A Randomized Phase II/III Study of Paclitaxel/Carboplatin/Metformin (NSC#91485) Versus Paclitaxel/Carboplatin/Placebo as Initial Therapy for Measurable Stage III or IVA, Stage IVB, or Recurrent Endometrial Cancer
Phase: PHASE2
Conditions: Endometrial Adenocarcinoma, Endometrial Clear Cell Adenocarcinoma, Endometrial Serous Adenocarcinoma, Endometrial Undifferentiated Carcinoma, Recurrent Uterine Corpus Carcinoma, Stage III Uterine Corpus Cancer AJCC v7, Stage IIIA Uterine Corpus Cancer AJCC v7, Stage IIIB Uterine Corpus Cancer AJCC v7, Stage IIIC Uterine Corpus Cancer AJCC v7, Stage IV Uterine Corpus Cancer AJCC v7, Stage IVA Uterine Corpus Cancer AJCC v7, Stage IVB Uterine Corpus Cancer AJCC v7
Sponsor: Gynecologic Oncology Group

Philip J. DiSaia, M.D. 
President 
 
Administrative Office 
Four Penn Center 
1600 John F. Kennedy Boulevard, Suite 1020 
Philadelphia, Pennsylvania 19103 
Phone: 215-854-0770 Fax: 215-854-0716 
 
Laura L. Reese 
Executive Director of Operations 
Larry J. Copeland, M.D. 
Vice President  
 
Finance/Development Office 
2127 Espey Court 
Suite 100 
Crofton, Maryland 21114 
Phone: 410-721-7126 Fax: 301-261-3972 
 
Mary C. Sharp 
Chief Financial Officer 
 
i 
 
SUMMARY OF CHANGES 
For Protocol Amendment #6 
NCI Protocol #: GOG-0286B 
Local Protocol #: GOG-0286B 
 
NCI Version Date: 6-21-17 
Protocol Date: 6-21-17 
# 
Section 
Page(s) 
Comments 
1. 
Title 
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1 
NCI Version Date has been updated. 
Includes Amendments 1-6 has been added. 
Jennifer Love Isaac has been replaced by Sarah Reese as the 
Study Nurse Contact. 
Revised has been added to the footer 
 
2 
7.31 
45 
Blood collection has been updated in the specimen collection 
requirements table. 
3 
10.2 
62 
The Translational Research folder has been updated in the 
Data Management Forms Table. 
4 
App V 
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The Translational Research Appendix has been updated.  
5 
ICD 
 
NCI Version Date has been updated. 

 
1 
 
PROTOCOL GOG-0286B 
A RANDOMIZED PHASE II/III STUDY OF PACLITAXEL/CARBOPLATIN/METFORMIN 
(NSC#91485) VERSUS PACLITAXEL/CARBOPLATIN/PLACEBO AS INITIAL THERAPY 
FOR MEASURABLE STAGE III OR IVA, STAGE IVB, OR RECURRENT ENDOMETRIAL 
CANCER NCT #02065687 
IND EXEMPT STUDY 
POINTS: 
PER CAPITA - 20 
MEMBERSHIP –6  
NCI Version Date: June 21, 2017 
Includes Amendments 1-6 
TR PER CAPITA – Award based on specimen submission with 1 point for each FFPE and 1 point for whole blood 
(MAX = 5 points).  
Lead Organization: NRG / NRG Oncology (08/18/14) 
 
 
 
  
STUDY CHAIR 
VICTORIA BAE-JUMP, M.D., PH.D. 
THE UNIVERSITY OF NORTH CAROLINA AT 
CHAPEL HILL 
DIV. OF GYNECOLOGIC ONCOLOGY 
DEPT. OF OB & GYN 
CB# 7572, B105 PHYSICIANS OFFICE BUILDING 
CHAPEL HILL, NC  27599-7572 
PHONE:  919-843-4899 
FAX:  919-966-2646 
EMAIL:  vbae@unch.unc.edu  
STUDY CO-CHAIR 
PAOLA GEHRIG, M.D. 
SEE GOG WEBSITE DIRECTORY 
MEDICAL ONCOLOGY CO-
CHAIR 
CAROL AGHAJANIAN, MD 
SEE GOG WEBSITE DIRECTORY 
STUDY STATISTICIAN 
MICHAEL SILL, PH.D. 
SEE GOG WEBSITE DIRECTORY 
NURSE CONTACT  
SARAH REESE  
THE UNIVERSITY OF NORTH CAROLINA AT 
CHAPEL HILL 
DIV. OF GYNECOLOGIC ONCOLOGY 
DEPT. OF OB & GYN 
CB# 7572, B180 PHYSICIANS OFFICE BUILDING 
CHAPEL HILL, NC  27599-7572 
PHONE:  919-966-7676 
FAX:  919-966-2646 
EMAIL:  sarah_reese@med.unc.edu  
TRANSLATIONAL RES. 
SCIENTIST 
HEATHER A. LANKES, PH.D., 
MPH 
SEE GOG WEBSITE DIRECTORY 
STUDY PATHOLOGIST 
WILLIAM H. RODGERS, MD, 
PH.D. 
SEE GOG WEBSITE DIRECTORY 
QUALITY OF LIFE CO-CHAIRS 
KAREN BASEN-ENGQUIST, PH.D, MPH 
SEE GOG WEBSITE DIRECTORY 
LARI WENZEL, PH.D 
SEE GOG WEBSITE DIRECTORY 
 
OPEN TO PATIENT ENTRY: MARCH 17, 2014; REVISED MAY 19, 2014; REVISED AUGUST 18, 2014; 
REVISED APRIL 28, 2015; TEMPORARILY CLOSED JULY 13, 2015; REVISED AND REOPENED 
DECEMBER 5, 2016: REVISED
This protocol was designed and developed by the Gynecologic Oncology Group (GOG). It is intended to be used 
only in conjunction with institution-specific IRB approval for study entry. No other use or reproduction is authorized 
by GOG nor does GOG assume any responsibility for unauthorized use of this protocol. 
Participating Organizations (08/18/14) 
ALLIANCE / Alliance for Clinical Trials in Oncology 
ECOG-ACRIN / ECOG-ACRIN Cancer Research Group 
SWOG / SWOG

 
 
GOG-0286B 
 
2 
 
 
SCHEMA (04/28/15)
 
ELIGIBILITY 
Stage III or IVA endometrial cancer with measurable disease 
Stage IVB endometrial cancer (whether there is measurable disease or not) 
Recurrent endometrial cancer (whether there is measurable disease or not) 
 
AND  
 
NO PRIOR CHEMOTHERAPY 
 
Randomization: 
 
Arm 1: 
Paclitaxel 175 mg/m2 IV over 3 hours day 1 
Carboplatin AUC = 5 IV day 1 
Metformin 850 mg oral QD, beginning on day 1. If tolerated for 4 weeks, the dose will be 
increased to Metformin 850 mg BID. 
Every 21 days x 6 cycles 
 
Maintenance regimen (for patients in complete response, partial response or stable disease) – 
Metformin 850 mg oral BID (one cycle of maintenance therapy = 21 days) 
 
Patients continue to receive maintenance treatment until disease progression or until adverse 
events prohibit further therapy. 
 
Arm 2: 
Paclitaxel 175 mg/m2 IV over 3 hours day 1 
Carboplatin AUC = 5 IV day 1 
Placebo for metformin 850 mg oral QD, beginning on day 1. If tolerated for 4 weeks, the dose 
will be increased to placebo for metformin 850 mg BID. 
Every 21 days x 6 cycles 
 
Maintenance regimen – (for patients in complete response, partial response or stable disease) – 
Matched placebo oral BID (one cycle of maintenance therapy = 21 days) 
 
Patients continue to receive maintenance treatment until disease progression or until adverse 
events prohibit further therapy. 
 
NOTE: Patients with stable disease (SD) or partial response (PR) who still have measurable 
disease at the completion of cycle 6 may continue to receive paclitaxel and carboplatin (with 
metformin or placebo) up to a total of 10 cycles (if deemed necessary by the treating 
investigator). Patients who continue with cycles 7-10 will continue with all study assessments as 
described for Prior to each cycle (cycles 1-6) (see Section 7, Study Parameters). 
TABLE OF CONTENTS 

 
 
GOG-0286B 
 
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   PAGE 
1.0 
OBJECTIVES ......................................................................................................................5 
1.1 
Primary Objective: .......................................................................................................... 5 
1.2 
Secondary Objectives: .................................................................................................... 5 
1.3 
Translational Research Objectives (12/05/16) ................................................................ 5 
1.4   
Patient-Reported Outcome Objectives ............................................................................ 6 
2.0 
BACKGROUND AND RATIONALE ................................................................................7 
2.1 
Current treatment of Endometrial Cancer ....................................................................... 9 
2.2 
Metformin and the mTOR Pathway ................................................................................ 9 
2.3 
Metformin as a Chemosensitizer .................................................................................. 10 
2.4 
Translational Research Background ............................................................................. 14 
2.5   
Patient-Reported Outcomes Research (12/05/16) ......................................................... 21 
2.6 
Inclusion of Women and Minorities ............................................................................. 23 
3.0 
ELIGIBILITY CRITERIA ................................................................................................24 
3.1 
Eligible Patients ............................................................................................................ 24 
3.2 
Ineligible Patients.......................................................................................................... 25 
4.0 
STUDY MODALITIES .....................................................................................................27 
4.1  
Paclitaxel (NSC #673089) ............................................................................................ 27 
4.2  
Carboplatin (Paraplatin® - NSC #241240) ................................................................... 28 
4.3 
Metformin (NSC#91485) .............................................................................................. 28 
4.4 
Emergency Unblinding ................................................................................................. 30 
5.0 
TREATMENT PLAN AND ENTRY/RANDOMIZATION PROCEDURE ....................31 
5.1   
Patient Entry and Registration ...................................................................................... 31 
5.2 
Treatment Plan (05/19/14) (08/18/14)(04/28/15)(12/05/16) ........................................ 32 
5.3 
Supportive Care Guidelines: ......................................................................................... 33 
5.4 
Criteria for removal from treatment .............................................................................. 34 
6.0 
TREATMENT MODIFICATIONS ...................................................................................35 
6.1  
Individual Dose Modification Levels ........................................................................... 35 
6.2  
General Guidelines for Hematologic Toxicity .............................................................. 35 
6.3  
Modifications for Hematologic Toxicity (Nadirs) ........................................................ 36 
6.4  
Modifications for Delayed Hematologic Recovery: ..................................................... 37 
6.5 
Adjustments Specific for Metformin/Placebo (hypoglycemia, glucose intolerance  and 
conditions predisposing to lactic acidosis) ............................................................................... 38 
6.6 
 Adjustments for Non-Hematologic Toxicity ............................................................... 38 
7.0 
STUDY PARAMETERS ...................................................................................................41 
7.1 
Observations and Tests (04/28/15) ............................................................................... 41 
7.2 
Stained Pathology Slide Requirements for Central Review to Confirm Protocol  
Eligibility .................................................................................................................................. 42 
7.3 
Translational Research (12/05/16) ................................................................................ 43 
7.4 
Patient-reported Outcomes Measures (05/19/14) ......................................................... 45 

 
 
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7.5 Anthropometrics (05/19/14)................................................................................................ 46 
8.0 
EVALUATION CRITERIA ..............................................................................................48 
8.1 
Antitumor Effect – Solid Tumors ................................................................................. 48 
9.0 
DURATION OF STUDY ..................................................................................................56 
9.1 
Patients will receive therapy until disease progression or intolerable toxicity  
intervenes.  The patient can refuse the study treatment at any time. ........................................ 56 
9.2 
All patients will be treated (with completion of all required case report forms) .......... 56 
9.3 
A patient is considered off study therapy when the patient has progressed or died, .... 56 
10.0 
STUDY MONITORING AND REPORTING PROCEDURES .......................................57 
10.1 
Adverse Event Reporting for a Commercial Agent ...................................................... 57 
10.2 
GOG Data Management Forms (04/28/15) .................................................................. 60 
11.0 
STATISTICAL CONSIDERATIONS ..............................................................................63 
11.1 
Study Design Summary: ............................................................................................. 63 
11.2 
Principal parameters: ................................................................................................. 63 
11.3 
Study Duration and Accrual Rate: ............................................................................ 64 
11.4 
Hypothesis and Sample Size (12/05/16) ..................................................................... 65 
11.5 
Secondary Endpoints .................................................................................................. 68 
11.6 
Exploratory and Translational Research (12/05/16) ............................................... 70 
11.7 
Study Monitoring (04/28/15) ........................................................................................ 81 
11.8 
Patient-Reported Outcomes (PROs) Research.............................................................. 81 
11.9 
Planned gender, minority and ethnic inclusion: ...................................................... 84 
12.0 
 BIBLIOGRAPHY .............................................................................................................86 
APPENDIX I - Clinical Staging (FIGO) ....................................................................................97 
APPENDIX II - Patient Medication Calendar (05/19/14)(04/28/15) .......................................98 
APPENDIX III - NRG Oncology General Therapy Guidelines (12/05/16) ............................99 
APPENDIX IV - CARBOPLATIN DOSE CALCULATION INSTRUCTIONS ................100 
APPENDIX V – Translational Research Specimen Procedures (12/05/16) .........................102 
APPENDIX VI – CT Scan Calculator .....................................................................................108 
APPENDIX VII – Translational Research Laboratory Testing Procedures (12/05/16) .....109 
 
 
 
 
 
 
 
 

 
 
GOG-0286B 
 
5 
 
1.0 OBJECTIVES 
 
1.1 
Primary Objective: 
 
1.11 
To determine if the addition of metformin to the standard regimen of 
carboplatin and paclitaxel prolongs progression-free survival (PFS) in 
women with advanced or recurrent endometrial cancer (Phase II).  To 
determine if the addition of metformin to the standard regimen of 
carboplatin and paclitaxel prolongs overall survival (OS) in the same 
population if a phase III study is conducted.  Both clinical trials (Phase II 
and III) will utilize OS as a primary endpoint if a phase III trial is opened. 
 
1.2 
Secondary Objectives: 
 
1.21 
To estimate the proportion of patients with objective response (RR) in the 
population of patients with measurable disease by treatment. 
 
1.22 
To estimate the duration of response in the population of patients with 
measurable disease who respond by treatment. 
 
1.23 
To estimate overall survival (OS) and relative hazards of death for each 
treatment arm if the study stops after the phase II trial is completed.  If the 
study continues with a phase III clinical trial, then PFS will be a secondary 
endpoint. 
 
1.24 
To determine the nature, frequency and degree of toxicity as assessed by 
CTCAE for each treatment arm. 
 
1.25 
To estimate possible differences in RR, PFS, OS, and toxicity rates for the 
treatment regimens by the patients’ level of obesity. 
 
1.3 
Translational Research Objectives (12/05/16) 
 
1.31 
Integrated Biomarker Research Objectives  
 
1.311 To test whether PIK3CA mutations/amplifications, PTEN mutations or 
PIK3R1/PIK3R2 mutations have a lower hazard of progression or death 
(PFS endpoint) among patients who are treated with metformin. 
 
1.312 To test whether higher expression of MATE 2 is associated with a lower 
hazard of progression or death (PFS endpoint) among patients who are 
treated with metformin. 
 
1.32 
Exploratory Biomarker Research Objectives  
 
1.321 To explore the association of metabolic factors (i.e., BMI, hip-to-waist 

 
 
GOG-0286B 
 
6 
 
 
ratio, diabetes status, HgbA1C, fasting insulin and glucose levels, HOMA 
 
scores) with treatment response to metformin/paclitaxel/carboplatin, PFS, 
 
and OS.  
 
1.322 To test whether genomic profiles (i.e.PIK3CA mutations/amplifications, 
PTEN mutations or PIK3R1/PIK3R2 mutations) differ between the tumors 
of obese and non-obese EC patients.   
 
1.323 To correlate expression of key targets of the insulin/IGF-1/mTOR signaling 
pathway (p-IGF1R, p-S6 and p-4EBP-1) with treatment response to 
metformin/paclitaxel/carboplatin, PFS, OS and obesity status.  
 
1.324 To determine if the genetic variants of the metformin transporters 
correspond with treatment response to metformin/paclitaxel/carboplatin, 
PFS and OS. 
  
 
1.4   
Patient-Reported Outcome Objectives 
 
1.41 
To estimate differences in physical functioning, physical activity, and 
fatigue between treatment arms.  Hypothesis:  Patients assigned to the 
metformin treatment arm will report better physical functioning and 
physical activity and less fatigue compared to patients assigned to the 
placebo treatment arm.  This between-arm difference will be most 
prominent in the obese patients. 
 
1.42 
To explore the association between metabolic factors (i.e., BMI, hip-to-
waist ratio, diabetes status, HgbA1C, fasting insulin and glucose levels, 
HOMA scores) and physical functioning, physical activity, and fatigue.  
Hypothesis:  We postulate that patients with increased BMI, hip-to-waist 
ratio, diabetes, HgbA1C, fasting insulin and glucose levels, and HOMA 
scores will report higher rates of health status decline, as measured by 
physical functioning, physical activity, and fatigue self-report scores. 

 
 
GOG-0286B 
 
7 
 
2.0 BACKGROUND AND RATIONALE 
 
Obesity and diabetes have been linked to poorer survival and increased recurrence rates 
in endometrial cancer. We postulate that the metabolic and endocrine effects of obesity 
likely play a role in the pathogenesis of endometrial cancer and invariably lead to 
biologically different cancers than those that arise in leaner women, possibly 
necessitating different treatment approaches. The anti-diabetic medication, metformin, 
has been shown to have anti-tumorigenic effects in vitro and in vivo. We aim to explore 
whether metformin is broadly useful as a chemotherapeutic agent for all women with 
endometrial cancer or more efficacious in the obese population. Thus, we will conduct a 
two arm, randomized, placebo-controlled phase II/III trial designed to assess the efficacy 
and safety of metformin in combination with paclitaxel and carboplatin versus paclitaxel 
and carboplatin alone in women with advanced and recurrent endometrial carcinoma. We 
predict that the addition of metformin will improve the efficacy of carboplatin and 
paclitaxel in advanced and recurrent endometrial cancer patients and have minimal added 
toxicity. In addition, we hypothesize that metformin/carboplatin/paclitaxel will be more 
efficacious in obese women as compared to their non-obese counterparts. The primary 
endpoint of the randomized phase II trial is efficacy (progression-free survival).  The 
primary endpoint of the phase III trial is efficacy (overall survival). Data will be collected 
on the patients’ level of obesity (BMI and hip-to-waist ratio). The trial will be stratified 
by BMI (<30 or >=30). We will determine presence of diabetes (yes/no by baseline 
medical history and by baseline HgbA1C) and insulin resistance (fasting glucose and 
insulin levels at baseline and prior to cycle 3). 
 
Endometrial cancer is the fourth most common cancer among women in the United States 
and has been increasing in frequency secondary to an aging female population and 
changes in dietary and hormonal factors, with obesity as a major culprit. In 2012, 
approximately 47,130 new cases were diagnosed, and 8,010 women will succumb to this 
disease1. Obesity, diabetes and insulin resistance are well-known risk factors that drive 
the development of endometrial cancers2. Obesity is not only a risk factor for developing 
endometrial cancer, but is associated with an increased risk of death3-5. Obese women 
with endometrial cancer have a 6.25 fold increased risk of death from this disease as 
compared to their non-obese counterparts4. Metformin is an anti-diabetic medication 
from the biguanide class that is widely used as the first line treatment of type II diabetes. 
Recent epidemiological evidence suggests that metformin use lowers cancer risk and 
reduces cancer deaths among diabetic patients6-8. It is not known whether the underlying 
mechanism behind metformin’s potential anti-neoplastic effects relates to the systemic 
action of this drug, by reducing circulating insulin levels, or a direct action on cancer 
cells. 
 
Metformin’s immediate downstream target is AMP-activated protein kinase (AMPK), 
and its activation leads to regulation of multiple signaling pathways involved in the 
control of cellular proliferation, including inhibition of the mammalian target of 
rapamycin (mTOR) pathway (Figure 1). Alterations in the mTOR pathway have 
previously been implicated in endometrial cancer carcinogenesis. PTEN is a negative 
regulator of this pathway, and loss of PTEN expression is one of the most prevalent 

 
 
GOG-0286B 
 
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molecular abnormalities associated 
with endometrial cancers9-11. 
Mutations and amplifications of the 
catalytic subunit of PI3K (i.e. 
PIK3CA) are also commonly seen 
and result in hyperactivation of the 
mTOR pathway12-16. Our preliminary 
data finds that metformin is a potent 
inhibitor of cell proliferation in 
endometrial cancer cell lines, and that 
this effect is partially mediated 
through inhibition of the mTOR 
pathway17. In addition, treatment with 
metformin in combination with 
paclitaxel resulted in a synergistic 
anti-proliferative effect in these cell 
lines18. Thus, metformin may have 
important therapeutic implications for 
endometrial cancer, a disease strongly 
influenced by obesity and insulin 
resistance, with potential mechanisms 
of action including mTOR inhibition 
and chemo-sensitization. 
 
As previously stated, obesity and 
diabetes have been linked to an 
increased risk of mortality from 
endometrial cancer making metformin a 
particularly innovative treatment 
strategy for this disease. However, the 
important biological question remains 
whether metformin will be universally 
effective in endometrial cancer 
treatment or more efficacious in the obese population. We postulate that the interaction 
between the tumor and its host environment (i.e., the obese state) may be critical in 
endometrial cancer development and progression, and ultimately, in response to a therapy 
such as metformin. In addition, metformin is not normally given in normal weight, non-
diabetic individuals, raising the issue of tolerability between the obese and non-obese 
patient populations. Thus, the overall objective of this concept is to compare the efficacy 
and tolerability of metformin in combination with paclitaxel and carboplatin versus 
paclitaxel and carboplatin in women with advanced or recurrent endometrial cancer. We 
recognize that metformin is not likely to produce numerous untoward toxicities; however, 
if our prediction that the addition of metformin will improve the efficacy of carboplatin 
and paclitaxel in advanced and recurrent endometrial cancer patients and have minimal 
added toxicity, patients may experience less disease burden and thus report better 
reported health outcomes and functioning than those without metformin. Further, if, as 
Figure 1. A schematic of the relationship 
between 
metformin 
and 
the 
mTOR 
pathway. (From Hartford, CM, and Ratain 
MJ, Rapamycin: Something Old, Something 
New, 
Sometimes 
Borrowed 
and 
Now 
Renewed, 
Clinical 
Pharmacology 
& 
Therapeutics (2007) 82, 381–388) 

 
 
GOG-0286B 
 
9 
 
hypothesized, metformin/carboplatin/paclitaxel is more efficacious in obese women as 
compared to their non-obese counterparts, this benefit may also be observed in obese 
patients in this arm reporting of less fatigue, better physical functioning, and more 
physical activity compared to their non-obese counterparts . Therefore, we will also 
estimate the efficacy, patient-reported outcomes and toxicity rates of the 
metformin/paclitaxel/carboplatin treatment regimen for obese versus non-obese patients. 
 
2.1 
Current treatment of Endometrial Cancer 
 
Women with early stage endometrial cancer have a relatively good prognosis with 
surgery alone or surgery plus radiation19. However, 10-15% of patients are 
diagnosed with stage III disease at the time of surgery, and have an estimated 5 
year survival rate of 40-70%20. Lastly, the remaining 10-15% of patients are 
diagnosed with stage IV disease and have a very poor 5 year survival of 0-10%21. 
Those patients with advanced disease are unlikely to be cured by surgery or 
radiation alone. In addition, the prognosis for recurrent disease is even more 
dismal, with expected overall survival of only 14-15 months.  
 
Much work needs to be done with regards to managing patients with advanced 
stage or recurrent disease. The current standard is systemic treatment with 
paclitaxel and carboplatin. A large phase III non-inferiority study performed by 
the Gynecologic Oncology Group (GOG) compared paclitaxel/carboplatin versus 
a three drug regimen of paclitaxel, doxorubicin and cisplatin (TAP) and showed 
that paclitaxel/carboplatin was not inferior to TAP in terms of PFS and OS.  
Overall, paclitaxel and carboplatin also had a favorable toxicity profile22.  
 
Second-line chemotherapy options for endometrial cancer are even less 
effective22, with Megace being the only FDA approved agent in this setting. The 
GOG has conducted multiple phase II trials of single-agent chemotherapy in the 
second-line treatment setting, with response rates all < 15% (except for paclitaxel 
in taxane naïve patients that had a response rate of 27%).  Clinical trials of 
therapies that target specific molecular abnormalities for endometrial cancer have 
shown some promise in the second line setting, such as mTOR inhibitors and 
vascular epidermal growth factor inhibitors (VEGF).  
 
2.2 
Metformin and the mTOR Pathway 
 
Metformin is a biguanide drug that is widely used for the treatment of type II 
diabetes. Recent epidemiological evidence suggests that metformin lowers all 
cancer risk and reduces cancer incidence and deaths among diabetic patients, 
including mortality from endometrial cancer6-8, 23. Furthermore, a recent 
retrospective cohort study of diabetic patients with early stage breast cancer found 
that those women receiving metformin and adjuvant chemotherapy had a higher 
response rate24. This has led to the idea that metformin many have a role in cancer 
treatment and prevention and multiple Phase I-III clinical trials are ongoing, most 
notably in breast and prostate cancer, to further test metformin’s anti-neoplastic 

 
 
GOG-0286B 
 
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effects25, 26. A Phase I study of temsirolimus and metformin in advanced solid 
tumors has already been completed and demonstrated acceptable toxicity and 
promising response rates in a heavily pre-treated group of patients27.  
 
Metformin is commonly thought of as an insulin sensitizer because it enhances 
signaling through the insulin receptor, leading to an improvement in insulin 
resistance, followed by a reduction in circulating insulin levels. More recently, 
evidence suggests that metformin’s key target of action is the inhibition of hepatic 
gluconeogenesis28, resulting in a secondary decline in insulin levels. Although the 
molecular mechanism of metformin has been well-studied in liver, muscle and fat, 
little is known about its effects on epithelial tissues, including the endometrium. 
Metformin inhibits complex I activity in the mitochondria. This leads to activation 
of its downstream target, AMPK, which regulates multiple signaling pathways 
controlling cellular proliferation, including inhibition of the mTOR pathway 
(Figure 1)30. AMPK regulates energy metabolism and is activated in response to 
cellular stresses that deplete cellular energy levels and increase the AMP/ATP 
ratio30. AMPK functions to detect cellular energy and ensure that cell division 
only proceeds if there are sufficient metabolic resources to support proliferation. 
Once activated, AMPK restores cellular energy levels by stimulating catabolic 
pathways, such as glucose uptake, glycolysis and fatty acid oxidation and halting 
ATP-consuming processes such as fatty acid, cholesterol and protein synthesis. 
LKB1 is the kinase responsible for phosphorylating and activating AMPK28.  
 
AMPK activation through LKB1 leads to regulation of multiple downstream 
pathways involved in the control of cellular proliferation, including inhibition of 
the mTOR pathway. Given the interrelationship between these two pathways, 
metformin is thought to behave as a novel mTOR inhibitor and has been shown to 
dramatically decrease proliferation in a number of different human cancer cell 
lines in vitro17, 31-33. As demonstrated in our previous work in endometrial cancer 
cell lines, metformin-mediated AMPK activation decreases cell growth through 
inhibition of mTOR and a decrease in phosphorylation of its downstream target, 
S617. This ultimately results in the inhibition of translation and critical mRNAs 
involved in cell cycle progression32, 34. Treatment with metformin has also been 
shown to effectively repress tumor growth in xenograft animal models of breast, 
prostate and colon cancer35-37. Some preclinical data in animal models also 
suggests that the anti-tumorigenic efficacy of metformin is dependent on the 
metabolic composition of its host (i.e. obese and insulin resistant). Metformin has 
been found to inhibit the growth of Lewis lung LLC1 carcinoma cells in diet-
induced obese, insulin-resistant C57BL/6J mice but not in mice fed a control 
diet38. Similar results were also found using breast tumor cells in lean and obese 
Balb/c mice39. 
 
2.3 
Metformin as a Chemosensitizer 
 
mTOR inhibitors are thought to be potent chemotherapeutic chemosensitizers in 
many types of cancer cells, including our previous work in endometrial cancer 

 
 
GOG-0286B 
 
11 
 
cell lines40, 41. Given the parallels in the effects of metformin and mTOR 
inhibitors on the mTOR signaling cascade, it is not surprising that metformin may 
also behave as a chemosensitizer when used in combination with cytotoxic agents. 
Paclitaxel is commonly used in the treatment of endometrial cancer; and thus, we 
examined the effects of metformin used in combination with paclitaxel in human 
endometrial cancer cell lines. We found a synergistic relationship between 
paclitaxel and metformin in regards to inhibition of cell proliferation and 
induction of apoptosis in endometrial cancer cells18. Treatment with metformin 
and paclitaxel also resulted in decreased phosphorylation of S6, a critical 
downstream target of the mTOR pathway18. These findings suggest that the 
combination of metformin and paclitaxel may be an effective treatment strategy in 
the management of endometrial cancer patients. 
 
Recent studies in breast, prostate and lung cancer cell lines confirm our work that 
the combination of metformin and paclitaxel shows great promise in the 
management of these various cancers42, 43. The combination of metformin with 
paclitaxel, carboplatin or doxorubicin has been shown to prolong relapse rate in 
prostate and lung xenograft models42. Most importantly from this study, 
metformin had comparable effects on tumor regression and prevention of relapse 
even when combined with a reduced dose of doxorubicin that is not usually 
effective as a monotherapy42. A subsequent study found similar results in that 
metformin and paclitaxel resulted in decreased proliferation via cell cycle arrest in 
G2 phase in lung and breast cancer cell lines43. In this same study, the 
combination of paclitaxel and metformin was also found to more effectively 
decrease tumor growth and induce apoptosis in a xenograft model of lung cancer 
as compared to the individual drug treatments alone43. Thus, it seems logical that 
the addition of metformin to standard paclitaxel and carboplatin treatment for 
endometrial cancer will lead to improved efficacy with a minimal increase in 
toxicity.  
 
Metformin has many advantages over using a specific mTOR inhibitor for cancer 
treatment, including low cost, oral route of administration and low toxicity. 
Common side effects for mTOR inhibitors (occurring in >30% of patients) 
include weakness and fatigue, anemia, thrombocytopenia, rash, mouth sores, 
nausea, poor appetite, peripheral edema, hyperglycemia, hypercholesterolemia, 
hypophosphatemia, increased liver enzymes and increased blood creatinine levels. 
In contrast, metformin has a good safety profile and is well-tolerated in patients. 
The main side effect of metformin is gastrointestinal distress, manifested 
generally as transient nausea and diarrhea that rarely requires discontinuation of 
the drug. A rare but serious risk of metformin is fatal and non-fatal lactic acidosis, 
which is usually associated with predisposing risk factors such as uncontrolled 
congestive heart failure, liver disease, renal failure or alcohol abuse. The cost of 
metformin is approximately a dollar a day as compared to weekly infusions of an 
mTOR inhibitor such as temsirolimus at $4,000 per infusion. Lastly, metformin is 
an oral agent, potentially providing an improved quality of life benefit for cancer 
patients. 

 
 
GOG-0286B 
 
12 
 
 
Thus, the overall goal of this proposal is to compare the response rates and 
tolerability of metformin in combination with carboplatin and paclitaxel versus 
carboplatin and paclitaxel alone in patients with advanced and recurrent 
endometrial cancer. Given that metformin is considered cytostatic and relatively 
non-toxic, metformin treatment may be extended beyond the cytotoxic therapy in 
patients with a complete clinical response (CR), partial clinical response (PR) or 
stable disease (SD) and be given until evidence of disease progression. A 
secondary goal is to estimate the outcomes and toxicities between obese and non-
obese patients who are on the arm receiving metformin/paclitaxel/carboplatin. We 
hypothesize that the metabolic composition of the patient may have a bearing on 
which chemotherapeutic strategies would be the most beneficial and if this is true, 
metformin may logically be a more effective treatment in the obese population. 
Carboplatin and paclitaxel were chosen as therapeutic partners for metformin 
based on our preclinical work demonstrating synergy between metformin and 
paclitaxel as well as the standard use of carboplatin and paclitaxel as first line 
treatment in this patient population. This trial will offer the potential benefit of a 
targeted therapy (metformin) to this traditional regimen which should be 
appealing to both patients and their oncologists. Potential biomarkers of response 
to treatment will be explored. This proposal is innovative in that it will be the first 
clinical trial of metformin for endometrial cancer treatment. Most importantly, 
this clinical trial will not only evaluate the utility of metformin as a 
chemotherapeutic agent but will also explore whether metformin is more 
beneficial and more tolerable in obese versus non-obese endometrial cancer 
patients. The findings from this study may lead to the individualization of 
endometrial cancer treatment based on both tumor biology and the metabolic 
composition of the patient. 
 
As summarized above, epidemiological and preclinical studies suggest that 
metformin has potential as a chemotherapeutic agent for a variety of cancers, 
including endometrial cancer. Given this, multiple phase I-III clinical trials are 
ongoing to further test metformin’s anti-neoplastic effects25, 26. In fact, there are 
36 clinical trials listed on “www.clinicaltrials.gov” for metformin in regards to 
cancer, including translational and pre-operative window studies, 
chemotherapeutic trials, prevention trials and survivorship studies. Although there 
are many ongoing trials for metformin in regards to cancer treatment and 
prevention, the results of only four clinical trials have been reported in the 
literature. In a pre-operative window study in operable breast cancer patients, the 
percentage of cells staining for Ki-67 fell significantly after only 2 weeks of 
treatment with metformin, with parallel beneficial effects on cell signaling 
pathways such as the mTOR pathway44. This was followed by a randomized 
control trial of metformin versus placebo for 4 weeks in a similar group of 
operable breast cancer patients45. In contrast to the previous study, metformin 
before surgery did not significantly affect Ki-67 staining overall45. However, 
metformin did show a significant benefit in patients with insulin resistance or high 
BMI45, suggesting that metformin’s anti-tumorigenic activity may be more related 

 
 
GOG-0286B 
 
13 
 
to its systemic effects on improving the metabolic milieu as opposed to its direct 
effects on the tumor cells. Another short term clinical trial demonstrated that a 
one month treatment of metformin resulted in a decrease in proliferation and the 
size and number of colorectal aberrant crypt foci, an endoscopic surrogate marker 
of colorectal cancer46. Lastly, a phase I study of temsirolimus and metformin in 
advanced solid tumors has been completed that demonstrated acceptable toxicity 
and promising response rates in a heavily pre-treated group of patients27.  
 
Carboplatin and paclitaxel were chosen as therapeutic partners for metformin 
based on our preclinical work demonstrating synergy between metformin and 
paclitaxel as well as the standard use of carboplatin and paclitaxel as first line 
treatment in this patient population. 
 
For this proposed clinical trial, we plan to enroll the following advanced 
endometrial cancer patients: 
• Stage III or IVA with measurable disease 
• Stage IVB endometrial cancer (whether measurable disease is present or 
not) 
• Recurrent endometrial cancer (whether measurable disease is present or 
not) 
 
This is the same patient population as enrolled in the recently completed GOG 
study in this population (GOG-0086P, A three arm randomized phase II study of 
paclitaxel/carboplatin/bevacizumab, paclitaxel/carboplatin/temsirolimus and 
ixabepilone/carboplatin/bevacizumab as initial therapy for measurable stage III or 
IVA, stage IVB, or recurrent endometrial cancer). GOG-0086P accrued 334 
evaluable patients. As of the most recent GOG statistical report (January 2013), 
the histologic types on central review are endometrioid grade 1 (12%), 
endometrioid grade 2 (25%), endometrioid grade 3 (24%), serous (21%), clear 
cell (5%), mixed epithelial (4%), undifferentiated (2%), adenocarcinoma, NOS 
(3%), and other/pending review (3%). 
 
We propose limiting eligibility in this study to endometrioid grade 1, 
endometrioid grade 2, endometrioid grade 3, serous, clear cell, mixed epithelial, 
undifferentiated and adenocarcinoma, NOS. Mucinous and squamous cell cancers 
are rare and represent a different biology, and therefore will not be eligible for 
this study. From the above GOG-0086P data, we would expect only 12% of the 
patients to have low grade tumors (endometrioid grade 1). 
 
The system used to grade endometrial cancer is the FIGO grading system. This 
grading system requires evaluation of histologic features that are difficult to 
access reproducibly. Furthermore, the current classification system for subtypes, 
in particular the distinction between endometrioid grade 3 and serous carcinomas 
is also limited by its reproducibility.  In regards to obesity and histologic 
subtypes, traditional dogma for endometrial cancer has been that serous cancers 
occur more often in thin, African American women versus endometrioid cancers 

 
 
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14 
 
that occur more often in Caucasian, obese women. We performed a retrospective 
look at 881 patients with endometrial cancer treated in the last five years at UNC2. 
Of these patients, 691 of these tumors were of endometrioid histology and 190 
were of serous histology. As we would expect, 17% of the endometrioid tumors 
and 30% of the serous tumors were from African American women. Of the 
endometrioid and serous endometrial cancers, 84% and 78% were overweight or 
obese (p=NS) respectively and 62% and 51% were obese (p=0.007). 95% of the 
African American patients were overweight or obese compared to 80% of the 
Caucasian patients (p<0.001). African Americans had a higher mean BMI than 
Caucasians for endometrioid (37.0 v 33.9, p<0.005) and serous (34.9 v 30.2, 
p<0.001) cancers. For serous endometrial cancers, African Americans had 6.62 
times the odds of being overweight or obese (95CI 1.93, 22.6) as Caucasians. The 
proportion of diabetes did not differ between endometrioid and serous 
endometrial cancer patients (23.1 v 20.5%, p=0.456). So as we expected, obesity 
and diabetes seem to be equally important in serous as endometrioid endometrial 
cancers. In a recently reported pooled analysis of 25 studies in the Epidemiology 
of Endometrial Cancer Consortium, BMI was also positively associated with both 
endometrioid and serous endometrial cancers47. 
 
Preliminary review of the data derived from GOG-0210 also suggests that obesity 
is associated with both low grade and high grade disease. Of the 2,244 women 
with G1-2 endometrioid tumors, 20% were overweight and 52% were obese. For 
the 354 women with G3 endometrioid tumors, 25% were overweight and 45% 
were obese. This is comparable to the women with serous tumors in this study 
(n=321) of which 25% were overweight and 40% were obese. 
 
Lastly, although our hypothesis is that metformin may be more efficacious in the 
obese population, we do not know this for sure. Although there are many ongoing 
trials for metformin in regards to cancer treatment and prevention, only four 
clinical trials have been reported in the literature44-46, 48. These clinical trials were 
not limited to obese patients but did show beneficial effects of metformin 
treatment. In my laboratory, we have also tested human uterine serous cell lines 
and found metformin to be equally effective for inhibiting cell growth as in the 
endometrioid cell lines (unpublished data). Given that obesity may be related to 
all endometrial cancers and that the impact of obesity on the efficacy of 
metformin is not clear, it seems reasonable to include all biological subtypes on 
this trial. 
 
2.4 
Translational Research Background 
 
The correlation between obesity and diabetes and risk of endometrial cancer 
growth will be examined. The ability of metformin to influence that risk will be 
assessed. Baseline BMI, hip-to-waist ratio, HgBA1C, and diabetes status will be 
documented. Fasting insulin and glucose levels will be obtained prior to cycles 1 
and 3. The Homeostasis Model Assessment Score (HOMA) (fasting insulin 
(microunits.ml) X fasting glucose (mmol/22.5)) will be calculated for each 

 
 
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15 
 
patient, which is a measurement of insulin resistance. 
 
PI3K/AKT/MTOR pathway mutations and amplification will be analyzed. DNA 
extracted from formalin-fixed, paraffin-embedded (FFPE) tumor and whole blood 
will be used for targeted sequencing using a hybrid capture approach in 
conjunction with next generation sequencing.  PIK3CA mutations/amplifications 
and PIK3R1/PIK3R2 mutations will be specifically assessed. FFPE will be used 
for metformin transporter proteins (OCT 1-3, MATE 1/2 and PMAT), PTEN, 
AMPK, and LKB1 immunohistochemistry. 
 
OBESITY, THE MTOR PATHWAY AND ENDOMETRIAL CANCER 
 
Obesity is a risk factor and a poor prognostic indicator 
for many cancers, including endometrial cancer. 
Obesity has reached epidemic proportions in the 
United States, with over 30% of adults considered 
obese and 65% considered overweight based on their 
body mass index (BMI). Impaired glucose regulation 
and insulin resistance are consequences of obesity, 
often culminating in type 2 diabetes59. It is postulated 
that hyperglycemia and hyperinsulinemia resulting 
from over-nutrition in obese patients may provide 
abundant nutrients and growth factors to cancer cells, 
resulting in the ideal environment for tumor initiation 
and promotion. The mTOR pathway is critical for the 
regulation of cell proliferation and metabolism and is 
often hyperactivated in both obesity and endometrial 
cancer, suggesting a possible link between these two 
disease processes. We hypothesize that the metabolic 
and endocrine effects of obesity play a role in 
the pathogenesis of endometrial cancer and 
invariably lead to biologically different cancers 
than those that arise in leaner women, possibly 
through aberrant modulation of mTOR 
signaling. Thus, obese endometrial cancer 
patients may derive increased benefit from 
chemotherapeutic agents related to inhibition of 
this pathway, such as metformin. 
 
ELEVATED IGF/PI3K/AKT/MTOR SIGNALING, SERUM GLUCOSE 
AND METABOLISM OF GLUCOSE MAY MEDIATE INITIATION 
AND/OR PROMOTION OF ENDOMETRIAL CANCER 
 
Overweight and obese states may be linked to endometrial cancer through 
nutrient-sensitive signaling cascades, such as the insulin/insulin growth factor 
(IGF) and PI3K/Akt/mTOR pathways. Hyperinsulinemia, IGF-1, and IGF-1 
Figure 5. Diagram of the 
relationship between obesity, mTOR 
hyperactivation and cancer. The 
biological question remains whether 
obesity-driven cancers will be more 
susceptible to treatment with inhibitors 
of the mTOR pathway, such as 
metformin. 

 
 
GOG-0286B 
 
16 
 
receptor (IGF-1R) levels are important in endometrial cancer development and 
progression60, 61. Signaling through IGF-1R leads to activation of the 
PI3K/Akt/mTOR pathway, and components of this pathway are often mutated, 
amplified or aberrantly expressed in endometrial cancers62, 63. Activation of the 
PI3K/Akt/mTOR pathway through PIK3CA amplifications and mutations and 
PTEN loss of function has been linked to more aggressive behavior, especially in 
early stage disease12, 63. Thus, mTOR inhibitors as a targeted therapy for 
endometrial cancer are already being actively investigated in Phase 1, 2 and 3 
clinical trials62, 63.  
 
Glucose metabolism and growth control are tightly linked in proliferating cells 
and involve signaling pathways including the PI3K/Akt/mTOR pathway. Elevated 
serum glucose and glucose metabolism by the tumor, termed the “Warburg 
effect,” also play a role in endometrial cancer pathogenesis64, 65; facilitative 
glucose transporters (GLUTs) are strongly expressed in endometrial cancer, and 
their expression correlates with clinical stage of disease66-71. Taken together, 
obesity is a high-energy, pro-inflammatory condition that culminates in increased 
growth factor signaling via the insulin/IGF axis, as well as a saturating nutrient 
environment via increased glucose (and other nutrients), ultimately resulting in 
excessive stimulation of the PI3K/Akt/mTOR pathway (Figure 5)25, 26, 72, 73. In 
experimental animal models, diet-induced obesity leads to activation of Akt and 
mTOR in a variety of epithelial tissues74, 75. Conversely, calorie restriction has the 
opposite effect and represses signaling through this pathway74, 75. Therefore, 
obesity may create a unique environment in which a therapeutic approach could 
take advantage of as a strategy to improve endometrial cancer outcomes. Thus, it 
is logical that a targeted agent, such as metformin, that indirectly decreases 
circulating glucose and insulin levels and specifically disrupts the 
PI3K/Akt/mTOR pathway may break the link between obesity and cancer and be 
particularly useful in obesity-driven cancers. Traditional mTOR inhibitors have 
been found to upregulate gluconeogenesis, resulting in detrimental metabolic side 
effects such as insulin resistance and hyperglycemia76. The great advantage of 
metformin over traditional mTOR inhibitors is its ability to inhibit mTOR without 
inducing hyperglycemia, due to its concomitant effects on inducing energy stress. 
 
PROPOSED MECHANISMS OF THE ANTI-TUMORIGENIC EFFECTS 
OF METFORMIN  
 
Metformin is believed to have both indirect and direct effects on tumor growth 
(Figure 6)77. Its indirect effects include inhibition of hepatic gluconeogenesis, 
resulting in an improvement in insulin sensitivity and a reduction in blood glucose 
and circulating insulin levels which may lead to decreased growth factor 
stimulation to tumor cells. On a more cellular or direct level, metformin inhibits 
respiratory complex 1 in the mitochondria, interfering with oxidative 
phosphorylation and resulting in decreased ATP production and energetic stress77. 
In tumor cells that are unable to cope with energetic stress, energetic crisis and 
ultimately cell death will occur77. Genetic defects that may predispose a tumor 

 
 
GOG-0286B 
 
17 
 
cell to increased sensitivity to energy stress are loss of function of AMPK or of 
LKB1, the major kinase that phosphorylates and activates AMPK77. 
 
In tumor cells that can adequately respond to energetic stress, AMPK is 
subsequently activated after exposure to metformin which leads to the regulation 
of multiple signaling pathways that control cellular proliferation, including 
inhibition of the mammalian target of rapamycin (mTOR) pathway (i.e. 
specifically mTORC1 inhibition). Metformin has also been found to inhibit the 
mTOR pathway via AMPK-independent mechanisms through its effects on the 
Ragulator complex (Rag GTPase) and REDD1 upregulation78.  
 
Alterations in the mTOR pathway, involving LKB1 and PTEN, have been 
implicated in endometrial carcinogenesis in up to 83% of endometrial cancers9-11, 
79. Unlike most other tumor types, loss of PTEN expression is observed in 
premalignant hyperplastic lesions of the endometrium, suggesting that PTEN loss 
may be a potential initiator of endometrial cancer development9. Mutations and 
amplifications of the catalytic subunit of PI3K (i.e. PIK3CA) are also commonly 
seen in endometrial cancer and result in hyperactivation of the mTOR pathway12-
16. PIK3R1 and PIK3R2 code for the p85α and p85β inhibitory subunits of PI3K 
and have also been found to be frequently mutated in endometrial cancers80. Thus, 
we hypothesize that hyperactivation of the mTOR pathway in tumor cells may 
lead to increased susceptibility to the anti-tumorigenic effects of metformin. One 
exception may be activating mutations in PIK3CA which have been linked with 
insulin insensitivity in tumor cells and theoretically may render tumor cells less 
responsive to metformin81. 

 
 
GOG-0286B 
 
18 
 
 
 
One critical, unanswered question in regards to the action of metformin is the 
extent to which this drug accumulates in neoplastic tissues. Metformin is highly 
hydrophilic with a net positive charge at all physiologic pH values. Therefore, it 
requires cation-selective transport proteins that mediate its entry into cells. The 
cation-selective transporters organic cation transporter (OCT)1-3, plasma 
membrane monoamine transporter (PMAT) and multidrug and toxin extrusion 
transporters (MATE) 1-2 mediate metformin transport in the liver, intestine, and 
kidney82-88. Less is known about the expression of these transporter proteins in 
Figure 6. Schematic of the proposed mechanisms of the anti-tumorigenic effects of metformin. 
As part of this proposed clinical trial, we will investigate potential biomarkers associated with 
critical underlying mechanisms of metformin’s anti-tumorigenic actions, including (1) indirect 
effects on the metabolic environment seen in obese cancer patients, (2) genetic defects in tumor cells 
related to poor coping with energetic stress, (3) evidence of hyperactivation of the mTOR pathway 
in tumor cells and (4) expression of cation-selective transporter proteins responsible for the uptake 
of metformin into tumor cells. This figure was edited from Investigating Metformin for Cancer 
Prevention and Treatment: The End of the Beginning, Cancer Discovery (2012), 2:778-790. 

 
 
GOG-0286B 
 
19 
 
solid tumors. Studies in our laboratory show that metformin uptake into human 
breast cancer cell lines are dictated by the expression levels of these cation-
selective transporters 
(unpublished data).  
 
Therefore, it is reasonable to 
assume that metformin 
uptake into endometrial 
cancer cell lines and tissues 
must be mediated by 
specific cation-selective 
transporters. 
 
Initial real-time polymerase 
chain reaction (RT-PCR) 
experiments to determine if 
cation-selective metformin 
transporters are expressed in 
endometrial cancer cell lines 
showed that all transporters 
of interest are present at 
varying levels (Figure 7) 
(unpublished data, 
submitted as an abstract to 
the Annual Meeting of the 
Society of Gynecologic 
Oncology). MATE1 and 2 
were the most highly expressed transporters in endometrial cancer cell lines 
(Ishikawa and ECC-1) while OCT2 and 3 were the least expressed transporters. In 
the serous endometrial cancer cell line (SPEC-2), MATE1 and PMAT expression 
predominated, with much decreased expression of MATE2. We also assessed the 
expression of the metformin transporter proteins in fifteen human endometrial 
cancer specimens and adjacent benign tissues. MATE1 was found to be the 
predominant transporter in endometrial tumor and benign tissues; however, 
PMAT and OCT3 were also expressed in significant amounts (Figure 8). Thus, 
we anticipate that the highly expressed MATE1 will facilitate metformin 
intracellular uptake into endometrial tumors and be predictive of treatment 
response to this agent. 
 
 
 
 
 
 
 
 
Figure 7. Relative Transporter Expression in 
Human Endometrial Cancer Cell Lines. All 
common cation-selective transporters were 
expressed in endometrial cancer cell lines, except 
OCT3 which was undetectable in SPEC-2 cells. 
MATE2 was the predominant transporter in ECC-1 
and Ishikawa cell lines, followed by MATE1. In 
SPEC-2 cells, MATE1 showed highest expression 
followed by PMAT, whereas the expression of 
MATE2 was 1033-and 338-fold lower than that 
observed in ECC-1 and Ishikawa cells, respectively.  
 
 
 

 
 
GOG-0286B 
 
20 
 
 
 
METFORMIN, 
OBESITY AND 
ENDOMETRIAL 
CANCER 
 
Our laboratory has 
shown that metformin-
mediated AMPK 
activation decreases 
endometrial cancer 
cell growth through 
inhibition of mTOR 
signaling17, and that 
metformin and 
paclitaxel have 
synergistic anti-
proliferative effects18. 
Others have also 
shown that metformin 
blunts cell and tumor 
growth in vitro and in xenograft models17, 31-33, 35-37. In an animal mouse model for 
endometrial hyperplasia, metformin exhibited anti-proliferative effects on the 
endometrium that coincided with inhibition of downstream targets of the mTOR 
pathway. Some preclinical data suggest that the anti-tumorigenic efficacy of 
metformin is dependent on the obese and insulin resistant state38, 39. The potential 
impact of obesity on response rates to metformin has not been explored in the 
setting of a clinical trial. 
 
Recently, we have conducted a multi-institutional, retrospective cohort analysis of 
all endometrial cancer patients with type 2 diabetes treated from 2005-2010 
(unpublished data, submitted as an abstract to the Annual Meeting of the Society 
of Gynecologic Oncology). 1561 endometrial cancer patients were identified, of 
which 377 were diabetic. Of these, 54% used metformin. The mean age was 63 yo 
(SD 11.6), and the mean was BMI 39.1 (SD 11.3). The majority of patients had 
tumors of endometrioid histology (297, 75%). Stage distribution included the 
following: 308 (78%) stage I, 16 (4%) stage II, 52 (13%) stage III and 18 (4%) 
stage IV. Median follow-up was 33 months (range of 19 to 87 months). 
Metformin use was significantly associated with improved progression free 
survival (HR 0.57, 95CI 0.391-0.852). Metformin users also had significantly 
improved overall survival with a hazard ratio of 0.05 (95CI 0.33-0.78, p<0.001). 
After adjusting for BMI, stage and adjuvant treatment, metformin use was 
associated with improved progression free survival (HR 0.56, 95CI 0.36-0.86) and 
overall survival (HR 0.47, 95CI 0.29-0.77). These results provide further support 
that metformin may have a role as adjuvant and maintenance therapy for 
Figure 8. Relative Transporter Expression in 
Endometrial Tumor Tissues and Adjacent Non-
malignant Tissues. In (A) 15 human endometrial 
tumor tissues and (B) 5 adjacent non-malignant 
tissues examined, MATE1 was the predominant 
transporter; however, PMAT and OCT3 were also 
expressed in significant amounts. The transporter 
expression pattern in tumor tissues and adjacent 
non-tumor tissues was similar. 
B 
A 

 
 
GOG-0286B 
 
21 
 
endometrial cancer.  
 
 
Thus, we aim to explore whether metformin in combination with paclitaxel and 
carboplatin is broadly useful for all women with endometrial cancer or more 
efficacious in the obese population. We hypothesize that the metabolic milieu of 
the host may play a critical role in oncogenesis, cancer proliferation, and 
transformation of the tumor into a unique pathology and prognosis dependent on 
the presence of obesity. As such, we propose that obese endometrial cancer 
patients would benefit most from treatment with metformin, which would inhibit 
or mitigate these obesity-driven tumor biological pathways. Through the 
comprehensive analysis of patients’ metabolic factors as well as targets of 
metformin/mTOR signaling and expression of the metformin transporter proteins 
in their tumors, we strive to gain valuable insight into the relationship between the 
tumor, its host environment, and the critical role obesity plays in endometrial 
cancer treatment. As delineated in Figure 6, we will investigate potential 
biomarkers associated with critical underlying mechanisms of metformin’s anti-
tumorigenic actions, including (1) indirect effects on the metabolic environment 
seen in obese cancer patients, (2) genetic defects in tumor cells related to poor 
coping with energetic stress, (3) evidence of hyperactivation of the mTOR 
pathway in tumor cells and (4) expression of cation-selective transporter proteins 
responsible for the uptake of metformin into tumor cells We speculate that 
hyperactivation of mTOR pathway-derived, metabolically-dependent, 
proliferative targets will be more characteristic of tumors derived from obese 
versus non-obese women with endometrial cancer and correspond to increased 
susceptibility to the metformin/paclitaxel/carboplatin regimen. Overall, obese 
women will be more responsive to metformin/paclitaxel/carboplatin treatment, 
and metabolic and molecular biomarkers will emerge that are predictive of 
sensitivity to this treatment regimen. 
 
2.5   
Patient-Reported Outcomes Research (12/05/16)    
 
Rationale for assessing physical function, fatigue, and physical activity.  
Many of the advanced or recurrent endometrial cancer patients suffer from 
obesity-driven comorbidities, including Type II diabetes, hypertension, heart 
disease, osteoarthritis, metabolic syndrome, and pulmonary disease.  Their obesity 
affects not only comorbidity but also quality of life, including levels of fatigue 
and physical functioning.91, 106 As stated in 2.4, if obese women are more 
responsive to metformin/paclitaxel/carboplatin treatment, this responsiveness may 
be reflected in an improved self- reported- health status, or in an advanced cancer 
patient population, a lower rate of decline than patients not treated with 
metformin. 
 
This disease and its co-morbidities are linked to manifestations of fatigue92.  In 
turn, cancer-related fatigue benefits from physical activity93, 94, 95, including 
among endometrial cancer patients.96 Fatigue has also been linked to shorter 
recurrence-free and overall survival.97 Cancer-related fatigue (CRF) is a very 

 
 
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22 
 
common and distressing symptom related to cancer and its treatment98, is known 
to adversely affect quality of life, and may be a dose limiting toxicity for some 
agents.  Because of its prevalence and impact, CRF has been the subject of much 
research [i.e., NCI Symptom Management and Quality of Life Steering 
Committee Cancer-related Fatigue CTPM Executive Summary, April 2010].  One 
recommendation from this CTPM was to “study host, disease, treatment, and 
environmental factors that result in different manifestations of CRF.”  We suggest 
that these two PRO objectives provide a novel, and significant way in which to 
examine the mechanisms of CRF within an advanced cancer clinical trial, in 
which the disease, the host environment (e.g., obesity, level of physical activity), 
and the hypothetically beneficial exposure to Metformin could improve how CRF 
is conceptualized in this population. 
 
This is not without precedent.  For example, the NCIC is currently conducting a 
Phase III Randomized Trial of Metformin vs Placebo in Early Stage Breast 
cancer, in which health-related quality of life, physical activity, and diet are 
included as secondary outcome measures 
(www.clinicaltrials.gov/NCT01101438).  Further, an NCI-funded BIQSFP 
supplement was awarded to NSABP to examine the association between markers 
of inflammation and symptoms of fatigue among patients with and without 
exposure to Metformin. Notably, one of the objectives of their supplement is to 
determine whether metformin is associated with reductions in inflammatory 
markers and corresponding decreases in fatigue.  We are proposing to collect data 
in the phase II trial, so that the GOG moves to implement a phase III trial on 
metformin versus placebo, we will have the necessary data to estimate outcome 
variances and effect sizes for planning the patient reported outcome component of 
the trial. Because the effects of metformin on this population of advanced cancer 
patients is unknown (and thus estimates of effect size are unavailable), it is 
important for us to collect PRO data in phase II for appropriate planning of Phase 
III.  
Additionally, if the study moves on to phase III, 300 patients will be recruited but 
the survival data will be analyzed together with the 240 patients from phase II, for 
a total sample size of 540. If PRO data are not collected during phase II the 
sample size for PRO data will be limited to only 300 patients, which may be 
inadequate for determining differences.  In the phase II study we expect positive 
trends supporting these hypotheses. This would support inclusion of the outcomes 
in the phase III trial, which would have an adequate sample size for definitive 
testing of hypotheses examining between group differences.   
Our proposed study differs from this trial in three fundamental ways:  1) This is 
an advanced cancer patient population, for which effective treatments and 
survival are paramount; 2) A sizeable proportion of this advanced cancer patient 
population will be obese, thereby creating hypothesized survival advantages if 
randomized to metformin.  3) Physical well-being, physical activity, and 
decreasing obesity are of critical importance to this patient advocacy population 
(per GCSC and NCI summary statement critique 11/12).   Therefore, we include a 

 
 
GOG-0286B 
 
23 
 
brief physical activity assessment (which collects information on vigorous and 
moderate activity, as well as walking and sitting) as part of the patient-reported 
outcome component of this trial for several reasons. First, physical activity 
provides a secondary measure of physical functioning. Measures of physical 
functioning reflect a patient’s ability to do basic activities of daily living, but 
leisure time physical activity provides an indicator of functioning above and 
beyond the basic level that may be particularly useful among patients who have 
sufficient functioning to do basic daily activities and thus may have a ceiling 
effect on physical functioning measures. Second, leisure time physical activity has 
been associated with improved quality of life and outcomes in randomized trials 
of patients with a range of cancer types99.  Furthermore, higher physical activity 
levels after diagnosis have been associated with improved overall and disease 
specific survival in breast and colon cancer100.  The measure chosen also assesses 
time spent sitting. Sedentary behavior, independent of moderate to vigorous 
intensity physical activity, is increasingly being linked to negative health 
outcomes107, including endometrial cancer108. Time spent sitting is also likely to 
provide an indication of physical functioning. Data exists on physical activity in 
endometrial cancer survivors with early stage disease.  These women report lower 
levels of moderate to vigorous activity than women their age who have not had 
cancer. In a survey of 120 survivors of early stage endometrial cancer, only 22% 
were meeting recommendations of 150 minutes of moderate to vigorous physical 
activity per week, compared with 46% of the general population 106. In this survey 
survivors who were sedentary had poorer physical functioning and more fatigue 
than those who were more active. However, data on physical activity in patients 
with advanced and recurrent endometrial cancer do not exist. Collecting data on 
physical activity and sedentary behavior in this trial would provide preliminary 
data which could aid in designing a physical activity intervention trial. Finally, 
physical activity targets some of the same mTOR related pathways as metformin 
(i.e., via AMPK pathway)101, and thus could be analyzed along with the 
translational endpoints of the trial. In addition, physical activity can be anti-
inflammatory102 and reduce insulin resistance103.  
 
2.6 
Inclusion of Women and Minorities 
 
The Gynecologic Oncology Group and GOG participating institutions will not 
exclude potential subjects from participating in this or any study solely on the 
basis of ethnic origin or socioeconomic status.  Every attempt will be made to 
enter all eligible patients into this protocol and therefore address the study 
objectives in a patient population representative of the entire endometrial cancer 
population treated by participating institutions. 

 
 
GOG-0286B 
 
24 
 
 
3.0 ELIGIBILITY CRITERIA 
 
3.1 
Eligible Patients 
 
3.11    Patients must have measurable Stage III, measurable Stage IVA, Stage 
IVB (with or without measurable disease) or recurrent (with or without 
measurable disease) endometrial carcinoma. 
 
Histologic confirmation of the original primary tumor is required. 
Patients with the following histologic epithelial cell types are eligible: 
Endometrioid adenocarcinoma, serous adenocarcinoma, undifferentiated 
carcinoma, clear cell adenocarcinoma, mixed epithelial carcinoma, 
adenocarcinoma not otherwise specified (N.O.S.). 
 
3.12 
Measurable disease is defined by RECIST (version 1.1).  Measurable 
disease is defined as at least one lesion that can be accurately measured in 
at least one dimension (longest diameter to be recorded).  Each lesion 
must be ≥ 10 mm when measured by CT, MRI or caliper measurement by 
clinical exam; or ≥ 20 mm when measured by chest x-ray.  Lymph nodes 
must be > 15 mm in short axis when measured by CT or MRI (See section 
8).  
 
 
 
 
3.13 
Patients must have a GOG Performance Status of 0, 1, or 2. 
 
3.14 
Patients must have adequate: 
NOTE: Institutional/laboratory upper limit of normal (ULN) 
 
Institutional/laboratory lower limit of normal (LLN) 
 
3.141 Bone marrow function:  
• Absolute neutrophil count (ANC) greater than or equal to 
1,500/mcl 
• Platelets greater than or equal to100,000/mcl.  
 
 
3.142 Renal function:   
• Creatinine less than 1.4 mg/dl. 
(Per the manufacturer, metformin is contraindicated in the 
presence of renal dysfunction defined as a serum creatinine 
≥1.4 mg/dL in females and in patients with abnormal 
clearance.) (05/19/14) 
 
3.143 Hepatic function: 
• Bilirubin less than or equal to 1.5 x ULN 
• AST and ALT less than or equal to 3 x ULN 
• Alkaline phosphatase less than or equal to 2.5 x ULN 
 

 
 
GOG-0286B 
 
25 
 
3.15 
Prior Therapy: 
 
3.151 Patients must NOT have received prior chemotherapy or targeted 
therapy, including chemotherapy used for radiation sensitization 
for treatment of endometrial carcinoma. 
 
3.152 Patients may have received prior radiation therapy for treatment of 
endometrial carcinoma.  Prior radiation therapy may have included 
pelvic radiation therapy, extended field pelvic/para-aortic radiation 
therapy, and/or intravaginal brachytherapy.  All radiation therapy 
must be completed at least 4 weeks prior to the first date of study 
therapy. 
 
3.153 Patients may have received prior hormonal therapy for treatment of 
endometrial carcinoma.  All hormonal therapy must be 
discontinued at least one week prior to the first date of study 
therapy. 
 
3.16 
Patients must be able to swallow and retain orally-administered 
medication. 
 
3.17 
Patients must have signed an approved informed consent and authorization 
permitting release of personal health information. Individuals with 
impaired decision-making capacity are not eligible to participate on the 
study. 
 
3.18 
Patients must meet eligibility criteria as specified in section 7.0. 
 
3.19 
Patients must be 18 years or older. 
 
3.2 
Ineligible Patients 
 
3.21 
Patients must NOT be taking metformin or have been on metformin in the 
past 6 months.   
 
3.22 
Patients with a history of other invasive malignancies, with the exception 
of non-melanoma skin cancer are excluded if there is any evidence of 
other malignancy being present within the last three years.   
 
3.23 
Uncontrolled intercurrent illness including, but not limited to, ongoing or 
active infection, symptomatic congestive heart failure, unstable angina 
pectoris, cardiac arrhythmia, or psychiatric illness/social situations that 
would limit compliance with study requirements. 
 

 
 
GOG-0286B 
 
26 
 
3.24 
Patients who are pregnant or nursing. If patients are of reproductive age 
and have not undergone hysterectomy, they must use an effective 
contraceptive method for the duration of this study. 
 
3.25 
Any condition associated with increased risk of metformin-associated 
lactic acidosis. (e.g. congestive heart failure defined as New York Heart 
Association {NYHA} Class III or IV functional status, history of acidosis 
of any type; habitual intake of 3 or more alcoholic beverages per day) 
 

 
 
GOG-0286B 
 
27 
 
4.0 STUDY MODALITIES 
 
4.1  
Paclitaxel (NSC #673089) 
 
4.11  Formulation: Paclitaxel is supplied as a 6mg/mL non-aqueous solution in 
multi-dose vials containing 30mg/5mL, 100mg/16.7mL, or 300mg/50mL 
of paclitaxel.  In addition to 6mg of paclitaxel, each mL of sterile non-
pyrogenic solution contains 527mg of purified Cremophor® EL 
(polyoxyethylated castor oil) and 49.7% (v/v) dehydrated alcohol, USP. 
 
4.12  Storage: Unopened vials of paclitaxel are stable to the date indicated on 
the package when stored between 20 to 25°C (68 to 77°F). Protect from 
light. 
 
4.13 
Stability: Commercially available paclitaxel will be labeled with an 
expiration date. All solutions of paclitaxel exhibit a slight haziness directly 
proportional to the concentration of drug and the time elapsed after 
preparation, although when prepared as described below, solutions of 
paclitaxel (0.3-1.2 mg/ml) are physically and chemically stable for 27 
hours. 
 
4.14 
Preparation: Paclitaxel must be diluted prior to infusion. Paclitaxel should 
be diluted in 0.9% Sodium Chloride for Injection, USP; 5% Dextrose 
Injection, USP; 5% Dextrose and 0.9% Sodium Chloride Injection, USP; 
or 5% Dextrose in Ringer’s Injection to a final concentration of 0.3 to 
1.2mg/mL.  The solutions are physically and chemically stable for up to 
27 hours at ambient temperature (approximately 25°C / 77°F) and room 
lighting conditions.   
 
NOTE: In order to minimize patient exposure to the plasticizer DEHP, 
which may be leached from PVC infusion bags or sets, diluted paclitaxel 
solutions should be stored in bottles (glass, polypropylene) or plastic 
(polypropylene, polyolefin) bags and administered through polyethylene-
lined administration sets. 
 
Paclitaxel should be administered through an inline filter with a 
microporous membrane not greater than 0.22 microns. Use of filter 
devices such as IVEX-2® or IVEX-HP®, which incorporate short inlet 
and outlet PVC-coated tubing has not resulted in significant leaching of 
DEHP.   
 
All patients should be premedicated with corticosteroids, 
diphenhydramine, and H2 antagonists prior to paclitaxel administration in 
order to prevent severe hypersensitivity reactions. Patients who experience 
severe hypersensitivity reactions to paclitaxel should not be re-challenged 
with the drug. 

 
 
GOG-0286B 
 
28 
 
 
4.15 
Adverse Effects: Consult the package insert for the most current and 
complete information.  
 
4.16  Supplier/ How Supplied: Commercially available both from Bristol-Myers 
Squibb Oncology as well as generic manufacturers. Consult the American 
Hospital Formulary Service Drug Information guide, Facts and 
Comparisons, or the package insert for additional information. 
 
 
4.2  
Carboplatin (Paraplatin® - NSC #241240) 
 
4.21  Formulation: Carboplatin is supplied as a sterile, pyrogen-free, 10mg/mL 
aqueous solution in multi-dose vials containing 50mg/5mL, 150mg/15mL, 
450mg/45mL, or 600mg/60mL of carboplatin. 
 
4.22  Storage: Unopened vials of carboplatin are stable to the date indicated on 
the package when stored at 25°C (77°F). Excursions from 15 to 30°C (59 
to 86°F) are permitted. Protect from light. Carboplatin multi dose vials 
maintain microbial, chemical, and physical stability for up to 14 days at 
25°C following multiple needle entries. 
 
4.23  Preparation: Carboplatin aqueous solution can be further diluted to 
concentrations as low as 0.5mg/mL with 5% Dextrose in Water or 0.9% 
Sodium Chloride for Injection, USP. When prepared as directed, 
carboplatin aqueous solutions are stable for 8 hours at room temperature 
(25°C / 77°F). Since no antibacterial preservative is contained in the 
formulation, it is recommended that carboplatin solutions be discarded 8 
hours after dilution. 
 
NOTE: Aluminum reacts with carboplatin causing precipitate formation 
and loss of potency; therefore, needles or intravenous sets containing 
aluminum parts that may come in contact with the drug must NOT be used 
for the preparation or administration of carboplatin. 
 
4.24  Adverse Effects: Consult the package insert for the most current and 
complete information. 
 
4.25  Supplier: Commercially available both from Bristol-Myers Squibb 
Oncology as well as generic manufacturers. Consult the American 
Hospital Formulary Service Drug Information guide, Facts and 
Comparisons, or the package insert for additional information. 
 
4.3 
Metformin (NSC#91485) 
 
4.31  Formulation: Metformin hydrochloride ( N , N -
dimethylimidodicarbonimidic diamide hydrochloride) is not chemically or 

 
 
GOG-0286B 
 
29 
 
pharmacologically related to any other classes of oral antihyperglycemic 
agents. Metformin hydrochloride is a white to off-white crystalline 
compound with a molecular formula of C 4 H 11 N 5 •HCl and a molecular 
weight of 165.63. Metformin hydrochloride is freely soluble in water and 
is practically insoluble in acetone, ether, and chloroform. The pKa of 
metformin is 12.4. The pH of a 1% aqueous solution of metformin 
hydrochloride is 6.68. Metformin tablets contain 850 mg, of metformin 
hydrochloride. Each tablet contains the inactive ingredients povidone and 
magnesium stearate. In addition, the coating for the tablets contains 
hypromellose.  
 
4.32  Storage: Metformin/placebo will be stored at controlled temperatures of 
20-25 degrees Celsius (68-77 degrees Fahrenheit). Metformin/placebo is 
dispensed in a light-resistant container 
 
4.33  Preparation: Metformin is an oral agent and no preparation is needed.  
 
4.34  Adverse Effects: Consult the package insert for the most current and 
complete information. 
 
4.35  Supplier: Metformin and matched placebo will be supplied and distributed 
by Biologics. Metformin will be supplied in 850 mg capsules.  Matched 
placebo will be supplied. 
 
4.36 
Drug Distribution: Following submission and approval of all required 
regulatory documents (as stated in Section 5.0), the Pharmacy Information 
Form will be forwarded to Biologics notifying them that an institution has 
been approved for patient entry.  
 
4.361 Initial Supply: Upon notification of randomization, Biologics will 
ship an initial supply of study drug of the following quantities to 
institution: 
- 
Metformin 850mg OR Placebo capsules - 126 count bottle 
to complete the initial 3 cycles. 
 
4.362  Subsequent Supply: At approximately 7 weeks, Biologics will 
contact the institution to confirm if additional study drug is needed.  
Biologics will ship a subsequent supply of study drug of the 
following quantities to site: 
- 
Metformin 850mg OR Placebo capsules - 126 count bottle 
to complete the next 3 cycles 
 
All study drugs will be shipped with a patient specific label adhered to the 
bottle.  Each bottle will be placed in a Ziploc bag with a study specific 
label adhered to the outside to avoid confusion at the sites. 
 

 
 
GOG-0286B 
 
30 
 
Each shipment includes a patient specific label with the following 
information: 
• The Study Number (i.e. GOG-0286B) 
• Patient identification 
• IND caution statement and/or local regulatory statements 
• Expiration Information 
• Dosing instructions (Take as Directed per Protocol) 
• Storage instructions  
• 
Emergency contact instructions 
 
All drug orders are shipped via FedEx Priority Overnight delivery for 
shipments to US sites. Study Drug is shipped in a Biologics branded 
package with appropriate materials to maintain temperature stability.   
 
4.363 Institution Instruction Upon Receipt of Study Drug: The 
designated site coordinator validates contents of package matches 
information provided on packing slip, signs off on the packing slip, 
and faxes completed form to Biologics to validate shipment has 
been received and is accurate. 
 
4.37 
Drug Accountability: All study drug must be accounted for during the 
course of this study. Sites must maintain a NCI drug accountability log. 
 
4.38 
Drug Destruction: At the conclusion of the study, remaining inventory is 
documented in the accountability records and unused drug is to be 
destroyed as per institution policy and record on the accountability record.   
 
4.4 
Emergency Unblinding 
In the event of an emergency during normal business hours (Monday through 
Friday 9:00 am to 5:00 pm Eastern Time), contact the GOG Statistical and Data 
Center by phone at 1-800-523-2917. At all other times, call: 716-901-2853.  If 
there is no answer, leave a message including a telephone number for a return 
call. A staff member from the GOG Statistical and Data Center will return your 
call. Remember, this is only in the event of an emergency! This procedure is to 
be used by the physician when the physician needs to know whether the patient is 
taking metformin or a placebo to manage an acute illness. Patients should be 
instructed that if they have any questions or symptoms they should contact the 
treating physician’s office. The GOG Statistical and Data Center will require the 
protocol number (e.g., “GOG-0286B”), the patient ID number (e.g., “999-0286B-
001”), and the patient initials (e.g., “FML”) to unblind the patient. 

 
 
GOG-0286B 
 
31 
 
5.0 TREATMENT PLAN AND ENTRY/RANDOMIZATION PROCEDURE   
 
Before patient entries will be accepted, submit the following documents to the GOG 
Administrative Office via mail (Attn: Regulatory Department, Protocol GOG-0286B): 
• 
IRB approval* 
• 
IRB-approved informed consent 
• 
IRB Membership list or IRB assurance number 
• 
Study-specific signed original FDA Form 1572 for institution PI** 
• 
Current CV (signed and dated) for institution PI and all sub-investigators 
listed on FDA Form 1572 
• 
Medical License for institution PI and sub-investigators  listed on FDA 
Form 1572 
• 
Lab license, certificates, and Normal Lab Values (NLV) for labs listed on 
FDA Form 1572 
• 
Signed Investigator Signature Page for protocol** 
• 
Signed financial Disclosure Form for investigators listed on FDA Form 
1572** 
• 
Pharmacy Information Form** 
 
The GOG Administrative Office will receive, review, and approve all regulatory 
documents. Please allow 7-10 days for review and approval of all documents prior to 
randomization of first patient.   
 
* When submitting the IRB approval to the GOG, the CTSU IRB Certification Form 
must be used (form can be downloaded at www.ctsu.org). All initial, continuing and 
amendment reviews must be sent to the GOG Administrative Office. 
 
** Please see GOG-0286B protocol documentation page to download forms by clicking 
on the “Regulatory Forms” link. 
 
5.1   
Patient Entry and Registration 
 
When a suitable candidate has been obtained for protocol entry, the following 
steps should be taken: 
 
OPEN (Oncology Patient Enrollment Network) Registration: All site 
staff will use OPEN to enroll patients to this study.  OPEN can be accessed on the 
GOG web menu page by clicking on the OPEN link.  
  
Prior to accessing OPEN, site staff should verify the following: 
  
• 
All eligibility criteria have been met within the protocol stated 
timeframes. Site staff should use the registration forms provided on the 
group web site as a tool to verify eligibility. 
• 
All patients have signed an appropriate consent form and HIPAA 
authorization form (if applicable).  

 
 
GOG-0286B 
 
32 
 
  
Access requirements for OPEN: 
  
• 
Site staff will need to be registered with CTEP and have a valid and active 
CTEP-IAM account. This is the same account (user id and password) used 
for the CTSU members' web site.  
• 
To perform registrations, the site user must have been assigned the 
'Registrar' role on the GOG or CTSU roster.  
• 
To perform registrations you must have an equivalent 'Registrar' role on 
the Lead Group roster.  Role assignments are handled through the Groups 
in which you are a member.  
  
Note:  The OPEN system will provide the site with a printable confirmation of 
registration and treatment information.   Please print this confirmation for your 
records.  
  
Further instructional information is provided on the CTSU members' web site 
OPEN tab or within the OPEN URL. For any additional questions contact the 
CTSU Help Desk at 1-888-823-5923 or ctsucontact@westat.com. 
 
5.2 
Treatment Plan (05/19/14) (08/18/14)(04/28/15)(12/05/16) 
Randomization will be stratified within the following groups: 
1. Performance status (0 or 1 versus 2) 
2. Disease status (Stage III versus IV or recurrent) 
3. Patient BMI (<30 or >=30) 
 
Randomization: 
Arm I:  
Paclitaxel 175 mg/m2 IV over 3 hours day 1 
Carboplatin AUC = 5 IV day 1 
Every 21 days x 6 cycles 
Metformin 850 mg oral QD, beginning on day 1. If tolerated for 4 weeks, the dose 
will be increased to Metformin 850 mg BID.  
Maintenance regimen (for patients in complete response, partial response or stable 
disease) – Metformin 850 mg oral BID 
Patients continue to receive maintenance treatment until disease progression or 
until adverse events prohibit further therapy. 
 
Arm II: 
Paclitaxel 175 mg/m2 IV over 3 hours day 1 
Carboplatin AUC = 5 IV day 1 
Every 21 days x 6 cycles 
Placebo for metformin 850 mg oral QD, beginning on day 1. If tolerated for 4 
weeks, the dose will be increased to placebo for metformin 850 mg BID.  
 

 
 
GOG-0286B 
 
33 
 
Maintenance regimen – (for patients in complete response, partial response or 
stable disease) – Matched placebo oral BID 
Patients continue to receive maintenance treatment until disease progression or 
until adverse events prohibit further therapy. 
 
Metformin or placebo for metformin 850 mg will begin on cycle 1 day 1.  
Metformin or Placebo doses will be taken orally, once daily approximately 24 
hours apart.  If patients tolerate the once daily dosing of metformin/placebo for 4 
weeks, the dose will be increased to twice daily, approximately 10-12 hours apart. 
Patients will be given a Patient Medication Calendar to complete (Appendix II).  
The Patient Medication Calendar should be reviewed prior to the start of each 
cycle. 
 
Paclitaxel will be infused over approximately 3 hours. 
 
Carboplatin will be infused over approximately 30 minutes, following paclitaxel. 
 
For all cycles where paclitaxel is to be administered, it is recommended that a 
preparative regimen be employed, to reduce the risk associated with 
hypersensitivity reactions.  This regimen should include dexamethasone (either IV 
or PO), an anti-histamine H1 (diphenhydramine 25-50 mg IV or orally, or an 
equivalent dose of an alternate H1blocker such as loratadine or fexofenadine), and 
a standard dose of antihistamine H2 IV (Famotidine is preferred, but ranitidine 
can also be used. Cimetidine is not recommended as it may increase the serum 
concentration of metformin).  The preparative regimen can be altered at the 
discretion of the treating physician. 
 
NOTE: Patients with SD or PR who still have measurable disease at the 
completion of cycle 6 may continue to receive paclitaxel and carboplatin (with 
metformin or placebo) up to a total of 10 cycles (if deemed necessary by the 
treating investigator). Patients who continue with cycles 7-10 will continue with 
all study assessments as described for Prior to each cycle (cycles 1-6) (see Section 
7, Study Parameters). 
 
Dosing of Carboplatin: See Appendix IV for Carboplatin Dose Calculation 
Instructions. 
 
Chemotherapy administration:  See Appendix III for NRG Oncology General 
Therapy Guidelines. 
 
5.3 
Supportive Care Guidelines:  
 
5.31 
Nausea/Vomiting:  It is anticipated that nausea and vomiting may be a 
significant side effect (due to carboplatin administration).  It is 
recommended that ASCO, NCCN and/or your institutional anti-emetic 
guidelines be consulted.  Antiemetic regimens can be altered at the 

 
 
GOG-0286B 
 
34 
 
discretion of the treating physician. (05/19/14) 
 
5.32 
Routine supportive measures for cancer patients such as erythropoietin, 
analgesics, blood transfusions, antibiotics, bisphosphonates, and myeloid 
colony stimulating factors are permitted (see Section 6 for specific 
guidelines). 
 
5.4 
Criteria for removal from treatment 
 
 
5.41 
Inability to tolerate the lowest doses because of toxicity. 
 
 
5.42 
Patients may withdraw from the study at any time for any reason.  Patients 
with evidence of disease progression or significant side effects will be 
removed from study therapy. 
 
 
 

 
 
GOG-0286B 
 
35 
 
6.0 TREATMENT MODIFICATIONS 
 
In order to maintain dose-intensity and cumulative dose-delivery on this study, 
reasonable efforts will be made to minimize dose reduction and treatment delays as 
specified. Any patient whose treatment is delayed must be evaluated on a weekly basis 
until adequate hematologic and non-hematologic parameters have been met. No dose 
escalation is planned for this study. 
 
6.1  
Individual Dose Modification Levels 
 
 
All modifications are relative to the actual starting doses for the specific Regimen. 
For application of individual dose modifications, see specific guidelines below. 
Allowable drug dose levels and instructions are summarized in Tables A, B, and 
C. 
• General Guidelines for Hematologic Toxicity  
• Hematologic Nadirs, Table A  
• Delayed Hematologic Recovery, Table B  
• Non-Hematologic Toxicity, Table C 
 
6.2  
General Guidelines for Hematologic Toxicity 
 
6.21  Initial treatment modifications will consist of cycle delay and/or dose 
reduction as directed. 
 
6.22  Treatment decisions will be based on the absolute neutrophil count (ANC) 
rather than the total white cell count (WBC). 
 
6.23 
 Lower Limits for ANC and Platelet Count 
 
6.231  With Cytotoxic Chemotherapy - Subsequent courses of treatment 
which contain any cytotoxic chemotherapy (carboplatin, 
paclitaxel) will not begin (day 1 of each cycle) until the ANC is ≥ 
1,500 cells/ mcl and the platelet count is ≥ 100,000/ mcl. Such 
treatment will be delayed for a maximum of three weeks until 
these values are achieved. Patients who fail to recover adequate 
counts within a three-week delay will no longer receive any 
protocol-directed therapy. 
 
Exceptions:  
Patients who received filgrastim or pegfilgrastim prior to the 
current cycle may begin (day 1 of cycle) with ANC ≥ 1000 cells/ 
mcl, if clinically appropriate, to allow for transient reductions in 
ANC after discontinuation of myeloid growth factors.  Patients 
who are delayed more than 7 days may begin with ANC ≥ 1000 

 
 
GOG-0286B 
 
36 
 
cells/ mcl, if clinically appropriate; and if they will receive 
filgrastim or pegfilgrastim with subsequent therapy. 
 
6.232  During Maintenance – Metformin or Placebo is not expected to 
cause myelosuppression.  Required blood tests during maintenance 
are detailed in Table 7. 
 
6.24  Use of Hematopoietic Cytokines and Protective Agents 
 
6.241  It is anticipated that myelosuppression may be a significant side 
effect of cytotoxic chemotherapy.  Myeloid growth factors (either 
filgrastim or pegfilgrastim) can be used (it is recommended that 
ASCO, NCCN and/or your institutional guidelines be consulted).  
If myeloid growth factors are used, it is recommended that 
filgrastim (dose and schedule per institutional standard) or 
pegfilgrastim (dose and schedule per institutional standard) be 
administered. 
 
6.242  Patients will NOT receive prophylactic thrombopoietic agents. 
 
6.243  Patients may receive erythropoietin (EPO), iron supplements, 
and/or transfusions as clinically indicated for management of 
anemia. Treating physicians should be aware of prescribing 
information for the erythropoiesis stimulating agents (including 
Aranesp, Epogen and Procrit) which note that there is a potential 
risk of shortening the time to tumor progression or disease-free 
survival, and that these agents are administered only to avoid red 
blood cell transfusions. They do not alleviate fatigue or increase 
energy. They should NOT be used in patients with uncontrolled 
hypertension. They can cause an increased incidence of thrombotic 
events in cancer patients on chemotherapy. The updated package 
inserts should be consulted. 
http://www.fda.gov/Medwatch/safety/2007/safety07.htm 
 
6.3  
Modifications for Hematologic Toxicity (Nadirs) 
 
6.31  Initial occurrence of dose-limiting neutropenia (defined in 6.32) or dose 
limiting thrombocytopenia (defined in 6.33) will be handled according to 
Table A. 
 
6.32  Dose-Limiting Neutropenia (DLT-ANC) is defined by the occurrence of 
febrile neutropenia or prolonged Grade 4 neutropenia persisting ≥ 7 days. 
There will be no modifications for uncomplicated Grade 4 neutropenia 
lasting less than 7 days. Febrile neutropenia is defined as a disorder 
characterized by an ANC <1000/mm3 and a single temperature of >38.3 
degrees C (101 degrees F) or a sustained temperature of >=38 degrees C 

 
 
GOG-0286B 
 
37 
 
(100.4 degrees F) for more than one hour. Dose reductions will be handled 
according to Table A.  
 
6.33  Dose-limiting thrombocytopenia (DLT-PLT) is defined by any occurrence 
of Grade 4 thrombocytopenia or bleeding associated with Grade 3 
thrombocytopenia. There will be no modifications for uncomplicated 
Grade 3 thrombocytopenia. Dose reductions will be handled according to 
Table A.  
 
 
Table A:  Modification Instructions for Dose-Limiting Hematologic Toxicity 
DLT 
ANC 
DLT 
PLT 
First Occurrence 
Second Occurrence 
Third Occurrence 
Yes± 
No 
Reduce paclitaxel one 
dose level 
Reduce carboplatin one 
AUC unit* 
Discontinue protocol-directed 
cytotoxic chemotherapy 
Yes± 
Yes 
Reduce paclitaxel one 
dose level and 
carboplatin one AUC 
unit* 
Reduce paclitaxel one 
dose level 
Discontinue protocol-directed 
cytotoxic chemotherapy 
No 
Yes 
Reduce carboplatin one 
AUC unit* 
Reduce paclitaxel one 
dose level 
Discontinue protocol-directed 
cytotoxic chemotherapy 
*Minimum carboplatin dose = AUC 4. 
±Myeloid growth factors (either filgrastim or pegfilgrastim) can be used (it is recommended that 
ASCO, NCCN and/or your institutional guidelines be consulted).  See section 6.241. 
 
6.4  
Modifications for Delayed Hematologic Recovery: 
 
6.41  Delay on the basis of neutropenia (Delay-ANC) is defined if the ANC is 
less than 1,500 cells/mcl within 24 hours prior to scheduled day 1 therapy, 
or less than 1,000 cells/mcl, if the patient received filgrastim or 
pegfilgrastim during the previous cycle. 
 
6.42  Delay on the basis of thrombocytopenia (Delay-PLT) is defined if the 
platelet count is less than 100,000/mcl within 24 hours prior to scheduled 
day 1 therapy. 
 
6.43 
Modifications noted below are only required for management of delays in 
the absence of dose reductions stipulated by nadir DLT-ANC and/or DLT-
PLT (as noted above). In other words, if the patient experiences DLT-
ANC and Delay-ANC, make the modifications as indicated for the nadir 
counts without additional modifications based on delayed recovery. 
 
Table B:  Modifications for Delayed Hematologic Recovery 
Category 
Delay (days) 
Modification 
Delay -ANC± 1-7 
No Change mandated 
8-21 
Decrease paclitaxel one dose level 
>21 
Discontinue Protocol Directed Cytotoxic Chemotherapy 

 
 
GOG-0286B 
 
38 
 
Delay-PLT 
1-7 
No Change 
8-21 
Decrease carboplatin one AUC unit* 
>21 
Discontinue Protocol Directed Cytotoxic Chemotherapy 
 
*Minimum carboplatin dose = AUC 4. 
±Myeloid growth factors (either filgrastim or pegfilgrastim) can be used (it is 
recommended that ASCO, NCCN and/or your institutional guidelines be 
consulted).  See section 6.241. 
 
6.5 
Adjustments Specific for Metformin/Placebo (hypoglycemia, glucose intolerance 
 
and conditions predisposing to lactic acidosis)  
 
6.51 
If a patient develops a hypoglycemic condition (grade 3 or 4), the 
metformin/placebo will be discontinued until the condition resolves. If a 
patient develops a second episode of grade 3 or 4 hypoglycemia, the 
metformin/placebo will be discontinued, and unblinding will occur for 
appropriate continued management of the hypoglycemia. No dose 
adjustments will be made for hypoglycemia.  
 
6.52 
If a patient develops grade 2 or grade 3 glucose intolerance, any anti-
diabetic medication can be initiated except for metformin, and the patient 
can remain on the trial with full follow-up. If grade 4 glucose intolerance 
develops, the metformin/placebo will be discontinued, and unblinding will 
occur for appropriate continued management of the glucose 
intolerance.6.53 
If a patient develops any condition predisposing to 
lactic acidosis such as renal dysfunction (serum creatinine ≥ 1.4 mg/dl), 
CHF and dehydration, the metformin/placebo will be held until the 
condition resolves.  
 
6.54 
NOTE:  Temporarily discontinue metformin/placebo in patients 
undergoing radiologic studies in which intravascular iodinated 
contrast media are utilized.  It is generally recommended that metformin 
be should be temporarily discontinued prior to or at the time of 
intravascular administration of iodinated contrast media (potential for 
acute alteration in renal function). Metformin is recommended to be 
withheld for 48 hours after the radiologic study.  Advise patients on 
temporarily discontinuing metformin/placebo for radiologic studies with 
intravascular iodinated contrast media according to your institutional 
policies. 
 
6.6 
 Adjustments for Non-Hematologic Toxicity 
 
Table C:  Modifications for Toxicity 
Drug 
Regimen -2 
Level 
Regimen -1 
Level 
Regimen Starting 
Dose 
Paclitaxel 
110 mg/m2 
135 mg/m2 
175 mg/m2 

 
 
GOG-0286B 
 
39 
 
Carboplatin Discontinue 
AUC = 4 
AUC = 5  
 
6.61  The development of Grade 2 (or greater) peripheral neuropathy requires 
reduction of one dose level in paclitaxel.  If CTCAE Grade 3 or 4 
peripheral neuropathy occurs then subsequent doses of paclitaxel will be 
delayed for a maximum of three weeks until recovered to CTCAE Grade 
<=2. If peripheral neuropathy fails to recover to Grade <=2 by a maximum 
delay of three weeks from time therapy is due, then all paclitaxel should 
be withheld from all subsequent chemotherapy cycles.  
 
6.62  Renal toxicity (associated with reduction in GFR) is not expected as a 
direct complication of chemotherapy in this chemotherapy naive patient 
population using the prescribed dose and schedule of each regimen. 
However, if the serum creatinine increases to ≥ 1.4 mg/dl, metformin or 
placebo must be held. Metformin is contraindicated in women with a 
serum creatinine ≥ 1.4 mg/dl. Metformin or placebo may be re-started 
once the serum creatinine improves to < 1.4 mg/dl. See Appendix IV 
(Carboplatin Dose Calculation Instructions). (05/19/14) 
 
6.63  Hepatic toxicity is not expected as a direct complication of chemotherapy 
in this chemotherapy naive patient population using the prescribed dose 
and schedule for each regimen. However, the development of Grade 3 (or 
greater) elevations in AST, ALT, alkaline phosphatase or bilirubin 
requires reduction of one dose level in paclitaxel and delay in subsequent 
therapy for a maximum of three weeks until recovered to Grade 1. 
 
6.64  There will be no dose modifications for alopecia or controllable nausea, 
vomiting, constipation, or diarrhea. It is recommended that routine 
medical measures be employed to manage nausea, vomiting, constipation, 
and diarrhea. 
 
6.65 
 Potential modifications for uncontrolled nausea, vomiting, constipation or 
diarrhea or other non-hematologic toxicities Grade 2 (or greater) require 
discussion with the study chair except where noted below in Section 
6.651.  
 
6.651 Special Modifications Study Treatment6.651For any 
CTCAE Grade 3 non-hematologic adverse event (except 
controllable nausea/vomiting, constipation or diarrhea) 
considered to be at least possibly related to study treatment, 
protocol directed treatment should be held until symptoms 
resolve to <= CTCAE Grade 1.  Upon resumption of 
therapy, both paclitaxel and carboplatin should be reduced 
by one dose level.  If a CTCAE Grade 3 adverse event 
persists for > three weeks or recurs after resumption of 

 
 
GOG-0286B 
 
40 
 
therapy, the patient may be taken off protocol directed 
treatment after consulting with the Study Chair. 
 
6.65For any CTCAE Grade 4 non-hematologic adverse event, the 
patient may be taken off protocol directed treatment 
therapy after consulting with the Study Chair. 

 
 
GOG-0286B 
 
41 
 
7.0 STUDY PARAMETERS 
 
 
7.1 
Observations and Tests (04/28/15) 
 
The following observations and tests are to be performed and recorded on the 
appropriate forms. 
 
Parameter 
 
Pre-
Therapy 
Prior to 
Each 
Cycle 
(Cycles 1-
6) 
Prior 
to 
Cycle 
3 
Every 
9 
weeks 
Every 3 
Maintenance 
Cycles 
Prior to 
each Cycle 
(Cycle 7-
10) for 
patients 
continuing 
carboplatin
/paclitaxel 
Off All 
Study 
Therapy 
History & Physical 
1 
X 
 
 
X 
X 
 
Vital Status 
 
 
 
 
 
 
6 
Review of Patient Medication 
Calendar 
 
X 
 
 
X 
X 
 
Vital signs (Heart rate, blood 
pressure, and temperature) 
1 
X 
 
 
X 
X 
 
BMI (height and weight)* 
1 
 
X 
 
X 
 
 
Hip to waist ratio 
1, 7 
 
X 
 
X 
 
 
Performance status 
1 
X 
 
 
X 
X 
 
Toxicity Assessment  
2 
X 
 
 
X 
X 
 
CBC/Differential/Platelets 
2 
X (3) 
 
 
X 
X (3) 
 
Serum Pregnancy Test (for 
patients of childbearing 
potential) 
2 
 
 
 
 
 
 
Fasting glucose 
2 
 
X 
 
 
 
 
Fasting insulin level 
2 
 
X 
 
 
 
 
Electrolytes, BUN, creatinine 
2 
X (3) 
 
 
X 
X (3) 
 
Bilirubin, AST, ALT, 
Alkaline Phosphatase, 
Albumin 
2 
X (3) 
 
 
X 
X (3) 
 
HgbA1C 
1 
 
 
 
 
 
 
ECG 
1 
 
 
 
 
 
 
Chest imaging (X-ray or CT 
scan of the chest) 
1 
 
 
4# 
4# 
 
4# 
Radiographic tumor 
measurement (CT or MRI) 
1 
 
 
5# 
5# 
 
5# 
Patient Reported Outcomes 
X 
 
X 
X8 
 
 
 
*Height only needs to be measured at baseline (pre-therapy).  Weight should be measured and 
recorded at indicated intervals and BMI recalculated with current weight at the indicated 
intervals. 

 
 
GOG-0286B 
 
42 
 
# Until disease progression, death or initiation of a subsequent anti-cancer therapy.  
Continue with the same frequency if the patient goes off of study therapy for reasons 
other than disease progression or death.  
ONE CYCLE = 3 weeks 
NOTE:  Patients that discontinue metformin or placebo, but continue treatment with 
paclitaxel and/or carboplatin are considered on study therapy.  All observations/tests 
and form completion must continue. 
 
Notes: 
1. Must be obtained within 28 days prior to initiating protocol therapy. 
2. Must be obtained within 14 days prior to initiating protocol therapy. 
3. Laboratory tests (including CBC/Differential/Platelets and creatinine) must be obtained within 4 days of 
re-treatment with cytotoxic therapy. 
4. Repeat chest imaging every 9 weeks (+/- 7 days) and at any other time if clinically indicated based on 
symptoms or physical signs suggestive of progressive disease, if initially abnormal or if required to 
monitor tumor response.  Imaging assessments as part of this protocol can be discontinued if disease 
progression is confirmed according to guidelines in section 8.  If a patient discontinues study 
treatment for any reason other than progression, imaging assessments should be performed every 9 
weeks (+/- 7 days) until progression.  After 2 years of protocol therapy or follow-up (measured from 
approximately cycle 1, day 1), chest imaging interval will be conducted every 12 weeks (+/- 7 days).  An 
Excel tool is provided to assist in determining imaging dates (see Appendix VI).  
5. CT scan or MRI of the abdomen and pelvis every 9 weeks (+/- 7 days) and at any other time if clinically 
indicated based on symptoms or physical signs suggestive of progressive disease.  Imaging assessments 
as part of this protocol can be discontinued if disease progression is confirmed according to guidelines in 
section 8.  If a patient discontinues study treatment for any reason other than progression, CT scan 
or MRI of the abdomen and pelvis should be performed every 9 weeks (+/- 7 days) until 
progression.   After 2 years of protocol therapy or follow-up (measured from approximately cycle 1, day 
1), CT scan or MRI of the abdomen and pelvis interval will be conducted every 12 weeks (+/- 7 days).  
An Excel tool is provided to assist in determining imaging dates (see Appendix VI).  
6. After completion all protocol treatment and radiologic assessments:  Follow-up every 3 months for 2 
years and then every 6 months for 3 years.  Follow-up forms are collected for the 5 year follow-up period 
or until study termination. 
7. Instructions on measuring hip to waist ratio (see section 7.5) 
- Measure hip circumference the maximum extension of the buttocks 
- Measure waist circumference at the belly button. 
8. Patient reported outcomes are collected before cycle 1, 3, and 6, and at 26 weeks. 
 
7.2 
Stained Pathology Slide Requirements for Central Review to Confirm Protocol 
 
Eligibility  
 
7.21 
Eligibility Criteria:  Endometrial cancer. 
 
Patients with the following histologic epithelial cell types are eligible: 
Endometrioid adenocarcinoma, serous adenocarcinoma, undifferentiated 
carcinoma, clear cell adenocarcinoma, mixed epithelial carcinoma, and 
adenocarcinoma not otherwise specified (N.O.S.). 
 
7.22 
Primary Site:  At least two H&E stained slides from the primary tumor 
must be submitted.  These slides should document the range of histologic 

 
 
GOG-0286B 
 
43 
 
features of the tumor (e.g. grade; clear cell, solid, serous or endometrioid 
features).  More than 2 slides may be submitted.  If the tumor is 
histologically homogeneous, two randomly selected slides should be 
submitted. 
                                                                               
Most Advanced Stage:  If histologically determined, at least one H&E 
stained slide documenting the most advanced stage should be submitted.  
 
Recurrent Tumor: If histologically determined, at least one representative 
H&E stained slide documenting recurrent tumor should be submitted. 
 
When submitting pathology material to the NRG Statistics and Data 
Monitoring Center individual slides must be labeled with NRG Patient ID, 
patient initials and the surgical / pathology accession number (e.g., S08-
2355) and block identifier (e.g., A6).  Do not label the slides with disease 
site (e.g., right ovary) or procedure date.  Pack the labeled slides into 
plastic slide cassette(s).  Tape plastic slide cassettes shut and wrap in 
bubble wrap or another type of padded material prior to shipping.  Please 
include the NRG Patient ID, patient initials, and protocol number on all 
pages of the pathology report and black out the patient’s name.  Ship 
pathology slides, one copy of the official pathology report in your own 
shipping containing using postal mail at your own expense directly to the 
Pathology Materials Coordinator at the NRG Statistics and Data 
Monitoring Center, Roswell Park Cancer Institute, Research Studies 
Center, Carlton and Elm Streets, Buffalo, New York, 14263; phone 
(716) 845-5702 Please see section 10.2 for additional requirements and 
instructions. (04/28/15) 
 
 
7.3 
Translational Research (12/05/16) 
 
7.31 
Specimen Requirements (12/05/16)(XX/XX/XX) 
 
 
 
If the patient gives permission for her specimens to be collected and used 
for this optional translational research component, then participating 
institutions are required to submit the patient’s specimens as outlined 
below (unless otherwise specified). 
 
Required Specimen (Specimen Code) 
Collection Time Point 
Sites Ship Specimens To 
FFPE Primary Tumor (FP01)* 
1st Choice: block 
2nd Choice: 25 unstained slides (15 
charged, 5µm & 10 uncharged, 10µm) 
Prior to all treatment 
GOG Tissue Bank within 8 weeks of 
registration1 
FFPE Metastatic Tumor (FM01)* 
1st Choice: block 
2nd Choice: 25 unstained slides (15 
charged, 5µm & 10 uncharged, 10µm) 
FFPE Recurrent Primary Tumor 
Prior to study treatment 

 
 
GOG-0286B 
 
44 
 
(FRP01)* 
1st Choice: block 
2nd Choice: 25 unstained slides (15 
charged, 5µm & 10 uncharged, 10µm) 
FFPE Recurrent Metastatic Tumor 
(FRM01)* 
1st Choice: block 
2nd Choice: 25 unstained slides (15 
charged, 5µm & 10 uncharged, 10µm) 
Pre-Treatment Whole Blood (WB01) 
7-10mL drawn into purple top (EDTA) 
tube(s) 
Prior to study treatment 
GOG Tissue Bank the day the 
specimen is collected1 
* 
A copy of the corresponding pathology report must be shipped with all tissue specimens sent to the GOG Tissue Bank 
1 
GOG Tissue Bank / Protocol GOG-0286B, Nationwide Children’s Hospital, 700 Children’s Drive, WA1340, Columbus, OH 
43205, Phone: (614) 722-2865, FAX: (614) 722-2897, Email: BPCBank@nationwidechildrens.org 
  
 
 
7.32 
Laboratory Testing (12/05/16) 
Assay details are included in Appendix VII. 
 
7.321  Analysis of PI3K/AKT/MTOR Pathway Mutations and 
Amplifications (Integrated Biomarker) 
 
DNA extracted from FFPE and whole blood will be used for 
targeted sequencing using a hybrid capture approach in 
conjunction with next generation sequencing. PIK3CA 
mutations/amplifications and PIK3R1/PIK3R2 mutations will 
specifically be assessed. 
 
7.322 Immunohistochemical Analysis of Metformin Transporter Proteins 
(Integrated Biomarker) 
 
FFPE will be used for metformin transporter proteins (OCT 3, 
MATE 1/2, and PMAT) immunohistochemistry. 
 
7.323 Immunohistochemical Analysis of Key Targets of the Insulin/IGF-
 
1/mTOR Pathway (Exploratory Biomarker) 
 
 
 
FFPE will be used for p-IGF1R, p-S6 and p-4E-BP-1 
 
immunohistochemistry. 
 
7.324 Analysis of the Genetic Variants of the Metformin Transporters 
 
(Exploratory Biomarker) 
 
 
DNA extracted from FFPE and whole blood will be used for 
targeted sequencing using a hybrid capture approach in 
conjunction with next generation sequencing. Genetic variants of 
the metformin transporters will specifically be assessed (see Table 
2 of Appendix VII). 
 

 
 
GOG-0286B 
 
45 
 
 
 
7.33 
Future Research 
 
Details regarding the banking and use of specimens for future research can 
be found in Appendix V. 
 
7.4 
Patient-reported Outcomes Measures (05/19/14) 
 
 
Physical Function   The physical functioning (PF) subscale of the SF-36 has 
been used previously to study physical functioning and ability to do activities of 
daily living in endometrial cancer survivors [Basen-Engquist et al, 2009; Nout et 
al 2011].  Basen-Engquist et al found that endometrial cancer survivors’ scores on 
the physical functioning scale were 8.1 points lower, on average, than age-
matched norms.  In this sample, the SF-36 physical functioning scale was 
negatively correlated with ratings of pain, fatigue, and age, as expected (Basen-
Engquist, personal communication). The scale was used in a study of long term 
outcomes of endometrial cancer patients randomized to surgery plus radiation 
versus surgery alone; patients in the radiation treatment group reported poorer 
physical functioning 15 years after treatment than those who received surgery 
alone [Nout et al 2011]. The scale is also sensitive to differences in BMI in 
endometrial cancer survivors91, 96, and physical activity level96.  The PF subscale 
consists of 10 items designed to assess the ability to conduct activities of daily 
living requiring large motor skills; a high score indicates better PF and a 
difference of 7 points on the scale is suggested as a minimally important 
difference.109  The internal consistency of the subscale is excellent (Cronbach 
alpha, 0.89-0.92) [Ware et al, l997] and it correlates with other PF measures and 
distinguishes between serious and mild medical conditions [McHorney et al, 
l993].  
 
Fatigue The Functional Assessment of Chronic Illness Therapy-Fatigue Scale 
(FACIT-F Scale) is a 13-item questionnaire that assesses self-reported fatigue and 
its impact upon daily activities and function. The 13-item FACIT-F Scale is 
formatted for self-administration on one-page, and uses a 5-point Likert-type 
scale (0 = Not at all; 1 = A little bit; 2 = Somewhat; 3 = Quite a bit; and 4 = Very 
Much). The FACIT-F Scale has been found to be reliable and valid (Yellen et al, 
1997; Cella et al, 2002). Since its initial validation report published in 1997, the 
FACIT-F Scale has been used on well over 20,000 people with cancer, and 
represents the multistep process for validation [Smith et al, PM&R, 2010].  It is 
currently being used in GOG 249 for validation of the PROMIS Fatigue-SF1.  A 
difference of 4.1 scale points is the best estimate of the minimal clinically 
important difference (MCID) when comparing the FACIT-Fatigue subscale 
between treatment groups.    
 
Physical Activity   
To measure physical activity patients will complete short form of the International 
Physical Activity Questionnaire (IPAQ). The IPAQ includes 7 questions asking 
patients to report the frequency and duration of their vigorous, moderate, and 

 
 
GOG-0286B 
 
46 
 
walking (at least 10 minutes) activity in the past 7 days, and the amount of time 
they spend sitting per day. The IPAQ has very good test-retest reliability for the 
overall score; in a 12 country study of the IPAQ’s reliability and validity110, 75% 
of the test-retest estimates were greater than 0.65, and the pooled estimate was 
0.76. The test retest reliability of the sitting question alone is also very good, with 
2/3 of the estimates greater than 0.70. In comparisons with accelerometer data, the 
short IPAQ was moderately correlated with total activity (2/3 of the correlations 
coefficients r > 0.30) and sitting (over half of the correlation coefficients r >0,30). 
When the data are categorized based on meeting the criteria of >150 minutes per 
week, agreement coefficients between accelerometer and the short IPAQ ranged 
from .46 to .93, with 80% of the coefficients greater than 0.70. The question 
responses can be used to estimate MET minutes/week for walking, moderate 
intensity activity, and vigorous intensity activity, as well as total physical activity 
MET minutes/week.     
 
PRO Measurement Intervals 
Patients will complete the Questionnaires at 4 times: 
1) Prior to beginning therapy 
2) 6 weeks after initiating therapy (approximately prior to cycle 3) 
3) 15 weeks after initiation of treatment (prior to cycle 6) 
4) 26 weeks following initiation of therapy 
 
The timing of the first three assessments is scheduled to coincide with regular 
treatment follow-up visits and to take account of expected changes in this patient 
population during active treatment, which might include improvements in fatigue 
and decreased sedentary behavior in the experimental arm.  The final or 4th 
assessment at 26 weeks/approx 6 months post baseline will measure quality of life 
after patients have completed chemotherapy on this protocol, but will provide  an 
opportunity to detect potential early differences in treatment arms responses 
between those on or off maintenance therapy.  QOL assessments should be 
administered at assessment times, regardless of whether the patient progresses or 
is removed from study for any reason.  It is worth noting the GOG experience in 
two recent advanced endometrial cancer studies (GOG-0122, GOG-0209) 
indicates that compliance is substantial (>94%) initially, and continues to be 
impressive even at later assessment points (e.g., GOG-0209 @ 81%), with 
minimal missing data. Data monitoring and institution training for compliance 
and data quality is ongoing. 
 
7.5 Anthropometrics (05/19/14) 
 
Height, weight, and waist circumference will be measured at the clinic site by site 
staff for each participant enrolled in the trial.  The physical examination form site 
will be entered electronically via Medidata Rave. Specific instructions for 
measurement are detailed below.  
  
Height  

 
 
GOG-0286B 
 
47 
 
Each participant will have her height in indoor clothes and without shoes 
measured at baseline and yearly at the GOG clinic managing her regular oncology 
clinical exams. The participant should be standing evenly on a flat surface at a 
right angle to the movable vertical board or cap that is pulled down to lightly 
touch the most superior point of the head. Hair may need to be compressed to 
ensure correct measurement of the participant’s height. The height should be 
recorded on the physical exam form to the nearest 1.0 centimeter.  
 
Weight  
Each participant will have her weight measured at baseline and every three 
months (to be in line with follow-up physical exams) at the GOG clinic managing 
her regular oncology clinical exams. Participants will be weighed using a 
calibrated standard beam scale that was calibrated to zero just prior to use. The 
scale should be located on a level area and the participant in lightweight, indoor 
clothing and shoeless should step onto the scale so that her body weight is 
centered on the platform and distributed evenly between both feet. The weight 
should be recorded on the physical exam form to the nearest 0.5 kilogram.  
 
Waist Circumference 
At baseline and every 3 cycles (9 weeks) (along with weight and physical 
examination), the participant will have her waist circumference measured by local 
GOG oncology care clinic staff. Clothing should not be covering the waist area at 
the time of the waist-circumference measurement. The participant should be 
standing relaxed, with her hands hanging at her sides. The measurement should be 
taken by facing the participant and placing the measurement tape horizontally 
around the participant at the umbilicus. The tape measure should not be stretched 
nor the skin compressed when the waist circumference is being measured. Special 
care should be taken to ensure that the tape measure is straight and not up on one 
side or the other. Clinics may find it helpful to have another staff member or 
mirror present to confirm correct alignment of the tape measure.  The waist 
circumference should be recorded to the nearest 0.1 centimeters. 
 
Buttocks (Hip) Circumference 
The patient stands erect with feet together and weight evenly distributed on both 
feet. The examiner squats on the right side of the SP and places the measuring 
tape around the buttocks. The tape is placed at the maximum extension of the 
buttocks. Ensure that the tape is horizontal and parallel to the floor. The tape is 
held snug but not tight. The examiner takes the measurement from the right side 
and calls it to the recorder. The hip circumference should be recorded to the 
nearest 0.1 centimeters. 
 
 
 
 

 
 
GOG-0286B 
 
48 
 
8.0 EVALUATION CRITERIA  
 
 
8.1 
Antitumor Effect – Solid Tumors 
 
Response and progression will be evaluated in this study using the new 
international criteria proposed by the revised Response Evaluation Criteria in 
Solid Tumors (RECIST) guideline (version 1.1) [Eur J Ca 45:228-247, 2009].  
Changes in the largest diameter (unidimensional measurement) of the tumor 
lesions and the shortest diameter in the case of malignant lymph nodes are used in 
the RECIST criteria. 
 
 
8.11 
Definitions 
 
Evaluable for toxicity:  All patients will be evaluable for toxicity from the 
time of their first treatment on study. 
 
Evaluable for objective response:  Only those patients who have 
measurable disease present at baseline, have received at least one cycle of 
therapy, and have had their disease re-evaluated will be considered 
evaluable for response.  These patients will have their response classified 
according to the definitions stated below.  (Note:  Patients who exhibit 
objective disease progression prior to the end of cycle 1 will also be 
considered evaluable.) 
 
Evaluable Non-Target Disease Response:  Patients who have lesions 
present at baseline that are evaluable but do not meet the definitions of 
measurable disease, have received at least one cycle of therapy, and have 
had their disease re-evaluated will be considered evaluable for non-target 
disease.  The response assessment is based on the presence, absence, or 
unequivocal progression of the lesions.  
 
 
8.12 
Disease Parameters 
 
Measurable disease:  Measurable lesions are defined as those that can be 
accurately measured in at least one dimension (longest diameter to be 
recorded) as >20 mm by chest x-ray, as >10 mm with CT scan, or >10 mm 
with calipers by clinical exam.  All tumor measurements must be recorded 
in millimeters (or decimal fractions of centimeters). 
 
Note:  Tumor lesions that are situated in a previously irradiated area will 
not be considered measurable unless progression is documented or a 
biopsy is obtained to confirm persistence at least 90 days following 
completion of radiation therapy. 
 
Malignant lymph nodes:  To be considered pathologically enlarged and 
measurable, a lymph node must be >15 mm in short axis when assessed by 

 
 
GOG-0286B 
 
49 
 
CT scan (CT scan slice thickness recommended to be no greater than 5 
mm).  At baseline and in follow-up, only the short axis will be measured 
and followed. 
 
Non-measurable disease:  All other lesions (or sites of disease), including 
small lesions (longest diameter <10 mm or pathological lymph nodes with 
≥ 10 to <15 mm short axis), are considered non-measurable disease.  Bone 
lesions, leptomeningeal disease, ascites, pleural/pericardial effusions, 
lymphangitis cutis/pulmonitis, inflammatory breast disease, and 
abdominal masses (not followed by CT or MRI), are considered as non-
measurable. 
 
Note:  Cystic lesions that meet the criteria for radiographically defined 
simple cysts should not be considered as malignant lesions (neither 
measurable nor non-measurable) since they are, by definition, simple 
cysts. 
 
‘Cystic lesions’ thought to represent cystic metastases can be considered 
as measurable lesions, if they meet the definition of measurability 
described above. However, if non-cystic lesions are present in the same 
patient, these are preferred for selection as target lesions. 
 
Target lesions:  All measurable lesions up to a maximum of 2 lesions per 
organ and 5 lesions in total, representative of all involved organs, should 
be identified as target lesions and recorded and measured at baseline.  
Target lesions should be selected on the basis of their size (lesions with 
the longest diameter), be representative of all involved organs, but in 
addition should be those that lend themselves to reproducible repeated 
measurements.  It may be the case that, on occasion, the largest lesion 
does not lend itself to reproducible measurement in which circumstance 
the next largest lesion which can be measured reproducibly should be 
selected.  A sum of the diameters (longest for non-nodal lesions, short axis 
for nodal lesions) for all target lesions will be calculated and reported as 
the baseline sum diameters.  If lymph nodes are to be included in the sum, 
then only the short axis is added into the sum.  The baseline sum diameters 
will be used as reference to further characterize any objective tumor 
regression in the measurable dimension of the disease. 
 
Non-target lesions:  All other lesions (or sites of disease) including any 
measurable lesions over and above the 5 target lesions should be identified 
as non-target lesions and should also be recorded at baseline.  
Measurements of these lesions are not required, but the presence, absence, 
or in rare cases unequivocal progression of each should be noted 
throughout follow-up.  
 
 
8.13 
Methods for Evaluation of Measurable Disease 

 
 
GOG-0286B 
 
50 
 
 
All measurements should be taken and recorded in metric notation using a 
ruler or calipers.  All baseline evaluations should be performed as closely 
as possible to the beginning of treatment and never more than 4 weeks 
before the beginning of the treatment. 
 
The same method of assessment and the same technique should be used to 
characterize each identified and reported lesion at baseline and during 
follow-up. Imaging-based evaluation is preferred to evaluation by clinical 
examination unless the lesion(s) being followed cannot be imaged but are 
assessable by clinical exam. 
Clinical lesions: Clinical lesions will only be considered measurable when 
they are superficial (e.g., skin nodules and palpable lymph nodes) and ≥10 
mm diameter as assessed using calipers (e.g., skin nodules).  In the case of 
skin lesions, documentation by color photography, including a ruler to 
estimate the size of the lesion, is recommended.  
 
Chest x-ray: Lesions on chest x-ray are acceptable as measurable lesions 
when they are clearly defined and surrounded by aerated lung.  However, 
CT is preferable.  
 
Conventional CT and MRI: This guideline has defined measurability of 
lesions on CT scan based on the assumption that CT slice thickness is 5 
mm or less.  If CT scans have slice thickness greater than 5 mm, the 
minimum size for a measurable lesion should be twice the slice thickness.  
MRI is also acceptable in certain situations (e.g. for body scans), but NOT 
lung.   
 
Use of MRI remains a complex issue.  MRI has excellent contrast, spatial, 
and temporal resolution; however, there are many image acquisition 
variables involved in MRI, which greatly impact image quality, lesion 
conspicuity, and measurement.  Furthermore, the availability of MRI is 
variable globally.  As with CT, if an MRI is performed, the technical 
specifications of the scanning sequences used should be optimized for the 
evaluation of the type and site of disease.  Furthermore, as with CT, the 
modality used at follow-up should be the same as was used at baseline and 
the lesions should be measured/assessed on the same pulse sequence.  It is 
beyond the scope of the RECIST guidelines to prescribe specific MRI 
pulse sequence parameters for all scanners, body parts, and diseases.  
Ideally, the same type of scanner should be used and the image acquisition 
protocol should be followed as closely as possible to prior scans.  Body 
scans should be performed with breath-hold scanning techniques, if 
possible. 
 
PET-CT: At present, the low dose or attenuation correction CT portion of 
a combined PET-CT is not always of optimal diagnostic CT quality for 

 
 
GOG-0286B 
 
51 
 
use with RECIST measurements.  PET-CT scans are not always done with 
oral and IV contrast.  In addition, the PET portion of the CT introduces 
additional data which may bias an investigator if it is not routinely or 
serially performed.  For these reasons, the GOG will not allow PET-CT 
use for RECIST 1.1 response criteria. 
 
Ultrasound: Ultrasound is not useful in assessment of lesion size and 
should not be used as a method of measurement.  Ultrasound examinations 
cannot be reproduced in their entirety for independent review at a later 
date and, because they are operator dependent, it cannot be guaranteed that 
the same technique and measurements will be taken from one assessment 
to the next.  If new lesions are identified by ultrasound in the course of the 
study, confirmation by CT or MRI is advised.  If there is concern about 
radiation exposure at CT, MRI may be used instead of CT in selected 
instances. 
 
Endoscopy, Laparoscopy: The utilization of these techniques for objective 
tumor evaluation is not advised.  However, such techniques may be useful 
to confirm complete pathological response when biopsies are obtained or 
to determine relapse in trials where recurrence following complete 
response (CR) or surgical resection is an endpoint. 
 
Cytology, Histology: These techniques can be used to differentiate 
between partial responses (PR) and complete responses (CR) in rare cases 
(e.g., residual lesions in tumor types, such as germ cell tumors, where 
known residual benign tumors can remain). 
 
The cytological confirmation of the neoplastic origin of any effusion that 
appears or worsens during treatment when the measurable tumor has met 
criteria for response or stable disease is mandatory to differentiate 
between response or stable disease (an effusion may be a side effect of the 
treatment) and progressive disease. 
 
FDG-PET:  While FDG-PET response assessments need additional study, 
it is sometimes reasonable to incorporate the use of FDG-PET scanning to 
complement CT scanning in assessment of progression (particularly 
possible 'new' disease).  New lesions on the basis of FDG-PET imaging 
can be identified according to the following algorithm:  
a. 
Negative FDG-PET at baseline, with a positive FDG-PET at 
follow-up is a sign of PD based on a new lesion. 
b. 
No FDG-PET at baseline and a positive FDG-PET at follow-up:  
If the positive FDG-PET at follow-up corresponds to a new site of 
disease confirmed by CT, this is PD.  If the positive FDG-PET at 
follow-up is not confirmed as a new site of disease on CT, 
additional follow-up CT  scans are needed to determine if there is 
truly progression occurring at that site (if so, the date of PD will 

 
 
GOG-0286B 
 
52 
 
be the date of the initial abnormal FDG-PET scan).  If the positive 
FDG-PET at follow-up corresponds to a pre-existing site of 
disease on CT that is not progressing on the basis of the anatomic 
images, this is not PD. 
c. 
FDG-PET may be used to upgrade a response to a CR in a manner 
similar to a biopsy in cases where a residual radiographic 
abnormality is thought to represent fibrosis or scarring.  The use 
of FDG-PET in this circumstance should be prospectively 
described in the protocol and supported by disease-specific 
medical literature for the indication.  However, it must be 
acknowledged that both approaches may lead to false positive CR 
due to limitations of FDG-PET and biopsy resolution/sensitivity. 
  
 Note:  A ‘positive’ FDG-PET scan lesion means one which is FDG avid 
with an uptake greater than twice that of the surrounding tissue on the 
attenuation corrected image. 
 
 
 
 
8.14 
Response Criteria 
 
 
 
8.141 Evaluation of Target Lesions 
 
Complete Response (CR): Disappearance of all target lesions.  
Any pathological lymph nodes 
(whether target or non-target) must 
have reduction in short axis to <10 
mm. 
 
 
 
 
 
 
 
 
 
 
 
 
 
Partial Response (PR): 
 At least a 30% decrease in the sum of 
the diameters of target lesions, taking 
as reference the baseline sum 
diameters. 
 
Progressive Disease (PD): At least a 20% increase in the sum of 
the diameters of target lesions, taking 
as reference the smallest sum on study 
(this includes the baseline sum if that 
is the smallest on study).  In addition 
to the relative increase of 20%, the 
sum must also demonstrate an absolute 
increase of at least 5 mm.  (Note:  the 
appearance of one or more new lesions 
is also considered progression). 
 
Stable Disease (SD): 
Neither sufficient shrinkage to qualify 
for PR nor sufficient increase to 

 
 
GOG-0286B 
 
53 
 
qualify for PD 
 
 
 
 
 
8.142 Evaluation of Non-Target Lesions 
 
Complete Response (CR): 
Disappearance of all non-target 
lesions and normalization of tumor 
marker level.  All lymph nodes must 
be non-pathological in size (<10 mm 
short axis) 
 
 
 
Non-CR/Non-PD: 
 
Persistence of one or more non-
target lesion(s) 
 
 
 
Progressive Disease (PD): 
Appearance of one or more new 
lesions and/or unequivocal 
progression of existing non-target 
lesions.  Unequivocal progression 
should not normally trump target 
lesion status.  It must be 
representative of overall disease 
status change, not a single lesion 
increase. 
 
Although a clear progression of “non-target” lesions only is 
exceptional, the opinion of the treating physician should prevail in 
such circumstances, and the progression status should be 
confirmed at a later time by the review panel (or Principal 
Investigator). 
  
 
 
 
8.143 Evaluation of Best Overall Response 
 
The best overall response is the best response recorded from the 
start of the treatment until disease progression/recurrence (taking 
as reference for progressive disease the smallest measurements 
recorded since the treatment started).  The patient's best response 
assignment will depend on the achievement of both measurement 
and confirmation criteria. 
 
For Patients with Measurable Disease (i.e., Target Disease) 
 
Target 
Lesions 
Non-Target 
Lesions 
New 
Lesions 
Overall 
Response 
Best Overall Response 
when Confirmation is 
Required* 
CR 
CR 
No 
CR 
>4 wks. Confirmation** 
CR 
Non-
CR/Non-PD 
No 
PR 
 
>4 wks. Confirmation** 
CR 
Not evaluated 
No 
PR 

 
 
GOG-0286B 
 
54 
 
PR 
Non-
CR/Non-
PD/not 
evaluated 
No 
PR 
SD 
Non-
CR/Non-
PD/not 
evaluated 
No 
SD 
documented at least once >4 
wks. from baseline** 
PD 
Any 
Yes or No 
PD 
 
no prior SD, PR or CR 
Any 
PD*** 
Yes or No 
PD 
Any 
Any 
Yes 
PD 
*      See RECIST 1.1 manuscript for further details on what is evidence of a new lesion. 
**    Only for non-randomized trials with response as primary endpoint. 
*** In exceptional circumstances, unequivocal progression in non-target lesions may be 
accepted as disease progression. 
 
Note: 
Patients with a global deterioration of health status requiring discontinuation of 
treatment without objective evidence of disease progression at that time should be 
reported as “symptomatic deterioration.”  Every effort should be made to document 
the objective progression even after discontinuation of treatment. 
 
For Patients with Non-Measurable Disease (i.e., Non-Target 
Disease) 
 
Non-Target Lesions 
New Lesions 
Overall Response 
CR 
No 
CR 
Non-CR/non-PD 
No 
Non-CR/non-PD* 
Not all evaluated 
No 
not evaluated 
Unequivocal PD 
Yes or No 
PD 
Any 
Yes 
PD 
*  ‘Non-CR/non-PD’ is preferred over ‘stable disease’ for non-target disease since SD is 
increasingly used as an endpoint for assessment of efficacy in some trials so to assign this 
category when no lesions can be measured is not advised 
 
8.15  Duration of Response 
 
Duration of overall response:  The duration of overall response is 
measured from the time measurement criteria are met for CR or PR 
(whichever is first recorded) until the first date that recurrent or 
progressive disease is objectively documented  (taking as reference for 
progressive disease the smallest measurements recorded since the 
treatment started). 
 
The duration of overall CR is measured from the time measurement 
criteria are first met for CR until the first date that progressive disease is 
objectively documented.  
 
Duration of stable disease:  Stable disease is measured from study entry 
until the criteria for progression are met, taking as reference the smallest 
measurements recorded since the treatment started, including the baseline 
measurements.  

 
 
GOG-0286B 
 
55 
 
 
8.16  Progression-Free Survival 
 
Progression-Free Survival (PFS) is defined as the duration of time from 
date of study entry to time of progression or death, whichever occurs first. 
 
      
8.17 
Survival 
 
Survival is defined as the duration of time from date of study entry to time 
of death or the date of last contact. 
 
 
 
 

 
 
GOG-0286B 
 
56 
 
9.0 DURATION OF STUDY 
 
9.1 
Patients will receive therapy until disease progression or intolerable toxicity 
 
intervenes.  The patient can refuse the study treatment at any time. 
 
9.11 
NOTE: Patients with SD or PR who still have measurable disease at the  
completion of cycle 6 may continue to receive paclitaxel and carboplatin 
(with metformin or placebo) up to a total of 10 cycles (if deemed 
necessary by the treating investigator). Patients who continue with cycles 
7-10 will continue with all study assessments as described for Prior to 
each cycle (cycles 1-6) (see Section 7, Study Parameters). (04/28/15) 
 
9.2 
All patients will be treated (with completion of all required case report forms)  
until disease progression or study withdrawal.  Patients will then be followed 
every three months for the first two years and then every six months for the next 
three years.  Patients will be monitored for delayed toxicity and survival for this 
5-year period with Follow-Up Forms submitted to the GOG Statistical and Data 
Center, unless consent is withdrawn.  Follow-Up Forms will no longer be required 
if the study is terminated prior to the completion of the 5-year follow-up period. 
 
9.3 
A patient is considered off study therapy when the patient has progressed or died,  
a non-protocol drug or therapy (directed at the disease) is initiated or all study 
therapy is totally discontinued.  Report all treatment received on Cycle Drug 
Information Forms and adverse events on Adverse Event Forms up until the 
patient qualifies as being off study therapy. 

 
 
GOG-0286B 
 
57 
 
10.0 STUDY MONITORING AND REPORTING PROCEDURES 
 
10.1 
Adverse Event Reporting for a Commercial Agent 
 
10.11 Definition of Adverse Events (AE) 
 
An adverse event (AE) is any unfavorable and unintended sign (including 
an abnormal laboratory finding), symptom, or disease that occurs in a 
patient administered a medical treatment, whether the event is considered 
related or unrelated to the medical treatment.  
 
The descriptions and grading scales found in the revised NCI Common 
Terminology Criteria for Adverse Events (CTCAE) version 4.0 will be 
utilized for AE reporting. All appropriate treatment areas should have 
access to a copy of the CTCAE version 4.0. A copy of the CTCAE version 
4.0 can be downloaded from the CTEP web site (http://ctep.cancer.gov). 
The CTCAE v4.0 Manual is also available on the GOG member web site 
(http://www.gog.org under MANUALS). 
 
10.12 Reporting Expedited Adverse Events (05/19/14) 
 
Depending on the phase of the study, use of investigational or commercial 
agents, and role of the pharmaceutical sponsor, an AE report may need to 
reach multiple destinations.  For patients participating on a GOG trial, all 
expedited AE reports should be submitted by using the CTEP automated 
system for expedited reporting (CTEP-AERS).  All CTEP-AERS 
submissions are reviewed by GOG before final submission to CTEP-
AERS.  Submitting a report through CTEP-AERS serves as notification to 
GOG, and satisfies the GOG requirements for expedited AE reporting.  
All adverse reactions will be immediately directed to the Study Chair for 
further action. 
 
The requirement for timely reporting of AEs to the study sponsor is 
specified in the Statement of Investigator, Form FDA-1572.  In signing the 
FDA-1572, the investigator assumes the responsibility for reporting AEs 
via CTEP-AERS.  In compliance with FDA regulations, as contained in 21 
CFR 312.64, AEs should be reported by the investigator. 
 
10.13 Phase 2 and 3 Trials Utilizing a Commercial Agent:  CTEP-AERS 
Expedited Reporting Requirements for Adverse Events That Occur Within 
30 Days of the Last Dose of Any Commercial Study Agent (05/19/14) 
 

 
 
GOG-0286B 
 
58 
 
Reporting Requirements for Adverse Events that occur within 30 Days¹ of the Last Dose of the 
Commercial Agent on Phase 2 and 3 Trials 
 
 
Grade 1 
 
Grade 2 
 
Grade 2 
 
Grade 3 
 
Grade 3 
Grades 
4 & 52 
Grades 
4 & 52 
Unexpected  
and Expected 
 
 
Unexpected 
 
 
Expected 
Unexpected 
Expected 
Unexpected Expected 
With  
Hospitali-
zation 
Without 
Hospitali- 
zation 
With  
Hospitali- 
zation 
Without 
Hospitali- 
zation 
Unrelated 
Unlikely 
Not 
Required 
Not 
Required 
Not 
Required 
7 Calendar 
Days 
Not 
Required 
7 Calendar 
Days 
Not 
Required 
7 Calendar 
Days 
7 Calendar 
Days 
Possible 
Probable 
Definite 
Not 
Required 
7 Calendar 
Days 
Not 
Required 
7 Calendar 
Days 
7 Calendar 
Days 
7 Calendar 
Days 
Not 
Required 
24-Hrs; 
3 Calendar 
Days 
7 Calendar 
Days 
1 Adverse events with attribution of possible, probable, or definite that occur greater than 30 days after the last dose of 
treatment with a commercial agent require reporting as follows: 
CTEP-AERS 24-hour notification followed by complete report within 3 calendar days for: 
• Grade 4 and Grade 5 unexpected events  
      CTEP-AERS 7 calendar day report: 
• Grade 3 unexpected events with hospitalization or prolongation of hospitalization 
• Grade 5 expected events 
 
2 Although a CTEP-AERS 24-hour notification is not required for death clearly related to progressive disease, a full 
report is required as outlined in the table. 
 
 Please see exceptions below under the section entitled, “Additional Instructions or Exceptions to CTEP-AERS 
Expedited Reporting Requirements for Phase 2 and 3 Trials Utilizing a Commercial Agent.”          March 2005 
 
Note:  All deaths on study require both routine and expedited reporting regardless of 
causality. Attribution to treatment or other cause must be provided. 
 
• 
Expedited AE reporting timelines defined: 
 “24 hours; 3 calendar days” – The investigator must initially report the AE via CTEP-
AERS within 24 hours of learning of the event followed by a complete CTEP-AERS 
report within 3 calendar days of the initial 24-hour report. 
“7 calendar days” – A complete CTEP-AERS report on the AE must be submitted within 
7 calendar days of the investigator learning of the event. 
 
• 
Any medical event equivalent to CTCAE grade 3, 4, or 5 that precipitates hospitalization 
(or prolongation of existing hospitalization) must be reported regardless of 
attribution and designation as expected or unexpected with the exception of any 
events identified as protocol-specific expedited adverse event reporting exclusions. 
 
• 
Any event that results in persistent or significant disabilities/incapacities, congenital 
anomalies, or birth defects must be reported to GOG via CTEP-AERS if the event occurs 
following treatment with a commercial agent. 
 
• 
Use the NCI protocol number and the protocol-specific patient ID provided during trial 

 
 
GOG-0286B 
 
59 
 
registration on all reports. 
 
Additional Instructions or Exceptions to CTEP-AERS Expedited Reporting Requirements for 
Phase 2 and 3 Trials Utilizing a Commercial Agent: 
The following SAEs will be exempted from expedited reporting through CTEP-AERS  
• All Grade 2, 3 and 4 myelosuppression (including leukopenia, neutropenia, anemia, and 
thrombocytopenia) is exempt from expedited reporting. 
• G3-4 Neutropenia/febrile neutropenia, regardless of hospitalization. 
• G3-4 Diarrhea, Nausea, Vomiting, or Dehydration, regardless of hospitalization. 
 
10.14 Procedures for Expedited Adverse Event Reporting: (05/19/14) 
 
10.141 CTEP-AERS Expedited Reports: Expedited reports are to be 
submitted using CTEP-AERS available at http://ctep.cancer.gov. 
The CTEP, NCI Guidelines: Adverse Event Reporting 
Requirements for expedited adverse event reporting requirements 
are also available at this site.  
 
Secondary Malignancy:  
A secondary malignancy is a cancer caused by treatment for a 
previous malignancy (e.g., treatment with investigational 
agent/intervention, radiation or chemotherapy). A secondary 
malignancy is not considered a metastasis of the initial neoplasm.  
CTEP requires all secondary malignancies that occur following 
treatment with an agent under an NCI IND/IDE be reported via 
CTEP-AERS. Three options are available to describe the event:  
 
• 
Leukemia secondary to oncology chemotherapy (e.g., acute 
 
myelocytic leukemia [AML])  
• 
Myelodysplastic syndrome (MDS)  
• 
Treatment-related secondary malignancy  
 
Any malignancy possibly related to cancer treatment (including 
AML/MDS) should also be reported via the routine reporting 
mechanisms outlined in each protocol.  
 
Second Malignancy:  
A second malignancy is one unrelated to the treatment of a prior 
malignancy (and is NOT a metastasis from the initial malignancy).  
Second malignancies require ONLY routine reporting via CDUS 
unless otherwise specified.* 
 
In the rare event when Internet connectivity is disrupted a 24-hour 
notification is to be made to GOG by telephone at: 215-854-0770. 
An electronic report MUST be submitted immediately upon re-
establishment of internet connection. Please note that all paper 

 
 
GOG-0286B 
 
60 
 
CTEP-AERS forms have been removed from the CTEP website 
and will NO LONGER be accepted. 
 
10.2 
GOG Data Management Forms (04/28/15)(XX/XX/XX) 
 
The following forms must be completed for all patients registered and submitted to the GOG Statistical and Data 
Center (SDC) according to the schedule below. GOG electronic case report forms must be submitted through 
the Medidata Rave Electronic Data Entry System (www.imedidata.com). All amendments to forms must also 
be submitted through Medidata Rave. Pathology reports can be sent to the GOG Statistical and Data Center 
via postal mail or uploaded in Medidata Rave. The upload option is an alternative method for submitting 
paper reports. Patient Reported Outcomes (PRO) questionnaires are to be completed on the original Scantron 
forms and submitted via postal mail. 
Form 
Comments 
Baseline Folder  
(Forms due within 2 weeks of registration) 
Baseline/History Forms: 
- 
Visit Information – Baseline Form 
- 
Registration Form 
- 
History Information Form 
- 
Primary Surgery Form 
- 
Pre-Treatment Summary Form 
- 
Specimen Consent 
 
Solid Tumor Evaluation Forms: 
- 
Target Lesions Form 
- 
Non-Target Lesions Form 
 
Non-Measurable Evaluation: 
- 
No Target Lesions Form 
- 
Non-Target Lesions Form 
 
Concomitant Medications Form 
 
Diabetes and Cardiovascular History Form 
 
The appropriate forms will load in the Baseline Folder 
based on the answers reported on the corresponding 
Baseline Visit Information form. 
 
Visit Folder 
(Forms due within 2 weeks of the completion of each cycle) 
Cycle Information and Treatment Forms: 
- 
Visit Information Form 
- 
Cycle Patient Information Form 
- 
Cycle Drug Information Form  
- 
Labs and Chemistries Form 
- 
Blood Pressure Assessment Form 
 
Toxicity Forms: 
- 
Section 1 Form 
- 
NADIRS Form 
- 
Adverse Event Form 
- 
Adverse Event Grades 
 
Solid Tumor Evaluation Forms: 
- 
 Target Lesions Form 
- 
 Non-Target Form 
The appropriate forms will load in the Visit Folder based 
on the answers reported on the corresponding Visit 
Information forms. 
 

 
 
GOG-0286B 
 
61 
 
- 
 New Lesion Form 
- 
 Status and Response Form 
 
Non-Measurable Evaluation: 
- 
No Target Lesions Form 
- 
Non-Target Lesions Form 
- 
New Lesion Form 
- 
Status and Response Form 
 
Pathology Folder 
(Reports and slides due within 6 weeks of registration) 
 
Primary disease: 
Pathology Report 
Stained Slides 
 
Recurrent or Persistent Disease: 
Pathology Report 
Stained Slides 
 
 
Submit stained slides with two copies of the pathology report to SDC 
via postal mail or upload the pathology report online via 
RAVE. 
 
Stained pathology slides are required for central review by the GOG 
Pathology Committee. See Section 7.2 for mailing instructions  
for Pathology eligibility. All stained slides MUST be 
submitted via postal mail. 
 
 
Translational Research Folder 
 
TR Form: 
- 
FFPE Primary Tumor (FP01) 
- 
FFPE Metastatic Tumor (FM01) 
- 
FFPE Recurrent Primary Tumor (FRP01) 
- 
FFPE Recurrent Metastatic Tumor (FRM01) 
- 
Pre-Treatment Whole Blood (WB01) 
 
A completed copy of Form TR must accompany each specimen 
shipped to the GOG Tissue Bank (or alternate laboratory). 
Handwritten forms will not be accepted. 
 
FP01, FM01, FRP01, and FRM01 are due 8 weeks from registration. 
WB01 is due 2 weeks from registration. 
 
Patient-Reported Outcomes Measures (PRO) Folder 
PRO Scantron Form 
Patients will complete the Questionnaires at 4 times: 
 1) Prior to beginning therapy 
 2) 6 weeks after initiating therapy (approx. prior to cycle 3) 
 3) 15 weeks after initiation of treatment (prior to cycle 6) 
 4) 26 weeks following initiation of therapy 
 
Treatment Completion Folder 
(Forms due within 2 weeks of treatment completion) 
Treatment Completion Form 
 
Follow-up Visit Folder 
(Forms due within 2 weeks of follow-up visits, disease progression or death) 
Visit Information Follow-Up Form 
Follow-Up Form 
Follow-Up Period Adverse Event: 
Follow-up visits should be scheduled quarterly for 2 years, semi-
annually for 3 more years, and annually thereafter. 
The appropriate forms will in the Follow-up Visit Folder based on the 
answers reported on the corresponding Follow-up Visit 
Information forms. 

 
 
GOG-0286B 
 
62 
 
- 
Reporting Form – Part 1 
- 
Reporting Form – Part 2 
Solid Tumor Evaluation: 
- 
 Target Lesions Form 
- 
 Non-Target Form 
- 
 New Lesion Form 
- 
 Status and Response Form 
Non-Measurable Evaluation: 
- 
No Target Lesions Form 
- 
Non-Target Lesions Form 
- 
New Lesion Form 
- 
Status and Response Form 
 
 
   
This study will be monitored by the Complete Clinical Data System (CDUS) Version 3.0.  
CDUS data will be submitted quarterly to CTEP by electronic means. 

 
 
GOG-0286B 
 
63 
 
11.0 STATISTICAL CONSIDERATIONS 
 
 
11.1 
Study Design Summary:  
This is a phase II/III, placebo controlled study. The phase II study is a randomized 
clinical trial designed to assess the impact of metformin in combination with 
carboplatin and paclitaxel (arm 1) against the reference regimen of placebo with 
carboplatin and paclitaxel (arm 2) through PFS.  The study will accrue 240 
patients and test the equivalence of PFS between the regimens after 60 PFS events 
are observed in the reference regimen at a 20% level of significance.  If the true 
HR of arm 1 to arm 2 is 2/3, then the study has 90% power.  The 60th event time 
may occur during study accrual, or up to four months after the study finishes 
accrual.  If the phase II study is positive, accrual will resume for a 300 patient 
phase III trial (i.e. targeting a total of 540) and use OS as a primary endpoint.  An 
interim futility analysis using OS will be conducted in both the phase II and phase 
III cohorts after 52 deaths are observed on the reference arm, which should yield 
at least 90 deaths (on average) in the entire study.  If the estimated risk of death is 
not greater on the experimental regimen than on the reference regimen, then the 
study will recommend continued accrual.  On the other hand, if the risk is greater, 
consideration will be given to closing the phase III portion of study for futility.  
Assuming the phase III study completes accrual, follow-up will continue until 180 
deaths are observed in the reference arm of the phase II and phase III studies. A 
log-rank test of equivalent OS will be conducted at the 5% level of significance 
(one sided).  Simulations indicate that a test utilizing both phases will reduce the 
false positive rate (i.e. alpha) to less than 5% while increasing overall power (i.e. 
the joint probability of having both a positive phase II study and a positive phase 
III study) when OS and PFS HR=2/3 to about 88%. 
 
This study will use an intent-to-treat principle.  A dynamic allocation procedure 
will be used that tends to allocate each treatment arm in the ratio 1:1 within the 
stratification factors defined below:  performance status (0 or 1 versus 2), disease 
status (Stage III versus Stage IV or recurrent), and patient body mass index (BMI 
< 30 versus >=30). (04/28/15) 
 
11.2 
Principal parameters:  
Parameters used to determine the relative benefit of each treatment are listed 
below: 
 
11.21 Primary Endpoints 
 
11.211 Progression-free survival (PFS) for the Phase II study only.  
Overall survival (OS) if the Phase III study is opened.  In this case, 
OS for the patients enrolled onto the phase II portion will also 
become a primary endpoint for the Phase II/III study. 
 
11.22 Secondary Endpoints 
 
11.221 Among patients with measurable disease, the proportion of patients 

 
 
GOG-0286B 
 
64 
 
responding to therapy by treatment. 
 
11.222 The duration of response by treatment. 
 
11.223 OS if the study closes before opening the phase III study (i.e. for 
the phase II study only).  If the study continues with a phase III 
clinical trial, then PFS will be a secondary endpoint. 
 
11.224 The frequency and severity of adverse events as assessed by 
CTCAE v4 within each arm. 
 
11.225 Proportion responding, PFS, OS, and toxicity by treatment and 
level of obesity. 
 
11.23 Translational Research Endpoints (12/05/16) 
 
Specific Integrated Biomarker Endpoints 
 
11.231 Whether or not a patient has PIK3CA mutations/amplifications, 
PTEN mutations or PIK3R1/PIK3R2 mutations with PFS.  
 
11.232 Expression of MATE 2 (dichotomized into two groups as high 
expression and low expression) with PFS.  
 
11.233 BMI, hip-to-waist ratio, diabetes status, HgBA1C, fasting insulin 
and glucose levels, HOMA scores by treatment and tumor 
response, PFS and OS. 
 
Other Exploratory Endpoints 
 
11.232 Expression levels of key targets of the metformin/mTOR signaling 
pathway (p-IGF1R, p-S6 and p-4EBP-1) by treatment and tumor 
response, PFS and OS.  
 
11.3 
Study Duration and Accrual Rate:  
The anticipated rate of accrual onto this study is 13 patients per month (156 
patients per year).  The period of active accrual to the phase II portion of the study 
is expected to be 18.5 months.  The study will suspend for the observation of 60 
PFS events in the reference arm.  The 60th event is anticipated to occur about 17 
months after the trial activates (likely range from 15 to 20 months, making the 
period of trial suspension small to non-existent).   The period of active accrual to 
the phase III study is expected to be 23 months (at least 42 months for the entire 
study).  The time of final data maturation is expected to occur around 70 months 
after the phase II trial opens (5 to 6 years). 
 

 
 
GOG-0286B 
 
65 
 
11.4 
Hypothesis and Sample Size (12/05/16) 
 
The population under examination has a survival function for both PFS and OS 
that can be modeled quite well with a gompertz model: 
 
S(t) = exp{(α/β)*(1-exp(β* t)} 
 
For PFS, alpha (α) has been estimated at 0.0909 and beta (β) has been estimated at 
0.0431.  For OS, alpha was estimated at 0.0326 and beta at 0.0126.  A plot of the 
survival functions is provided below: 
 
 
Figure 11.1: Plots of survival functions for OS (solid line)  
and PFS (dashed line). 
 
Because the survival functions indicate a proportion of patients who are cured as 
well as having decreasing hazards of death or progressing, it is necessary to 
accrue larger sample sizes than typically seen in phase II studies. 
 
Phase II 
 
The null hypothesis is that the PFS hazard ratio (HR) of the experimental regimen 
to the reference regimen is 1.0.  That is, Ho: HR ≥ 1.0. Ordinarily, phase II studies 
restrict the level of significance to 10%.  However, the current study is 
exceptional in several respects.  Most importantly, metformin is not as toxic as 
many experimental agents examined in clinical trials.  Therefore the harm is not 
as great if the drug was incorrectly taken into a phase III study.  Furthermore, 
there is a desire to keep the accrual momentum high, which can be more easily 

 
 
GOG-0286B 
 
66 
 
achieved if the study does not suspend for long periods of time (if at all).  
 
When testing the equivalence of hazard rates with a stratified log-rank test at α = 
20% (1-sided) and β = 10%, Schoenfeld’s equation indicates that we need to 
observe 110 PFS events in total when clinically significant hazard ratios of 2/3 or 
less are deemed interesting (i.e. Ha: HR ≤ 2/3).  Requiring the observation of 60 
events in the reference arm under the alternative will give us a design with these 
characteristics (simulation).  The expected time to observe 60 events with an 
accrual rate of 13 patients per month and a maximum sample size of 240 is about 
17 months (simulation).  Follow-up for OS will continue as a secondary endpoint 
(if the study is negative) or as a primary endpoint for combining with a 
subsequent phase III study.  In the case that a phase III study commences, data for 
the phase II and phase III patients will be frozen when 52 deaths are observed in 
the reference arm.    The purpose of the data freeze when 52 deaths are seen is to 
conduct an interim analysis of OS for guidance in carrying out the rest of the 
study.  If this analysis indicates a greater risk of death in the experimental 
regimen, the phase III study will be considered for early termination for futility.  
The timing of this analysis is expected to occur approximately 26 months after the 
phase II study opens (simulation) if there is no delay in opening the phase III trial. 
 
Phase II/III Trial 
 
The null hypothesis for the Phase III and Phase II/III study is that the overall 
survival hazard ratio is 1.0 (Ho: OS HR ≥ 1.0).  For the purposes of this study, 
reductions in the hazard by 1/3 or more are deemed clinically significant (i.e. Ha: 
OS HR ≤ 2/3).  A case can be made for smaller reductions being considered 
clinically significant (e.g. HR ≤ 0.80), but such designs are not practically 
feasible. 
 
This study will target an accrual of 300 patients to the phase III portion (540 in 
total). The primary endpoint will be OS.  The null hypothesis of equivalent 
hazards will be assessed at the final analysis with a stratified log-rank test statistic 
at an overall 1-sided 5% level of significance when 180 deaths are observed in the 
reference arm (phase II and phase III patients).  This should guarantee the 
observation of at least 300 deaths in the entire study under the alternative 
hypothesis (OS HR = 2/3 and PFI HR=2/3) and provide approximately 88% 
power (probability of both phase II and phase II/III trials being positive). 
 
An interim analysis will be conducted after the observation of 52 deaths in the 
reference arm (phase II and phase III patients as stated above).  The purpose of 
this analysis is primarily for futility.  Assuming no interruption of accrual, this 
analysis will be conducted approximately when 60% of the targeted accrual is 
attained.  If the futility analysis indicates a higher hazard of death on the 
experimental arm (as assessed with a stratified log-rank statistic before squaring 
being greater than zero) using the method of Wieand90, then the trial could close 
at that time. 

 
 
GOG-0286B 
 
67 
 
 
There will also be a test for substantial activity in the regimen that administers 
metformin using a Lan-DeMets alpha spending function: 
 
alpha(t) = alpha * t^3. 
 
This spending function mimics an O’Brien-Fleming procedure when the total 
alpha is 0.05 (if alpha was 0.025, then t^4 may be more suitable). If the interim 
analysis is conducted at 28% information time, then 0.001 alpha will be spent at 
that time.  The estimated probability (through simulation) of observing a 
sufficient result for early stopping when the PFI and PFS HR are both equal to 
0.67 and the OS HR is equal to 0.67 is 33%. 
 
The time of final data maturation on the phase II/III study is considerably longer 
than the phase II study.  It is anticipated that it will take about 70 months after 
initiating the phase II study (about 5 to 6 years) before 180 deaths are observed in 
the reference arm (phase II and phase III patients).  
 
 
Table 11.1 of Operating Characteristics 
 
Event 
P(Event)1 
HRPFI = 1.0 
2 HRPFS = 1.0 
HROS=1.0 
P(Event)1 
HRPFI = 0.667 
HRPFS = 0.77 
HROS=1.0 
P(Event)1 
HRPFI = 0.80 
HRPFS = 0.80 
HROS=0.80 
P(Event)1 
HRPFI=0.667 
HRPFS = 0.71 
HROS=0.80 
P(Event)1 
HRPFI = 0.667 
HRPFS = 0.667 
HROS=0.667 
Start Phase II/III from Phase II 
PFS 
(Starting at Study Initiation) 
20.01 
73.24 
64.52 
85.23 
90.92 
Complete Phase II/III Study After 
OS IA3 at 52 Deaths 
(Starting at Study Initiation) 
13.67 
42.09 
59.39 
76.52 
89.52 
Reject H0 at End Phase II/III 
Study 
(Starting at Study Initiation) 
1.46 
3.96 
43.99 
55.70 
88.16 
Reject H0 at End Phase II/III 
Study 
(Beginning at Phase III After a 
Positive Phase II Study)3 
7.30 
5.40 
68.18 
65.35 
96.96 
1 Probabilities are provided in percentages (%). 
2 HRPFS was estimated through simulation 
2 
IA = Interim Analysis 
3 
The higher probabilities than the design’s advertised level of significance in the first two 

 
 
GOG-0286B 
 
68 
 
columns is not a serious concern since Phase II/III trials are designs to examine the 
properties from study initiation. 
 
The null hypothesis in the table is that the experimental regimen does not reduce 
the risk of death (i.e. Ho: OS HR=1.0).   
 
The above table provides a sampling of the operating characteristics of the design 
at interesting points in the parameter space using simulation (n=10000) based on 
the Gompertz model provided above.       
 
The first column shows the characteristics when the metformin has no effect on 
PFI, PFS, or OS.  Only 20% of the phase II trials lead to opening a phase III trial, 
and only 14% of all studies continue on to complete the phase III study after the 
phase II/III interim analysis utilizing OS.  The probability of a phase II/III study 
(that combines data from both phases) declaring metformin active is 1.46% of all 
studies that are initiated.  Conditional on a phase II study being positive, the 
probability of the phase II/III study being positive is about 7.3%. 
 
The second column gives an example where metformin has an effect on PFI and 
PFS but not on OS.  In this case, the majority of phase II trials open to phase III 
studies, however, when the data are analyzed by OS, the Phase II/III study reach 
the correct conclusion within the required probability (<5%). 
 
The third column provides an example of modest activity on both PFI and OS, 
and the 5th column provides the characteristics of the design when the drug is 
fully active by both PFS and OS.   
 
 
 
11.5 
Secondary Endpoints 
With about 25% of patients being non-measurable, we could expect 200 
measurable patients in each arm at the end of a phase II/III study.  Testing a one-
sided alternative hypothesis at α = 5%, the study should have 90% power of 
detecting a difference when the P(response | arm 1) = 65% and P(response | arm 
2) = 50%.  The 95% CI for the probability of response should be no greater than 
14% per treatment arm (actual length depending on the number of measurable 
patients).  The duration of response will be assessed from the date of response to 
disease progression, death, or date last seen.  These durations will be 
characterized by treatment using Kaplan-Meier curves and quartile estimates.   
 
Toxicities will be assessed according to CTCAE v4 by organ or organ system.  
For each category of toxicity, each patient will be evaluated by the worst grade 
experienced during the course of therapy.  These data will then be summarized by 
their frequency and severity according to the regimen administered.  Comparisons 
between regimens will be examined through exact Chi-Square methods by 
breaking the severity of the worst toxicities experienced by each patient into 

 
 
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severe versus not severe (or into groups of none to mild, moderate, and severe or 
worse).  
 
Obesity will be quantitatively assessed by body mass index (BMI) and will be 
assessed for its predictive and prognostic significance. 
 
The interaction between BMI and metformin treatment will be examined with an 
interaction term in a Cox proportional hazards model. Historical data indicate that 
approximately 50% of people are obese (BMI>=30).  For the purposes of 
examining the relationship, we will classify patients into two levels (high versus 
low) at the median BMI, which will increase the likelihood of detecting an 
interaction between metformin treatment and obesity. The power to detect an 
association between metformin treatment and obesity in this manner is 
approximated in the graph below where R = Δ1 / Δ2 and Δ1 is the hazard ratio of 
control to experimental therapy for obese people and Δ2 is the hazard ratio of 
control to experimental therapy for people who are not obese: 
 
 
 
Figure 11.2: Power to detect a significant association between weight and metformin 
therapy as a function of R when testing a one-sided alternative hypothesis at the 5% 
level of significance. The dashed line gives power after observing 110 events (phase II 
data only); the solid line gives power after observing 342 events (phase II and III data 
combined). 
 
The power to detect associations using interaction terms is unfortunately not high 
for clinically significant values of R (e.g. in the range of 1.5). In addition to this 
rigorous test, we will examine the impact of obesity with subset analyses as well 

 
 
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as inspect the impact of BMI (as a continuous variable) in a multivariate Cox PH 
model or logistic regression (depending on the endpoint). The latter analyses will 
be reported if the model diagnostics indicate that they are adequate. The operating 
characteristics for a log-rank test of equivalent PFS or OS by regimen within 
obese patients as a function of HR (control to experimental) are provided in the 
figure and paragraph below: 
 
 
 
 
Figure 11.3: Power to detect the activity of metformin in a subset analysis as a function 
of the hazard ratio (HR) within obese patients assuming that approximately 50% of the 
population are obese in (A) the phase II study only [long dashed line] with 50 PFS events 
within this group, and (B) the combination of both phase II and III studies [solid line] 
with 170 events in this group. 
 
The power to detect metformin activity within the phase II study is 20% and 42% 
when the HR is 1.25 and 1.5, respectively. The power to detect metformin activity 
with the studies combined is 42% and 84% when the HR is 1.25 and 1.5, 
respectively. If metformin is more active in obese patients than non-obese 
patients, then the power of the subset analysis may be less than indicated here 
because fewer patients will event in the experimental arm of this subgroup at the 
time when the analysis is triggered. 
 
11.6 
Exploratory and Translational Research (12/05/16) 
 
Integrated Biomarker Analysis (Hypothesis Testing) 
 

 
 
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There will be two hypotheses tested within the metformin treatment arm (i.e. 
these will be subset analyses). The first hypothesis will examine whether patients 
with PIK3CA mutations/amplifications, PTEN mutations or PIK3R1/PIK3R2 
mutations have a lower hazard of progression or death (PFS endpoint). Therefore 
patients will be split into two groups: those with mutations and those without, and 
the clinical outcome will be PFS. The null hypothesis will be that the hazard ratio 
(HR) of the PFS event for those with mutations to without is 1.0, i.e. Ho: HR = 
1.0. The alternative will be 1-sided indicating that patients with mutations have a 
lower risk of the PFS event compared to those without mutation, i.e. Ha: HR < 
1.0. The null hypothesis will be tested at the 2.5% level of significance. 
 
The number of events at the time of data maturation for the phase II study will be 
relatively small. By design, we anticipate about 110 PFS events in the entire 
study, and we expect there to be about 50 to 55 PFS events within the 
experimental regimen, depending on the relative level of activity of the regimen 
compared to the reference arm. If the number of patients with any mutation is 
approximately 50%, then the power of the study will be at a maximum. If patients 
with mutations have a hazard of 50% for the PFS event compared to those without 
mutations, then power will be 69%. On the other hand, if 2/3 of patients have 
mutations, the power of the study will be 63%. A graph of the study’s power as a 
function of the proportion with any mutation is provided below. 
 
 
 
0.0
0.2
0.4
0.6
0.8
1.0
0.2
0.4
0.6
0.8
1.0
 
Figure: The asymptotic approximation of the study’s power as a function of the 
proportion of patients who have the mutation of interest when the HR is 50% with 
50 events. 
 
If the number of PFS events in one group is less than 10, then a permutation test 
will be conducted instead of an asymptotic test to assess the null hypothesis. 
 
As can be seen from the graph, the power of the subset analysis will not be very 
high for the phase II study. If the study stops after the phase II study, it is possible 
that a subsequent analysis could be done at a later time as part of a long-term 

 
 
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follow-up study. 
 
If the study continues on to the phase III study, then we expect to see many more 
PFS events. We anticipate on seeing at least 120 PFS events and possibly up to 
216 within the experimental regimen. The variability is greater because the 
primary endpoint will be overall survival for the second study. It is reasonable to 
assume we will see 170 PFS events within the metformin arm at the conclusion of 
the data maturation. The power of the study will be 99% in this case, regardless of 
whether the proportion is 50% or 67%. A graph is provided below to give the 
reader a better understanding of the relationship in this setting: 
 
0.0
0.2
0.4
0.6
0.8
1.0
0.2
0.4
0.6
0.8
1.0
 
Figure: The asymptotic approximation of the study’s power as a function of the 
proportion of patients who have the mutation of interest when 170 PFS events are 
observed in the metformin arm when the HR is 50%. 
 
If 67% of the patients have the mutation, then the power of the study will be 
2.5%, 28%, 59%, and 88% when the HR is 1.0, 0.80, 0.70, and 0.60, respectively. 
The graph as a function of the hazard ratio is also provided below: 
 
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
0.2
0.4
0.6
0.8
1.0
 
Figure: The asymptotic approximation of the study’s power as a function of the 
HR for patients who have the mutation of interest to those who do not when 170 

 
 
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PFS events are observed in the metformin arm with 67% of patients carrying the 
mutation. 
 
The second integrated biomarker to be analyzed will be MATE 2 expression. 
Expression will be examined by IHC with intensity of staining and the percentage 
of cells staining positive. From these statistics, we will calculate an H-score which 
is the (intensity score) x (% staining). The distribution of the H-score is expected 
to be fairly continuous, so we will further dichotomize expression as high 
expression and low expression at the median to maximize the power of the study. 
If the median is equal to zero, then the dichotomized variable will be broken into 
those patients whose scores are 0 against those whose scores are greater than 0. 
The null hypothesis will be that the HR for those who have high expression to 
those with low expression is 1.0, i.e. Ho: HR = 1.0. The alternative hypothesis 
will be that the HR for those who have high expression to those with low 
expression is less than 1.0, i.e. Ha: HR < 1.0. The null hypothesis will be tested at 
the 2.5% level of significance. 
 
The operating characteristics for the testing of this biomarker will be identical to 
the one listed above. However, since the distribution of the H-score is expected to 
be fairly continuous, we have a higher chance of seeing 50% with high 
expression. Below provides a graph of the power when the groups are split 50:50: 
 
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
0.2
0.4
0.6
0.8
1.0
 
 
If 50% of the patients have high expression, then the power of the study at the end 
of the phase III part will be 2.5%, 31%, 64%, and 91% when the HR is 1.0, 0.80, 
0.70, and 0.60, respectively. 
 
Although not explicitly included as part of the integrated analysis to be a part of 
the official hypothesis testing procedure, a test for an interaction between 
treatment regimen and biomarker level (e.g. mutant or high value versus wild type 
or low value) will be conducted as part of the exploratory data analysis. The 
relationship between power and the parameter R where R = Δ1 / Δ2 and Δ1 is the 
hazard ratio of experimental to control therapy for biomarker level 1 people and 
Δ2 is the hazard ratio of experimental to control therapy for biomarker level 2 

 
 
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people, p = the proportion of people who have biomarker level 1, d = the total 
number of events in the study, and Zα is the standardized critical value for 
rejecting the null hypothesis that R = 1, and α = the probability of a type I error, is 
given by: 
 
 
 
The operating characteristics will be the same as the test involving the hazard 
ratios for obese and non-obese subjects when the proportion within each group is 
50%. If the proportion is different, then the power will not be as high. For 
example, if 67% of the patients have biomarker level 1 and 33% have biomarker 
level 2, then power for the test of interaction will drop from 44% (at p=0.50) to 
40% (at p=0.33 or 0.67) when 110 patients event and R = 0.50. The following 
graph provides the operating characteristics for the phase II and phase II/III 
studies. 
 
0.2
0.4
0.6
0.8
1.0
0.2
0.4
0.6
0.8
1.0
 
Figure: Power to detect a significant interaction association between biomarker level and 
metformin therapy as a function of R when testing a one-sided alternative hypothesis at the 2.5% 
level of significance. The dashed line gives power after observing 110 events (phase II data only); 
the solid line gives power after observing 342 events (phase II and III data combined) when the 
proportion with biomarker level 1 is 67%. 
 
 
 
Analyses similar to those involving BMI will also be done on related measures of 
obesity and diabetes.  Measurements to be examined include hip-to-waist ratios, 
HgBA1C values, fasting insulin and glucose levels, and HOMA scores.  
Continuous measures will be dichotomized as it is proposed for BMI, so the 
power of these studies is expected to be similar.  In addition, these variables will 
be analyzed as continuous covariates (or as appropriate with transformations such 
as the logarithm) with Cox models or logistic regression.  If systematic 
differences exist by institution, consideration will be given for including them as 
covariates or using random effects models.  If serial measurements are available, 
the data may be analyzed for associations with risks of progression or death as 

 
 
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time dependent covariates in Cox models for PFS or OS. 
 
The field of translational research is one that is rapidly developing technologically 
and quickly changing as our knowledge of the relationships between various 
biomarkers and cancer development evolves. Therefore, it is difficult to predict 
which hypotheses will be of primary interest at this time.  In spite of these 
problems, a general outline of the analytical procedures used to uncover important 
associations and interactions can still be made. 
 
Two important goals of the study will be to determine whether the biomarkers 
under consideration have prognostic value or predictive value.  A variable is 
prognostic if varying levels of the factor influences the hazard of death (for 
example), irrespective of treatment.  Performance status and stage of disease are 
common examples.  A variable is predictive of response to treatment if varying 
levels of the factors differentially influences the hazard of death by the treatment 
administered.  A biomarker would be predictive if, for example, high levels of 
protein expression were associated with a substantial reduction in the risk of 
progression within the arm that administered metformin but not in the arm that 
administered chemotherapy only.  Such factors can be identified with analyses 
that look at biomarker-treatment interactions.  The identification of these factors 
would help guide physicians to the appropriate therapy. 
 
Two mutually exclusive sets of patients will be used to assess the value of the 
biomarkers as prognostic factors or predictive factors.  The first set of patients 
will serve as a training dataset, which will be used to screen through possibly 
many biomarkers in order to develop a model (or a small number of models) that 
is believed to relate the biomarker data to clinical outcomes (such as survival, 
PFS, and response) in a meaningful way.  Commonly used model building 
techniques involve data examination (with the possible exclusion of observations 
believed to be outliers resulting from laboratory errors), subjective judgment by 
the statistician about whether to include certain variables, and collaboration with 
knowledgeable investigators who can comment on the possible meaning of the 
results.  Although the method is useful for arriving at models that functionally 
relate biomarker data to clinical outcomes, given the relatively large number of 
relationships examined, it would be difficult to determine the reliability of the 
final set of models strictly from this process because the usual limits on type I and 
type II errors are no longer applicable.  Fortunately, there will be enough patients 
in the trial to test the reliability of these models with the use of validation datasets.  
Variables that demonstrate statistically significant associations in the validation 
dataset should increase our level of confidence that relationship detected in the 
model building phase is real. 
 
Associations between biomarkers and overall survival or progression-free survival 
will be examined in a Cox proportional hazards model that includes significant 
prognostic variables based on prior research such as performance status and stage 
of disease.  Since the translational research analysis will not occur before the 

 
 
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reporting of clinical outcomes, the number of events for survival analysis should 
be adequate to screen biomarkers in the model building phase.  For example, an 
independent, normally distributed biomarker that has a prognostic relationship to 
the hazard of survival of 1.5 for one standard deviation increase in value is almost 
certainly going to be detected (assuming 170 events with α = 0.05 while testing 
against a two-sided alternative; power is nearly 1 in this case).  If the coefficient 
of determination with other biomarkers is relatively high, for example equal to 
50%, then the marginal probability of detecting the relationship is still greater 
than 95%.  However, if the biomarker is dichotomous with roughly equal 
proportions of each value, the relationship between the hazard of death and the 
biomarker must be stronger to assure a good chance of detection.  For example, if 
the hazard ratio associated with the biomarker is only 1.5, then the marginal 
probability of detection is about 75%.  On the other hand, if the hazard ratio is 2, 
the marginal probability of detection is nearly certain with this event size. 
 
Unfortunately, when many biomarkers are being screened for associations, the 
chances of erroneously including at least one biomarker into the model is quite 
high.  Assuming independence between biomarkers (not likely in reality but 
assumed in order to give a flavor of the likelihood of these errors), the probability 
of including at least one biomarker erroneously into the model is 40% with 10 
variables, 64% with 20 variables, and 92% with 50 variables.  Compounding the 
problem are multiple analyses (e.g. analyses of response in addition to survival 
and PFS), making use of the validation dataset a valuable part of the analysis. 
 
The detection of significant predictive biomarkers is going to be more difficult 
because it involves an analysis of interactions.  Following Peterson and George’s 
definition for interaction, it is possible to look at the probability of detection of a 
dichotomous biomarker.  For example, if the hazard ratio for treatment on 
metformin to no metformin is 0.5 for patients with biomarker level L1, and the 
hazard ratio is equal to 1.0 for those patients who have biomarker level L2, the 
probability of detecting the interaction is approximately 59% (assuming about 160 
events in the training dataset).  In order to detect the interaction with 80% power, 
the hazard ratio in L1 needs to be 0.41 or 2.43.  To make such a detection with 
90% power, the hazard ratio in L1 needs to be 0.36 or 2.79. 
 
The analysis of biomarkers may be conducted as categorical data types, ordinal 
categorical data, or continuous data.  Mutations are most naturally thought of as 
categorical data for statistical analysis.  The IHC intensity scores are inherently 
ordinal but can be analyzed as categorical.  The IHC percent staining positive are 
continuous, and the histoscore data may be thought of as being continuous. For 
survival analysis, we intend to carry out the analyses of biomarker data as either 
categorical data or continuous data.  The operating characteristics of the analyses 
using dichotomized data are expected to behave similarly to the analysis of 
dichotomized BMI data since we will attempt to split into two levels at the 
median. The analysis of continuous biomarker data requires more care because it 
is susceptible to influential cases having an undue effect on the results. This can 

 
 
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be risky with some data if they are skewed or have outlying cases. However, these 
analyses tend to have more statistical power. See Table 11.2 which demonstrates 
the difference in power when analyzing a biomarker as a numerical covariate 
versus a dichotomized covariate (when the covariate is unlikely to yield 
influential cases). As can be seen from the Table 11.2, the gain in power from 
analyzing the covariate as a raw numerical score can be anywhere from 6% to 
about 14% when the parameter is significantly within the alternative parameter 
space.  
 
Table 11.2. Statistical power of test for association between a categorized or numerical covariate and the 
hazard of eventing (death, progression, etc.) when 240 patients are enrolled with approximately 20% of 
patients censored. 
 
Hazard Ratio1 
Power (categorized)2 
Power (numerical)2 
1.01 
5.50 
5.26 
1.13 
28.67 
36.86 
1.21 
52.88 
66.65 
1.30 
78.09 
89.08 
1.41 
92.33 
97.89 
1 Hazard ratio is defined by the numerical covariate as the hazard at 1 standard deviation above the median 
covariate value to the hazard rate at the median covariate value. 
2 Power estimated with 10,000 simulations. The covariate is either categorized or analyzed as a numerical 
value. 
 
Table 11.3. Statistical power of test for association between a categorized or numerical covariate and the 
hazard of eventing (death, progression, etc.) when 120 patients (phase II study) are examined within the 
subset of interest with approximately 20% of patients censored. This table should approximate the power of a 
study after the first data freeze for clinical analysis (about 100 events). 
 
Hazard Ratio1 
Power (categorized)2 
Power (numerical)2 
1.01 
5.05 
5.03 
1.13 
16.32 
19.71 
1.21 
29.86 
38.69 
1.30 
48.28 
61.19 
1.41 
65.73 
79.68 
1.53 
81.20 
92.74 
1 Hazard ratio is defined by the numerical covariate as the hazard at 1 standard deviation above the median 
covariate value to the hazard rate at the median covariate value. 
2 Power estimated with 10,000 simulations. The covariate is either categorized or analyzed as a numerical 
value. 
 
Table 11.3B. Statistical power of test for association between a categorized or numerical covariate and the 
hazard of eventing (death, progression, etc.) when 270 patients (phase III study) are examined within the 
subset of interest with approximately 20% of patients censored. This table should approximate the power of a 
study after the last data freeze for clinical analysis. 
 
Hazard Ratio1 
Power (categorized)2 
Power (numerical)2 
1.00 
5.28 
5.48 
1.13 
30.45 
38.93 
1.21 
57.85 
71.30 
1.30 
82.55 
92.66 
1.41 
94.89 
98.90 
1 Hazard ratio is defined by the numerical covariate as the hazard at 1 standard deviation above the median 
covariate value to the hazard rate at the median covariate value. 
2 Power estimated with 10,000 simulations. The covariate is either categorized or analyzed as a numerical 
value. 
 
 
 
A reason why this analysis is not carried out universally has to do, in part, with the 

 
 
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problem of influential cases. Cases with potentially undue influence will need to be 
examined with deviance or martingale residual diagnostics, changes in the estimated 
effects (e.g. diff betas), and likelihood displacement measures to assure that particular 
cases (or set of cases) are not overwhelming the results. If a problem exists, subsequent 
analyses will be conducted with these cases removed as part of an exploratory analysis 
(these cases will be explicitly stated as being dropped from the analysis). Furthermore, 
martingale or deviance residuals can help uncover non-linear relationships between the 
hazard function and various numeric covariates which can help create transformed 
covariates that are more sensitive prognostic factors. 
 
al
-3
-2
-1
0
1
2
3
cov
2
3
4
5
6
7
8
 
Figure 11.4: Example of a deviance residual plot where the covariate is truly linearly related to the hazard of 
death (higher values have lower risk).  
 
It is possible to expect patients on the extremes of a biomarker scale to be at a higher or 
lower risk of death than those who have more normal levels. Analyzing a relationship 
such as this with either a dichotomized transformed variable or as raw values in a linear 
model will not likely reveal an association between the covariate and the hazard.  In cases 
like these, plotting the martingale or deviance residual can help investigators uncover 
more complex relationships. For example as shown in Fig 11.5 is a theoretical 
relationship between the hazard function and a biomarker which can be discerned in a 
sample of 240 patients who are about 20% censored (Fig 11.6). 
 

 
 
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0
2
4
6
8
10
0.02
0.04
0.06
0.08
0.10
 
Fig 11.5: Theoretical Hazard Rate as a Function of a Covariate 
 
ual
-3
-2
-1
0
1
2
3
cov
1
2
3
4
5
6
7
8
9
 
Fig 11.6: Example Deviance Residual Plot with a Smoothed Spline Function  
Corresponding to the Theoretical Hazard Function Plotted Above  
(240 patients) 
 
If such a relationship was suspected with real data, a preliminary analysis could 
be conducted with an indicator variable that designated a patient into a higher risk 
group if the covariate value was less than 3 or greater than 7.  This variable could 
be included into a Cox PH model and would yield a greater likelihood of 
detecting a true relationship. On the other hand, since the analysis would be 
exploratory, a follow-up analysis with independent data would be necessary to 
validate such findings.  Assuming the study goes to a phase III trial, a natural 
point for data splitting would be at the interim between the phase II and phase III 
components.  Alternatively, all of the data could be randomly split at the end of 

 
 
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the study, which has several advantages. 
 
Logistic regression will be used to help assess the value of biomarkers in 
predicting response to a particular treatment or determine associations with 
response.  Covariates determined by previous research to be likely associated with 
response will be included in all models.  If the biomarker is normally distributed 
and associated with response such that when it increases by one standard 
deviation above the mean, the odds of responding increases by 50% (i.e. the odds 
ratio for those at μ + σ to those at μ is 1.5; P0 = 0.30; Pa = 0.39), then there is a 
marginal probability of detecting this association in 225 evaluable patients is 
about 80% (with the assumption of a 30% baseline response rate).  If the 
coefficient of determination with other covariates is 50%, the probability of 
detection is 50%.  However, if the odds ratio is 2 (P0 = 0.30; Pa = 0.46), the 
marginal probability of detection is about 92% (even with a 50% coefficient of 
determination).  If the biomarker is dichotomous, the ability to detect an 
association becomes more difficult.  In the case of an independent biomarker 
where the odds ratio is 2, the probability of detection is approximately 71%.  On 
the other hand, the probability of detection is approximately 92% when the odds 
ratio is 2.5 (P0 = 0.30; Pa = 0.52).  If the coefficient of determination is 50%, 
power drops to 65%. Some additional examples follow: 
 
Table 11.4: Statistical power of test for association between a categorized or numerical covariate 
and the odds ratio of responding at a high value to a lower value for various sample sizes within 
the subset of interest using logistic regression or Fisher’s Exact Test (FET).  
Odds Ratio  
(categorized)1 
Odds Ratio  
(numerical)2 
Power 
 (categorized)3 
Power 
 (numerical)3 
Power 
(FET)4 
Sample 
Size 
1.02 
1.01 
5.18 
5.09 
3.51 
60 
1.78 
1.40 
18.99 
22.39 
14.91 
60 
2.71 
1.80 
47.46 
58.00 
40.96 
60 
3.73 
2.20 
70.32 
81.33 
64.29 
60 
4.81 
2.60 
83.85 
92.35 
79.35 
60 
5.92 
3.00 
90.96 
96.98 
88.14 
60 
 
 
 
 
 
 
1.02 
1.01 
5.67 
4.71 
4.79 
120 
1.78 
1.40 
35.43 
42.56 
32.09 
120 
2.71 
1.80 
76.72 
87.34 
73.84 
120 
3.73 
2.20 
94.12 
98.27 
92.79 
120 
4.81 
2.60 
98.73 
99.84 
98.38 
120 
 
 
 
 
 
 
1.02 
1.01 
5.98 
6.31 
5.90 
270 
1.18 
1.10 
57.60 
69.99 
57.21 
270 
1.27 
1.15 
88.51 
95.37 
88.39 
270 
1.37 
1.20 
98.28 
99.73 
98.25 
270 
1.78 
1.40 
100.00 
100.00 
100.00 
270 
1 The parametric value of the odds ratio of response for values of a biomarker dichotomized above the 
median to those below assuming the underlining biomarker is distributed as a uniform with a marginal 
probability of response equal to 50%. 
2 Odds ratio is defined by the numerical covariate as the odds at 1 standard deviation above the median 
covariate value to the odds at the median covariate value where the covariate is uniformly distributed. 
3 Power estimated with 10,000 simulations using logistic regression. The covariate is either categorized 
(dichotomized) or analyzed as a numerical value. The analysis depends on asymptotic results to make 
inferences. 
4 Power using Fisher’s Exact Test (estimated). This estimate differs from standard power calculations 
because the simulation used a parametric median to classify biomarkers as high or low values. This procedure 
does not depend on asymptotic results but has smaller statistical power. 

 
 
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11.7 
Study Monitoring (04/28/15) 
 
Safety monitoring: The Safety Review Committee (SRC) reviews accumulating 
summaries of adverse event reports and all serious adverse event (SAE) reports 
submitted during the interval between group meetings. This committee also reviews 
deaths in which treatment may have contributed to the cause. Based upon these 
reviews, the SRC may recommend study amendments to the DMC pertaining to 
patient safety. 
 
11.8 
Patient-Reported Outcomes (PROs) Research  
The PRO research questionnaire consists of three patient-reported outcomes 
measures: 
• The physical functional subscale of the SF-36, 
• International physical activity questionnaire (IPAQ) short form, 
• FACIT-F subscale. 
 
PRO Measurement time points 
Patients will complete the Questionnaires at 4 times: 
1) Prior to beginning therapy 
2) 6 weeks after initiating therapy (approximately prior to cycle 3) 
3) 15 weeks after initiation of treatment (prior to cycle 6) 
4) 26 weeks following initiation of therapy 
 
Scoring of PRO measures and Minimal Important Differences (MID)  
• The physical functioning subscale of the SF-36 is a 10-item scale of the 
Medical Outcome Study-Short Form. Each item is rated on a 3-point scale of 
limitation of activity due to the patient’s health: 1=limited a lot, 2=limited a 
little, 3=not limited at all. If at least 50% of items provide valid answers, the 
physical functioning score will be calculated as follows: 
1) First averaging the item responses across all non-missing items, 
2) Transform scale to 0-100 using following formula: 
 
 
 
A higher score indicates a better physical functioning.  
 
A difference of 7 points is suggested as the minimally important difference 
(MID) for physical functioning subscale of the SF-36 (Jayadevappa R, 
Malkowicz SB, Wittink M, Wein AJ, Chhatre S. Comparison of distribution- 
and anchor-based approaches to infer changes in health-related quality of life 

 
 
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82 
 
of prostate cancer survivors. Health Services Research, 47:5, 1902-1924, 
(2012)). The GOG has applied physical functioning subscale of the SF-36 to a 
recently closed randomized phase III trial (GOG #213) which evaluates the 
effect of the addition of bevacizumab to second-line and maintenance phases 
of treatment on overall survival in patients with recurrent platinum sensitive 
ovarian cancer. A total of 522 patients have participated in QOL component 
of this study. The estimated standard deviation of physical functioning 
subscale scores at baseline is about 26. Assuming the same standard deviation, 
the MID of 7 points is corresponding to an effect size (ES) of 0.27 by Cohen’s 
criteria.  
 
 
• International physical activity questionnaire (IPAQ) short form consists of 4 
generic items regarding the types (e.g. vigorous-intensity, moderate-intensity, 
walk, and sitting) of physical activities and the time people spend per week as 
part of their everyday lives. The volume of physical activity is summarized 
with MET-minute, which weights each type of activity with its energy 
requirements defined in Metabolic Equivalent of Task (MET). The following 
formula is used to calculate the MET-minute per week: 
 
MET-minutes/week =  MET level × minutes of activity/day × days per week  
 
The MET levels used in IPAQ short form are: Walking= 3.3 METs, Moderate 
intensity=4.0 METs, and Vigorous intensity = 8.0 METs. The total physical 
activity MET-minutes/week is calculated as the sum of walking MET-
minutes/week, Moderate MET-minutes/week, and Vigorous MET-
minutes/week. A larger number of MET-minutes/week indicates larger 
amount of physical activity per week. Since no MCID has been established for 
IPAQ scale, a medium effect size of 0.5 would be an interest to detect in this 
study. We will also analyze the single item measuring minutes of sitting time. 
 
• The FACIT-F Scale is a 13-item questionnaire that assesses self-reported 
fatigue and its impact upon daily activities and function. It uses a 5-point 
Likert-type scale (0 = Not at all; 1 = A little bit; 2 = Somewhat; 3 = Quite a 
bit; and 4 = Very Much). A subscale score will be computed if more than 50% 
of subscale items were answered.  A subscale score Si with Ni items was 
calculated as: 
 
 
∑
∑
×
×
=
i j
i j
i j
i
i
s
N
S
δ
δ
)
(
 
 
where δij is equal to 1 when the jth item has a valid response; otherwise it is equal 
to 0 and sij is the score of the jth item. A higher score indicates worse fatigue. A 
difference of 4 scale points is considered the best estimate of the minimal 
clinically important difference (MCID) when comparing the FACIT-Fatigue 
subscale between treatment groups (Yost KJ, Eton DT. Combining distribution- 
and anchor-based approaches to determine minimally important difference, the 

 
 
GOG-0286B 
 
83 
 
FACIT experience. Evaluation & the health professions, Vol. 28 No. 2, June 
2005, 172-191). The FACIT-Fatigue subscale has been applied to a recently 
closed randomized phase III study (GOG 249) which evaluates the effect of 
vaginal cuff brachytherapy followed by three cycles of chemotherapy on 
recurrence or death rate. A total of 546 patients completed baseline QOL 
assessment. The estimated standard deviation of the FACIT-Fatigue subscale is 
approximately 11 points. Therefore this MCID of 4 points is corresponding an 
effect size of 0.36 assuming a standard deviation of 11. 
 
Statistical power and analysis on patient-reported outcomes research 
The primary objective on patient-reported outcomes in this study is to estimate 
treatment differences in physical functioning, physical activity, and fatigue 
between treatment arms.  
Since this study is randomized phase II/III study for primary clinical endpoints, 
the hypothesis testing for PROs will be conducted only if the phase III is resumed. 
The overall type I error for testing entire PRO outcomes is set as 5% (two-sided) 
for phase III study. In order to control the PROs-wide error for three PROs, a type 
one error of 1.67% will be allocated to each of the PROs.  
 
Based on the GOG experiences on advanced endometrial cancer studies (e.g. 
GOG 0209), at least 86% eligible patients are evaluable for PRO assessments. A 
patient is evaluable for PRO assessment if she completes baseline and at least one 
follow-up assessment. Assuming the same evaluable percentage for this study, we 
expect at least 232 patients in each group will be evaluable for PRO in phase III 
trial of this study.  
 
Since the PRO outcomes are measured repeatedly over time, the repeated 
outcomes are most likely correlated each other. The following table displays the 
statistical powers to detect MCIDs or ESs if the correlations (e.g. compound 
symmetry) among the repeated measures are ranged 0.6 to 0.7. The effect size is 
defined as the ratio of treatment difference to the standard deviation in the control 
group at baseline. If the phase II trial moves forward to a phase III study, the 
standard deviations of the physical functioning and FACIT Fatigue scales will be 
re-estimated using the data collected from phase II trial and statistical power will 
be adjusted correspondingly.  
 
 
MCID 
STD 
Effect size 
correlations 
Statistical Power  
Physical Functioning 
7 
26 
0.27 
0.6 ~0.7 
80.5% ~ 84.2% 
FACIT-Fatigue 
4.0 
11 
0.36 
0.6~0.7 
97.4%~98.4% 
Physical activity 
 
 
0.50 
0.6~0.7 
99.9%~99.9% 
 
A linear mixed model will be applied to assess the treatment difference of each 
PRO scores and its interaction with assessment points. The baseline PRO score 
will be included in the model as a covariate along with the patient’s age, and 
performance status at baseline. Patients will be categorized by their randomized 
treatment group rather than the treatment received. Patients who complete 

 
 
GOG-0286B 
 
84 
 
baseline and at least one follow-up PRO assessment will be included in the PRO 
analysis. The interaction between treatment assignment and assessment time will 
be assessed first. If there is statistically significant evidence that the relative 
treatment effects vary over assessment time, then treatment comparisons will be 
performed for each assessment time points.  
Missing information 
Patient death, noncompliance, missed appointments, and patient illiteracy, can 
cause missing information.  One or more of the QoL assessments may be missing 
for an individual on any occasion.  Missing information is troublesome 
particularly in studies involving repeated patient assessments.  Data management 
procedures will be used to reduce missing data.  To this end, a calendar of events 
which lists the dates for the required QoL assessments for each patient will be 
made available to the patient's health care provider as soon as the patient has been 
registered onto this study.  Also, the clinic staff will use the GOG web-based 
forms tracking system to obtain reminders of the upcoming QoL assessments.     
 
At semi-annual group meetings the data managers and nurses will be given 
presentations, which describe the goals of this study and stress the importance of 
obtaining complete assessments.  A study contact person will be designated to 
answer questions that arise throughout the study.  
 
Women, who are unable to read or have difficulty reading, will not be required to 
participate in the QoL part of this study.  Also, any woman, who does not wish to 
participate in the QoL portion of this study, can refuse and remain eligible for the 
therapeutic portion of the study. 
 
 
 
11.9 
Planned gender, minority and ethnic inclusion: 
The following are the race and ethnicity distributions anticipated for this trial 
based upon historical data.  All patients in this study will be female by definition 
of disease. 
 
 
Ethnic Category 
 
Number of patients anticipated 
Hispanic or Latino 
22 (    4.1%) 
Not Hispanic or Latino 
518 (  95.9%) 
Total 
540 (100.0%) 
 
Racial Category 
 
Number of patients anticipated 
American Indian/Alaskan Native 
4 (    0.7%) 
Asian 
10 (    1.9%) 

 
 
GOG-0286B 
 
85 
 
Native Hawaiian or Other Pacific Islander 
0 (    0.0%) 
Black or African American 
 67 (  12.4%) 
White 
459 (  85.0%) 
Total 
540 (100.0%) 
 

 
 
GOG-0286B 
 
86 
 
12.0 
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Mitchell, S.A., Cancer-related fatigue: state of the science. PM R, 2010. 2(5): p. 364-83. 
 
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Bower, J.E., Treating cancer-related fatigue: the search for interventions that target 
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Minton, O., et al., A systematic review and meta-analysis of the pharmacological 
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Speck, R.M., et al., An update of controlled physical activity trials in cancer survivors: a 
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Ballard-Barbash, R., et al., Physical activity, biomarkers, and disease outcomes in cancer 
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Hursting, S.D., et al., Obesity, energy balance, and cancer: new opportunities for 
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102. 
Friedenreich, C.M., et al., Inflammatory marker changes in a yearlong randomized 
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103. 
Friedenreich, C.M., et al., Changes in insulin resistance indicators, IGFs, and adipokines 
 
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107.  Thorp AA, Owen N, Neuhaus M, Dunstan DW. Sedentary behaviors and subsequent  
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108. 
Moore SC, Gierach GL, Schatzkin A, Matthews CE. Physical activity, sedentary  
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Craig, C. L., et al. (2003). "International physical activity questionnaire: 12-country  
reliability and validity." Med Sci Sports Exerc 35(8): 1381-1395. 

 
 
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APPENDIX I - Clinical Staging (FIGO) 
  
 
CARCINOMA OF THE ENDOMETRIUM 
FIGO CLASSIFICATION 
2009 
 
 
 
Stage I* 
Tumor confined to the corpus uteri. 
IA* 
No or less than half myometrial invasion 
IB* 
Invasion equal to or more than half of the myometrium 
Stage II* 
Tumor invades cervical stroma, but does not extend beyond the uterus** 
Stage III* 
Local and/or regional spread of the tumor 
IIIA* 
Tumor invades the serosa of the corpus uteri and/or adnexae# 
IIIB* 
Vaginal and/or parametrial involvement# 
IIIC* 
Metastases to pelvic and/or para-aortic lymph nodes# 
IIIC1* 
Positive pelvic nodes 
IIIC2* 
Positive para-aortic lymph nodes with or without positive pelvic lymph         
nodes 
Stage IV* 
Tumor invades bladder and/or bowel mucosa, and/or distant metastases 
IVA* 
Tumor invasion of bladder and/or bowel mucosa 
IVB 
Distant metastases, including intra-abdominal metastases and/or inguinal 
lymph nodes 
 
*Either G1, G2, or G3. 
 
**Endocervical glandular involvement only should be considered as Stage I and no longer as 
Stage II. 
 
#Positive cytology has to be reported separately without changing the stage. 
 

 
 
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APPENDIX II - Patient Medication Calendar (05/19/14)(04/28/15) 
 
PILL DIARY 
INSTRUCTIONS FOR THE PATIENT: 
This is a calendar on which you are to record the total number of pills (study medication) you take each day  
and the time of day you take your pills (study medication). You will take metformin (850 mg) or placebo once a day 
for 4 weeks and then twice a day for the remainder of the study.  
 
Bring the bottle(s) with any unused pills and your calendar with you each time you have an appointment.   
 
If you have any questions, please contact: ____________________________________ 
Telephone: ________________________ 
Your next appointment is: __________________________________________________ 
 
 
#of pills 
taken_____ 
 
Time: 
a.m.   □   
 
Time 
 p. m. □ 
 
 
#of pills 
taken_____ 
 
Time: 
a.m.   □   
 
Time 
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#of pills 
taken_____ 
 
Time: 
a.m.   □   
 
Time 
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#of pills 
taken_____ 
 
Time: 
a.m.   □   
 
Time 
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#of pills 
taken_____ 
 
Time: 
a.m.   □   
 
Time 
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#of pills 
taken_____ 
 
Time: 
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Time 
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#of pills 
taken_____ 
 
Time: 
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Time 
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#of pills 
taken_____ 
 
Time: 
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Time 
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#of pills 
taken_____ 
 
Time: 
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Time 
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#of pills 
taken_____ 
 
Time: 
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Time 
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#of pills 
taken_____ 
 
Time: 
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Time 
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#of pills 
taken_____ 
 
Time: 
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Time 
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#of pills 
taken_____ 
 
Time: 
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Time 
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#of pills 
taken_____ 
 
Time: 
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Time 
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#of pills 
taken_____ 
 
Time: 
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Time 
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#of pills 
taken_____ 
 
Time: 
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Time 
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#of pills 
taken_____ 
 
Time: 
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Time 
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#of pills 
taken_____ 
 
Time: 
a.m.   □   
 
Time 
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#of pills 
taken_____ 
 
Time: 
a.m.   □   
 
Time 
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#of pills 
taken_____ 
 
Time: 
a.m.   □   
 
Time 
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#of pills 
taken_____ 
 
Time: 
a.m.   □   
 
Time 
 p. m. □ 
 
This section is to be completed by the physician, nurse or staff: 
 
Reporting period (mm/dd/yy):  Start _______ Stop _______Total # of pills taken: ____   # pills left in bottle: ____  
 
 
Patient’s Signature: __________________________________________________ Date: _________                

 
 
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APPENDIX III - NRG Oncology General Therapy Guidelines (12/05/16)  
 
• For 21 or 28 day cycles, a patient will be permitted to have a new cycle of therapy 
delayed up to 7 days (without this being considered to be a protocol violation) for 
major life events (e.g., serious illness in a family member, major holiday, vacation 
which is unable to be re-scheduled).  Documentation to justify this decision should be 
provided. 
• It will be acceptable for individual doses to be delivered within a “24-hour window 
before and after the protocol-defined date” for “Day 1” treatment of 21 or 28 day 
cycles.  If the treatment due date is a Friday, and the patient cannot be treated on that 
Friday, then the window for treatment would include the Thursday (1 day earlier than 
due) through the Monday (day 3 past due). 
• For weekly regimens, it will be acceptable for individual doses to be delivered within a 
“24-hour window,” for example; “Day 8 therapy” can be delivered on Day 7, Day 8, or 
Day 9 and “Day 15 therapy” can be given on Day 14, Day 15, or Day 16. 
• Doses can be “rounded” according to institutional standards without being considered a 
protocol violation (most institutions use a rule of approximately +/- 5% of the 
calculated dose). 
• Doses are required to be recalculated if the patient has a weight change of greater than or 
equal to 10%.  Patients are permitted to have chemotherapy doses recalculated for less 
than 10% weight changes. 
 

 
 
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APPENDIX IV - CARBOPLATIN DOSE CALCULATION INSTRUCTIONS 
 
1) The Cockcroft-Gault formula will be used in GOG trials. 
2) Conversion of IDMS creatinine levels to “non-IDMS” values will not be permitted.  
3) The carboplatin calculation tool is available on the GOG website (Web Menu, Tools). 
 
Dosing of Carboplatin: 
 
1) The carboplatin dose will be calculated to reach a target area under the curve (AUC) 
according to the Calvert formula using an estimated glomerular filtration rate (GFR) from the 
Cockcroft-Gault formula. 
 
2) The initial dose of carboplatin must be calculated using GFR. In the absence of renal toxicity 
greater than or equal to CTCAE Grade 2 (serum creatinine >1.5 x ULN) or toxicity requiring 
dose modification, the dose of carboplatin will not need to be recalculated for subsequent 
cycles, but will be subject to dose modification for toxicity as noted in the protocol. 
 
3) Carboplatin doses are required to be recalculated if the patient has a weight change of greater 
than or equal to 10%.  Patients are permitted to have chemotherapy doses recalculated for < 
10% weight changes. 
 
4) At the time of dose modification, if the patient’s age had changed (the patient has had a 
birthday), the site can use the current age. 
 
5) In patients with an abnormally low serum creatinine (less than 0.7 mg/dl), the creatinine 
clearance should be estimated using a minimum value of 0.7 mg/dl.  For trials where 
patients enter and are treated within less than or equal to 12 weeks of surgery:  If a more 
appropriate (higher) baseline creatinine value is available from the pre-operative period 
(within 4 weeks of surgery date), that value may also be used for the initial estimation of 
GFR. 
 
CALVERT FORMULA: 
 
Carboplatin dose (mg) = target AUC x (GFR + 25) 
 
NOTE:  the GFR used in the Calvert formula should not exceed 125 ml/min. 
 
Maximum carboplatin dose (mg) = target AUC (mg/ml x min) x 150 ml/min. 
 
The maximum allowed doses of carboplatin are: 
 
AUC 6 = 900 mg 
 
AUC 5 = 750 mg 
 
AUC 4 = 600 mg 

 
 
GOG-0286B 
 
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For the purposes of this protocol, the GFR is considered to be equivalent to the estimated 
creatinine clearance. The estimated creatinine clearance (ml/min) is calculated by the method of 
Cockcroft-Gault using the following formula: 
 
Creatinine Clearance (mL/min) =    [140-Age (years)] x Weight (kg) x 0.85  
                                                             
72 x serum creatinine (mg/dl)  
 
Notes: 
1) Weight in kilograms (kg): 
a.  Body Mass Index (BMI) should be calculated for each patient.  A BMI calculator is 
available at the following link:  http://www.nhlbisupport.com/bmi/  
b. Actual weight should be used for estimation of GFR for patients with BMI of less than 25. 
c. Adjusted weight should be used for estimation of GFR for patients with BMI of greater 
than or equal to 25. 
d. Adjusted weight calculation: 
Ideal weight (kg) = (((Height (cm)/2.54) – 60) x 2.3) + 45.5 
Adjusted weight (kg) = ((Actual weight – Ideal weight) x 0.40) + Ideal weight 
 
2) The Cockcroft-Gault formula above is specifically for women (it includes the 0.85 factor). 
 
At the time of a dose modification for toxicity:  If the creatinine at the time of a dose 
modification is lower than the creatinine used to calculate the previous dose, use the previous 
(higher) creatinine; if the creatinine at the time of a dose modification is higher than the 
creatinine used to calculate the previous dose, use the current (higher) creatinine.  This will 
ensure that the patient is actually receiving a dose reduction.  
 

 
 
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APPENDIX V – Translational Research Specimen Procedures (12/05/16)(XX/XX/XX) 
 
I. Summary of Specimen Requirements 
 
If the patient gives permission for her specimens to be collected and used for this optional 
translational research component, then participating institutions are required to submit the 
patient’s specimens as outlined below (unless otherwise specified). 
 
Required Specimen (Specimen Code) 
Collection Time Point 
Sites Ship Specimens To 
FFPE Primary Tumor (FP01)* 
1st Choice: block 
2nd Choice: 25 unstained slides (15 
charged, 5µm & 10 uncharged, 10µm) 
Prior to all treatment 
GOG Tissue Bank within 8 weeks of 
registration1 
FFPE Metastatic Tumor (FM01)* 
1st Choice: block 
2nd Choice: 25 unstained slides (15 
charged, 5µm & 10 uncharged, 10µm) 
FFPE Recurrent Primary Tumor (FRP01)* 
1st Choice: block 
2nd Choice: 25 unstained slides (15 
charged, 5µm & 10 uncharged, 10µm) 
Prior to study treatment 
FFPE Recurrent Metastatic Tumor 
(FRM01)* 
1st Choice: block 
2nd Choice: 25 unstained slides (15 
charged, 5µm & 10 uncharged, 10µm) 
Pre-Treatment Whole Blood (WB01) 
7-10mL drawn into purple top (EDTA) 
tube(s) 
Prior to study treatment 
GOG Tissue Bank the day the 
specimen is collected1 
* A copy of the corresponding pathology report must be shipped with all tissue specimens sent to the GOG Tissue Bank 
1 GOG Tissue Bank / Protocol GOG-0286B, Nationwide Children’s Hospital, 700 Children’s Drive, WA1340, Columbus, OH 
43205, Phone: (614) 722-2865, FAX: (614) 722-2897, Email: BPCBank@nationwidechildrens.org 
 
II. Obtaining a GOG Bank ID for Translational Research Specimens 
 
Only one GOG Bank ID (# # # # - # # - G # # #) is assigned per patient. All translational 
research specimens and accompanying paperwork must be labeled with this coded patient 
number. 
 
A Bank ID is automatically assigned once the Specimen Consent is completed and 
indicates that a patient has agreed to participate in the translational science component. If a 
patient has previously been assigned a Bank ID, please ensure the Bank ID appearing in 
Rave is the same as the previously assigned Bank ID. 
 
Please contact GOG User Support if you need assistance or have assigned more than one 
Bank ID to a patient (Email: support@gogstats.org; Phone: 716-845-7767). 
 
III. Requesting Translational Research Specimen Kits 
 
Kits are not provided for this protocol. 
 

 
 
GOG-0286B 
 
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IV. Labeling Translational Research Specimens 
 
A waterproof permanent marker or printed label should be used to label each translational 
research specimen with: 
 
GOG Bank ID (# # # # - # # - G # # #) 
GOG protocol number (GOG- # # # #) 
specimen code (see section I) 
collection date (mm/dd/yyyy) 
surgical pathology accession number (tissue specimens only) 
block number (tissue specimens only) 
 
Note: If labeling slides, only label on the top, front portion of the slide. Do not place a label 
on the back of the slide or over the tissue. The label must fit on the slide and should not be 
wrapped around the slide or hang over the edge. 
 
V. Submitting Formalin-Fixed, Paraffin-Embedded Tissue 
 
Formalin-fixed, paraffin embedded (FFPE) tissue should be the most representative of the 
tumor type (primary, metastatic, recurrent). 
 
Every attempt should be made to provide a FFPE block; however, if a block cannot be 
provided on a permanent basis, then 25 unstained consecutive slides (15 charged, 5µm + 10 
uncharged, 10µm) should be submitted. All tissue sections should be cut sequentially from 
one same block. 
 
Note: If stained slides (required to confirm patient eligibility by central pathology review) 
will be cut from the same block that will be submitted for translational research, your 
pathology department should cut the slides for staining prior to submitting the block for 
translational research. 
 
The type of specimen (block or slides) should be specified on Form TR. If submitting 
slides, the slide type, thickness, and count should also be specified. 
 
All FFPE tissue should be submitted with the corresponding pathology report. 
 
VI. Submitting Whole Blood 
 
1.  Label the lavender/purple top (EDTA) collection tube(s) as described above. Multiple 
tubes may be used to collect the required amount. 
 
2. Draw 7-10mL of blood into the labeled lavender/purple top tube(s). A minimum of 
3mL is needed for processing. 
 
3.  Immediately after collection, gently invert the tube 5-10 times to mix the blood and 
EDTA. 

 
 
GOG-0286B 
 
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4. Whole blood specimens should be refrigerated (4°C) until the specimens can be 
shipped. Ship whole blood to the GOG Tissue Bank the day the specimen is collected 
(i.e., Monday through Friday, but not the day before a holiday). If the whole blood 
absolutely cannot be shipped the day it is collected, the tube(s) should be refrigerated 
(4°C) until the specimen can be shipped. 
 
VII. Submitting Form TR 
 
A specimen transmittal form (i.e., Form TR) for each biospecimen will be available in the 
Translational Research Folder in Rave, once the Specimen Consent (located in the 
Baseline Folder) has been completed. 
 
An electronically (i.e., Rave) completed copy of Form TR must accompany each specimen 
shipped to the GOG Tissue Bank. Handwritten forms will not be accepted. 
 
Note: A copy does not need to be sent to the GOG Tissue Bank if specimens are not 
collected. 
 
Form TR should be printed from the Translational Research Form screen in Rave using the 
“PDF File” link at the top of the form. Do not use the “Printable Version” or “View 
PDF” links at the bottom of the form or any other method to print the form, as these 
formats will not be accepted. 
  
Retain a printout of the completed form for your records. 
  
Please contact User Support if you need assistance (Email: support@gogstats.org; Phone: 
716-845-7767). 
 
VIII. Shipping Translational Research Specimens 
 
A completed copy of Form TR must be included for each translational research 
specimen.  
 
A. FFPE Tissue 
 
FFPE tissue and a copy of the corresponding pathology report should be shipped using 
your own container at your own expense to: 
 
GOG Tissue Bank / Protocol GOG-0286B 
Nationwide Children’s Hospital 
700 Children’s Dr, WA1340 
Columbus, OH 43205 
Phone: (614) 722-2865 
FAX: (614) 722-2897 
Email: BPCBank@nationwidechildrens.org 

 
 
GOG-0286B 
 
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Do not ship FFPE tissue for Saturday delivery. 
 
B. Whole Blood 
 
All whole blood specimens should be shipped to the GOG Tissue Bank (address above). 
  
Whole blood specimens can be shipped to the GOG Tissue Bank Monday through Friday 
for Tuesday through Saturday delivery. Please do not ship whole blood the day before a 
holiday. Use your own shipping container to ship specimens via FedEx priority 
overnight. 
 
When shipping whole blood specimens, please be aware that your institution must 
comply with IATA standards (www.iata.org). If you have questions regarding your 
shipment, contact the GOG Tissue Bank at BPCBank@nationwidechildrens.org or by 
phoning 866-GOG-BANC (866-464-2262).  
 
To ship whole blood specimens you will need (1) a sturdy shipping container (e.g., a 
cardboard or styrofoam box), (2) a leak proof biohazard envelope with absorbent 
material*, (3) a puncture and pressure resistant envelope (e.g. Tyvek envelope), (4) an 
Exempt Human Specimen sticker, and (5) a pre-paid FedEx air bill. 
 
*If you will be shipping whole blood specimens from more than one patient, please put 
each specimen in a separate plastic zip-lock bag before placing the specimens in the 
shipping bag. You may include up to four different blood specimens in one biohazard 
envelope. 
 
If you do not have these materials available at your institution, you may order them from 
any supplier (e.g., Saf-T-Pak; Phone: 800-814-7484; Website: www.saftpak.com). 
 
Instructions for Shipping Whole Blood Using Your Own Shipping Container 
 
1. Place the whole blood specimen in a biohazard envelope containing absorbent 
material. Expel as much air as possible before sealing the bag. 
  
2. Wrap the biohazard envelope in bubble wrap or another padded material. 
 
3. Place the padded tube(s) into a Tyvek envelope. Expel as much air as possible before 
sealing the envelope. 
 
4. Place the Tyvek envelope in a sturdy shipping contained (e.g., cardboard FedEx box). 
 
5. Insert a copy of the TR Form(s) into the box.  
 
6. Attach an Exempt Human Specimen sticker to the outside of the shipping container. 
 

 
 
GOG-0286B 
 
106 
 
7. Print a pre-paid FedEx air bill using the Kit Management system 
(https://ricapps.nationwidechildrens.org/KitManagement/). Attach the air bill. 
 
8. Make arrangements for FedEx pick-up through your usual institutional procedure or 
by calling 800-238-5355. 
 
IX. Distributing Translational Research Specimens 
 
The GOG Statistical and Data Center and Tissue Bank will coordinate the distribution of 
translational research specimens to approved investigators. 
 
Investigators will not be given access to any personal identifiers. 
 
Investigators will be responsible for the direct supervision and oversight of translational 
research and for keeping accurate records. 
 
Investigators will ensure the results are linked to the appropriate translational research 
specimen-specific identifiers and are responsible for transferring relevant laboratory data to 
the GOG Statistical and Data Center. 
 
At the discretion of the Chair of the Committee on Experimental Medicine and the Director 
of the GOG Tissue Bank, investigators may be required to ship any specimens (or by-
products) remaining after the completion of the translational research to the GOG Tissue 
Bank. 
 
A. FFPE 
 
FFPE will be batch shipped to: 
 
Dr. David Neil Hayes  
UNC Lineberger Comprehensive Cancer Center 
School of Medicine CB#7295 
University of North Carolina at Chapel Hill 
450 West Drive 
Chapel Hill, NC  27599-7295 
Phone: 919-966-3786 
Fax: 919-966-1587 
Email: hayes@med.unc.edu 
 
B. Whole Blood 
 
The GOG Tissue Bank will extract DNA from whole blood. DNA will be batch shipped 
to Dr. David Neil Hayes (address above). 
 
X. Banking Translational Research Specimens for Future Research 
 

 
 
GOG-0286B 
 
107 
 
Specimens will remain banked in the GOG Tissue Bank and made available for approved 
research projects if the patient has provided permission for the use of her specimens for 
future health research. The patient’s choices will be recorded on the signed informed 
consent document and electronically via the Specimen Consent. At the time of specimen 
selection for project distribution, the most recent consent information will be used. 
 
Institutions can amend a patient’s choices regarding the future use of her specimens 
at any time if the patient changes her mind. 
 
If the patient revokes permission to use her specimens, the GOG Tissue Bank will destroy 
or return any remaining specimens. The patient’s specimens will not be used for any further 
research; however, any specimens distributed for research prior to revoking consent cannot 
be returned or destroyed. In addition, the patient cannot be removed from any research that 
has been done with her specimens distributed prior to revoking consent. 
 
Note: If return of specimens is requested, shipping will be at the institution’s expense.

 
 
GOG-0286B 
 
108 
 
APPENDIX VI – CT Scan Calculator 
 
Patient Number:
Initials:
Date of First Dose:
Week 9 
Week 18
Week 27
Week 36
Week 45
Week 54
Week 63
Week 72
Week 81
Week 90
Week 99
Week 111
Week 123
Week 135
Week 147
Week 159
Week 171
Instructions:
Enter Patient Number in Cell B1
Enter Patient Initials in Cell B2
Enter Date of Cycle 1, Day 1 (first dose) in Cell B3
Projected CT Scan dates will appear in cells B4 
through B20.  Please use these dates to schedule
CT scans for this patient.  You may print this sheet 
for your reference.
 

 
 
GOG-0286B 
 
109 
 
 
Figure 1: Indirect and Direct Anti-Cancer 
Effects of Metformin. 
 
APPENDIX VII – Translational Research Laboratory Testing Procedures (12/05/16) 
 
A. OVERVIEW: Endometrial cancer (EC) is the most 
common cancer of the female reproductive organs. 
More than 50,000 new cases are diagnosed annually in 
the USA, and more than 8,000 women die in the same 
period. Despite available chemotherapy, overall 5-year 
survival for advanced EC remains poor at 21-56%. 
Thus, there is a great need for novel agents to improve 
EC outcomes. 
 
Obesity, diabetes and insulin resistance are 
associated with increased risk and worse outcomes for 
EC1-3. Metformin is a widely used first line treatment 
for type 2 diabetes. Epidemiological evidence4-6 and 
our own retrospective studies in EC patients7 suggest 
that metformin use lowers cancer risk and reduces 
cancer deaths among diabetic patients. Although 
controversial, metformin is thought to exert anti-tumorigenic activity through indirect effects on 
the metabolic milieu (↓insulin, ↓glucose) and direct effects on the tumor through inhibition of 
mitochondrial complex 1 and subsequent AMPK activation/mTOR inhibition via cation-selective 
transporters (Fig 1)8-11.  
 
We found differential patterns of gene expression in endometrioid ECs arising in obese 
women compared with non-obese women in The Cancer Genome Atlas (TCGA) database, 
including upregulation of modulators of the insulin/insulin growth factor-1 (IGF-1) pathway, 
suggesting that ECs arising in obesity may have distinct metabolic targets for treatment. In 
addition, we completed a phase 0 clinical trial in obese EC patients and found a favorable 
response to short-term metformin treatment12. Treatment with metformin resulted in decreased 
expression of the IGF-1 receptor and targets of the mTOR pathway. Metabolomic profiling 
revealed that responders to metformin treatment had higher pre-treatment serum levels of several 
fatty acids and glycolipids, suggesting that these biomarkers could predict increased benefit to 
metformin therapy. Lastly, in the LKB1fl/flp53fl/fl genetically engineered mouse model of 
endometrioid EC, diet-induced obesity (DIO) resulted in a doubling of tumor size, and 
metformin had increased efficacy against EC in obese versus lean mice.  
 
Based on this preliminary work, we will assess the contribution of indirect effects (via 
downregulation of insulin/IGF-1 signaling) and direct effects (via transporter-dependent cell 
entry and modulation of mitochondrial complex 1/AMPK/mTOR signaling) of metformin to its 
overall anti-cancer efficacy in GOG-0286B, an ongoing two arm, randomized phase 2/3 clinical 
trial of metformin versus placebo, in combination with paclitaxel/carboplatin, for the treatment 
of EC [through the NRG Oncology Group]. Since obesity is linked to upregulation of 
insulin/IGF-1 signaling, and metformin is believed to elicit its anti-cancer effects, in part, by 
lowering plasma insulin/glucose, we hypothesize that metformin (in combination with 
paclitaxel/carboplatin) will be more efficacious in obese women versus their non-obese 
counterparts. In addition to obesity, expression of transporters and hyperactivation of 
insulin/IGF-1/mTOR signaling in the tumor tissue will predict response to metformin. 
 
SIGNIFICANCE AND PRELIMINARY DATA: EC is the fourth most common cancer 

 
 
GOG-0286B 
 
110 
 
among women in the United States. EC frequency has been increasing, secondary to an aging 
female population and changes in dietary and hormonal factors, with obesity as a major culprit. 
In 2015, approximately 54,870 new cases will be diagnosed, and 10,170 women will succumb to 
this disease13. Obesity, diabetes and insulin resistance are well-known factors associated with 
both increased risk of developing EC 14-16 and increased risk of death1-3,17. Obese women with EC 
have a 6.25-fold increased risk of death from this disease, as compared to their non-obese 
counterparts2. 
 
It has been postulated that hyperglycemia and hyperinsulinemia in obese patients provide 
abundant nutrients and growth factors to cancer cells, resulting in an ideal environment for tumor 
signaling cascades, such as the insulin/insulin-like growth factor-1 (IGF-1) and PI3K/Akt/mTOR 
pathways18. Hyperinsulinemia, IGF-1 and IGF-1 receptor (IGF1R) levels are important in EC 
development and progression19,20. Signaling through IGF-1R leads to activation of the 
PI3K/Akt/mTOR pathway, and components of this pathway are often mutated, amplified or 
aberrantly expressed in EC21-24. Activation of the PI3K/Akt/mTOR pathway, through PIK3CA 
amplifications, PIK3CA/PIK3R1/PIK3R2 mutations and phosphatase and tensin homolog 
(PTEN) mutations/loss of function, is common in EC and has been linked to more aggressive 
tumor behavior22-25.  
 
Taken together, obesity is a high-energy, pro-inflammatory condition that culminates in 
increased growth factor signaling via the insulin/IGF-1 axis, as well as a nutrient-saturated 
environment via increased glucose (and other 
nutrients), 
ultimately 
resulting 
in 
excessive 
stimulation of the PI3K/Akt/mTOR pathway26-29. In 
experimental animal models, diet-induced obesity 
leads to activation of Akt and mTOR in epithelial 
tissues30,31. Conversely, calorie restriction represses 
signaling through this pathway30,31. Therefore, 
obesity may create a unique environment that can be 
exploited by a therapeutic approach as a strategy to 
improve EC outcomes.  
 
Obesity drives significant EC progression in LKB1fl/flp53fl/fl 
mice. We have assessed the impact of obesity on EC 
development in a unique endometrioid EC mouse model 
(LKB1fl/flp53fl/fl). The LKB1fl/flp53fl/fl mouse has specific and 
somatic deletions of the tumor suppressor genes, LKB1 and 
TP53, in adult endometrial epithelial cells. Knockout of LKB1 
and p53 are conditional and only activated via injection of an 
adenoviral vector expressing Cre (AdCre) into the uterine horn 
of adult female mice. Within 12 weeks (wks) post induction, 
tumors develop in the affected uterine horn, while the un-
injected uterine horn remains normal (Fig 2). 
 
 
LKB1fl/flp53fl/fl mice were fed a control low fat diet 
(LFD; 10% calories from fat) versus a high fat diet (HFD; 60% 
calories derived from fat) to mimic diet-induced obesity, 
starting at 3 wks of age. AdCre was injected at 6 wks to induce invasive EC. There was a 
significant difference in body weight between the two groups (obese mice: 32.8 g ± 8.6, lean 
 
 
Figure 2. (A) Endometroid ECs develop in the 
LKB1fl/flp53fl/fl mouse model, 12 weeks after 
AdeCre injection, as evidenced by anisocytosis, 
anisokaryosis, stromal invasion/fusion of glands, 
cellular atypia and prominent nucleoli. (B) 
Invasion 
of 
endometrial 
tumor 
into 
the 
myometrium.  
 
 Figure 
3. 
Obesity 
increases 
endometrial 
tumor 
size 
in 
LKB1fl/flp53fl/fl mice. Mice were 
fed low fat or high fat diets to 
induce obesity for 15 wks during 
tumorigenesis 
(N=10 
mice/group). 
Comparison 
of 
tumor size from lean and obese 
mice (*p=0.040). 

 
 
GOG-0286B 
 
111 
 
 
Endometrial Cancer 
Cell 
Obesity and Insulin Resistance
High Circulating Insulin and 
Glucose Levels
(1) Insulin/IGF-1 stimulated cell 
growth
(2) Hyperactivation of the mTOR
pathway
•PTEN mutations/loss of 
function
•PIK3CA/PIK3R1/PIK3R2 
mutations
MATE 1/2
Metformin
 
Figure 5. Potential benefits of 
metformin in an obesity-driven 
cancer, such as EC, that is associated 
with (a) increased circulating insulin 
and glucose levels and (b) 
hyperactivation of the mTOR 
pathway. 
mice: 24.0 g ± 2.8; p=0.033). After 15 wks of exposure to HFD/LFD, tumors in HFD-fed (obese-
ECs) mice were more than double the size of those in LFD-fed (lean-ECs) mice (mean tumor 
size 2.1 cm2 versus 0.79 cm2, p=0.040) (Fig 2). Thus, the obese state promotes tumor 
aggressiveness in the LKB1fl/flp53fl/fl mouse model of endometrioid EC. 
 
Metabolically 
relevant 
alterations 
in 
gene 
expression are associated with BMI in human 
endometrioid ECs. To assess genomic differences 
between human endometrioid ECs and BMI, we 
took advantage of the publically available gene 
expression analysis from The Cancer Genome Atlas 
(TCGA) database. 181 genes were found to be 
significantly up- or down-regulated with BMI status 
(BMI < 30 versus BMI ≥ 30) among the 
endometrioid 
ECs 
(q-value<0.1), 
including 
metabolically relevant genes related to lipid 
metabolism 
and 
the 
insulin/IGF-1 
signaling 
pathway (Fig 4), including up-regulation of 
lipoprotein lipase, insulin receptor substrate-1 (IRS-
1), insulin-like growth factor binding protein 7 
(IGFBP7) and insulin-like growth factor binding 
protein 4 (IGFBP4). This suggests that the 
metabolic consequences of obesity may be crucial in 
EC pathogenesis, resulting in biologically distinct cancers than those that arise in a non-obese 
environment. 
 
Proposed Mechanisms of the Anti-tumorigenic Effects of 
Metformin. Approved in the US in 1994, metformin is an 
effective, well-tolerated and inexpensive medication for 
normalizing hyperglycemia in the treatment of type 2 
diabetes32. Metformin is also used to treat menstrual 
dysfunction and infertility in women with polycystic 
ovarian syndrome33, and prevents the development of 
diabetes 
in 
patients 
with 
obesity 
and 
metabolic 
syndrome34,35. Epidemiological evidence, including our own 
retrospective analysis of metformin and EC outcomes7, 
suggests that metformin lowers cancer risk and reduces 
cancer incidence and deaths among diabetic patients4-6. This 
has led to the hypothesis that metformin has a role in cancer 
treatment and prevention.  
 
Metformin may have both indirect and direct effects 
on tumor growth (Fig 1)9. Its indirect effects are postulated 
to be due to a reduction in circulating glucose and insulin 
levels in the host via inhibition of gluconeogenesis in the 
liver, and subsequent decreased growth factor stimulation in 
tumor cells. On the cellular - or direct - level, metformin inhibits mitochondrial respiratory 
  
 Figure 4. Genomic differences between 
endometrial tumors from obese versus non-
obese 
women 
reveal 
alterations 
in 
metabolically relevant genes and the IGF-
1/insulin pathway. Heat map representation of 
181 genes that were significantly up- or down-
regulated in endometrial tumors from obese 
versus non-obese women (q-value<0.1).  

 
 
GOG-0286B 
 
112 
 
Lean
Obese
0
1
2
3
4
Tumor weight(gms)
Contral
Metformin
**
*
*
Lean
Obese
0
1
2
3
4
Tumor weight(gms)
Contral
Metformin
**
*
*
Control
Metformin
 
 Figure 6. Metformin inhibited endometrial tumor 
size in obese and lean LKB1fl/flp53fl/fl mice. In 
obese mice, metformin decreased tumor volume 
by 85% compared to obese control animals. 
Tumor volume was only decreased by 61% in the 
lean mice compared to lean controls.  
complex 1, leading to suppression of tricarboxylic acid (TCA) cycle flux, interrupted oxidative 
phosphorylation and decreased mitochondrial ATP production8-11. Tumors engineered to express 
a surrogate for complex 1 that is refractory to metformin were found to be resistant to metformin 
in vivo11, supporting that metformin’s effects on mitochondrial metabolism are critical to direct 
inhibition of tumor growth. The resulting cellular energetic stress from inhibition of complex 1 
raises the AMP/ATP ratio, resulting in increased AMPK signaling and stimulated glycolysis and 
fatty acid oxidation. AMPK is a central regulator of multiple signaling pathways that control 
cellular proliferation and metabolism, including inhibition of the mTOR pathway (i.e. 
specifically mTORC1 inhibition)9. In addition, metformin has also been found to inhibit the 
mTOR pathway via AMPK-independent mechanisms, potentially through its effects on the 
Ragulator complex (Rag GTPase) and REDD1 upregulation or via enhanced PRAS40 binding to 
RAPTOR9,36-39. Thus, it is logical that a drug such as metformin, that indirectly decreases 
circulating glucose and insulin levels and specifically disrupts the insulin/IGF-1 and 
PI3K/Akt/mTOR pathways, may break the link between obesity and cancer, and be particularly 
useful in obesity-driven cancers, such as EC (Fig 5). 
 
Metformin has anti-EC effects in vitro and in 
vivo: Our laboratory has shown that metformin-
mediated AMPK activation decreases EC cell 
growth via inhibition of mTOR signaling40, and 
that metformin and paclitaxel have synergistic 
anti-proliferative effects41. Others have shown 
that metformin blunts cell and tumor growth in 
vitro and in xenograft models40,42-47. In a mouse 
model of endometrial hyperplasia, metformin 
induced 
anti-proliferative 
effects 
on 
the 
endometrium that coincided with inhibition of 
downstream targets of the mTOR pathway48.  
 
Importantly, metformin has increased 
anti-tumorigenic 
efficacy 
in 
our 
obese 
LKB1fl/flp53fl/fl mouse model. LKB1fl/flp53fl/fl mice were subjected to a HFD or LFD diet starting 
at 3 wks of age, and AdCre injection was performed at 6 wks of age. Once tumor growth was 
confirmed by palpation of a 1 cm tumor, obese and lean mice were treated with vehicle or 
metformin orally (200 mg/kg/day) for 4 wks (N=10 mice/group). Metformin inhibited tumor 
volume growth in both the HFD-fed and LFD-fed mice after 4 wks of treatment (Fig 6). 
However, metformin-induced decreases in tumor volume in HFD-fed animals were significantly 
greater than in LFD-fed animals (85% versus 62%, respectively, p=0.033). Thus, our pre-clinical 
studies suggest that metformin’s anti-tumorigenic efficacy may be dependent on obese and 
insulin resistant states, which has also been shown in breast and lung cancer mouse models as 
well as our own work in ovarian cancer49-52. 
 
Metformin inhibits endometrial cell proliferation in EC Patients: Based on our preliminary 
in vitro work40,41, we conducted a phase 0 clinical trial of metformin in EC patients at UNC-
CH12. Twenty obese women, who were to undergo surgical staging for EC, received short-term 
metformin treatment until the day before their surgery. Metformin significantly reduced Ki-67 
staining, a marker of cell proliferation, in the endometrial tumors when comparing post-treatment 

 
 
GOG-0286B 
 
113 
 
 
Figure 
7. 
Metformin 
inhibited 
cellular 
proliferation in EC patients. Obese EC patients 
(n=20) underwent short-term metformin treatment 
(mean of 14.7 days) in a pre-operative window 
study. 
Percent 
Ki-67 
staining 
decreased 
significantly with metformin treatment (mean 
decrease of 21.9%, p=0.008). (A) Pre-treatment 
endometrium and (B) post-treatment endometrium. 
hysterectomy 
specimens 
to 
pre-treatment 
endometrial biopsies (p=0.008)12 (Fig 7). 
Overall, 65% of patients (13/20) responded to 
metformin treatment, with a mean decrease in 
Ki-67 staining of 21.9% among responders to 
metformin12. 
Responders 
to 
metformin 
treatment were defined as those patients with an 
absolute decrease in %Ki-67 staining (decrease 
range of 7-50%). Non-responders were defined 
as those who had no decrease in %Ki-67 
staining. Glucose levels decreased in both 
metformin responders and non-responders, but 
were only statistically significant in responders 
(p=0.007)12. Metformin also decreased expression of downstream targets of the insulin/IGF-1 
and mTOR pathway in the endometrial tumors, including phosphorylated (p)-IGF1R (24%, 
p=0.035), p-S6 (51.2%, p=0.0002) and p-4E-BP-1 (p=0.001), as demonstrated by 
immunohistochemistry12.  
 
For the EC patients enrolled on the phase 0 clinical trial, metabolomic profiling was 
performed on serum pre- and post-metformin treatment. When comparing pre- and post-
treatment serum, metformin significantly altered the concentration of 173 metabolites (37 up and 
136 down) in obese EC patients12. Comparison of global biochemical serum profiles revealed 
several key metabolic differences between metformin responders and non-responders. 
Metformin-driven metabolic alterations in the responders were primarily related to elevated 
lipolysis, more efficient amino acid metabolism and altered gut microbiome-associated 
metabolites.  
 
Metabolomic profiling revealed that responders had higher pre-metformin treatment 
Metabolic 
Pathway 
Metabolic Sub-Pathway 
Biochemical Name 
Responders/ 
Non-
responders* 
Amino Acid 
Glycine, Serine and Threonine 
Metabolism 
N-acetylthreonine 
1.47 
 
Alanine and Aspartate Metabolism 
aspartate 
1.81 
 
Histidine Metabolism 
trans-urocanate 
1.8 
 
Creatine Metabolism 
guanidinoacetate 
1.94 
Peptide 
Dipeptide 
phenylalanylphenylalanine 
2.78 
 
 
phenylalanyltryptophan 
2 
Carbohydrate 
Glycolysis, Gluconeogenesis, and 
Pyruvate Metabolism 
lactate 
1.23 
 
Pentose Metabolism 
xylulose 
0.67 
Lipid 
Polyunsaturated Fatty Acid (n3 and 
n6) 
arachidonate (20:4n6) 
1.73 
 
Fatty Acid, Monohydroxy 
2-hydroxypalmitate 
1.64 
 
Lysolipid 
1-stearoylglycerophosphoinositol 
2.94 
 
 
1-
arachidonoylglycerophosphoinositol 
1.67 
 
 
1-arachidonoylglyercophosphate 
3.35 
 
Secondary Bile Acid Metabolism 
 
glycodeoxycholate 
0.36 
Nucleotide 
 
xanthine 
1.86 
Table 1. Baseline Metabolic Differences in Serum between Responders and Non-responders to 
Metformin Treatment (*p<0.05). 
 

 
 
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114 
 
 
 
Figure 8. Metformin significantly reduced size of 
tumors overexpressing OCT3, compared to tumors 
with low transporter expression in xenograft mouse 
models of breast cancer. 
 
Figure 9. Relative transporter expression in 
human EC cell lines. 
serum levels of amino acids, dipeptides, glycolytic intermediates, arachidonic acid, 
monohydroxy fatty acids and lysolipids when compared to non-responders (p<0.05) (Table 1). 
These metabolites could serve as potential biomarkers predictive of response to metformin 
treatment. Higher pre-treatment serum levels of several fatty acids and glycolipids may be 
indicative of increased insulin resistance underlying increased benefit to metformin therapy. 
 
Cation-selective transporters mediate efficacy of 
metformin. Metformin is one of the most 
hydrophilic drugs known (logD of -6.13 at pH 7.4) 
with a net positive charge at all physiologic pH 
values, necessitating transporters to mediate its 
entry into cells. Metformin transport in the 
intestine, liver and kidney is mediated by organic 
cation transporters (OCT)1-3, and/or the plasma 
membrane monoamine transporter (PMAT), and/or 
MATE1/253-59. Studies in our laboratory show that 
metformin uptake into human breast cancer cell 
lines is dictated by the expression levels of these 
cation-selective transporters60.  
In addition, we have found that metformin response in breast cancer requires metformin transport 
capability61. Xenograft mouse models of breast cancer were generated using MCF7 cells (low 
OCT3 transporter-expressing control group) and OCT3 overexpressing MCF7 cells (OCT3-
MCF7 group). Cells (2 x 106) were injected subcutaneously into athymic nude mice. When 
tumor volumes reached 100 mm3, mice were treated with intraperitoneal saline or metformin (50 
mg/kg, daily). After a 20-day treatment, metformin-induced decrease in tumor size was greater in 
OCT3-MCF7 tumors compared to MCF7 tumors (74.5% versus 24.7%, respectively) (Fig 8). 
Thus, overexpression of a transporter increased response to metformin, suggesting variation in 
expression of transporters may contribute to differential susceptibility of tumors that arise in 
lean and obese animals. 
 
Metformin transporters are expressed in EC cell lines and tissues: Initial quantitative real-
time PCR (qRT-PCR) experiments demonstrated the expression of cation-selective metformin 
transporters at varying levels in EC cell lines (Fig 9). MATE1 and 2 were the most highly-
expressed transporters in two EC cell lines, namely Ishikawa and ECC-1, while OCT2 and 3 
were the least expressed. In the serous EC cell line SPEC-2, MATE1 and PMAT expression 
predominated, with low expression of MATE2. 
Metformin transporter expression in 15 pairs of 
human EC specimens and adjacent benign tissues 
showed that MATE1 and 2 were expressed in 
significant amounts as well as PMAT (Fig 10). In 
our pre-operative window study of metformin in 
EC patients, MATE2 (26%, p=0.0017) expression 
decreased with metformin treatment, and 
approached significance in predicting response to 
metformin (p=0.0625). Thus, we anticipate that the 
highly-expressed MATE2 may facilitate metformin 

 
 
GOG-0286B 
 
115 
 
 
 Figure 10. Relative transporter expression in (A) 15 
endometrial tumor tissues and (B) five adjacent non-
malignant tissues.  
intracellular uptake into endometrial 
tumors and be predictive of treatment 
response to this agent. 
 
It is also reasonable to postulate that 
genetic diversity in cation-selective 
transporters could affect outcomes of 
metformin treatment in cancer, as genetic 
variations in transporters have been shown 
to influence metformin’s pharmacokinetics 
and glucose-lowering effects in humans62-
66. Single nucleotide polymorphisms 
(SNPs) are the most common genetic 
variations in the human genome; therefore, SNPs in transporters can cause variability in response 
to drug treatment. The effect of genetic variations in transporters has yet to be explored in 
clinical trials of metformin for cancer treatment, and will be assessed for the first time as part of 
the proposed work. 
 
SUMMARY: Controversy surrounds whether metformin’s anti-tumorigenic effects are indirect, 
via decreasing circulating insulin and glucose levels, or direct, through inhibition of 
mitochondrial complex 1, AMPK activation and insulin/IGF-1/mTOR pathway inhibition. 
Furthermore, it is unknown if metformin will be more effective in obese and insulin resistant 
patients, given its favorable impact on the metabolic syndrome and/or disruption of obesity-
driven, intra-tumoral metabolic changes. Since obesity drives EC formation through alterations 
in metabolic pathways and hyperactivation of insulin/IGF-1/mTOR signaling, it is highly likely 
that an agent such as metformin, in combination with other chemotherapeutic agents, will be 
more efficacious in obese patients through its ability to lower plasma insulin/glucose, disrupt 
mitochondrial function and inhibit insulin/IGF-1/mTOR activation. Metformin enters cells via 
cation-selective transporters (OCT1-3, MATE1/2, and PMAT). However, the expression/action 
of these transporters in solid tumors is poorly understood. Our preliminary work suggests that 
metformin transporter expression patterns differ among breast, ovarian and ECs. If metformin is 
to be taken up by tumor cells in vivo, the expression of these transporters will be critical to its 
efficacy among cancer types and individual patients, as demonstrated by our preliminary studies 
in xenograft mouse models of breast cancer. This knowledge would ultimately aid the 
development of improved metformin-based drugs that specifically target individual transporters. 
The proposed studies will yield important insights into the relationship between the direct and 
indirect anti-cancer effects of metformin in EC. If our hypothesis is proven, we may need to 
consider obesity status and transporter expression in the individualization of metformin therapy 
in EC. Most importantly, for the first time in a clinical trial, this proposal will explore the inter-
relationship between both tumor and metabolic environment as predictors of drug efficacy.  
 
If metformin is found to be efficacious in this phase 2/3 clinical trial, the metabolic and 
molecular factors investigated in this study will be critical in identifying the best candidates for 
this unique targeted therapy and guiding further studies and management strategies for EC. 
Alternatively, if metformin is not found to be overall efficacious in this phase 2/3 trial, we may 
be able to identify subgroups of patient who did derive benefit from this therapy.  
 
I. Insulin/IGF-1/mTOR Pathway 

 
 
GOG-0286B 
 
116 
 
 
Integrated Biomarker Research Objective 
 
(1) 
To 
test 
whether 
PIK3CA 
mutations/amplifications, 
PTEN 
mutations 
or 
PIK3R1/PIK3R2 mutations are associated with a lower hazard of progression or death 
(PFS endpoint) among those EC patients treated with metformin. Targeted DNA sequencing 
(PIK3CA mutations/amplifications, PTEN mutations and PIK3R1/PIK3R2 mutations) analysis 
will be performed of the EC tumor specimens from patients enrolled on both arms of this trial. 
Hazard of progression or death will be compared between patients with and without PIK3CA 
mutations/amplifications, PTEN mutations or PIK3R1/PIK3R2 mutations in their tumors on both 
arms of this trial, although we hypothesize that these mutations/amplifications will only be 
predictive of a lower hazard of progression or death in the metformin arm. 
 
Other Exploratory Research Objectives 
 
(1) To test whether genomic profiles (i.e.PIK3CA mutations/amplifications, PTEN 
mutations or PIK3R1/PIK3R2 mutations) differ between the tumors of obese and non-
obese EC patients.  Targeted DNA sequencing (PIK3CA mutations/amplifications, PTEN 
mutations and PIK3R1/PIK3R2 mutations) analysis will be performed of the EC tumor 
specimens from patients on both arms of this trial, and genomic profiles compared between the 
endometrial tumors of obese and non-obese women.  
 
(2) To correlate expression of key targets of the insulin/IGF-1/mTOR signaling pathway (p-
IGF1R, p-S6 and p-4EBP-1) with treatment response to metformin/paclitaxel/carboplatin, 
PFS, OS and obesity status. Immunohistochemical (IHC) analyses will be performed of the EC 
tumor specimens from patients on both arms of this trial to determine expression of targets of the 
insulin/IGF-1/mTOR pathway, including p-IGF1R, p-S6 and p-4EBP-1. Hazard of progression 
or death will be compared between patients with and without high expression of p-IGF1R, p-S6 
and p-4EBP-1 in their tumors, although we hypothesize that these alterations will only be 
predictive of a lower hazard of progression or death in the metformin arm. In addition, 
expression of p-IGF1R, p-S6 and p-4EBP-1 will also be compared between the endometrial 
tumors of obese and non-obese women.  
  
 
Expected Findings: Among those EC patients treated with metformin (plus 
paclitaxel/carboplatin), we hypothesize that patients with PIK3CA mutations/amplifications, 
PTEN mutations or PIK3R1/PIK3R2 mutations in their tumors will have a lower hazard of 
progression or death (PFS endpoint) than those without these genomic alterations. As seen in our 
TCGA data on BMI and EC (Fig 3), we anticipate that the tumors from obese versus non-obese 
women will have evidence of hyperactivation of the insulin/IGF-1/mTOR pathway, as will be 
demonstrated by (1) increased expression of p-IGF1R (2) PTEN mutations, (3) PIK3CA 
mutations/amplifications and PIK3R1/PIK3R2 mutations, and (4) increased expression of 
downstream targets of the mTOR pathway, including p-S6 and p-4E-BP1. In addition, these 
alterations in insulin/IGF-1/mTOR signaling will be predictive of responsiveness to metformin 
(plus paclitaxel/carboplatin) and improvements in PFS and OS. If we unexpectedly find that 
hyperactivation of the insulin/IGF-1/mTOR signaling pathway is not associated with heightened 

 
 
GOG-0286B 
 
117 
 
efficacy of metformin, it may be metformin’s indirect effects on improving the metabolic milieu 
are much more critical in beneficial response to this agent, as will be explored by following the 
metabolic characteristics of patients enrolled on this trial (i.e. BMI, fasting insulin glucose levels, 
HOMA scores and hip-to-waist ratio).  
 
II. Metformin Transporters 
 
Integrated Biomarker Research Objective 
 
(1) To test whether higher expression of MATE2 is associated with a lower hazard of 
progression or death (PFS endpoint) among EC patients who are treated with metformin. 
Using pre-treatment EC specimens from patients enrolled on both arms of this trial, IHC 
analyses of the metformin transporters (OCT3, MATE1/2, PMAT) will be performed. Hazard of 
progression or death will be compared between patients with and without high expression of 
these transporters in their tumors, although we hypothesize that high MATE2 expression alone 
will be predictive of a lower hazard of progression or death in only the metformin arm. 
 
Other Exploratory Research Objectives 
 
(1) To determine if the genetic variants of the metformin transporters correspond to 
metformin responsiveness, PFS and OS, next generation sequencing of the EC tumor 
specimens from the patients treated with metformin (plus paclitaxel/carboplatin) will be 
performed targeted to the regions coding the SNPs associated with increased or decreased uptake 
of metformin or related to efficacy of this agent (Table 2)64. We anticipate that the racial 
distribution of our patients will be largely Caucasian (83%) and African American (10%); and 
thus, we will primarily focus on SNPs related to these two ethnic groups.  Hazard of progression 
or death will be compared between patients with and without these SNPs of interest.  
 
 
Expected Findings: As per our preliminary data in EC cell lines and tumors (Fig 9 and 10), we 
expect that the MATE2 transporter will be expressed in the EC tumors of women enrolled on 
GOG286B. We hypothesize that higher expression of this transporter will correspond with a 
lower hazard of progression or death (PFS endpoint) among patients who are treated with 
metformin. The MATE transporters are commonly thought to mediate metformin secretion in the 
kidney8, although some evidence suggests that MATE1 is a bi-directional transporter67. If 
expression of MATE2 does not correspond with a lower hazard of progression of death (PFS), 
this could be because the MATE2 transporter is primarily related to metformin efflux in the 
endometrium and not uptake of this agent. In this case, we would assess expression of the other 
cation-selective transporters, including MATE1, OCT1, OCT3 and PMAT, and determine if 
these were alternatively predictive of response to metformin.  
 
Although the phase 2/3 clinical trial is a combination of metformin with paclitaxel and 
carboplatin, it should not interfere with our ability to assess the relationship between transporter 
expression and response to metformin. Others have shown that the organic anion transporting 
polypeptide 1B3 and copper transporter 1 are essential for the uptake of paclitaxel and 
carboplatin, respectively in human EC, and not OCT1-3, PMAT or MATE1/268. 

 
 
GOG-0286B 
 
118 
 
Genetic variants of 
transporters associated 
with decreased uptake 
of metformin will 
correlate with 
decreased efficacy of 
this drug, which could 
be related to either its 
indirect effects (uptake 
by the liver) or direct 
effects (uptake by the 
endometrial tumors). 
Conversely, genetic 
variants of transporters 
associated with 
increased uptake or enhanced efficacy will correlate with improved response to metformin. 
 
III. Metabolic Factors 
 
(1) To correlate biomarkers of obesity and insulin resistance with response to metformin 
treatment (plus paclitaxel/carboplatin), PFS and OS, BMI, waist-to-hip ratio, HgBA1C and 
diabetes status will be documented throughout treatment. Fasting insulin and glucose levels will 
be obtained prior to cycles 1 and 3. Homeostasis Model Assessment Score (HOMA) (fasting 
insulin (microunits.ml) X fasting glucose (mmol/22.5)), a measurement of insulin resistance, will 
be calculated prior to cycles 1 and 3.  
 
Expected findings: For the phase 2/3 clinical trial, we postulate that increased baseline BMI, 
waist-to-hip ratio, and fasting insulin and glucose levels will predict response to treatment with 
metformin/paclitaxel/carboplatin, with a greater response seen in the obese patients. We expect 
that BMI and fasting insulin and glucose levels will decrease with 
metformin/paclitaxel/carboplatin treatment as compared to placebo/paclitaxel/carboplatin. If 
metabolic factors are not predictive of response to metformin treatment, it would suggest that 
metformin’s direct effects may be most important for its anti-tumorigenic benefits. In this case, 
metformin may be universally effective for cancer treatment and not limited to patients who are 
obese and insulin resistant; other biomarkers of response to metformin treatment could be 
elucidated from this work such as the transporters and alterations in the insulin/IGF-1/mTOR 
pathway as being explored above. 
 
B. Laboratory Testing Procedures 
 
Insert detailed procedures including assay specifics and quality control elements. 
 
IMMUNOHISTOCHEMICAL ANALYSIS 
 
Chromogenic Immunohistochemistry (IHC): IHC will be performed with the assistance of the 
UNC-CH Anatomic Pathology Translational Core Lab as previously done for our phase 0 study 
Table 2. Transporter Coding SNPs Associated with Response to Metformin 
Transporter 
dsSNP ID 
Amino Acid 
Allele 
Frequencies 
Metformin 
Uptake 
OCT1 
rs34447885 
Ser14Phe 
AA: 3.1% 
↓ 
 
rs12208357 
Arg61Cys 
C: 9.1-9.7% 
↓ 
 
rs34104736 
Ser189Leu 
C: 0.5% 
↓ 
 
rs361003319 
Gly220Val 
AA: 0.5% 
↓ 
 
rs34130495 
Gly401Ser 
C: 3.2% 
↓ 
 
rs628031 
Met408Val 
AA:73.5% 
C: 59.7% 
Positive 
predictor of 
efficacy 
 
rs202220802 
Met420del 
C:15.7% 
↓ 
 
rs34059508 
Gly465Arg 
C: 1.5% 
↓ 
OCT3 
rs8187715 
Thr44Met 
AA: 0.6% 
C: 0.6% 
↑ 
 
rs8187725 
Thr400lle 
Val423Phe 
C: 0.5% 
↓ 
MATE1 
rs35790011 
Val338lle 
C: 0.4% 
↓ 
 
rs35395280 
Cys497Phe/Ser 
AA: 2.4% 
↓ 
AA=African American, C=Caucasian 

 
 
GOG-0286B 
 
119 
 
of metformin in obese EC patients12. All rabbit monoclonal and polyclonal antibodies used in 
this study are listed in the Table 3. All IHC stains will be carried out in the Bond fully-
automated slide staining system (Leica Biosystems). Briefly, slides will be dewaxed in Bond 
Dewax solution (AR9222) and hydrated in Bond Wash solution (AR9590). Heat induced antigen 
retrieval for each antibody will be performed for 30 min at 100ºC in Bond-Epitope Retrieval 
solution 1 pH-6.0 (AR9961) or solution 2 pH-9.0 (AR9640) as shown in Table 3. The antigen 
retrieval will be followed with 5 min Bond peroxide (Bond Polymer Refine kit DS9800) and 10-
30 min protein blocking (DAKO, DS9800) steps. After pretreatment, slides will be incubated 
with the appropriately diluted primary antibody followed by chromogenic detection and 
hematoxylin counterstaining using Bond Polymer Refine Detection Kit (DS9800). Stained slides 
will be dehydrated and coverslipped. Positive and negative controls (no primary antibody) will 
be included for each run. 
 
Antibody  
Host 
M=mo
nocl 
P=poly
cl 
Vendor  
Catalog 
# 
Clone 
or 
Phos-
phory
lated 
Site 
Antigen 
Retrieval  
Peroxidase 
Block  
(Bond 
Polymer 
Refine Kit 
DS9800). 
Min 
Protein 
Block 
(DAKO 
X0909). 
min 
Primary 
Ab 
dilution 
Primary 
Ab 
Incubation 
 Bond 
Polymer 
Refine 
Kit 
(DS9800) 
Analysis 
Software 
MATE1: 
SLC47A1 
Rabbit 
P 
Thermo 
Scientifi
c  
PA5-
25272 
  
pH-6.0, 
30 min 
(AR9961)  
5 
10 
1:400 
1 h 
yes 
Aperio 
membrane 
algorithm 
MATE2: 
SLC47A2 
Rabbit 
P 
Sigma-
Aldrich 
HPA044
412 
  
pH-6.0, 
30 min 
(AR9961)  
5 
10 
1:200 
1 h 
yes 
Aperio 
Nuclear 
Oct3: 
SLC22A3 
Rabbit 
M  
OriGene 
TA3108
52 
EPR6
630 
pH-6.0, 
30 min 
(AR9961)  
5 
10 
1:600 
30 min 
yes 
Definiens 
Tissue Studio. 
H score and 
% of positive 
cells in 
epithelial 
enriched ROI  
(not stroma) 
Phospho- 
4E-BP1 
Rabbit 
M  
Cell 
Signalin
g 
Technol
ogy  
2855 
Thr37/
46, 
236B4 
pH-6.0, 
30 min 
(AR9961)  
5 
N/A 
1:400 
1 h 
yes 
Aperio 
cytoplasmic 
& color 
deconvolution 
algorithms 
Phospho-
IGF1R 
Rabbit 
P IgG 
Santa 
Cruz 
Biotechn
ology 
sc-
101703 
Tyr 
1161 
N/A 
5 
10 
1:200 
1 h 
yes 
Aperio 
cytoplasmic 
algorithm 
Phospho- 
S6 
Ribosoma
l Protein 
Rabbit 
M 
Cell 
Signalin
g 
Technol
ogy  
4858 
D57.2.
2E 
pH-6.0, 
30 min 
(AR9961)  
5 
10 
1:200 
2 h 
yes 
Aperio 
cytoplasmic 
& color 
deconvolution 
algorithms 
PMAT 
Rabbit 
P 
Thermo 
Scientifi
c  
PA5-
11228 
  
pH-6.0, 
30 min 
(AR9961)  
5 
10 
1:200 
1 h 
yes 
Aperio 
cytoplasmic 
algorithm 

 
 
GOG-0286B 
 
120 
 
Table 3: Rabbit Monoclonal and Polyclonal Antibodies and Protocols 
 
 
Digital imaging and image analysis: Stained whole tissue sections (WTS) slides will be 
digitally imaged using 20X objective in the Aperio ScanScope XT (Leica Biosystems). Digital 
images will be stored within the Aperio eSlide Manger Database. 
 
Phosphorylated (p)-4E-BP1, p-AKT, PMAT, p-IGF1R and p-S6  biomarkers will be 
quantified using Aperio v9 color deconvolution (area analysis) and Aperio Cytoplasmic v2 (cell 
quantification) algorithms. Aperio membrane v9 and nuclear v9 cell quantification algorithms 
will be used for MATE1 and MATE2, respectively. Definiens Tissue Studio image analysis 
software (Architect XD v 2.0.4, Tissue Studio v 3.5, Munich, Germany) will be used to quantify 
PTEN and OCT3 positive cells within the stroma and epithelial rich regions defined by the 
algorithm. 
 
Protocol for Optimization of Automated Analysis of Digital Images from IHC Stained 
Slides: 
 
Control slides will be constructed for each antibody with endometrial tissues known to be 
representative of both positive and negative biomarker status, in addition to being representative 
of a range of staining intensities. These control slides will be used to confirm that staining 
conditions remain consistent between staining runs. Additionally, sections of control slides will 
be cut at the same time as patient sample slides and all will be stored together in nitrogen 
desiccator chambers. In addition, we will minimize the number of batches run and ensure the 
same lot number is used for reagents, including that of the antibodies used.  
 
Background IHC staining: Background staining levels for each IHC marker shall be defined as 
the average DAB staining intensity on negative control tissues (tissues that are negative for each 
marker). Average background staining intensities will be measured on digital images using the 
Color Deconvolution algorithm (v9, Aperio). The positive intensity threshold for each IHC 
marker will then be set above the average DAB background staining intensity.  
 
Determination of % Positive cells: The algorithms that will be in this study are commercially 
available from Aperio and Definiens. Our Core facility has had extensive experience analyzing a 
wide range of normal and tumor tissues using both of these analysis platforms. We have worked 
with pathologists in the past to optimize algorithms, and our automated analysis results 
correlated well with both manual review of TPL stains and in comparison to original clinical 
diagnostics from multiple hospital sites (please see TPL references Allott et al., 2015 and Taylor 
et al., 2015 for our published work with the Carolina Breast Cancer Study and the AMBER 
consortium; manuscripts evaluating additional markers are currently under review or in 
preparation).  
PTEN 
Rabbit 
M  
Cell 
Signalin
g 
Technol
ogy  
9559S 
138G6 
pH-9.0, 
30 min 
(AR9640)  
5 
10 
1:400 
30 min 
yes 
Definiens 
Tissue Studio. 
H score and 
% of positive 
cells in the 
stroma and 
epithelial 
enriched 
ROIs 

 
 
GOG-0286B 
 
121 
 
 
To ensure that automated algorithms are accurately quantifying DAB positive cells in 
endometrial tissues, for each marker a pathologist (Dr. S. O’Connor, pathologist involved with 
Dr. Bae-Jump’s other IHC studies conducted at UNC-CH) will manually review a control TMA 
with tissue from multiple patients. We will then optimize automated algorithms to match these 
manual evaluations. Typically algorithms are adjusted for cell shape and size, stain and 
counterstain optical densities, and background staining levels. Our goal is to reach at least 90% 
agreement with manual reviews. 
 
Intensity scores: For some markers such as phosphorylated H2AX (p-H2AX), there is a direct 
relationship between amounts of DNA damage and intensity levels of protein as measured by 
both Western blot and IHC staining (please see TPL reference Nikolaishvilli-Feinberg et al., 
2014; automated analysis of p-H2AX using both AQUA and Tissue Studio was compared to 
Western blot data and radiation dosage).  For other markers such as the use of CK5/6 or EGFR 
for basal-like subtyping in breast cancer, staining intensity is not considered. 
 
The relevance of the staining intensity for the IHC markers that will be used in this study is 
unknown at this time but can still be evaluated (this information may be useful as the study 
continues).  Intensity scores above background (1+, 2+, 3+) will be set for each IHC marker 
based on pathologist (Dr. S. O’Connor) manual review of control tissues with a range of staining 
intensities (e.g. a TMA slide with tissue cores from multiple patients).  We will measure the 
average DAB intensity for each of these manually determined intensity score categories using 
the Color Deconvolution algorithm. These values will be utilized to set 1+, 2+ and 3+ thresholds 
in Cellular Analysis algorithms (average intensities will represent the center of each intensity 
score bin). Our goal is to reach at least 90% agreement with manual reviews. 
 
Determining positive vs negative cutoffs for tissues: IHC markers that are clinically approved use 
a variety of factors to determine cut-offs for tissue positivity, including treatment efficacy and 
molecular subtyping.  The markers that will be used for this study are not currently used 
clinically so no such guidelines exist. Based on our (TPL) experience however, a cut-off of at 
least 1% positive cells with automated analysis is advisable to exclude false positive signal due 
to slide artifact such as small folds or surgical dyes.  However, a 1% cut-off is just a starting 
point, and can be adjusted based on statistical analysis of metrics between groups of interest (i.e. 
(1) obese and non-obese women and (2) responders and non-responders to metformin plus 
paclitaxel/carboplatin).  H Score data (which takes into account both intensity and positivity) can 
also be evaluated in this manner. 
 
Antigen Stability Evaluation Protocol 
 
It is anticipated that some GOG tissue slides will have been stored at 4°C under vacuum for 18 
months or more prior to analysis. To ensure that these sections will be useful for IHC, this pilot 
study was conducted to investigate the stability of target antigens that will be used in this trial. 
While TPL does not routinely store sections at 4°C/vacuum, we do utilize two other commonly 
used methods of storage that can be evaluated. 
 
Staining and analysis: TMA sections containing endometrial tissue were stored for 18 months at 

 
 
GOG-0286B 
 
122 
 
-20°C or at room temperature in a nitrogen gas chamber (RT/N2). Fresh sections from these 
TMAs were cut and stained at the same time as aged sections using Bond autostainers. 
Antibodies and staining conditions have been described previously (see lab appendix).  
 
After staining, slides were digitally imaged at 20X magnification and individual TMA cores 
were analyzed using Aperio analysis algorithms. Analysis results for each TMA core were 
compared by paired t-test using GraphPad Prism software (version 5.01, GraphPad Software 
Inc., La Jolla, CA). 
 
Results and recommendations for further antibody use and further study: MATE1, MATE2 and 
phosphorylated (p)-S6 antigens were essentially stable for 18 months under both of the storage 
conditions tested (see supplemental Table 1).  OCT3 and p-4E-BP1 were stable at -20°C, but had 
some loss of antigenicity at RT/N2. Adjustment of positivity thresholds during automated 
analysis can compensate for this loss, so that older sections can be compared to freshly cut 
sections with the same staining conditions.  
 
Antigenicity for p-IGF-1R was stable at -20°C, but significantly decreased under room 
temperature/N2 conditions. Given these equivocal results, we plan to further test this antigen 
with aged slides stored at 4°C under vacuum. 
 
PMAT had loss of antigenicity under both storage conditions, although the decrease at -20°C 
was less substantial. This antibody will also be evaluated at 4°C/vacuum.  
 
Two antibodies gave abnormal results with the slides stored at -20°C in that there was an 
increase in signal on the aged slides as compared to the freshly cut sections. Antigen signal for p-
AKT for the slide stored at -20°C was much higher than that for the freshly cut slide while the 
slide stored at RT/N2 had a small decrease in antigenicity. It would be difficult to predict 
whether p-AKT would be a reliable marker for the IHC evaluation of tissues stored at 
4°C/vacuum. Results with p-IRS1 also showed an increase in signal under storage at -20°C, but 
was almost completely decayed at RT/N2. This antibody should not be used for aged slides.   
 
OCT1 was not tested in this stability study.  We found that the antibody itself (Santa Cruz 
Biotechnology, sc-133866) was not stable, and that the cell compartment that was stained 
(cytoplasmic, membrane or nuclear staining) varied with each lot of this antibody. Other 
commercial antibodies for OCT1 are now available from other companies and we will conduct 
tests for both antibody and antigen stability at 4°C to determine the viability of OCT1 as an IHC 
marker for this investigation. 
 
Given these results, we have removed IHC analysis from this protocol for OCT1, 
phosphorylated-Akt and phosphorylated-IRS-1. PMAT and phosphorylated-IGF-1R will be re-
tested under 4°C/vacuum. In addition, we will re-test phosphorylated-IGF-1R, using a 
monoclonal antibody as opposed to a polyclonal antibody which should be more stable and help 
with more reproducible results.  
 
Table 1. Results from paired t-tests from antigen stability study. 
Stain 
% Positive cells -20°C 
% Positive cells RT/N2 

 
 
GOG-0286B 
 
123 
 
MATE1 
no significant difference, p= 0.1946 
no significant difference, p= 0.1127 
MATE2 
no significant difference, p= 0.6403 
mean aged = 48%, mean fresh =52% 
pS6 
no significant difference, p=0.7354 
mean aged = 6%, mean fresh = 9% 
OCT3 
no significant difference, p=0.9949 
mean aged = 41%, mean fresh = 52% 
p-4E-BP1 
no significant difference, p= 0.654 
mean aged = 8.4%, mean fresh = 17% 
p-IGF-1R 
no significant difference, p= 0.6244 
mean aged = 12%, mean fresh = 45% 
PMAT 
mean aged = 68%, mean fresh  76% 
mean aged = 63%, mean fresh = 81% 
p-AKT 
mean aged = 56%, mean fresh = 32% 
mean aged = 18%, mean fresh =22% 
p-IRS1 
mean aged  45%, mean fresh =27% 
mean aged = 1.4%,  mean fresh = 19% 
OCT1 
Not tested 
Not tested 
 
Translational Pathology Laboratory (TPL) selected references (TPL members are 
highlighted in bold): 
 
Allott, E.H., Cohen, S.M., Geradts, J., Sun, X., Khoury, T., Bshara, W., Zirpoli, G.R., Miller, 
C.R., Hwang, H., Thorne, L.B., et al. (2015). Performance of three biomarker 
immunohistochemistry for intrinsic breast cancer subtyping in the AMBER consortium. Cancer 
Epidemiol Biomarkers Prev. 
 
Busch, E.L., Keku, T.O., Richardson, D.B., Cohen, S.M., Eberhard, D.A., Avery, C.L., and 
Sandler, R.S. (2015). Evaluating markers of epithelial-mesenchymal transition to identify cancer 
patients at risk for metastatic disease. Clin Exp Metastasis. 
 
Carson, C.C., Moschos, S.J., Edmiston, S.N., Darr, D.B., Nikolaishvili-Feinberg, N., Groben, 
P.A., Zhou, X., Kuan, P.F., Pandey, S., Chan, K.T., et al. (2015). IL2 Inducible T-cell Kinase, a 
Novel Therapeutic Target in Melanoma. Clin Cancer Res 21, 2167-2176. 
 
Karginova, O., Siegel, M.B., Van Swearingen, A.E., Deal, A.M., Adamo, B., Sambade, M.J., 
Bazyar, S., Nikolaishvili-Feinberg, N., Bash, R., O'Neal, S., et al. (2015). Efficacy of 
Carboplatin Alone and in Combination with ABT888 in Intracranial Murine Models of BRCA-
Mutated and BRCA-Wild-Type Triple-Negative Breast Cancer. Mol Cancer Ther 14, 920-930. 
 
Nikolaishvilli-Feinberg, N., Cohen, S.M., Midkiff, B., Zhou, Y., Olorvida, M., Ibrahim, J.G., 
Omolo, B., Shields, J.M., Thomas, N.E., Groben, P.A., et al. (2014). Development of DNA 
damage response signaling biomarkers using automated, quantitative image analysis. J 
Histochem Cytochem 62, 185-196. 
 
Prasad, R.Y., Chastain, P.D., Nikolaishvili-Feinberg, N., Smeester, L., Kaufmann, W.K., and 
Fry, R.C. (2012). Titanium dioxide nanoparticles activate the ATM-Chk2 DNA damage response 
in human dermal fibroblasts. Nanotoxicology. 
 
Schlegel, J., Sambade, M.J., Sather, S., Moschos, S.J., Tan, A.C., Winges, A., DeRyckere, D., 
Carson, C.C., Trembath, D.G., Tentler, J.J., Eckhardt, S.G., Kuan, P.F., Hamilton, R.L., Duncan, 
L. M., Miller, C. R., Nikolaishvili-Feinberg, N., Midkiff, B.R. (2013). MERTK receptor 
tyrosine kinase is a therapeutic target in melanoma. J Clin Invest 123, 2257-2267. 

 
 
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124 
 
 
Song, G., Darr, D.B., Santos, C.M., Ross, M., Valdivia, A., Jordan, J.L., Midkiff, B.R., Cohen, 
S., Nikolaishvili-Feinberg, N., Miller, C.R., et al. (2014). Effects of tumor microenvironment 
heterogeneity on nanoparticle disposition and efficacy in breast cancer tumor models. Clin 
Cancer Res 20, 6083-6095. 
 
Stevens, E.V., Banet, N., Onesto, C., Plachco, A., Alan, J.K., Nikolaishvili-Feinberg, N., 
Midkiff, B.R., Kuan, P.F., Liu, J., Miller, C.R., et al. (2011). RhoGDI2 antagonizes ovarian 
carcinoma growth, invasion and metastasis. Small GTPases 2, 202-210. 
 
Sun, X., Gierach, G.L., Sandhu, R., Williams, T., Midkiff, B.R., Lissowska, J., Wesolowska, E., 
Boyd, N.F., Johnson, N.B., Figueroa, J.D., et al. (2013). Relationship of mammographic density 
and gene expression: analysis of normal breast tissue surrounding breast cancer. Clin Cancer Res 
19, 4972-4982. 
 
Taylor, N.J., Nikolaishvili-Feinberg, N., Midkiff, B.R., Conway, K., Millikan, R.C., and 
Geradts, J. (2015). Rational Manual and Automated Scoring Thresholds for the 
Immunohistochemical Detection of TP53 Missense Mutations in Human Breast Carcinomas. 
Appl Immunohistochem Mol Morphol. In press.  
 
TARGETED DNA SEQUENCING ANALYSIS 
 
DNA Sequencing and Quantification: The analysis will be done in collaboration with the 
UNC-CH High Throughput Sequencing Facility. DNA will be extracted from archival formalin-
fixed, paraffin-embedded (FFPE) tumor specimens using the Qiagen DNEasy column. RNA will 
also be collected and stored for future sequencing if funding becomes available. Given that the 
samples will be received as paraffin sections, there is an opportunity to dissect the tissue, which 
further assures the histopathologic features of tissue input into the assay.  
 
Targeted tumor DNA sequencing will be performed using a hybrid capture approach and 
the Agilent custom SureSelect protocol in conjunction with next generation sequencing using the 
Illumina HiSeq 2000 platform to specifically assess (1) PIK3CA mutations/amplifications, 
PTEN mutations, PIK3R1/PIK3R2 mutations and (2) the MATE1/2 and OCT1/3 transporters. 
For exon capture and targeted sequence construction, we have constructed more than 1000 
libraries using the Agilent SureSelect capture system. During library preparation, sequence 
indexes are added to the captured libraries so that multiple libraries can be sequenced on the 
same lane using multiplexed sequencing. FFPE samples will be sequenced using the paired end 
protocol with PCR duplicates removed to address the greater problem of lower complexity and 
PCR artifacts in paraffin samples. Additionally, performance is improved when matched normal 
is also embedded in paraffin. This accommodation normalizes hybridization and sequencing 
artifacts that can otherwise emerge (primarily C to A transversions). Currently, we are using a 50 
base paired-end sequencing strategy, which provides ample gene coverage while also making it 
possible to identify potential gene fusions and alternative gene transcripts. To accurately identify 
somatic gene variants we sequence cancer samples to an average depth of 200x coverage. With 
current computational protocols, this depth achieves 100% sensitivity for SNPs and small indels, 
and >90% sensitivity for large indels, assuming the allele is present in at least 10% of the 
sample. At this depth, specificity for somatic events is also very high (>99.999%) when paired 

 
 
GOG-0286B 
 
125 
 
normal is available. With current instrument capabilities, we can usually generate this coverage 
running 8 multiplexed samples per lane. Targeted sequencing in this manner is fast, reproducible, 
economical, and has excellent performance characteristics (sensitivity and specificity).  
 
Data Analysis: SeqWare initiates processing of the raw data based on the protocols specified in 
LIMS. Extensive repeatability and accuracy studies are performed prior to final workflow 
specification to ensure computational protocols provide expected performance criteria. These 
studies, in addition to published work, resulted in an alignment and somatic mutation calling 
workflow optimized for detecting somatic mutations and copy number aberrations. Initial 
alignment to the genome is performed using Stampy, a highly sensitive DNA alignment 
algorithm69. This alignment alone generates a relatively high false positive rate due to overly 
sensitive alignment. This issue is mitigated by our local Assembly Based ReAlignment (ABRA) 
algorithm, which reassembles the initial alignments in each targeted region of interest. The 
resulting contigs are used as estimates of the individual’s genome, and all reads are re-aligned 
with this reference. This procedure generates significantly better alignment accuracy than 
alignment or assembly alone, aligns complex structural changes or mutations in close proximity, 
greatly reduces reference bias, and yields improved sensitivity and specificity for any subsequent 
genotyping procedure. Currently, the resulting alignments are then processed with UNCseqR to 
generate somatic mutation calls70. Somatic copy number aberrations are detected in targeted 
regions using a negative binomial model of counts for each gene to test for differences between 
tumor and normal. Any detection of candidate viral RNAs are further processed to ensure 
adequate signal above background is available. Once mutation, structural variation, copy number 
and viral variation is detected, these are annotated as appropriate for the study and reported back 
to the project specific database.  
 
Sequencing quality is assessed to ensure that performance of an individual assay meets 
the expected performance criteria. Quality measures include fraction of sequence in the targeted 
region, overall depth of coverage, depth at each locus of interest, and adequate complexity as 
measured by fraction of duplicate sequences and fraction of duplicate start positions. Thresholds 
for each measure vary by protocol, and are determined with control samples when incorporating 
new protocols. The sample identity is verified by targeting and genotyping the loci in the identity 
panel described above. 
 
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