bluuebunny/crossref_metadata_2025 huggingface dataset:
A tailored predication model to improve outcomes and mortality in geriatric indigenous women with breast cancer of western Australia.
Authors: Khan Azim , Martin Hilary Laura , Spalding Lisa , Redfern Andrew D.
Published: May 2020

e14000

Background: Indigenous women with breast cancer (BrCa) have higher mortality than Non-Indigenous women. Remoteness, aggressive tumour biology, treatment acceptance and compliance are all contributory factors. A retrospective study of geriatric indigenous women (aged 55 years more) with BrCa has shown high co-morbidity and treatment-related toxicity scores translating to high mortality comparing to the non-indigenous geriatric population (aged 65 years or more). An algorithm allowing prediction of risk of morbidity and mortality to enable optimisation of care for this patient group, catering to needs, values, beliefs and resources of geriatric Indigenous patients is warranted. Methods: To create a predictive algorithm and model of care based on the demographics, social environment, biology, co-morbidity burden and predicted chemotherapy toxicity scores of Indigenous women with BrCa to then be applied to a validatory prospective study.A cohort of 132 indigenous geriatric patients was identified from the Western Australian Cancer Registry from 2001 to 2010 along with remoteness matched geriatric non-Indigenous women in a 1:1 ratio. Data was collated on cancer biology, chemotherapy toxicity, mortality, co-morbidity burden (by the Charlson Co-Morbidity Index -CCI) and predicted chemotherapy toxicity (by CARG toxicity score). An algorithm was created based on these factors to identify patients at greatest risk of morbidity and mortality. Results: Elements identified as predicting morbidity and mortality were; remoteness based on distance from Perth (scored 0-3), lymph node status (scored 0-1), social isolation (scored 0-1), the Carg score (scored 0-3), and the CCI (scored 0-4). The KR Geri-Indigenous Rx Model was created using these components and scored out of 12. Conclusions: The score has been used to create three risk categories, low (1-3) intermediate (4-8) and high risk (9-12). A model of care was designed with staged increases in service elements and intensity for increasing risk category. These include the addition of Indigenous health worker review initially in clinic as well as at home during chemotherapy, initial geriatric assessment and potential geriatrician review, optimization of co-morbidities and medications, consideration of dose adjustments, social worker visits to increase services, increased surveillance, with telehealth contact and GP update with treatment planning as well as on treatment completion. A prospective study based on this model is planned.
Applying the Collaborative Care Model to treat depression and anxiety in breast cancer (BC) patients.
Authors: Johns Kevin Nathaniel , Kalister Steven , Mwandha Noah , Adams Elizabeth J. , Basinger Heidi , Fugett Susan , Crawford Danielle , Draime Michelle , Shinde Namrata Vilas , Kassem Mahmoud , Radcliff Lindsey , Unthank Brittany , Cherian Mathew Amprayil , Sardesai Sagar D. , Stover Daniel G. , Vandeusen Jeffrey , Wesolowski Robert , Williams Nicole Olivia , Ramaswamy Bhuvaneswari , Lustberg Maryam B.
Published: May 2020

e14004

Background: Depression and anxiety are frequent causes of excess morbidity for cancer patients, but access to mental health care, including outpatient psychiatry, is limited across academic and community settings. The Collaborative Care Model (CoCM) is a team-based model of care that is proven to leverage limited mental health resources across a wider population in primary care (Raney L, Am J Psychiatry, 2015). Evidence suggests that CoCM can be effectively applied to cancer populations too (Walker J, Gen Hosp Psychiatry, 2009). Methods: The investigators adapted CoCM to treat depression and anxiety in an academic BC clinic. A quality protocol was approved by the institutional board. All English-speaking BC patients were screened for depression using the PHQ-2 and PHQ-9. Patients with PHQ-9 ≥15 or clinical suspicion of severe anxiety or depression were referred to CoCM. The CoCM intervention included social work care management and regular case reviews by a psychiatrist. Medical oncologists prescribed all psychiatric medications. Patients were referred to mental health services in the community as needed. Results: From November 2018 through January 2020, a total of 74 patients were enrolled in CoCM. Median age was 50 years (range: 28-74 years) with BC stages I-III (n = 49, 66.2%) and stage IV (n = 25, 33.8%). Treatments within the cohort include: endocrine therapy (n = 39, 52.7%), chemotherapy (n = 18, 24.3%), observation (n = 9, 12.1%), single agent HER2 inhibitor targeted therapy (n = 4, 5.4%), immunotherapy (n = 2, 2.7%), single agent CDK4/6 inhibitor (n = 1, 1.4%), and radiation (n = 1, 1.4%). Of the 74 patients, 28 had PHQ-9 scores ≥15 at enrollment. On average, ending PHQ-9 scores decreased 39% from the initial score (average beginning score of 19.3 and ending score of 11.3 [n = 19]). 50 patients had GAD-7 ≥10 at enrollment. On average, ending GAD-7 scores decreased 36% from the initial score (average beginning score of 15.4 and ending score of 9.9 [n = 32]). On a 5-point scale, the average patient satisfaction score was 4.3 [range: 4.1-4.5] and the average medical oncology satisfaction score was 4.6 [range: 4.5-4.7]. Financial viability is promising based on projections that 93.4% of psychiatry costs (10% salary + benefits) are covered by reimbursements for care and 2 existing social workers serving as care managers. Conclusions: The collaborative care model is an effective and financially sustainable approach to promptly address depression and anxiety symptoms in BC. Further studies are needed to assess its applicability to patients with other forms of cancers.
Advancing precision medicine in clinical oncology: Whole exome paired tumor-normal DNA and RNA sequencing at a single-institution cancer center.
Authors: McDonnell Kevin , Kulkarni Amit , Woodhouse Melissa , Smith Sidney A , Hong Christine , Melas Marilena , Heller Kathleen , Lazaris Charalampos , Sturgeon Duveen , Ricker Charité , Solomon Ilana , Culver Julie , Lowstuter Katrina , Morales Pichardo Jennifer , Jones Veronica , Idos Gregory , Bonner Joseph D , Gruber Stephen B.
Published: May 2020

e14006

Background: Next generation sequencing (NGS) allows for reliable, comprehensive and cost-effective identification of clinically actionable genetic and genomic alterations. The increasing adoption of NGS in clinical oncology has increased our ability to identify germline alterations predisposing to cancer development as well as somatic changes enabling prescription of individualized cancer treatment and enhanced clinical trial participation. Here we summarize implementation of an NGS-based precision medicine initiative involving oncology patients from a single institution cancer center. Methods: IRB-approved NGS matched whole exome (WES) germline and solid tumor somatic tumor sequencing together with somatic tumor RNA sequencing (RNA-seq) were performed using germline DNA extracted from peripheral blood lymphocytes and nucleic acids for tumor DNA and RNA sequencing obtained from formalin-fixed, paraffin-embedded tumor specimens. Results of sequencing and analyses were presented to a multi-disciplinary tumor board to establish recommendations for management of germline pathogenic variation, therapeutic drug matching, clinical trials eligibility and molecularly informed patient prognosis. Results: A total of 1,005 patients completed sequencing. Germline and somatic WES exceeded 100X and 250X mean target coverage, respectively; somatic RNA-seq exceeded 200 million mean reads. Patients ranged in age from 17 to 90 years. The study cohort comprised comparable numbers of female (51%) and male (49%) patients. Ethnicities and races were broadly represented with 22% of participants identifying as Hispanic, 14% as Asian, 4% as Black, 55% as Non-Hispanic White and 5% as other. The most common solid tumor histological classification was colorectal (18%), followed by breast (16%), prostate (7%), head and neck (7%), sarcoma (7%), ovarian (5%), melanoma (4%) and lung (3%). Bioinformatic analyses and precision medicine tumor board review established that 12% of patients harbored a germline pathogenic variant and 43% carried clinically actionable genetic/genomic alterations; a majority of patients met molecular requirements for participation in a clinical trial. Conclusions: This study confirms the feasibility and utility of clinical NGS and precision medicine tumor board review in clinical oncology to identify germline genetic pathology, deliver personalized cancer therapeutics, increase clinical trial enrollment and clarify diagnosis and prognosis.
Effect of accreditation best practice systems on time to treatment delays: Three-year review of a South African internationally accredited site.
Authors: Benn Carol , Rapoport Bernardo Leon , van Loggerenberg Dominic
Published: May 2020

e14009

Background: The Netcare Breast Care Centre has been an operational multi-disciplinary centre since 2000. It was proposed that formalised accreditation would improve patient services thus three years of patient time to treatment data were analysed comparing the pre-accreditation average to the mid accreditation and post accreditation. Methods: Patients for this comparison met the following criteria: The treating physicians were members of the unit, no arm of the care was external; they underwent a combination therapy of surgery; systemic treatment (neo-adjuvant or post-operative); The patients fell within a clearly defined treatment time frame. The time to treatment was judged from date of diagnosis to date starting first treatment whether surgery or systemic. patients who delayed treatment for longer than 100 days beyond the reasonable allowance within the centre’s Standard operating procedure were excluded. Results: Pre accreditation time to treatment was an average of 36.5 days from the date of initial diagnosis,. An even split of primary surgery (48%) to systemic (52%). During the implementation of new accredited systems, the time to treatment average saw a reduction of 20%, down to 29 days from the date of diagnosis. An even split of primary surgery (47%) to systemic (53%) remained. After full implementation of the new systems and full international accreditation being achieved the time to treatment saw a further 20% improvement (41% from initial value) of 21 days from diagnosis to treatment. Case mix was now heavily weighted towards the systemic treatment of 71% and primary surgery 29%. Conclusions: The implementation of accredited systems and optimization of the MDM environment saw a dramatic improvement to the service provided to patients. Over the course of the accreditation period, the systems allowed for a two-week reduction in time to treatment (40% improvement) from a previous 5-week standard.
Lessons learned from clinical trial queries on small biopsy collections from an academic cancer center.
Authors: Voyten Jamie , Holtzman Matthew Peter , Pantanowitz Liron , Dhir Rajiv , Beasley H. Scott , Cuda Jackie , Monaco Sara Elizabeth
Published: May 2020

e14016

Background: Small biopsies and cytology specimens are becoming increasingly important for clinical trials and biomarker testing. Thus, institutions must ensure that there is adequate lesional material meeting specifications for a multitude of different protocols, which can involve rapid on-site evaluation (ROSE). The aim of this study is to look at the recent clinical trial biopsy characteristics and feedback on these collections at our institution. Methods: Clinical trial biopsies performed at our institution and trial feedback (so called “queries”) were analyzed from the past two years (2017-2019). The query data was reviewed in detail, in addition to protocol modifications related to biopsy requirements and study protocol changes. Results: A total of 698 biopsy collections were performed for clinical trial purposes for 95 trials, with the majority of these requiring biopsies at more than 1 time point (63.2%), for phase 1 or 2 trials (92.6%), and for specific tumor types (67.4%). Only 18 (18.9%) of the 95 trials requiring fresh tissue biopsies provided feedback. This included 90 (12.9%) cases, of which 27 (30.0%) had queries regarding insufficient (10, 37.0%) or borderline (17, 63.0%) tumor, and only 1 (3.7%) of these had ROSE by cytology. ROSE was performed due to institutional guidelines (45.3%), requirement by study (1.1%), or trial modification (5.3%).(Table). Conclusions: This investigation shows the high volume of clinical trial biopsies managed at our academic cancer center. Feedback from trials is low at 18.9%, and frequently involves suboptimal cases without ROSE at acquisition, which has led to more widespread adoption of ROSE to mitigate insufficient biopsies and repeat procedures. The high volume of clinical trial biopsies and variability in trial needs necessitates a collaborative multi-disciplinary network to facilitate these important biopsies for cancer patients. [Table: see text]
Impact of an oncology clinical pharmacist specialist in an outpatient multiple myeloma clinic.
Authors: Virani Amin , Schlei Zach , Jakubowiak Andrzej J. , Jasielec Jagoda , Gleason Christine , Ackermann Megan , Wolfe Brittany , Major Sarah , McIver Amanda , Parsad Sandeep
Published: May 2020

e14030

Background: Improvements in cancer treatment, supportive care, and the approval of oral chemotherapy medications over the past decade have resulted in an increasing number of cancer patients treated in outpatient settings. Transitioning cancer treatments to the outpatient setting places greater emphasis on proper medication counseling and optimal side effect management. Current literature demonstrates improvements in medication adherence and effective cancer related symptom management with the addition of an oncology pharmacist. Historically, the University of Chicago Medical Center (UCMC) has not employed pharmacists into their ambulatory oncology clinics. UCMC is evaluating pharmacist’s roles in these clinics. Methods: The primary objective of this project is to evaluate the clinical and financial impacts of an oncology clinical pharmacist specialist in an interdisciplinary multiple myeloma (MM) clinic. This will be evaluated by monitoring the interventions made by the pharmacist in clinic through a validated scoring tool. This tool associates a value for each type of intervention made based on current literature and internal evaluations at UCMC. The oncology clinical pharmacist specialist will be available for consult by the MM clinic staff. The pharmacist may be consulted for any medication related inquiry. Based on the consult the pharmacist will categorize their interventions into twelve predefined intervention categories. Results: Study results showed the implementation of a clinical pharmacist specialist into the MM clinic over 39 clinic days resulted in 241 patient consults and 474 interventions made by the pharmacist. The most frequent interventions made by the pharmacist were medication teaching (97), dose adjustments by pharmacist (82) and medication reconciliation (63). Based on the dollar values associated with each intervention type, the value of interventions made by the pharmacist during the study period was $189,441 with a predicted annual value of $757,764. Conclusions: An clinical pharmacist specialist in the MM clinic lead to dramatic and sustainable financial and clinical impacts. Further investigation into other oncology clinics is warranted.
Early nutrition intervention in cancer patients: A systematic review and meta-analysis.
Authors: Batukbhai Bhavina D O , Champagne Madeleine , Rai Karan , Barrett-Campbell Odeth , Riblet Natalie
Published: May 2020

e14037

Background: Malnutrition is a common finding seen in 50-80% of cancer patients. Malnutrition negatively impacts quality of life (QOL), treatment-related outcomes, and survival. While early nutrition interventions (ENI) may be beneficial in preventing malnutrition, they are not part of routine cancer care. We conducted a systematic review and meta-analysis to assess the benefits of ENI compared to standard of care (SC) on nutritional status, QOL, and survival in patients with newly diagnosed cancer. Methods: We searched MEDLINE, EMBASE, Cochrane, and CINAHL databases from inception through October 2019 to identify randomized control trials (RCT) and cohort studies comparing ENI to SC in adult patients with newly diagnosed oncologic malignancies. We required that nutrition interventions began within 8 weeks of diagnosis and lasted at least 6 weeks. Outcomes of interest included nutritional status (change in weight or BMI), mortality, and QOL. We assessed for risk of bias among included studies using the Cochrane Risk of Bias tool (RCT) and the Newcastle-Ottawa scale (cohort studies). We summarized change in weight (kilogram, kg) at 3 and 6 months using standardized mean differences (SMD) and 95% Confidence Intervals (CI). Because some studies had insufficient data on weight to allow for quantitative analysis, we summarized their findings qualitatively. We used a qualitative approach to summarize QOL. Mortality at 1 and 2 years was compared using relative risk (RR) and 95%CI. Random effects models were used to pool data as there was meaningful and statistically significant heterogeneity (I2 > 50%; p-value < 0.1). Results: 2,781 studies were identified and screened by two independent reviewers for eligibility. 18 independent studies (subjects = 1936) met inclusion criteria. Among 9 studies included in the quantitative analysis for nutritional status, patients who received ENI had better nutritional status compared to SC (SMD at 3 months 0.49kg, 95%CI 0.08-0.79; at 6 months: 0.27kg, 95% CI 0.09-0.45). ENI was also associated with a lower risk of death compared to SC, although the findings were not significant (1 year RR: 0.41[95% CI 0.09-1.81]; 2 year RR: 0.79 [95 CI 0.58-1.09]). Of the ten studies reporting on QOL, six found that patients in the ENI group had improved QOL outcomes. There was insufficient data available to perform subgroup analysis based on cancer type. The quality of studies was generally good and 11 of the included studies had low risk of bias. Conclusions: Patients with newly diagnosed cancer may benefit from ENI due to better weight outcomes, QOL, and survival.
Utilization of online crowdfunding for alternative cancer treatments at home and abroad.
Authors: Gruhl Joshua David , Peterson John Stuart , Davis Sydney , Olsen Jaxon Carey , Parsons Matthew , Kann Benjamin H , Stoddard Gregory J. , Johnson Skyler B
Published: May 2020

e14044

Background: The use of alternative cancer treatments has been associated with decreased survival. However, little is known about the types of individuals who seek out these therapies, the alternative therapies pursued, and the associated costs. We utilized GoFundMe campaigns to characterize the types of patients who sought alternative cancer treatments, the types of therapies pursued, and the associated costs. Methods: We queried GoFundMe ( www.gofundme.com ) for English language campaigns using the term “alternative cancer treatment” using custom code for web scraping on 10/25/2019. We identified 1000 campaigns between 2011-2019, of which, 795 had received donations and were used for analysis. We studied each individual campaign in detail and extracted relevant information. Results: The majority of patients were female (63.5%). The most common cancer types were breast (25.3%), colorectal (10.8%), and lung (5.5%) cancer. Of patients reporting cancer stage, 79.3% had stage IV disease. Of those reporting prior cancer treatment history, 34.8% had never undergone traditional cancer treatment; 42.1%, 46.8%, and 26.6% had undergone prior surgery, chemotherapy, and radiotherapy, respectively. The most common proposed alternative treatments were vitamins and minerals (23.4%), herbs and botanicals (16.1%), special diets (13.0%), supplements (10.8%), heat or light therapy (7.8%), IV infusions (7.8%), homeopathic/naturopathic therapies (7.2%), and hyperbaric oxygen therapy (5.9%). Among all campaigns, a total of $36,394,110 was requested and a total of $8,601,759 (23.9%) was raised. The median campaign fundraising goal was $25900 (US dollars; Interquartile range [IQR] $1000 – $50000); the median amount donated was $5805 (IQR $2595 – $12580). These costs include travel as 629 (79.1%) campaigns were for patients residing in the USA, 70 (8.8%) in Europe, 62 (7.8%) in Canada, and 34 (4.3%) in other regions, from which, 54.3% of patients stated that they planned to travel internationally—most commonly to Mexico—for their alternative cancer treatments. Conclusions: Millions of dollars have been requested and raised between 2011 and 2019 for alternative cancer treatments. The majority of patients sought treatment at alternative clinics internationally, were females, had stage IV disease and primary tumors of the breast, colon/rectum or lung, who had previously undergone traditional cancer therapies, and highlight a group of patients where improved communication/education between providers and patients appears to be needed.
Clinical utility of comprehensive genomic pathway and integrated network analyses in personalized oncology.
Authors: Sahajpal Nikhil Shri , Mondal Ashis , Ahluwalia Meenakshi , Njau Allan Njoroge , Kota Vamsi , Okechukwu Nwogbo , Jackson Gretchen Purcell , Brotman David , Weeraratne Dilhan , Jillella Anand P. , Snowdon Jane , Kolhe Ravindra
Published: May 2020

e14051

Background: Adoption of next-generation sequencing (NGS) technology in routine clinical practice has enabled the detection of genetic aberrations such as single nucleotide variants, copy number alterations, and gene fusions. Pathway and network analyses (PNA) are key components for evaluation of NGS data in a clinical setting to explain findings involving thousands of altered genes and proteins with a smaller and more interpretable set of altered processes. Though PNA have been applied to identify driver genes and pathways in cohort-based analyses, its application in precision oncology remains unexplored. We investigate the potential utility of the Watson for Genomics (WfG) pathway analyses tool in interpreting complex and multiple genomic alterations in individual cancers. Methods: DNA and RNA isolated from 70 patient tumors across 30 different cancer types were processed with Illumina’s TST170 NGS platform. WfG’s feature of pathway analyses was used to identify gene variants, signaling pathways, networks, and the drugs targeting these alterations based on evidence in the clinical literature and FDA drug databases. Results: Analyses defined 5 different pathway/network models: 1) downstream therapeutic targets, 2) synthetic lethality, 3) combinatorial downstream targets + synthetic lethality, 4) two or more pathways converging to downstream targets, and 5) complex profile analyses. The five PNA models are illustrated by the following unique cases. 1) A thyroid cancer case with HRAS variant and activated RAF1 downstream pathway showed MAPK1/3 were suggestive of relevant targets. 2) An acute myeloid leukemia case with BRCA1, BRCA2 and PTEN variants, targeting a common synthetic lethal partner PARP1 was ideal for therapy. 3) A penile carcinoma case with BRAF, CDKN2A and TP53 variants, targeting the BRAF downstream pathway in combination with either CDKN2A or TP53 were the likely choice for therapy. 4) A glioma case with activated PI3K and MEK downstream pathway, targeting a common downstream marker would block both pathways. 5) A breast carcinoma case with a complex pathogenic variant profile provided relevant clinical information and levels of evidence for multiple drug targets. Conclusions: We discovered that the integrated WfG pathway analyses tool is ideal for visualization of the variants with levels of evidence from clinical literature and FDA drug databases that can help inform treatment options and provides a holistic understanding of a specific tumor profile allowing the treating clinician to select personalized targeted therapy.
Computational pathological identification of prostate cancer following neoadjuvant treatment.
Authors: Dogdas Belma , Kanan Christopher , Raciti Patricia , Tian Shaozhou Ken , Brookman-May Sabine Doris , Wetherhold Lisa , Smith Andressa , Rooney Oliver Brendan , McCarthy Sharon Anne , Alvarez John David , Lopez-Gitlitz Angela , Casson Adam , Godrich Ran , Kunz Jeremy D. , Ceballos Rodrigo , Leibowitz Carla , Grady Leo , Fuchs Thomas J.
Published: May 2020

e14052

Background: The need for accurate pathological identification and quantitation of prostate cancer (PC) following neoadjuvant treatment with androgen deprivation therapy (ADT) and androgen receptor antagonists is increasing as PC treatment continues to evolve. In clinical practice, pathological assessment of residual tumor is a tedious and time-consuming process due to the volume of tissue from radical prostatectomy (RP). In addition, neoadjuvant treatments can greatly alter both benign and neoplastic prostate tissue morphology making the pathology assessment difficult for even specialized pathologists. Paige Prostate 1.0 is a clinical-grade artificial intelligence (AI) system for PC detection. It was trained and evaluated in over 50,000 prostate biopsy slides with validation across more than 800 institutions worldwide using multiple slide scanners. Methods: We evaluated the performance of Paige Prostate 1.0 at identifying prostatic tumor on 64 hematoxylin and eosin stained slides exhibiting neoadjuvant treatment effect from apalutamide, enzalutamide, and/or ADT. Results: Analysis of the receiver operating characteristic curve demonstrated an area under the curve of 0.96. Using the Paige Prostate 1.0 operating point, it achieved a sensitivity of 91% and a specificity of 94%, corresponding to the correct identification of challenging treated morphology in 59/64 slides using expert pathologists as the reference. False negative cases were typically represented by atypical small acinar proliferation that required expert pathological consensus confirmation. Conclusions: To our knowledge, this is the first AI based evaluation of residual disease in PC with hormone neoadjuvant therapy. Paige Prostate 1.0 effectively identified tumor despite treatment effects. Future work will include optimization of Paige Prostate 1.0 by training with RP specimens from a larger cohort of appropriate samples, as well as precise measurement of residual tumor burden to further improve its accuracy and reproducibility. Paige prostate residual disease detection 1.0 has the potential to impact emerging clinical practice at the patient level and to complement the pathological assessment of RPs in global phase 3 clinical trials, such as PROTEUS, in a standardized, reproducible, and robust way.
Phenotyping clinical trial treatment regimens in cancer: An integrative ontology, artificial intelligence and knowledge engineering approach.
Authors: Mai Yun , Lee Kyeryoung , Liu Zongzhi , Li Zhiqiang , Jones Scott , Ma Meng , Fink Marc , Ghosh Krish , Chen Rong , Schadt Eric , Wang Xiaoyan
Published: May 2020

e14054

Background: Matching clinical attributes (i.e. indications, lab tests, treatment regimens) of clinical trial eligibility criteria with real world patient data is extremely challenging. Attribute phenotyping is one of the key components of Trial2Patient, a customized system developed by Sema4 to find patients for clinical trials. Transforming treatment regimens to a standard ontology and encoding drugs with standard nomenclatures will facilitate the semantic retrieval of treatments mentioned in clinical trial criteria. This will also enable the interoperation between different data sources that is often required for fast-learning and scalable healthcare information system. Methods: Free text containing treatment regimen/medication terms were extracted and preprocessed from three sources: 1) clinical trials listed in a commercial database citeline.com, 2) clinical trials listed in clinicaltrials.gov, and 3) National Comprehensive Cancer Network (NCCN) Guidelines. The regimen terms such as neoadjuvant therapy for non-small cell lung cancer, checkpoint inhibitor, EGFR inhibitor, androgen deprivation therapy (ADT), among many others, were profiled by AI methods (i.e. pattern reorganization and rule-based) and knowledge engineering via Sema4’s in-house knowledge base (CAV), Pharmaprojects in citline.com and NCCN Guidelines. The drugs related to each regimen were identified and mapped to RxCUI via RxNorm ontology. Results: We identified 76 regimen terms for non-small cell lung cancer (NSCLC), small cell lung cancer (SCLC) and prostate cancer (e.g. PD-L1 ≥1% nonsquamous NSCLC, bone antiresorptive therapy for M1 castration resistant prostate cancer), and 14,476 drug-category pair (e.g. pembrolizumab is a PD-1 inhibitor, pembrolizumab is used as the third line and beyond systemic therapy for M1 CRPC). All drugs identified were mapped to RxCUI for real world patient matching. Conclusions: This approach systematically extracted and normalized regimens and medications mentioned in clinical trials in NSCLC, SCLC and prostate cancer to standard codes. These standardized data can be used in mapping treatment histories of a patient to the eligibility criteria for clinical studies or for identifying studies relevant to a patient. The outcome of profiling cancer treatment regimens through standard ontology RxNorm may be particularly valuable in cancer studies based on real-world evidence.
Impact of augmented intelligence (AI) on identification and management of depression in oncology.
Authors: Frownfelter John , Blau Sibel , Zettler Marjorie , Miller Kelly , Kish Jonathan , Valley Amy W. , Gajra Ajeet
Published: May 2020

e14059

Background: Depression is common in patients with cancer and is associated with worse cancer treatment outcomes. Depression is often underdiagnosed/treated as cancer clinicians are focused on the complex aspects of therapy and care coordination. AI has a potential application in the identification of patients at high risk for depression. Jvion has developed a prescriptive analytics solution (the Machine), which uses AI algorithms and machine learning techniques applied to combined clinical and exogenous datasets to identify patients with a propensity for poor clinical outcomes. The Machine was applied to depression risk (within next 6 months), and recommended patient-specific, dynamic, and actionable insights. While the Machine requires no additional documentation within the electronic health record (EHR) to generate its insights, those insights can be integrated back in to any EHR. Herein, we report the results of a pilot study evaluating the impact of AI-driven insights on depression screening and management at a single oncology practice. Methods: All patients were scored weekly using the Machine depression vector. The Machine risk-stratified the patients and generated recommendations for the provider to consider as they developed a care plan. Patients identified as “at risk” by the Machine were assessed for depression (PHQ-9) by the clinical team regardless of prior screening results. The rate per 1000 unique patients per month (PPM) of depression screenings, case management evaluations, and antidepressant prescriptions were calculated for the 5 months prior to and 17 months post deployment of the Machine in the practice. Results: The oncology practice has 21 providers managing an average of 4329 unique PPM. The mean rate of depression screenings increased from 6.0 per 1000 PPM pre- deployment to 16.2 per 1000 PPM post deployment (+271%). The downstream workflow outcomes of case management evaluations increased from 11.6 to 21.4 per 1000 PPM (+184%) and antidepressant prescriptions increased from 9.2 to 15.5 per 1000 PPM (+168%) pre and post-implementation respectively. The providers reported high satisfaction with the use of the AI solution in depression screening. Conclusions: This oncology practice found deployment of the Jvion AI solution to be feasible. The Machine-generated insights for depression risk were actionable, could be incorporated into workflow, and increased the number of patients identified. If confirmed in larger studies, AI-driven insights may improve the identification and management of depression in patients with cancer.
A mixed methods study for the evaluation of a digital health solution for cancer multidisciplinary team meetings using simulation-based research methods.
Authors: Gardner Clarissa , Ghafur Saira , Fontana Gianluca , Guo Chaohui , Prime Matthew Stewart , Ashrafian Hutan
Published: May 2020

e14063

Background: Simulation-based research (SBR) methods involve setting up structured scenarios to replicate real-world situations, with the aim of eliciting real-world reactions and behaviours. SBR is useful for the evaluation of new healthcare solutions without compromising patient safety or navigating complex ethical review processes. However, there are few trials using SBR methods for the evaluation of digital health interventions (DHIs), the adoption of which has been hindered in the NHS due to a lack of evidence-base for their efficacy. SBR methods could be an appropriate tool for testing digital solutions. Methods: The Institute of Global Health Innovation (IGHI) developed a series of simulated lung cancer multidisciplinary team (MDT) meetings to test the NAVIFY Tumor Board solution by Roche Diagnostic Information Solutions, a digital solution for the preparation and conduct of cancer MDTs. To provide an environment for participants to evaluate the capabilities of the NAVIFY Tumor Board solution, 10 simulation sessions were organised in which groups of five to six clinicians were recruited to discuss up to 10 mock patient cases across two simulated MDTs, first using standard tools commonly used to conduct MDTs and then using the NAVIFY Tumor Board solution. The cases were developed by the study team at IGHI and consultants in respiratory medicine and oncology. 56 healthcare professionals (respiratory physicians, oncologists, radiologists, histopathologists, clinical nurse specialists and thoracic surgeons) were recruited. The sessions were video recorded and observations were noted by the study team, followed by a focus group in which participants provided feedback about their experience of the simulated MDTs. Results: Through this study we were able to generate evidence and multi-professional recommendations for Roche regarding the functionality, usability and applicability of the solution in the NHS, as well as beneficial features and those which could be improved. Participants reported that the simulations were realistic and a meaningful way of conducting evaluations of digital health solution without impacting clinical practice or patient safety. Conclusions: This study demonstrated the utility and validity of testing commercial tools in simulated settings to generate meaningful insights from endusers. SBR methods were shown to be an acceptable way for frontline practicing clinicians to participate in the testing and development of digital health tools in a standardised setting.
Prioritizing targeted therapies in an evidence-based manner, integrating biological context and functional precision medicine.
Authors: Boca Simina , Bhuvaneshwar Krithika , Fernandez-Vega Virneliz , Kancherla Jayaram , Rao Shruti , Madhavan Subha , Riggins Rebecca , Beckman Robert A. , Corrada Bravo Hector , Scampavia Louis , Spicer Timothy
Published: May 2020

e14065

Background: It is becoming increasingly common for cancer patients to undergo molecular profiling of their tumors in order to see whether there are any actionable DNA, gene expression, or protein expression signatures. For example, individuals with ER+ or HER2+ breast cancer or KRAS wild type (non-mutated) colorectal cancer are prescribed specific targeted therapies. When an individual’s molecular alterations do not match any currently-approved recommendations for their tumor type, their clinician may consider prescribing a therapy approved in a different tumor type. Unfortunately, tumors often eventually become resistant to the therapies they are exposed to, leading to a narrowing of options after each therapy line. Methods: We previously developed CDGnet, an evidence-based approach and web-based tool for prioritizing targeted therapies based on tumor molecular profiles based on known pathways which provide biological context. CDGnet considers approved therapies with biomarkers among the alterations for the individual’s tumor type and other tumor types as the first and second evidence level categories respectively. These are followed by therapies that target or have as biomarkers genes or proteins downstream of altered oncogenes, considering curated pathways for the individual’s tumor type and other tumor types as the third and fourth evidence level categories respectively. We are currently expanding CDGnet in order to include data from high-throughput screening (HTS) experiments of NCI oncologic drugs performed on patient-derived organoids. The concept of “functional precision medicine” consists of using functional drug efficacy determination directly on individual patients, in this case by considering drugs with low half maximal effective concentrations (EC50) which are tested on tissues derived from the actual patients. Results: We will present extensions to CDGnet that allow users to upload both the molecular profiles and the HTS data to see whether any drugs are predicted by both approaches or whether specific combinations appear promising for further testing. Preliminary results on a set of glioblastoma samples will be presented. Conclusions: We hope that extending CDGnet to also include HTS data will eventually allow a truly multi-factorial personalized oncology approach, whereby both molecular alterations at the DNA, RNA, and protein levels and patient-derived organoids will be considered in deciding on treatment plans for individuals.
Patient engagement in clinical trial design and implementation: A pragmatic approach to valued insights.
Authors: Aubel Dawn , Hayes Susan , Cook Geoffrey , Salamakha Alexey , Ciupek Andrew , Patel Gunjan , Mason Ginny
Published: May 2020

e14084

Background: Evidence-based guidance on how to best facilitate patient engagement in the drug development process is lacking1,2.Well-informed, engaged patients and advocates can contribute meaningfully throughout drug development process for new therapeutics3. Regulatory agencies, including the FDA and EMA, concur4,5. However, identifying and effectively implementing meaningful, and structured patient engagements has been challenging for research organizations6. Methods: Patient advocates with deep knowledge of the special needs of patients within their respective disease areas and experience in patient-centered research and programming were identified by the patient relations’ team of the organization. Participating advocates were generally contracted for consulting support across the development lifecycle to provide timely feedback for emerging topics and agreed to provide feedback within a 3-4 week timeframe to requests for support. Called “Global Oncology Patient Insight Panel” (GOPIP), the approach facilitated fast-moving development teams to quickly engage advocates for advice while enabling projects remain on timeline. GOPIP members engaged with teams within the organization on an ad-hoc basis across the drug development process, as early as protocols’ and informed consents’ review and PRO selection, among many others patient-centered projects. Results: GOPIP engagements included 49 advocates from 19 countries, across 14 disease areas, in the program’s first 18 months. 54 projects were completed. Multiple engagements for three clinical trials, part of the development program, have been accomplished. Seven plain language summaries of published data results were reported publicly with consultants’ input. Satisfaction was high among advocates as well as internal teams. Conclusions: The Patient Insight Panel model is a valuable resource to facilitate patient engagement efforts. More research is needed to evaluate the outcomes and effectiveness of the method. References: 1. Chalasani, M., et al. 2018. Res Involv Engagem. 4,10 2. de Wit, M., et al. 2019. The Lancet, 393(10176),1095-1096 3. Kuehn, C. M. 2018. Ther. Innov. Regul. Sci., 52(5),661–668 4. FDA. 2020. https://www.fda.gov/about-fda/oncology-center-excellence/patient-focused-drug-development . Accessed February 8, 2020 5. EMA. 2016. https://www.ema.europa.eu/en/documents/other/appendix-2-guideline-evaluation-anticancer-medicinal-products-man_en.pdf Accessed February 8, 2020 6. Geissler, J., et al. 2017. Ther. Innov. Regul. Sci., 51(5),612-619.
Assessment of ovarian function as an endpoint in breast cancer clinical trials: A systematic review.
Authors: Cui Wanyuan , Francis Prudence A. , Loi Sherene , Hickey Martha , Stern Catharyn , Partridge Ann H. , Loibl Sibylle , Phillips Kelly-Anne
Published: May 2020

e14098

Background: Premature ovarian failure (POF) is an established adverse effect of chemotherapy for breast cancer (BC); little is known about the ovarian toxicity of newer treatments (e.g. PARPi, CDK4/6 inhibitors, immunotherapy, anti-HER2 therapy). This study examined whether contemporary BC (neo)adjuvant clinical trials will provide data on POF. Methods: Eligible studies were phase 3 (neo)adjuvant trials of pharmacologic agents aimed to reduce BC events, that enrolled premenopausal women between June 2008 - Sept 2019. MEDLINE and EMBASE were searched using MESH terms / keywords: “breast neoplasm” or “breast adj2 (cancer* or tumo?r* or carcinoma*)”, “adjuvant chemotherapy” or “neoadjuvant therapy” or “neoadjuvant” or “adjuvant”. Clinicaltrials.gov and EudraCT were searched using the filters “breast cancer”, “interventional studies”, “phase 3”. Data extracted from eligible trial publications, protocols and clinicaltrials.gov/EudraCT summaries, included whether each trial assessed the variables - post treatment pregnancy/birth, attempt at pregnancy, menstrual data, antral follicle count, FSH, LH, estrogen, progesterone and AMH levels. Results: Of 1665 trials screened, 141 were eligible. Investigational treatments included chemotherapy (71%), endocrine (20%), anti-HER2 (26%), immunotherapy (8%), CDK4/6 inhibitor (5%), PARPi (2%). Few trials (28%) assessed quality of life (QOL). Study chairs were from Nth America (19%), Europe (36%), Asia (43%); most (67%) were male. POF and fertility measures were prespecified endpoints in 9 (6%) and 0 trials respectively. Twenty (14%) trials collected POF and/or fertility data; post treatment pregnancy (11/20), amenorrhoea (6/20), estrogen (8/20), FSH (7/20), LH (5/20), AMH (3/20), and progesterone levels (1/20) and antral follicle count (3/20). Assessment of POF was associated with collection of QOL data (p = 0.02) but not with BC phenotype, timing, treatment type, or sex or nationality of study chair. Conclusions: POF measures are rarely prespecified endpoints and are infrequently collected in phase 3 BC (neo)adjuvant trials. Trialists should consider POF, given the potential for this data to enhance informed decision making for premenopausal patients.
An innovative approach to manage cancer patient information for care coordination and monitoring using a digital health platform for a nonprofit tertiary cancer care institute in India.
Authors: Hensley Alford Sharon , Gupta Rishi Kumar , Sonawane Nayan , Mahatma Srishti , Mahatma Shilpa , Kumar Tarun
Published: May 2020

e14102

Background: In India, most patients maintain a personal copy of their medical record in paper form which is hard to manage and collate. Newly diagnosed cancer patients and their doctors have an insurmountable task of consuming and using that information. Making this information available in an easy to consume format as well as recording a patient’s cancer experience is a technical challenge. We piloted an innovative mobile application to address this pain point. Methods: A pilot engagement was performed with 100+ cancer patients visiting a non-profit oncology center between March 2019 and January 2020 in a suburb of New Delhi, India. Systematic medical information was captured on stage, TNM, molecular markers, tumor size, tests, and treatments. We assessed the utility of a digital health application for the purpose of cancer information management and ease of use by the medical team. Results: Data for a total of 65 patients with a single cancer and diagnosis and treatment information was captured. Patients were between the ages of 23 and 78, mean age 49. Of these patients, there were 21 (32%) early stage cases ( < Stage 3), 23 (35%) late stage cases ( > Stage 2), and 21 (32%) cases with missing stage. Overall, 39 (60%) received a chemotherapy only regimen, 2 (3%) radiation only, 6 (9%) surgery only, 6 (9%) received chemotherapy and radiation, 11 (17%) received surgery and chemotherapy, and 1 (1%) patient received surgery, chemotherapy, and radiation. Clinic staff reported that the application was easy to use and helpful. We continue to enhance the application with staff feedback. Conclusions: We demonstrate the feasibility and value of moving to a next generation digitized mobile platform circumventing the need for a traditional EMR system at a busy non-profit oncology clinic in the suburbs of New Delhi, India. Using the pilot, we make a case that this type of technology has the ability to transform cancer care for rural and low-resource cancer centers. We also demonstrate that it is easy to capture patient longitudinal data using this innovative platform.
Preferences and motivational factors for the use of digital technology in real world oncology studies.
Authors: Dias Nadeeka , Yamamoto Rinah , Faulkner Kenneth G
Published: May 2020

e14119

Background: Patient engagement and motivation during real world oncology studies can be challenging. Digital tools have proven to be effective in reducing patient burden and improving data collection and accuracy. Given the rapid advances in health technology, patient preferences and motivations for these tools are continually evolving. This study investigates preferences for digital technology use with smartphones and wearables, telehealth, and identifies motivational factors that would drive real world study compliance for daily symptom reporting. Methods: Patients (n = 50; 68% male) diagnosed with cancer answered questions via an anonymous online survey about their technology preferences. Results: Although the majority of the population uses smartphones (99%), few oncology patients use wearable technology such as a fitness watch (18%). Of those who do not own a fitness watch, 90% were willing to use the technology in a clinical study and share their data if a watch were provided to them. However, the majority of patients (74%) would prefer to use their own smartphone during a clinical study to answer questions and conduct video calls with the study doctor. For patients who currently visit their doctor in person (98%), almost half (48%) would prefer a remote alternative such as a video call for convenience, especially when in pain. Patients reported several factors that would encourage consistent daily reporting of study data. Out of 10 options, the most motivating factor was access to health information that would help manage their symptoms on a daily basis (92%). Subsequent factors of interest included in-app reminders (88%) and assurance that a doctor would review the reported data and contact them to discuss reported symptoms (94%). Conclusions: The global market for digital health is growing rapidly, and there is a strong preference from oncology patients to shift in-person engagement and reporting to digital platforms. Patients are also highly motivated by information about their own health. Designing real world studies to incorporate these preferences may result in enhanced patient engagement, improved compliance, and higher quality data.