Gender Difference in sidE eFfects of ImmuNotherapy: a Possible Clue to Optimize cancEr tReatment
Conditions: Melanoma, Lung Cancer, Head and Neck Cancer, Urogenital Neoplasms, Breast Cancer
Sponsor: Fondazione IRCCS Istituto Nazionale dei Tumori, Milano

Title
 
Gender Difference in sidE eFfects of ImmuNotherapy: a possible clue to 
optimize cancEr tReatment (G-DEFINER) 
Document type
Protocol
Version N.
5.0
Date
October 12nd 2021 
 
Page 1 of 26 
 
 
 
 
Gender Difference in sidE eFfects of 
ImmuNotherapy: a possible clue to optimize 
cancEr tReatment (G-DEFINER) 
 
NCT04435964 
 
Date of the document: October 12nd 2021 
 
 

Title
 
Gender Difference in sidE eFfects of ImmuNotherapy: a possible clue to 
optimize cancEr tReatment (G-DEFINER) 
Document type
Protocol
Version N.
5.0
Date
October 12nd 2021 
 
Page 2 of 26 
 
 
 
Gender Difference in sidE eFfects of ImmuNotherapy: a possible clue to 
optimize cancEr tReatment  
(G-DEFINER) 
 
 
 
 
 

Title
 
Gender Difference in sidE eFfects of ImmuNotherapy: a possible clue to 
optimize cancEr tReatment (G-DEFINER) 
Document type
Protocol
Version N.
5.0
Date
October 12nd 2021 
 
Page 3 of 26 
 
 
TABLE OF CONTENTS 
GENERAL INFORMATION ................................................................................................................................... 5 
ABBREVIATIONS ................................................................................................................................................. 5 
RELATED DOCUMENTS ...................................................................................................................................... 6 
SYNOPSIS ........................................................................................................................................................... 7 
1. INTRODUCTION ........................................................................................................................................... 12 
1.1 Study rationale ...................................................................................................................................... 12 
1.2 Research questions ................................................................................................................................ 14 
2. OBJECTIVES .................................................................................................................................................. 14 
2.1 Primary objectives ................................................................................................................................. 15 
2.2 Secondary objectives ............................................................................................................................. 15 
2.3 Translational objectives ......................................................................................................................... 15 
3. STUDY DESIGN ............................................................................................................................................. 15 
3.1 Overview of study design ...................................................................................................................... 15 
3.2 Study centers ......................................................................................................................................... 16 
3.3 Study population ................................................................................................................................... 16 
3.3.1 Inclusion criteria ............................................................................................................................. 16 
3.3.2 Exclusion Criteria ............................................................................................................................ 17 
4. DATA SOURCES/DATA COLLECTION PROCESS ............................................................................................. 17 
4.1 Investigators .......................................................................................................................................... 17 
4.2 Patient clinical data ............................................................................................................................... 17 
4.3 Patient biological data ........................................................................................................................... 18 
5. TREATMENT AND CLINICAL PROCEDURES .................................................................................................. 19 
6. STUDY CONDUCTION ................................................................................................................................... 19 
7. STATISTICAL ANALYSIS ................................................................................................................................. 19 
7.1 Clinical data analyses ............................................................................................................................. 19 
7.2 Translational analyses ........................................................................................................................... 20 

Title
 
Gender Difference in sidE eFfects of ImmuNotherapy: a possible clue to 
optimize cancEr tReatment (G-DEFINER) 
Document type
Protocol
Version N.
5.0
Date
October 12nd 2021 
 
Page 4 of 26 
 
7.3 Sample size considerations for the main sex-related analyses ............................................................. 21 
8. ADMINISTRATIVE AND REGULATORY DETAILS ............................................................................................ 21 
9. References ................................................................................................................................................... 23 
13. Document history- amendments .............................................................................................................. 26 
 
 
 

Title
 
Gender Difference in sidE eFfects of ImmuNotherapy: a possible clue to 
optimize cancEr tReatment (G-DEFINER) 
Document type
Protocol
Version N.
5.0
Date
October 12nd 2021 
 
Page 5 of 26 
 
GENERAL INFORMATION 
 
Coordinating 
Center 
Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy (INT)
Partner Centers
• 
Dept of Oncology-Pathology, Karolinska Institutet, Karolinska University 
Hospital, Sweden (Onk-Pat KI) 
• 
Dublin City University, Dublin -St Vincent’s University Hospital, Ireland 
(DCU- SVUH) 
• 
Oslo University Hospital – The Radium Hospital Norway (OUH) 
Study sponsor
 
 
 
 
ABBREVIATIONS  
Acronym
Description
AE
Adverse Events
CRF
Case Report Form
CT
Chemotherapy
CTMR
Centre for Translational Microbiome Research, Dept. of Microbiology, Tumor 
and Cellbiology, Karolinska Institutet, SciLifeLab, Stockholm, Sweden 
DCU- SVUH
St Vincent’s University Hospital - Dublin City University, Dublin -, Ireland 
F
Female
GDPR
General Data Protection Regulation
IC
Informed Consent
ICI
Immune Checkpoint Inhibitors
ICF
Informed Consent Form
INT
Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
irAE
Immune-related adverse event
M
Male

Title
 
Gender Difference in sidE eFfects of ImmuNotherapy: a possible clue to 
optimize cancEr tReatment (G-DEFINER) 
Document type
Protocol
Version N.
5.0
Date
October 12nd 2021 
 
Page 6 of 26 
 
Onk-Pat KI
Dept of Oncology-Pathology, Karolinska University Hospital, Sweden
OUH
Oslo University Hospital – The Radium Hospital Norway 
RCT
Randomized Clinical Trial
SAP
Statistical Analysis Plan
SOP 
Standard Operating Procedure 
 
RELATED DOCUMENTS 
Type
Title
ANNEX
ANNEX 01 - Informazione per il paziente – ITA
ANNEX 02 - Patient information - ENG 
ANNEX
ANNEX 03 - Administrative and regulatory details
ANNEX
ANNEX 04 - irAE List
ANNEX
ANNEX 05 - PS CTCAE RECIST
ANNEX 
FORM 
Study CRFs
SOPs
Study specific SOPs for Development, Approval and Review documents, data 
management, study conduction, AE registration, faeces sampling and microbiota 
analysis, blood sampling and SNP analysis, blood sampling and gene expression 
analysis. 
 
 
 

Title
 
Gender Difference in sidE eFfects of ImmuNotherapy: a possible clue to 
optimize cancEr tReatment (G-DEFINER) 
Document type
Protocol
Version N.
5.0
Date
October 12nd 2021 
 
Page 7 of 26 
 
SYNOPSIS  
Title of Study
Gender Difference in sidE eFfects of ImmuNotherapy: a possible clue to optimize 
cancEr tReatment (G-DEFINER)  
Type of study
Multicentric  prospective observational study
Indication
Melanoma, lung, head and neck, urogenital, breast cancer and, in addition, other 
solid tumors characterized by the presence of microsatellite instability (MSI-high), 
treated with immunocheckpoint inhibitors (ICI) irrespective of treatment schedule. 
It is possible to include patients treated with Immunotherapy in a compassionate 
use setting. No limitations to previous lines of treatment. ICI therapy may be either 
as single agent or in combination. Concomitant chemotherapy (CT) and 
radiotherapy (RT) is allowed.  
Sites
• Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy (INT) 
• Dept of Oncology-Pathology, Karolinska Institutet, Karolinska University 
Hospital, Sweden (Onk-Pat KI) 
• Dublin City University, Dublin -St Vincent’s University Hospital, Ireland (DCU- 
SVUH) 
• Oslo University Hospital – The Radium Hospital Norway (OUH) 
Number of 
patients 
Total 400, 200 females (F) and 200 males (M). Stratification : ICI: 100 F/100 M; ICI+ 
CT/RT: 100 F/100 M. 
Study 
Coordinator 
Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy (INT)
Principal Investigator: Rosalba Miceli, PhD Unit of Clinical Epidemiology and Trial 
Organization 
Study sponsor
 
 
Background 
The study aim is to investigate the differences between sex and gender in the 
immune-related adverse events (irAEs) development associated with immune 
checkpoint inhibitors (ICI) treatment.  
In common feeling, sex and gender represent the same concept, that is the 
traditional division of individuals into females (F) and males (M) defined by 
differential organization of chromosomes, reproductive organs, and sex steroid 
levels. However, if we go beyond these aspects, which characterize "sex", we can 
see how the differences between people are also characterized by behaviors and 
relationships that are the product of the culture and sociality typical of human 
beings. This is what is called "gender", i.e. the process of social and cultural 

Title
 
Gender Difference in sidE eFfects of ImmuNotherapy: a possible clue to 
optimize cancEr tReatment (G-DEFINER) 
Document type
Protocol
Version N.
5.0
Date
October 12nd 2021 
 
Page 8 of 26 
 
construction that determines the behaviors that give life to the status of an 
individual. Gender is therefore learned and not innate. Sex and gender do not 
constitute two opposing but interdependent dimensions: the process of 
determining gender identity is triggered on biological characters. The relationship 
between sex and gender varies according to geographical areas, historical periods 
and cultures. 
Sex influences the adaptive immunity, and may influence irAEs types, frequency 
and severity. Together with genetic and biological differences, the roots of irAEs 
inequalities between F and M could be linked to psycho-social and behavioral 
determinants. IrAEs usually develop within the first few weeks to 6 months after 
treatment initiation; however, they can also present after cessation of ICI therapy. 
Most studies indicate that prolonged treatment does not result in an increased 
cumulative incidence of irAEs. Accumulating evidences support the existence of 
sex-driven differences in immune responses as potential factors contributing to 
disease outcome and response to therapy. Increasing use of ICI is associated with 
immune-related adverse events caused by non-specific activation of the immune 
system.  
We will conduct a multicenter prospective observational study investigating sex 
differences in irAEs in relation to clinical factors and genetic, immunological and 
hormonal profiles. By focusing on biological F/M differences possibly affecting 
discrepant irAEs incidence, we explicitly address sex inequality, complemented by 
the exploration of association between gender dimension and irAEs development. 
Exploring the irAEs occurrence in a “real world” (outside RCT) context will be more 
easily translated in a ready-to-use personalized approach to irAEs timely diagnosis 
and treatment. 
Study design
This is a multicentric  prospective observational study aimed at studying the 
incidence of irAEs in female and male cancer patients treated with ICI. To allow for 
balanced sex groups, we will include patients according to the following 
stratification : ICI: 100 F/100 M; ICI+ CT/RT: 100 F/100 M. Due to the current ICI 
use in clinical practice we are expecting to mainly populate the ICI strata during the 
first recruitment period. As the recruitment progresses, the sample will be 
enriched of ICI+CT and ICI+RT treated patients, since combinations are expanding 
for many cancers such for instance melanoma, lung and head and neck.  
Primary 
objectives 
• To estimate and compare the irAEs incidence in F and M patients, and estimate 
the incidence according to different clinical features and gender dimensions 
(behavioral and psychosocial differences associated with being female or male). 
• To estimate and compare the irAEs incidence in pre- and postmenopausal 
women. 
Secondary 
objectives 
• To develop irAEs predictive tools based on clinical characteristics. 
• To explore irAEs occurrence in relation to hormonal profiles, exploring the 
differences in F vs M patients, and in pre- vs post-menopausal F patients. 
• To explore the role of concomitant medications on irAEs occurrence. 
Translational 
To explore irAEs occurrence in relation to immune-related genes, germline 

Title
 
Gender Difference in sidE eFfects of ImmuNotherapy: a possible clue to 
optimize cancEr tReatment (G-DEFINER) 
Document type
Protocol
Version N.
5.0
Date
October 12nd 2021 
 
Page 9 of 26 
 
objectives
variations and microbioma, exploring the differences in F vs M, and in pre- vs post-
menopausal F patients.  
• Gene-expression analysis. Blood samples will be taken in order to identify 
immune-related genes in patients with various solid tumors treated with ICI 
associated with the development of irAEs. 
• SNPs analysis. Blood samples will be taken in order to perform a preliminary 
genome wide association study for the identification of germline variations 
associated with the development of irAEs. with the hypothesis that the 
individual’s genetic makeup may be related to irAEs. 
• Microbioma analysis. Stool collection will be performed to analyze gut 
microbiota aiming at performing RNA/DNA sequencing analysis for identifying 
components associated with the development of irAEs. 
Inclusion 
criteria 
• Signed informed consent. 
• Histologically confirmed diagnosis of one of the following cancers: melanoma, 
lung, head and neck, urogenital, breast cancer, and, in addition, other solid 
tumors characterized by the presence of microsatellite instability (MSI-high). It 
is possible to include patients treated with Immunotherapy in a compassionate 
use setting. 
• Any disease stage.  
• Patients eligible for ICI-containing regimens: 
o ICI single agent; 
o Combination of ICIs; 
o ICI-chemotherapy combination; 
o ICI-radiotherapy combination. 
• Any 
treatment 
setting 
(neoadjuvant, 
adjuvant, 
advanced 
disease, 
maintenance). 
• Patient age ≥18 years 
• Eastern Cooperative Oncology Group (ECOG) Performance Status of 0-2. 
• Adequate bone marrow, liver and renal function. 
• Life expectancy of at least 12 weeks. 
Exclusion 
criteria 
Patients not eligible for ICI-containing regimens.
 
Treatment 
schema 
As per clinical prescription. Any melanoma, lung, head and neck, urogenital and 
breast cancers patients, and, in addition, patients with other solid tumors 
characterized 
by 
the 
presence 
of 
microsatellite 
instability 
(MSI-
high),independently of disease stage and setting, treated with a regimen 
containing ICI.  
Clinical 
Procedures 
/Assessments 
Patients will undergo physical and laboratory evaluation and disease evaluation 
with CT and/or imaging scan according to current clinical practice at each study 
Center.  
Baseline radiological tumor measurements should be performed preferably within 
7 days, but in any case no more than 30 days before entering the study. 

Title
 
Gender Difference in sidE eFfects of ImmuNotherapy: a possible clue to 
optimize cancEr tReatment (G-DEFINER) 
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IrAEs of specific interest  will be recorded and graded according to most recent
Common Terminology Criteria for Adverse Events (CTCAE) . Patients who 
experience irAEs may continue treatment according to the clinical indications.  
Response will be evaluated by most recent RECIST according to current clinical 
practice at each study Center.  
Biological 
samples 
and 
sampling 
times 
Statistical 
considerations
The incidence of first severe (G≥ 2) irAEs of any type will be estimated in F and M 
as a proportion of patients developing the event respect to the total number of 
patients at risk; the corresponding binomial 95% confidence interval (CI) will also 
be reported. The main comparison F vs M will be performed by estimating the 
odds ratio (OR) in a univariable logistic model; F/M unbalance for different clinical 
and gender-related characteristics will be taken into account using the “matching 
weight” (MW) method; patient weights will be estimated in advance as a function 
of a model-based balancing score (applying the propensity score methodology).  
IrAE incidence will also be estimated according to irAE type and grade, tumor site, 
ICI treatment, patients’ age and gender-based characteristics by sex groups. 
Logistic models using MW and including the interaction between sex and the 
different clinical features will be fitted to estimate OR according to different 
feature categories or values (numerical variables). Considering the large number of 
statistical tests, correction for multiplicity will be done using the Benjamini–
Hochberg false discovery rate (FDR) method. The irAE probability predictive 
models will be developed after performing variable selection; the predictive model 
performance will be evaluated examining calibration of predictions and 
discriminative ability. The same methods described above will be applied for 
investigating the pre vs post menopausal differences and for developing the 
related predictive tool. 
IrAEs will also be tabulated describing the overall number of irAEs and the number 
of patients reporting irAEs grouped by CTCAE class and grading, and according to 
sex and menopausal status, ICI treatment, and tumor type. Univariable and 
multivariable Poisson regression models will also be applied to analyse irAE as 
count variable in relation to clinical variables  

Title
 
Gender Difference in sidE eFfects of ImmuNotherapy: a possible clue to 
optimize cancEr tReatment (G-DEFINER) 
Document type
Protocol
Version N.
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October 12nd 2021 
 
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Sample size considerations for the main sex-related analyses. Given that the 
events proportion could be even greater than 30%, we simulated scenarios in 
which the proportion is varying from 20% to 50%. To estimate the proportion by F 
and M, a sample size of 200 patients (200 F/200 M) will produce a two-sided 95% 
exact CI with a width as small as 0.115 (CI 0.147 to 0.262) if the proportion is 20%, 
increasing to 0.143 (CI 0.429 to 0.571) if the proportion is 50%.  
Study 
duration  
Patients recruitment: 24 months. Follow-up: 12 months. Total study duration: 36 
months. 
 
 

Title
 
Gender Difference in sidE eFfects of ImmuNotherapy: a possible clue to 
optimize cancEr tReatment (G-DEFINER) 
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Protocol
Version N.
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1. INTRODUCTION 
1.1 Study rationale  
Sex, defined by differential organization of chromosomes, reproductive organs, and sex steroid levels, is a 
biological variable that affects innate and adaptive immunity, acting through genetic, hormonal and 
environmental factors. [1] Recent studies demonstrate a link among microbioma, sex, and immunity since 
gut microbiota composition is able to shape the immune response. [2-4]. “Sex-driven dimorphism” in 
immune functions and responses can lead to differences between female and male in the pathogenesis of 
infectious diseases, response to viral vaccines, and prevalence of autoimmune diseases.[5-6] Females 
exhibit higher immune responses to antigenic challenges than males, which can reduce pathogen load and 
accelerate pathogen clearance but leads to increase in immune-related pathology. [7] 
Sex plays a role in cancer incidence, progression, response to chemotherapy (CT) treatments [8,9] and CT 
adverse events (AE); a recent gastric cancer randomized clinical trial (RCT) showed AE greater risk in women 
vs men, especially that of nonhematological events.[10] The association between sex and AEs development 
could be attributed to differences in pharmacokinetics and pharmacodynamics properties of the drugs; in 
addition, the AE onset and severity are related to the reproductive status, thus being influenced by sex 
hormones.  
Immunotherapy is a promising cancer treatment and immune checkpoint inhibitors (ICIs) have shown 
benefit in treating a range of cancer types. Accumulating evidence supports the existence of sex-driven 
differences in immune responses as potential factors contributing to disease outcome and response to ICI. 
[11,12] X-chromosome linked genes, sex hormones and sex-biased immunophenotype shape different 
anticancer response in females (F) and males (M) affecting ICIs efficacy. The differential response between 
F and M could be amplified by the combined use of ICI and CT, as shown by a recent meta-analysis of 
randomized trials [13] investigated sex hetherogeneity in response to anti-PD1/PD-L1 plus chemotherapy 
as compared with chemotherapy alone in lung cancer.  
However, by increasing the (non-specific) activity of the immune system, ICIs are associated with 
inflammatory side effects (immune-related adverse events, irAEs). IrAEs usually develop within the first few 
weeks to 6 months after treatment initiation; however, they can also present after cessation of ICI therapy. 
Most studies indicate that prolonged treatment does not result in an increased cumulative incidence of 
irAEs. A related issue is the association between aging and “low-grade pro-inflammatory state”, systemic 
condition characterized by aberrant cytokine production. As demonstrated by studies in mice and humans, 
[14] the lethal ICI side effects are markedly exacerbated with aging. IrAEs can potentially affect any organ 
system and the majority are mild to moderate in severity; grade 3-4 irAEs are of major concern as they may 
cause treatment discontinuation and can occasionally be life-threatening if not promptly treated. Some 
studies reported a 90% incidence for any-grade irAEs due to single-agent ICI; an RCT meta-analysis 
indicated an overall incidence <75% with anti-CTLA-4 monotherapy (ipilimumab) (≥ grade 3 in 43% of 
patients) and ≤30% in trials of anti-PD-1/PD-L1 agents.[15] The incidence of most irAEs with ICI 
monotherapy appears similar across tumor types, with minor differences for some selected irAEs, and 
incidence is higher when ICIs are used in combination.  
It has been hypothesized, but not yet proven, that irAEs types, frequency and/or severity may vary 
according to sex. Despite the thorough collection of irAEs data in registrational studies, the sex 
differentiation has never been published. Exploring irAEs incidence in relation to patients’ sex is of clinical 

Title
 
Gender Difference in sidE eFfects of ImmuNotherapy: a possible clue to 
optimize cancEr tReatment (G-DEFINER) 
Document type
Protocol
Version N.
5.0
Date
October 12nd 2021 
 
Page 13 of 26 
 
relevance, and developing tools to identify irAEs high risk patients would be of help to optimize patients 
selection for ICI treatments minimizing toxicity.  
In common feeling, sex and gender represent the same concept, that is the traditional division of 
individuals into females (F) and males (M) defined by biological differences. However, if we go beyond 
these aspects, which characterize "sex", we can see how the differences between people are also 
characterized by behaviors and relationships that are the product of the culture and sociality typical of 
human beings. This is what is called "gender", i.e. the process of social and cultural construction that 
determines the behaviors that give life to the status of an individual. Gender is therefore learned and not 
innate. Sex and gender do not constitute two opposing but interdependent dimensions: the process of 
determining gender identity is triggered on biological characters. The relationship between sex and gender 
varies according to geographical areas, historical periods and cultures. No studies have been conducted to 
reveal an association between irAEs and gender specific determinants, such as socio-economic context, 
exposure to lifestyle and psycho-social factors. An extensive study of gender-based inequalities in health 
[16] reported that some measures related to structural (e.g. age, family arrangement, occupation), lifestyle 
(smoking, drinking, physical activity, weight), and psycho-social context (mainly related to stress) are 
differently distributed between F and M, and most of them are related to health inequalities.  
The role of estrogen signaling in tumor development is well understood in breast and ovarian cancer. In 
melanoma the effect of hormones (estrogen in particular) is supported by the sex differences in patients 
outcome, observed also after immune therapy, but remains unexplained.[17] The hormones role in anti-
tumor immunity has not been extensively studied. Recent studies report hat estrogen signaling is 
responsible for immunosuppressive effects in the tumor microenvironment across cancer types through 
both the accumulation and activity of myeloid-derived suppressor cells (MDSCs), a set of immune cells 
associated with tumors treatment resistance.[18] Sexual hormones could affect autoimmunity via their 
effects on gene transcription, considering that some genes are thought to be regulated directly by estrogen 
or androgen receptors.[19] Moreover, since obesity results in increased systemic estrogen levels via 
adipocyte aromatase activity which converts androgens to estrogen,[17] there is a rationale to examine 
estrogen (e.g. estradiol) levels in F and also in M patients, given also the estrogen production in men at 
peripheral fat, and a possible influence on irAEs. 
Genes influence the risk of certain autoimmune diseases even in the absence of immune checkpoint 
blockade.[19] Evidence for genetic predisposition to individual response to ICI treatment and irAEs 
occurrence is emerging. Several studies have demonstrated an increased probability of clinical benefit 
when tumors are infiltrated by CD8 T cells, have a high mutation burden or have an interferon gamma 
signature. Moreover, expression of IFNG, the gene encoding IFN-γ, is associated with clinical response to 
the immune checkpoint blockade in non-small cell lung cancer, melanoma [20] and head and neck patients. 
This predisposition may be determined by germline DNA variations, for instance in genes encoding proteins 
involved in interactions between the immune system and tumors (e.g. in CTLA-4 gene and PD-L1).[21-23] 
Breunis et al. [21] looked for associations between seven CTLA4 polymorphisms and the occurrence of 
severe autoimmune reactions (grade 3/4), but they did not find any significant associations. Also, the 
candidate gene approach is limiting in the analysis of genetic variations predisposing to irAE, since the 
molecular bases of these side effects are not yet known. Thus, genomewide association studies are needed 
to establish a relationship between genetic factors and the risk of irAEs. Since several immune response 
genes localize to sexual chromosomes or are regulated by sex chromosome genes and sexual dimorphism 

Title
 
Gender Difference in sidE eFfects of ImmuNotherapy: a possible clue to 
optimize cancEr tReatment (G-DEFINER) 
Document type
Protocol
Version N.
5.0
Date
October 12nd 2021 
 
Page 14 of 26 
 
in both immune response and autoimmunity has been reported [24,25], we hypothesize that genetic 
germline variations can account for differences in ICI toxicity between F and M. 
Microbiota and their collective genomes, referred to as the microbiome, inhabiting the human body play a 
critical role in human biology, health and disease, acting collectively as a organ integrated in host’s 
metabolism, regulating innate and adaptive immunity, and participating in the control of the energy 
balance.[26] Recent studies demonstrate a link among microbiota, gender, and immunity since gut 
microbiota composition is able to shape the immune response [2-4] and shows gender-specific 
differences.[27-28]. In addition, microbiota can influence the endocrine system developing a cooperative 
model where signals from both hormones and microbiota are integrated for prevention of disease 
development.[29]. It has been hypothesized that the microbiologic composition of a patient’s 
gastrointestinal flora could be related to the development of irAEs.[19]. Moreover, in a review by Botticelli 
et al. [30] it was hypothesized that the identification of different microbiome profiles could help to 
establish classes of PD-1 and PDL-1 responders, or characterize patients at major risk to develop high grade 
toxicities.  
Finally, since elevated expression of certain cytokines may signal subclinical inflammation that evolves into 
severe irAEs with treatment [31,32], we believe that experimental efforts should explore the irAEs 
incidence in relation to patients sex.  
1.2 Research questions 
The present project focuses on differences between F and M that could affect the incidence of irAEs in 
cancer patients treated with ICI. We aim at integrating sex dimensions in applied health research on 
genetic, immunological and hormonal mechanisms related to irAEs, also exploring gender-related and 
ageing differences.  
We hypothesize that there are sex specific profiles that may explain differential occurrence of adverse 
events. In addition, we hypothesize that the menopausal status in F patients may be associated with 
specific profiles.  
The purpose is to address the unmet clinical need of identifying predictive factors of irAEs. We aim at 
supporting the development of an innovative culture of a “sex based” approach to modern cancer 
therapeutic approaches such as ICI. The study results, being obtained in a “real world” (outside 
experimental clinical trial setting) context, will be more easily translated in a ready to use irAEs timely 
diagnosis and personalization of treatment approaches. 
We expect to be able to improve our clinical practice and decision making in relation to the individualized 
anticipated toxicity profile, avoiding also inadequate inclusion in clinical trials. By irAEs monitoring, early 
recognition and timely treatment, we may impact on outcome and quality of life. Tools for irAEs prediction 
will also be developed. 
2. OBJECTIVES 
The study aim is to investigate the association between irAEs occurrence and sex and gender characteristics 
in patients with different tumor types treated with ICI.  
We will estimate the irAE incidence in F and M and also in relation to clinical, behavioral and psychosocial, 
genetic and immunological features. The differences related to menopausal status will also be investigated 
in the women subset. 

Title
 
Gender Difference in sidE eFfects of ImmuNotherapy: a possible clue to 
optimize cancEr tReatment (G-DEFINER) 
Document type
Protocol
Version N.
5.0
Date
October 12nd 2021 
 
Page 15 of 26 
 
By focusing on biological F/M differences possibly affecting discrepant irAEs incidence, we explicitly address 
sex inequality, complemented by the exploration of association between gender dimension and irAEs 
development. Exploring the irAEs occurrence in a “real world” (outside RCT) context will be more easily 
translated in a ready-to-use personalized approach to irAEs timely diagnosis and treatment. 
2.1 Primary objectives 
• To estimate and compare the irAEs incidence in F and M, and estimate the incidence according to 
different clinical features and gender dimensions (behavioral and psychosocial differences associated 
with being female or male). 
• To estimate and compare the irAEs incidence in pre- and postmenopausal F patients. 
2.2 Secondary objectives 
• To develop irAEs predictive tools based on selected clinical characteristics. 
• To explore irAEs occurrence in relation to hormonal profiles, exploring the differences in F vs M patients, 
and in pre- vs post-menopausal F patients. 
• To explore the role of concomitant medications on irAEs occurrence. 
2.3 Translational objectives 
To explore irAEs occurrence in relation to immune-related genes, germline variations and microbioma, 
exploring the differences in F vs M, and in pre- vs post-menopausal F patients. 
• Gene-expression analysis. Blood samples will be taken in order to identify immune-related genes in 
patients with various solid tumors treated with ICI associated with the development of irAEs.  
• SNPs analysis. Blood samples will be taken in order to perform a preliminary genome wide association 
study for the identification of germline variations associated with the development of irAEs. with the 
hypothesis that the individual’s genetic makeup may be related to irAEs. 
• Microbioma analysis. Stool collection will be performed to analyze gut microbiota aiming at and 
performing RNA/DNA sequencing analysis for identifying components associated with the development 
of irAEs. 
3. STUDY DESIGN 
3.1 Overview of study design 
This is a multicentre prospective observational study. A total number of 400 patients will be consecutively 
recruited in 4 centers, 200 F and 200 M. To allow for balanced sex groups, we will include patients 
according to the following stratification : ICI: 100 F/100 M; ICI+ CT/RT: 100 F/100 M. Due to the current ICI 
use in clinical practice we are expecting to mainly populate the ICI strata during the first recruitment 
period. As the recruitment progresses, the sample will be enriched of ICI+CT/RT treated patients, since 
combinations are expanding for many cancers such for instance melanoma or head and neck. Recruited 
patients will be treated with ICI according to usual clinical practice. Study duration: 24 months recruitment 
and 12 months follow-up ; total study duration: 36 months. 
 
Flow-chart:  
 

Title
 
Gender Difference in sidE eFfects of ImmuNotherapy: a possible clue to 
optimize cancEr tReatment (G-DEFINER) 
Document type
Protocol
Version N.
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Date
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3.2 Study centers 
• Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy (INT) 
• Dept of Oncology-Pathology, Karolinska University Hospital, Sweden (Onk-Pat KI) 
• Dublin City University, Dublin -St Vincent’s University Hospital, Ireland (DCU- SVUH) 
• Oslo University Hospital – The Radium Hospital Norway (OUH) 
 
3.3 Study population 
3.3.1 Inclusion criteria 
Inclusion criteria for ICI administration are well established according to clinical practice. Patients fulfilling 
the following criteria will be enrolled: 
• Signed informed consent. 
• Histologically confirmed diagnosis of one of the following cancers: melanoma, lung cancer, head and 
neck cancer, urogenital cancer, breast cancer, and, in addition, other solid tumors characterized by the 

Title
 
Gender Difference in sidE eFfects of ImmuNotherapy: a possible clue to 
optimize cancEr tReatment (G-DEFINER) 
Document type
Protocol
Version N.
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Date
October 12nd 2021 
 
Page 17 of 26 
 
presence of microsatellite instability (MSI-high). It is possible to include patients treated with 
Immunotherapy in a compassionate use setting. 
• Any disease stage.  
• Patients eligible for ICI-containing regimens irrespective of treatment schedule: 
o ICI single agent. 
o Combination of ICIs. 
o Combination of ICI and chemotherapy. 
o Combination of ICI and radiotherapy 
• Any treatment setting (neoadjuvant, adjuvant, advanced disease, maintenance). No limitations to 
previous lines of treatment. 
• Patient age  ≥18 years 
• Eastern Cooperative Oncology Group (ECOG) Performance Status of 0-2. 
• Adequate bone marrow, liver and renal function. 
• Life expectancy of at least 12 weeks. 
3.3.2 Exclusion Criteria 
Patients not eligible for ICI-containing regimens. Exclusion criteria for ICI administration are well 
established according to clinical practice.  
 
4. DATA SOURCES/DATA COLLECTION PROCESS 
The process for data management and CRF completion is outlined in the “SOP 02 Data management – CRF 
completion”. 
4.1 Investigators 
• 
Clinical investigator. Each Center will independently appoint eligible physicians, which will 
participate in the study as CI. The participating physicians will be responsible for the accuracy, 
completeness, legibility and timeliness of the data documented in the CRFs & eCRF’s and all 
required reports.  
• 
Data Manager. Each Center will appoint eligible data manager/s responsible for data collection and 
CRF/eCRF completion  
4.2 Patient clinical data 
Data collection/reporting will be conducted in a consistent way among different Centers to avoid bias in the 
data collection process. Data will be entered by all sites onto CRF/eCRFs specifically designed for the study.  
The study will collect data primarily from Oncology Units treating patients deemed to be eligible for the 
study in the participating Centers. Paper and electronic case report forms will be used by the study 
investigators to enter data.  
The greater part of the data that will be collected in this study are those usually collected at appointments 
for routine checks. Additional data will be collected to characterize gender aspects, and additional blood 
and stool samples will be taken to study genetic, immunological and hormonal profiles (par. 4.3). As regards 
study-specific gender related data, based on the results shown in [16] we will examine some measures 
related to structural factors (e.g. age, family arrangement, social support, occupation, socio-economic), 

Title
 
Gender Difference in sidE eFfects of ImmuNotherapy: a possible clue to 
optimize cancEr tReatment (G-DEFINER) 
Document type
Protocol
Version N.
5.0
Date
October 12nd 2021 
 
Page 18 of 26 
 
lifestyle factors (e.g smoking, drinking, physical activity, weight), and psycho-social factors (e.g. practical, 
family, emotional, and physical problems; stress). Moreover, we will investigate general health levels 
(mobility, self care, usual activities, pain anxiety).  
4.3 Patient biological data 
Biological samples will be collected to derive data for translational studies aimed at studying irAEs 
occurrence in relation to immune-related genes, germline variations and microbioma, exploring the 
differences in F vs M, and in pre- vs post-menopausal F patients.  
Biological samples and timing: 
Type of 
sample 
Times
N. additional 
samples required 
for the study 
T0
T1
T2
Gene-expression 
profiling 
for 
immune assessment  
Blood
X 
(3 ml) 
X
(3 ml)
X
(3 ml) 
Total 9 ml
Cytokines 
Blood
X
(7 ml) 
X
(7 ml)
X
(7 ml) 
Total 21 ml
SNPs 
Blood
X 
(10 ml)
Total 10 ml
Microbioma 
Stool
X
X
X
3 samples
Hormones 
Blood
X
X
X
Routine lab, no 
additional tubes 
T0: baseline; T1: at 2nd ICI infusion; T2: at occurrence of the first irAE Grade ≥2.
 
• Gene expression profiles for immune assessment. Blood samples will be collected in order to identify 
immune-related genes associated with the development of irAEs. Each center will ship the samples to 
the Piattaforma di Biologia Integrata at INT, where the analyses  will be carried out on T0 samples. T1 
and T2 samples will be  stored for future studies. 
• SNPs. Blood samples will be collected in order to perform a preliminary genome wide association study 
for the identification of germline variations associated with the development of irAEs with the 
hypothesis that the individual’s genetic makeup may be related to irAEs. Each center will ship the 
samples to the Genomic Core facility at OUH, where whole genome SNP genotyping will be carried out. 
• Microbioma. Stool collection will be performed to analyze gut microbiota aiming at the identification of 
components associated with the development of irAEs. Each center will ship the samples to CTMR 
Karolinska Institutet, Sweden, where extraction and analyses will be centralized and performed for all 
centres participating in the G-DEFINER project.  
• Hormones. Blood samples will be taken in order to explore irAEs occurrence in relation to  hormonal 
profiles. We will collect data Routine lab, no additional tubes are requested for the study. 
• Cytokines. Additional blood collected will be taken and stored for future studies aimed at identify 
cytokines associated with the development of irAEs.  
Blood samples will be collected at medical examinations by applying routine drawing procedure. Stool 
samples will be taken from patients at home; patients will be provided with special kits.  

Title
 
Gender Difference in sidE eFfects of ImmuNotherapy: a possible clue to 
optimize cancEr tReatment (G-DEFINER) 
Document type
Protocol
Version N.
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5. TREATMENT AND CLINICAL PROCEDURES 
Treatment will be as per clinical prescription for melanoma, lung cancer, head and neck cancer, renal 
cancer, urothelial cancer, breast cancer, and for other solid tumors characterized by the presence of 
microsatellite instability (MSI-high). It is possible to include patients treated with Immunotherapy in a 
compassionate use setting. 
Any ICI-containing regimen can be used in this study: 
o ICI single agent; 
o Combination of ICIs; 
o ICI-chemotherapy combination; 
o ICI-radiotherapy combination. 
Treatment may be administered in any setting: neoadjuvant, adjuvant, advanced disease, maintenance. 
Patients will undergo physical and laboratory evaluation and disease evaluation with CT and/or imaging 
scan according to current clinical practice at each study Center.  
Baseline radiological tumor measurements should be performed preferably within 7 days, but in any case 
no more than 30 days before entering the study. 
IrAEs of specific interest  will be recorded and graded according to most recent Common Terminology 
Criteria for Adverse Events (CTCAE) v. 5.0. Patients who experience irAEs may continue treatment according 
to the clinical indications.  
Response will be evaluated by most recent RECIST according to current clinical practice at each study 
Center. 
6. STUDY CONDUCTION 
The process for the study conduction is outlined in the “SOP 03 - Study conduction”. 
Being G-DEFINER an observational study, patients will not modify their course of treatment according to 
oncologists prescriptions, nor visits or instrumental examinations additional to those planned for the 
treatment of their disease. The decision for discontinuation from ICI lies with the treating physician in 
agreement with the patient’s decision and is not regulated by this protocol.  
 
7. STATISTICAL ANALYSIS 
This section presents an overview of the statistical analysis planned. Details will be presented in the 
statistical analysis plan (SAP). Patients recruitment will be periodically monitored. All patients who fulfill the 
study entry criteria will be included in the data set for analyses. The statistical analyses will be performed 
with SAS (Cary, NC, USA) and R software [R Core Team (2016): R: A language and environment for statistical 
computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.] 
7.1 Clinical data analyses 
All collected data and endpoint variables will be summarized. The variables will be described using standard 
statistical analyses to gain an understanding of the qualitative and quantitative nature of the data collected 
and of the characteristics of the sample studied. Results will be displayed using tables, listings, and/or 
figures.  
The incidence of first severe (G≥ 2) irAEs of any type (below referred to as “event”) will be estimated in F 
and M as a proportion of patients developing the event respect to the total number of patients at risk; the 

Title
 
Gender Difference in sidE eFfects of ImmuNotherapy: a possible clue to 
optimize cancEr tReatment (G-DEFINER) 
Document type
Protocol
Version N.
5.0
Date
October 12nd 2021 
 
Page 20 of 26 
 
corresponding binomial 95% confidence interval (CI) will also be reported. The main comparison F vs M will 
be performed by estimating the event odds ratio (OR) and its CI in a univariable logistic model; F/M 
unbalance for different clinical and gender-related characteristics will be taken into account using the 
“matching weight” (MW) method.[33] Patient weights will be estimated in advance as a function of a 
model-based balancing score by applying the propensity score methodology. To develop the event 
predictive signature on the bases of sex, gender, and other patients, disease and treatment characteristics 
we will perform variable selection in multivariable setting (e.g. by means of a random forest (RF) [34] for 
binary response or LASSO methods in binary logistic models [35]). The same methods described above will 
be applied for investigating the pre vs post menopausal differences and for developing the related 
predictive signature.  
IrAEs will also be tabulated describing the overall number of irAEs and the number of patients reporting 
irAEs grouped by CTCAE class and grading, and according to sex and menopausal status, ICI treatment, and 
tumor type. Univariable and multivariable Poisson regression models will also be applied to analyse irAE as 
count variable in relation to clinical variables. 
7.2 Translational analyses 
The analyses will be performed separately in the F and M groups to identify the features associated with 
event (defined above) occurrence.  
Hormonal, gene expression, and microbioma data analyses. For each type of analysis, baseline features 
data will be categorized according to their distribution quartiles and irAE event incidence and 
corresponding 95% confidence intervals will be estimated in the 4 strata defined by the combination ICI/sex 
(i.e. ICI F, ICI M, ICI+ CT/RT F, ICI+ CT/RT M). Estimation will also be performed according to ICI type and 
grade, tumor site, ICI treatment, patients’ age and gender-based characteristics. Sex-specific signatures to 
predict the event will be developed by performing class comparison (univariable) and class prediction 
(multivariable) analyses. In class comparison analysis, the features differentially expressed in patients with 
or without event will be identified by applying the Wilcoxon-Mann-Whitney (WMW) test. The features will 
be selected based on their 5% significance at class comparison analysis. The control of the False Discovery 
Rate (FDR) due to multiple testing will be done by adjusting the p-values with the Benjamini-Hochberg 
procedure [36]. In class prediction analysis, since the number of features to be investigated will be high, 
even after class comparison pre-filtering, variable selection techniques (e.g. [34] or [35]) will be used. The 
event predictions will be obtained by using statistical models for binary response, such as logistic 
regression, including the selected features. The same methodology will also be applied for the menopausal 
status-specific signatures to predict the event. As regards microbioma data, the statistical methods will take 
into account their peculiarities (e.g. over-dispersion and zero-inflation);[37] moreover, as in addition to 
baseline, samples will be taken at 2 other later times, models for longitudinal data will be applied.  
SNPs analysis. This has to be regarded as a very exploratory analysis, given that, after applying the FDR 
method [36] to adjust for test multiplicity, the statistical power of bioinformatical analyses will be 
sufficiently higher (>70%) only with irAE proportion> 30% and the OR associated to a single feature > 5. SNP 
allele frequencies will be tested in F and M for association with irAEs adjusting for confounders as ICI 
treatment, age, smoking habit, tumor type and stage. SNPs analyses will be performed separately in the F 
and M groups to identify the features associated with irAE event occurrence. The features differentially 
expressed in one group but not in the other one will also be selected. These analyses will be done by 

Title
 
Gender Difference in sidE eFfects of ImmuNotherapy: a possible clue to 
optimize cancEr tReatment (G-DEFINER) 
Document type
Protocol
Version N.
5.0
Date
October 12nd 2021 
 
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logistic regression using PLINK software. The FDR method [36] will be applied for test multiplicity 
correction. 
7.3 Sample size considerations for the main sex-related analyses 
Considering Puzanov et al. data,[15] the events proportion associated to 3 ICIs (CTLA-4,PD-1, PD-L1) 
considering all sites could be even greater than 30%. We simulated scenarios in which the proportion is 
varying from 20% to 50%. To estimate the proportion by F and M, a sample size of 200 patients (200 F/200 
M) will produce a two-sided 95% exact CI with a width as small as 0.115 (CI 0.147 to 0.262) if the proportion 
is 20%, increasing to 0.143 (CI 0.429 to 0.571) if the proportion is 50%. In the multivariable logistic model 
analysis of association between the event and sex, given that using MW each patient weights less than 1, 
the effective sample size is reduced. However, we foreseen that the number of events will be still adequate 
to the model degrees of freedom (DF).[38] In fact, even with a proportion of irAE as low as 20% and with an 
effective sample lowered to 200 patients (100 F/100 M) we would have 40 events, sufficient to reliably 
estimating the sex OR in a univariable model, or in a multivariable model with interaction sex x another 
covariate with 2 DF. In the multivariable logistic model analysis of association between first G≥2 irAEs and 
gender, a sample of 400 patients (200 females, 200 males) will produce a power ≥80% at a two-sided Wald 
test with α=5% in the following scenarios:  
R2
P0
OR
0.5
20%
≥2.40
0.5
25%
≥2.35
0.5
30%
≥2.30
0.5
≥35%
≥2.25
R2
P0
OR
0.6
20%
≥2.70
0.6
25%
≥2.55
0.6
30%
≥2.50
0.6
≥35%
≥2.45
R2
P0
OR
0.7
20%
≥3.05
0.7
25%
≥2.90
0.7
30%
≥2.85
0.7
≥35%
≥2.85
R2: correlation between gender and other model covariates; P0: baseline proportion of irAE; OR: testable 
gender odds ratio.  
8. ADMINISTRATIVE AND REGULATORY DETAILS 
Administrative and regulatory details are described in “ANNEX 03 - Administrative and regulatory details”. 
By signing this protocol, the Consortium Investigators participating to the G-DEFINER study agree to be 
responsible for implementing and conducting the study according to the all the principles described in the 
“ANNEX 03 - Administrative and regulatory details”.  
 

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Gender Difference in sidE eFfects of ImmuNotherapy: a possible clue to 
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Gender Difference in sidE eFfects of ImmuNotherapy: a possible clue to 
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Document type
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Title
 
Gender Difference in sidE eFfects of ImmuNotherapy: a possible clue to 
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13. Document history- amendments 
Version No.
Date
Reviewer
Details of changes
0.1
26.03.2019
RM
Very first version
0.2
27.05.2019
RM
Synopsis, added references to forms and SOPs
1.0
29.05.2019
RM, 
SA, 
GLR 
Substantial modifications; main: 1) Synopsis; 2) par. 2.3, 3) par. 3.3.1 and 3.3.2 
(eligibility); 4) timing: T1: 2nd infusion instead of 6th  week; 5) eliminated 
eligibility=biological make-up as male/female. 5) bib citation to be included: Conforti F 
2019 in introduction. 
1.1
05.06.2019
RM
Inclusion annex 03 and SAP, modification statistical analysis section
1.2
07.06.2019
RM
Corrections by GLR 
1.3 
10.6.2019
RM
Specified 10 ml for blood tubes. 
2.0 
19.6.2019
RM
Modifications: 1) Participating Units at INT (no person names), 2) incorporated 
corrections SA/LL mail 16/06/2019 and GL; 3) flow chart added 
3.0
21.10.2019
RM
Amendments post meeting with partners. As version 2 of the protocol was approved 
by the INT Ethical Committee, INT has to present protocol amendments (Em1.0-
21.10.2019-Prot3.0) . 
List of Amendments: 
1) substantial: exclusion criteria: eliminated all the criteria, because implicit for 
patients treated with ICI; patients excluded are those not eligible for ICI-containing 
regimens. Inclusion criteria: the partner established that patients with curative RT can 
be included (sections inclusion/exclusion criteria). 
2) substantial: new biological sampling scheme, with addition of 2 blood samples at 
times T1 and T2 for gene expression. Nonetheless, the total amount of blood for all 
the analyses is less than in version 2 of the protocol. Par. 4.3 modified accordingly and 
it was specified that gene expression analysis is performed on T0 samples in the 
present study; T1/T2 samples will be analysed in future studies.Par 4. of patient 
information was modified accordingly. 
3) substantial: Patients informed consent: patients who do not accept to participate 
to the study can give a separate consent to register their age, sex, disease type and 
reason for refusal to participate.  
4) not substantial: flow chart: added “sign consent” before enrollment and added 
cytokines among study-specific blood samples. 
5) not substantial: eliminated section 9 “flow chart” because of redundancy with flow 
chart picture in pag. 3.1. 
6) not substantial: par 5 patient information: specified that additional 10 ml of blood 
will be taken in case of serious adverse event.  
7) not substantial par. 6 patient information: more clearly specified that after 1 year 
no data will be directly requested to the patient, and only clinical chart data related to 
disease evolution will be registered. 
4.0
12.06.2020
RM
Amendment: possibility to include patients with solid tumors characterized by the 
presence of microsatellite instability (MSI-high). 
5.0
12.10.2021
RM
Amendments post meetings with partners. List of Amendments:
1) substantial: Synopsis, sections “Indications” and “Inclusion criteria”, par. “3.3.1 
Inclusion criteria”, par “5. TREATMENT AND CLINICAL PROCEDURES”: Possibility to 
include patients patients treated with Immunotherapy in a compassionate use setting 
(to comply DCU- SVUH  request). 
2) Substantial: Synopsis, section “Statistical considerations”, Flow-chart par. “3.1 
Overview of study design”, table par. “4.3 Patient biological data”: first severe irAEs 
defined as G≥ 2, instead of G≥3, is estimated as main end-point. This because the 
observed proportion of events G≥3 is low because patients are eligible if treated with 
ICI for the first time, and many patients are treated with single ICI. Considering G≥2 
fits well with the scenarios hypothesized in section “7.3 Sample size considerations for 
the main sex-related analyses”, where we simulated scenarios in which the irAEs 
proportion was varying from 20% to 50%.