I3LUNG: Integrative Science, Intelligent Data Platform for Individualized LUNG Cancer Care With Immunotherapy
Conditions: Non Small Cell Lung Cancer, Lung Cancer Metastatic, Lung Cancer, Nonsmall Cell, Lung Adenocarcinoma
Sponsor: Fondazione IRCCS Istituto Nazionale dei Tumori, Milano

 
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Observational Study Protocol 
Version n. 1.0 del 22/06/2022 
PI:  Prelaj Arsela, MD, Istituto Nazionale Dei Tumori di Milano 
Title of the 
study 
I3LUNG: Integrative science, Intelligent data platform for Individualized LUNG cancer 
care with Immunotherapy  
 
Background 
and Rationale 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
BACKGROUND 
Lung cancer is the leading cause of cancer-related mortality worldwide. The global incidence in 2020 
was an estimated 2.2 million cases with an estimated 1.8 million deaths. A 70% increase in the number 
of new cases of lung malignancy is expected over the next 2 decades (1). There are two major families 
of lung cancers: small cell lung cancer (SCLC), which accounts for approximately 15-20% of total 
cases, and non-small cell lung cancer (NSCLC), which accounts for approximately 80-85% (2). About 
70% of patients with NSCLC, at the time of diagnosis, have advanced disease not amenable to surgical 
resection while SCLC is always considered a systemic disease (3). For several decades, cytotoxic 
chemotherapy has been the only treatment able to prolong survival in advanced patients with both 
SCLC and NSCLC. In recent years, in NSCLC, progress in cell biology have led to the identification 
of specific genetic alterations, known as "driver" alterations, at the basis of tumorigenesis, including 
mutations of EGFR, KRAS and BRAF and rearrangements of ALK, ROS1, RET genes. The use of 
"target therapies" is now the treatment choice in this subgroup of patients (4-11). The advent of 
immunotherapy has further changed the therapeutic landscape in both NSCLC without "driver" 
alteration (NSCLC-WT) and SCLC. This is a treatment modality based on mobilization of the immune 
system in order to recognize and destroy tumor cells. Immune Checkpoint Inhibitors (ICIs), have been 
developed with the intent to act on those pathways of "self-tolerance" used by tumors to escape 
recognition and destruction by the immune system. By blocking these inhibitory pathways, which 
physiologically control the immune response, ICIs reactivate and sustain the immune system response 
against tumors cells (12). Cytotoxic T-lymphocyte-associated-4 (CTLA-4) and programmed cell 
death protein 1 (PD-1) are receptors expressed on T cells that, by interacting with CD80/CD86 (13) 
and programmed cell death protein 1 (PD-L1) or PD-L2 ligands, respectively (14), can promote and 
foster immune evasion by tumor cells. PD-L1 evaluation on tumor remains the only used and approved 
predictor of response to immunotherapy (IO) treatment. Although the probability that a patient will 
benefit from IO treatment is higher in high PD-L1 expression, the role of PD-L1 remains unclear as a 
predictive role (15-20). 
As a general guideline, the lack of alterations in target genes and PD-L1 expression > 50% allow to 
candidate a patient for first-line treatment, single-agent IO or its combination with chemotherapy 
based on platinum regimen (CHT). In patients with PD-L1 expression 1-49%, IO alone showed 
modest activity, while the CHT-IO therapy demonstrated to significantly prolong patient survival 
compared to CHT alone, in both squamous and non-squamous NSCLC (10). 
To date, FDA and EMA approved for use in advanced NSCLC patients of nivolumab and 
pembrolizumab (antibodies against PD-1), and atezolizumab and durvalumab (antibodies against PD-
L1). Due to the better efficacy and safety profile compared with chemotherapy, pembrolizumab, 
nivolumab, and atezolizumab were approved as monotherapies, pembrolizumab as first and second 
line treatment, whereas nivolumab and atezolizumab only in second line (15-19). Durvalumab is 

 
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instead indicated as maintenance therapy in patients with locally advanced NSCLC undergoing 
concomitant chemo-radiotherapy treatment (20). 
Approximately 16% of NSCLC patients are still alive at 5 years compared to 5% with cytotoxic CHT. 
However, about 50% of patients do not obtain a real long-term benefit from this treatment modality. 
(21) Furthermore, in subjects carrying driver alterations, no significant advantage of single-agent IO 
over CHT has been reported (15-20). Lastly, there is a subgroup of 13-26% patients for whom IO 
treatment may even be detrimental, inducing a hyper-progressive disease (HPD) (22-26). Beyond PD-
L1, several other biomarkers have been identified and used to profile patient prediction, including 
Tumor Mutational Burden (TMB) (27), tumor microenvironment (TME) (28), microRNA (miRNA) 
(29), immune gene signatures (30), gut microbiome (31), radiomics (32), or baseline clinical features 
or their combination in different scores (33,34). 
Radiomics regards the quantitative analysis of digital imaging, informs future precision medicine, and 
supports objective decisions for cancer diagnosis and treatment. MAASTRO clinic has successfully 
experienced and validated fifty-six 3D-radiomic features. Recently, radiomics has demonstrated 
correlation between CT images and clinicopathologic features in NSCLC-IO patients (35). Finally, 
radiomics have been lately applied to PET/CT, in which context metabolic tumor volume (MTV) and 
total lesion glycolysis (TLG) in NSCLC demonstrated correlation with response to IO (36). There are 
several ongoing EU projects that use radiomics for NSCLC, but none of them is IO-specific and 
integrates multi- OMICs. 
In the last decade, an impressive amount of knowledge has been achieved regarding the many other 
putative predictive factors, but no one has been applied in clinical practice. This is mainly due to 
unstructured and spared data among the different institutions, generating less consistent and therefore, 
less predictive datasets. Today, the medical community is claiming for an overall integration of 
clinical data and molecular biomarkers, in order to generate predictive information to address 
therapeutic administration in cancer patients, specifically in the immunotherapy era. In fact, it is 
unlikely that a single biomarker will profile prediction or prognosis with a high accuracy considering 
the role played by the immune system, and the complexity of the TME. 
Artificial Intelligence (AI) frameworks and Machine Learning (ML) processes synthesize and 
correlate information from multiple sources and develop learning methods from it. These are a 
powerful, innovative, and potentially highly efficient instrument to construct decision-making (DM) 
tools providing individualized prediction of response to treatment and finally cope with the 
overmentioned complexity (37).  
Therefore, the I3LUNG project aims to achieve the highest performance in personalized medicine 
through AI/ML modelled on multimodal patients’ data, together with implementing an AI/ML model 
in a real-life setting. A set of patient-centered ML tools designed and validated for the project, which 
make use of the novel virtual patient AVATAR entity for predicting progression and outcome. To 
maximize its impact, the use of Trustworthy Explainable AI (XAI) methodology will integrate the 
AI’s inherent performances with the input of Human Intuition (HI) to construct a responsible AI 
application able to fully implement truly individualized treatment decisions in NSCLC interpretable 
and trustworthy for clinicians. (37) The final objective is the establishment of a European (potentially 
worldwide) Data Sharing and Elaboration Platform (DSEP), presented in Figure 1. The DSEP will 
provide guiding tools for patients, providing information to generate awareness on treatments. Lastly, 
it gives access to researchers and the general scientific community to the most up-to-date data sources 
on NSCLC. 

 
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Within the I3LUNG project, an ad-hoc Individual Patient Decision Aid System (IPDAS) for NSCLC 
patients will be developed. Patient decision aids are tools that might be used by patients either before 
or within a consultation with physicians. Patient decision aids explicitly represent the decision to be 
made and provide patients with user-friendly information about each treatment option by focusing on 
harms and benefits. They allow patients to clarify what matters to them the most. These decisional 
support systems have been demonstrated to be effective in empowering patients, improving their 
knowledge, promoting their active participation in clinical decision-making about treatments, and 
improving overall patient satisfaction with care while decreasing decisional conflict and decisional 
regret (38-41) (see Psychological Study Annex I).  
Finally, within the I3LUNG project it will be assessed whether using the IPDAS during the clinical 
consultation would foster the quality of the shared decision-making as well as the quality of the doctor-
patient communication. Alongside the evaluation of the impact of the IPDAS, it will be also evaluated 
whether the inclusion of the AI/ML predictive models in clinical practice will be added value in 
supporting oncologists’ clinical decision-making and decreasing cognitive fatigue and decisional 
conflict (see Psychological Study Annex II).  
 
 
Figure 1. I3LUNG platform, tools, and overall structure. The AVATAR based platform and the 
AI/ML based tools. 
I3LUNG will foresee two study phases: Retrospective and prospective.  
I3LUNG adopts a two-pronged approach to develop a medical device through the creation and 
validation of retrospective and prospective AI-based models to predict immunotherapy efficacy for 
NSCLC patients using the integration of multisource data (real word and multi-omics data) through a 
retrospective – setting up a transnational platform of available data from 2000 patients – and a 
prospective – multi-omics prospective data collection in 200 NSCLS patients – study phase.  
The retrospective part of the I3LUNG project includes the analysis of a multicentric retrospective 
cohort of more than 2,000 patients. This cohort will be used to perform a preliminary knowledge 
extraction phase and to build a retrospective predictive model for IO (R-Model), that will be used in 
the prospective study phase to create a first version of the  Physician Decision Support System (PDSS) 
tool, an AI-based tool to provide an easy and ready-to-use access to predictive models, increasing care 
appropriateness, reducing the negative impacts of prolonged and toxic treatments on wellbeing and 

 
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healthcare costs. Also, CT and PET scans will be collected and a first radiomic signature will be 
created to feed the R-Model. 
The prospective part of the project includes the collection and the analysis of multi-OMICs data 
from a multicentric prospective cohort of about 200 patients. This cohort will be used to validate the 
results obtained from the retrospective model through the creation of a new model (P-Model), which 
will be used to create the final Physician Decision Support System (PDSS) tool. 
Objectives 
and clinical 
relevance  
 
Overall Primary Objective 
 
Prediction of response to immune checkpoint inhibitors in NSCLC by using clinical, 
biological, metabolomics, genomics, pathomics and radiomics data. 
Secondary Objectives 
 
Prediction of Progression-free survival (PFS) in response to immune checkpoint inhibitors in 
NSCLC by using clinical, biological, metabolomics, genomics, circulomics, pathomics, 
microbiome and radiomics data. 
 
Prediction of Objective Response Rate (ORR) in response to immune checkpoint inhibitors 
in NSCLC by using clinical, biological, metabolomics, genomics, circulomics, pathomics, 
microbiome and radiomics data. 
 
Prediction of Overall survival (OS) in response to immune checkpoint inhibitors in NSCLC 
by using clinical, biological, metabolomics, genomics, circulomics, pathomics, microbiome 
and radiomics data. 
Exploratory Objectives 
 
Added value of PET/CT into the prediction model for the Primary and Secondary Objectives. 
 
Prediction of adverse events to immune checkpoint inhibitors in NSCLC by using clinical, 
biological, genomics, pathomics and radiomics data. 
Evaluate the addition of sarcopenia analysis into the prediction model to the response of immune 
checkpoint inhibitors in NSCLC 
Specific Objectives:  
Retrospective Study Phase 
The primary objective of the retrospective clinical study within the I3LUNG project is to create a 
model that will be used in the prospective phase to compare the predictive accuracy of ML methods 
(created using multi-OMICs analyses) with current validated biomarkers (e.g., PD-L1 or available 
scores). All available jeopardized data of 2000 aNSCLC patients treated at any point with any IO-
based therapy will be collected in the first 18 months of the I3LUNG project, including all the data 
and radiomics detailed in the "Scientific Clinical Study Design" section. This model will be used to 
create a first version of the Physician Decision Support System (PDSS) tool. 
Prospective Study Phase 
The primary objective of the prospective phases to compare the predictive accuracy of ML methods 
(created using multi-OMICs analyses) to current validated biomarkers (e.g., PD-L1). In this 
prospective part of the study, multi-OMICs data (pathology, genomics, transcriptomics, 
metabolomics, circulating immune profiling, microRNA signature classifier, liquid biopsy, 
microbiome) of 200 patients recruited by 5 cancer centers from the Consortium (INT, GHD, VHIO, 

 
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MH and SZMC) will be collected and then integrated in the platform and used to feed the AI models 
learned on retrospective data, previously collected in the retrospective study phase.  
Other objectives of the prospective phase in particular will include: 
1. Integrate both retrospective and prospective data to improve and refine the predictive 
capabilities of the AI models compared to existing models; 
2. Integrate radiomics signature originating from both Computed Tomography (CT) and 
Positron Emission Tomography–CT (PET-CT) images of the prospective cohort to refine 
and validate the retrospective radiomics signature; 
3. Measure, within the prospective cohort new sensor-based QoL model and behavior data 
(e.g.: cough, physical and social activities, and mood states/emotions) that complements 
standard QoL measures (Patient Reported Outcome Measures or PROMs and Patient 
Reported Experience Measures or PREMs); the ad-hoc developed sensor- based monitoring 
system of patients’ QoL and standardized PROMs and PREMs (ii) if co-shared decision-
making impact QoL at least +10% compared to published prospective study data (Figure 1) 
4. Use AI/ML to create new models in order to increase cognitive abilities of patients and 
physicians in shaping trust, usability, acceptability and attitude towards AI/ML; 
5. Provide guidelines for an efficient collection of data to support diagnosis and select 
treatment in NSCLC will be developed thanks to the validated AI and Explainable AI (XAI) 
techniques; 
6. Put in place preliminary tasks for the CE marking certification and future exploitation of the 
“medical device developed tools”. 
 
Figure 1. QoL sensor-based and transparent monitoring using app and cloud-collection on patients' 
information.  
Clinical relevance  
Currently, no clear data support the use of any biomarker, beyond PD-L1, to predict the patients 
benefit from IO and clinicians are often left alone to make decisions. The I3LUNG study aims to 
combine different types of markers (clinical, radiologic, genetic, molecular and immunological) 
through Artificial Intelligence (AI) frameworks and Machine Learning (ML) techniques. This will 
permit to fit it in a model that will be tested and integrated in the prospective phase of the study. In 
the final scenario, a pragmatic tool will be provided to clinicians in order to help predicting the benefit 
of IO, choosing the best candidate to immunotherapy and finally guide the therapeutic choice (IO 
alone vs IO-CHT combinations vs IO-IO combinations). 

 
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Study 
Endpoints 
Primary endpoint 
 
The predictive model will be developed through artificial intelligence and machine learning 
analysis of the integrated clinical, biological, genomics, metabolomics, circulomics, 
pathomics, microbiome and radiomics data. 
Secondary endpoints 
 
Using the predictive model index as classificatory biomarker in Progression-free survival 
(PFS), defined as the time interval from the date of the first dose of the study treatment until 
the first date at which disease progression is objectively documented, or death due to any 
cause, whichever occurs first and calculated through censored Kaplan-Meier analysis. 
 
Prediction of Objective response rate (ORR) classification through the predictive model and 
compared to the assessed by the investigator and defined as the proportion of the participants 
with confirmed complete response (CR) or partial response (PR) according to Response 
Evaluation Criteria in Solid tumors (RECIST) v1.1, after initiation of immune checkpoint 
inhibitors treatment. 
 
Using the predictive model index as classificatory biomarker in Overall survival (OS) 
calculated from diagnosis to death or last follow-up through censored Kaplan-Meier analysis. 
Exploratory endpoints 
 
If available PET/CT will be added to the prediction model and the increment in the 
prediction performance will be analyzed for the primary and secondary endpoints and the 
adverse events categories. 
 
Machine learning algorithm will be used to predict adverse events categories in response to 
immune checkpoint inhibitors treatment through the integration of the clinical, biological, 
genomic, pathomics and radiomics. 
Sarcopenia will be calculated through radiomics by measuring the fat and muscular mass on L1 slice 
of the chest CT scan and added to the prediction model for the Primary and Secondary Objectives. 
Study 
population 
 
RETROSPECTIVE PHASE:  
The study cohort consists of aNSCLC patients treated with IO. Data from an estimated 2000 patients 
treated with IO-based therapy (any treatment line) in the period 2012-2021 will be collected from all 
the clinical partners (INT, GHD, VHIO, MH, SZMC and UOC). Inclusion criteria will be checked 
and informed consent for the study and for the re-use of prior collected medical data will be obtained 
before enrolment. If not feasible, i.e. patients not alive, the approval to Privacy Guarantee will be 
obtained. 
Inclusion criteria retrospective phase: 
1. Age ≥ 18 years 
2. Presence of an Eastern Cooperative Oncology Group (ECOG) performance status ≤ 2; 
3.Histologically confirmed diagnosis of stage III-IV Non-Small-Cell Lung Cancer patients receiving 
also maintenance therapy with durvalumab or other immune/combination are allowed; 
4. Patients who received any line of IO-based therapy (immunotherapy monotherapy or 
immunotherapy in combination with any other treatments); 
5. Patients with any CNS mets (stable or not) are allowed.  

 
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6. Patients with driver alteration (e.g., EGFR, BRAF, KRAS mutations or ALK, ROS1) who 
underwent an immunotherapy line will be included. 
Exclusion criteria retrospective phase: 
1. Patients without minimal treatment information data (e.g. start and stop date of immunotherapy and 
type of immunotherapy agent or protocol name) 
PROSPECTIVE PHASE:  
In the prospective phase, the study cohort consists of aNSCLC patients candidate for first-line IO-
based therapy with available surgical samples (enough to perform OMICs). Baseline data of an 
estimated 200 patients from 5 clinical centers (INT, GHD, VHIO, MH and SZMC) will be collected 
including complete clinical, multi- OMICs analysis, imaging of CT and PET scan at baseline IO, 
behavioral, health economic, QoL measurements with based-sensor techniques and standard QoL. 
Data treated with IO-based therapy (any treatment line) will be collected. Inclusion criteria will be 
checked and informed consent for the study and for the re-use of prior collected medical data will be 
obtained before enrolment. 
Inclusion criteria for prospective phase: 
1. Age >/= 18 years.  
2. Evidence of a personally signed and dated ICF indicating that the patient has been informed 
of and understands all pertinent aspects of the study before enrolment.  
3. Eastern Cooperative Oncology Group (ECOG) performance status " 0-2.  
4. Histologically confirmed diagnosis of stage IIIB/C-IV Non-Small-Cell Lung Cancer  
5. No activating EGFR mutations or ALK/ROS1 rearrangements  
6. No prior treatment for advanced disease  
7. Availability of at least 1 (one) FFPE block for -omics data generation (for cohort 200 pts) 
8. Clinical indication for frontline treatment with immunotherapy (alone or in combination), as 
first line of treatment for metastatic disease or consolidation treatment for locally advanced 
disease  
9. Patients with untreated or unstable CNS brain metastases are allowed 
Exclusion criteria for prospective phase: 
1. Prior systemic treatment for NSCLC for advanced disease 
2. Absence of sufficient amount of tumor tissue for -omics data generation for OMIC cohort 
(200 pts). 
3. Unavailability or inability to comply with the requested study procedures, including 
compilation of QoL questionnaires and behavioral data collection (mobile app).  
4. Unable to participate in the psychological study 

 
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Data for –omics analysis from patients considered screening-failure and, therefore, not eligible for the 
prospective phase of the protocol, will also be collected to feed the AI models using their real-world 
data generated during IO-based treatments. There is no limit for sample size in this phase and are not 
counted within the 200 patients who will be eligible for the multi-OMICs analysis.  
 
 
 
 
 
 
 
Methodology 
and study 
design 
 
Scientific Clinical Study Design 
I3LUNG will conduct an observational retrospective/prospective multicenter international project on 
advanced Non-Small-Cell Lung Cancer patients treated with IO. Its main objective is to study, train 
and validate the predictive accuracy of ML methods created by using real world data and multi-
OMICs analyses, compared to the current standard of care biomarkers (e.g., the PD-L1). 
The prospective study part of the project will start at month 3 and will continue up to 36 months, 
NSCLC patients candidates (n=200) to first-line IO-based therapy with available surgical samples 
(enough to perform -omics) will be enrolled. 
In particular, will be collected the following data for Retrospective Phase (when available) and for 
Prospective phase: 
Baseline patients’ data 
Retrospective phase 
Jeopardized 
Prospective phase 
Homogenized 
Demographic characteristics (age, 
gender, height, body weight, ethnicity, 
family history of cancer, Smoking 
History, asbestos exposure, alcohol 
consumption, drug use history). 
✓ 
✓ 
Medical history (personal medical 
history of cancer and other severe 
chronic diseases, allergies and 
concomitant medications) 
✓ 
✓ 
Health-economic details (site of 
treatment administration, lost 
workdays, etc.) 
If available 
✓ 
Lifestyle factors (diet and use of 
supplements, physical activity, daily 
heart rate, sleep patterns, QoL) 
 
✓ 
Clinical findings: 
- 
Performance Status (ECOG), 
Vital signs (blood pressure, 
oxygen saturation, (BP, 
SpO2), Temp, breaths/min, 
pulse). 
- 
Main symptoms leading to 
diagnosis, main symptoms at 
initiation of immunotherapy. 
Presence of main disease-
related symptoms (shortness 
of breath, cough, pain and 
fatigue). 
✓ 
✓ 

 
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- 
Body weight/height/BMI/BSA 
and weight loss prior to 
diagnosis and prior to 
initiation of IO (kg/months). 
- 
Major findings at clinical 
examination at diagnosis. 
Laboratory findings: Hb, WBC, 
Neutrophils, Lymphocytes, NLR 
(neutrophil/lymphocyte ratio) at IO 
initiation and post-treatment change in 
NLR, Platelets, Albumin, LDH, 
Ferritin, Creatinine, Creatinine 
clearance, Na, ALP, Glucose, CRP. 
✓ 
✓ 
Tumor characteristics: Histology, 
Grade, Ki 67, mutational status (e.g. 
EGFR, BRAF KRAS, ALK, ROS1) 
and PD-L1 expression, TNM stage, 
Sites/organs of metastases. Time from 
initial diagnosis to first metastatic 
disease. 
✓ 
✓ 
Treatment Characteristics: 
- 
previous treatments, previous 
platinum use and response, 
- 
1st line treatment combination 
(IO vs CT+IO or other IO 
combinations). 
- 
Regimen duration, number of 
cycles, details of maintenance 
treatment. Best response to 1st 
line. Duration of response, 
PFS, OS. 
- 
Toxicity details (type of 
toxicity, grade). Use of 
supportive medication (growth 
factors, anti-emetics). 
✓ 
✓ 
Radiomics 
✓ (CT +PET, the later 
when available) 
✓ (CT + PET scan) 
Immunohistochemistry data (e.g., 
CD3, CD4, CD8, CD20, CD11c, 
CD68, PD-1, PD-L1 and TIGIT) 
 
✓ 
Citomic: Immune phenotype and 
single cell analysis of circulating 
populations (i.e. PBMCs, PMNs) 
 
✓ 
Cytokines and MicroRNA signature 
(MSC test) 
 
✓ 
Cell free DNA and ctDNA 
sequencing 
 
✓ 

 
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The clinical/synthetic data will be collected within I3LUNG web-based platform. To each patient 
will be assigned a code. The CRF form will be filled in from each center with a dedicated account 
and all these workflows will be addressed using the Data Management Plan. 
In particular, for the prospective phase the following multi-OMICs data will be collected for all 200 
patients with available tissue and blood. For details see Table 2.  
 
 
 
Omics type 
Collection 
Analyses 
Tissue-based 
(Tasks 3.2, 3.3, 
3.4) 
Formalin-Fixed Paraffin 
Embedded (FFPE) tissue blocks 
from NSCLC patients who 
underwent surgery or large 
biopsy will be collected. Every 5 
collected samples, FFPE blocks 
will be shipped to GHD. GHD 
will then extract samples for 
immune profiling/TME and will 
prepare 4 total cores, 2 for 
genomic profiling and 2 for 
metabolomics. 
· 
TME and Tissue Immune Profiling - 
Reference tissue spots with known 
expression of the target proteins will 
be added to each microenvironment 
Tissue Microarrays (ME-TMA) 
· 
Genomic profiling (DNA and RNA 
extraction) and transcriptomics 
profiling. 
Metabolomics, metabolite extraction for 
targeted and untargeted metabolomics 
analysis 
Genomics analysis: tumor mutational 
burden (TMB), somatic copy number 
alterations, whole genome sequencing, 
immune infiltration transcriptomic 
profile 
 
✓ 
Metabolomics and Microbiome 
 
✓ 
Commentato [CP1]: Monica please check the tubes of WB.  

 
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Blood-based 
(Task 3.5) 
 
(NB: PNMs isolation 
and Single Cell 
analysis and 
will be performed 
only in INT, because 
PNMs lability will 
not permit freeze-
thaw). 
30 ml of WB samples will be 
collected from each patient only 
at baseline therapy. 
1x2 ml WB tube will be used for 
DNA extraction 
1x2 ml K2EDTA for cytokines 
assay 
2x10 and 1x6 ml K2EDTA 
tubes for PBMC, PNMs, 
miRNA Signature Classifier 
(MSC) and cell-free DNA 
(cfDNA) 
· 
Circulating Immune profiling PBMCs 
by flow cytometry staining. 
Assessment of membrane or 
intracellular markers (T-cells, T-
regulatory cells, natural killer cells, 
monocytes, neutrophils, myeloid 
derived suppressor cells, B-cells and 
T-cell exhaustion, immune 
suppression, activation, proliferation, 
cytotoxicity, polarizations) 
· 
Single cell analysis – T-cell receptor 
RNA sequencing 
· 
Cytokines plasma analysis 
· 
MSC test - panel of lung cancer 
related miRNAs will be measured in 
plasma samples 
· 
cfDNA and ctDNA sequencing. 
Microbiota 
analysis 
(Task 3.6) 
Baseline IO stool samples of all 
prospective patients enrolled 
will be collected and shipped to 
INT each 10 samples for 
microbiome analysis. 
 
· 
NGS libraries sequencing 
· 
Metagenomic DNA and RNA 
extraction 
Organism-specific gene hits will be 
assigned to the Kyoto Encyclopaedia of 
Genes and Genomes (KEGG) Orthology 
(KO) DB, metagenomes for each sample 
will be reconstructed into metabolic 
pathways 
Advanced 
Imaging 
(Task 3.7) 
CT scans in both in 
retrospective and prospective 
study phase and PET-CT in 
prospective phase scans are 
taken to perform the radiomic 
signature. The images will be 
anonymized and uploaded in a 
dedicated platform in order to be 
centralized to BGU. 
 
· 
Tumour and lymph nodes/metastasis 
manual segmentation, and interactive 
segmentation, using algorithms of 
varying complexities, already 
evaluated. 
· 
Extraction of four classes of image 
features describing tumour features: i) 
intensity, ii) shape, iii) texture and iv) 
Fourier or wavelet features (also 
MTV and TLG for PET-CT). 
Radiomic signatures: unsupervised 
clustering to reveal clusters of patients 
with similar radiomic expression patterns 
 
QoL Monitoring 
Within the prospective part of the study, among all the prospective enroller centers (INT, GHD, MH, 
VHIO and SZMC) we will evaluate whether making decisions using our prediction system would lead 
to an improvement in patient’s QoL using (i) the ad-hoc developed sensor- based monitoring system 
of patients’ QoL and standardized PROMs and PREMs (ii) if co-shared decision-making impact QoL 
at least +10% compared to published prospective study data (Figure 6); 
App Mobile 

 
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Sensor-based monitoring system of QoL This task foresees the development of a mobile application 
for the transparent monitoring of the patient’s quality of life. It will include: an automatic cough 
detector system, an assessment of patient’s mood state and emotional functioning, a human activities 
and gait recognition system and an evaluation of smartphone usage to monitor changes of social 
interactions and habits. Smartphone-based features collected daily will be correlated with clinical 
outcomes (e.g., monitoring toxicity and clinical benefit) collected at specific time points during the 
prospective clinical study in order to evaluate their capability to predict temporal variations of QoL, 
physical, social, and emotional functioning. Data collection within preliminary controlled studies are 
foreseen in this task to validate the app accuracy. 
Using self-reported QoL measures to identify trajectories over time 
Patients’ lifestyle factors, daily habits, and quality of life will be collected at baseline and at baseline 
IO at 3, 6, 9 and 12 months and when progressive disease occurs through validated and self-reported 
measures. Physical activity, metabolic equivalent of task, and QOL will be assessed with the WHO 
Global Physical Activity Questionnaire. Moreover, patients will be administered with reliable, valid, 
and standardized cancer QoL measures such as EORTC QLQ-C30 , EQ-5D and lung cancer-specific 
QoL measures such as QLQ-LC13. 
Image analysis methodology 
The analytical process for the medical images will include: 
 
Data acquisition 
 
Image segmentation 
 
Analysis and features extraction 
 
Data acquisition 
Digital Imaging and Communications in Medicine (DICOM) medical images will be exported from 
the PACS system pseudonymized and uploaded to the central cloud server for Radiomics analysis. 
Image segmentation 
The pseudonymized DICOM images will be process through automatic segmentation by artificial 
intelligence using deep learning algorithms to recognize the lung field and automatically detect and 
segment the lung tumor lesions. 
An independent qualified radiologist will centrally review the results and performance of the tumor 
segmentation algorithm. 
Image features extraction 
The study will extract and analyze a list of radiomics features according to the Image Biomarker 
Standardization Initiative (IBSI) recommendations. Features extracted from the segmented lesions 
will characterize different properties of the images, including: 
i) 
pixel intensity, 
ii) 
lesion shape, 
iii) 
lesion texture, 
iv) 
Fourier or wavelet-based features. 

 
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The parameters set obtained by RFE will be used to create radiomic signature following three steps: 
i) 
identification of cluster and possible expression patterns; 
ii) 
evaluation of relationship between these patterns and tumor/patient’s; 
iii) 
generation of radiomic signature; 
To remove redundancy within the radiomic information, one or two best-performing radiomic 
feature from each of the four-feature groups will be selected and combined into a multivariate Cox 
proportional hazards regression model for prediction of survival. For the training phase, feature 
stability will be assessed in inter-observer settings. 
The radiomic signature will be developed in the first 1000 CT and PET-CT images and will be then 
validated in the ~1000 remaining patients. The generated and validated radiomic signature will be 
integrate to the R-model generated to improve its prediction and make the R-model. 
Radiomic Signature 
To assess the value of radiomic features to capture phenotypic differences of tumors, the features 
obtained by the RFE will be subjected to unsupervised clustering to reveal possible clusters of 
patients with similar radiomic expression patterns. For PET-CT metabolic tumor volume (MTV), 
total lesion glycolysis (TLG) and radiomics features such as volume and heterogeneity will be added 
to the analysis of PET-CT images. 
The main clusters of patients will be related with other collected parameters, looking for association 
with tumor stage, histology etc. Finally, the generated radiomics signature will be validated and then 
integrated in the PDSS tool to improve prediction. 
 
Artificial intelligence methodology 
I3LUNG will develop a Data Sharing and Elaboration Platform (DSEP) that gathers and manages 
virtual entities, namely patient AVATARS, corresponding to real patients, and will provide a set of 
tools that integrate physician expertise and the knowledge extracted from the patient avatars data to 
create individualized predictions for NSCLC patients treated with IO. As depicted in Figure 2, data 
is gathered from the DB and a context is generated for each patient, which, in its turn, is fed to the 
ML models to make decisions. 
 
Figure 2. A summary of the ML pipeline we will adopt in the I3LUNG project. 

 
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Patient data collected through the  I3LUNG platform will be utilized to prototype, train, and host 
validated ML predictive algorithms to help with prognosis and IO response predictions. A careful 
ML data preparation for each of the data types (real world, radiomics) is essential to build both 
independent and merged models. ML informed features will be derived from the raw data types. 
Explainability of the AI Models 
The aims of the Explainable AI (XAI) are to produce more explainable models while maintaining a 
high level of learning performance (e.g., prediction accuracy) and to enable humans to understand, 
appropriately trust, and effectively manage the emerging generation of AI partners. 
In I3LUNG, a suite of XAI techniques will be used to make ML results interpretable and available to 
both clinicians and patients, comparing results with HI, so to support the clinical decision for the 
best IO-treatments for NSCLC, and the sharing of decisions with aware patients. XAI 
methodologies will be adopted and applied to the developed AI algorithms, such as the use of text, 
visual and local explanations, explanations by examples, and feature and 
input relevance (37). Moreover, simplifications to the model will be applied; for example, the 
creation of new models, based on the trained one, where part of the complexity is neglected without 
decreasing in the accuracy. 
 
 
Figure 3. XAI methodology approaches. 
Sample size 
and statistical 
analysis and 
AI analysis 
plan 
 
For the retrospective phase, we have planned to enroll patients based on a first check availability of 
already treated patients with IO-based therapy among centres.  
The primary objective of the study is to compare the predictive accuracy of ML methods (created 
using multi-OMICs analyses) with current validated biomarkers (e.g., PD-L1 or available scores, see 
Table 2). Considering the number of 200 prospective cases, the study will have at least 85% power to 
detect a 0.1 increase in predictive accuracy as measured by the area under the ROC curve (AUC), 
assuming an AUC for validated biomarker alone of at least 0.6, using a one-sided z-test at a 
significance level of 0,050. The data are assumed to be discrete (rating scale) responses. Stratification 
Factors: Country (Italy or others), Gender (male vs females), ECOG PS (0-1 vs 2), Histology 

 
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(squamous vs non-squamous), Stage (III vs IV), PD-L1 status, and treatment administered (Chemo-
Immuno vs immuno-alone vs others IO combinations) will be considered. 
AI analysis plan 
After Ethical approval in each Cancer Center, the third part of the study will start at month 3 and will 
continue up to 36 months. These periods will allow sufficient time for prospective enrolment and  
follow-up of patients for at least for 18 months. At month 30, an intermediate version of the P-Model 
will be released after the collection of the first batch (~100) of patients including multi-OMICs  data. 
At months 54, after the predictive models are build using all the data from 200 patients, the final P-
Model will allow to verify if: (i) new predictive system using multi-OMICs  results perform better 
compared to current ones (e.g., PD-L1 or other state of the art biomarkers), (ii) if the model using -
omics data perform better compared to the R model, (iii) if there is any specific -omics analysis which 
perform better compare to other -omics, which would give more value to the model (iv) if the P-Model 
perform better compared to current biomarker, and compare to the R-model when applied to specific 
subgroups of patients (e.g., non-squamous vs squamous patients). We will consider this phase 
successful if the new P-models outperforms current biomarkers prediction at least for 10% in terms 
of correct prediction of either 2-year PFS and 2-year OS, measured with Harrel’s C statistic. 
 
Ethical 
considerations  
and Informed 
Consent 
The study is conducted in accordance with the Declaration of Helsinki Ethical Principles and Good 
Clinical Practices and was approved at each site by an independent local ethics committee. 
During the clinical visit, after receiving adequate study information, the patient may sign an informed 
consent form, in accordance with the provisions of the local ethical regulations. regulations.  The 
consent of the patient must be signed and personally dated by the patient and by the 
investigator/person designated to conduct the informed consent discussion. The signed and dated 
declaration of informed consent will remain at the researcher site and must be safely archived by the 
investigator so that the forms can be retrieved at any time for monitoring, auditing and inspection 
purposes. A copy of the signed and dated information and consent should be provided to the patient 
prior to participation. 
In the retrospective part of the study, in case it is not possible to ask and obtain for the informed 
consent (due to death or inability to reach the patients), to process the patient data and samples, for 
ethical-administrative reasons, the study falls within the scope and purposes specified by the 
Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the 
protection of natural persons with regard to the processing of personal data and on the free movement 
of such data, and repealing Directive 95/46/EC (General Data Protection Regulation), in its article 
89.1. 
 
All clinical centers will be involved in the retrospective phase, while in the prospective phase only 5 
out 6 centers will participate (INT, GHD, VHIO, MH and SZMC). The study will involve exporting 
of CT and PET images from both retrospective and prospective studies from EU cancer centers and 
US to Israel, while clinical data will be exported from all the EU cancer centers and Israel to US for 
AI analyses. Biological samples (tissue, blood and stool samples) will be collected from SZMC 
(Israel) and will be imported in EU countries (Italy, Germany, Spain) for pathology, genomic, 
circulating and microbiome analyses. 
References 
1. 
Globocan (2018) Available from http://globocan.iarc.fr/Default.aspx (Accessed on 25-Nov- 
2020).  

 
OBSERVATIONAL STUDY 
PROTOCOL 
MOD-PRO-G-03-
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rev. n°1 del 22/06/2022 
 
 Pag. 16 di 18 
 
2. 
 Howlader N, et al. SEER Cancer Statistics Review, 1975−2012. National Cancer Institute, 
2015 [resource on the Internet]. Available from: 
http://seer.cancer.gov/csr/1975_2012/http://seer.cancer.gov/csr/1975_2012/. 
3. 
Sulpher JA, et al. Factors influencing a specific pathologic diagnosis of non-small-cell lung 
carcinoma. Clin Lung Cancer. 2013 May;14(3):238-44. 
4. 
Siegel RL,et al. Cancer Statistics, 2017. CA Cancer J Clin 2017; 67:7−30. 
5. 
Lynch TJ, et al. Activating mutations in the epidermal growth factor receptor underlying 
responsiveness of non-small-cell lung cancer to gefitinib. N Engl J Med 2004; 350: 2129-39. 
6. 
Rosell R, et al. Screening for epidermal growth factor receptor mutations in lung cancer. N 
Engl J Med 2009; 361: 958-67. 
7. 
Fukuoka M, et al. Biomarker analyses and final overall survival results from a phase III, 
randomized, open-label, first-line study of gefitinib versus carboplatin/paclitaxel in clinically 
selected patients with advanced non-small-cell lung cancer in Asia (IPASS). J Clin Oncol 2011; 29: 
2866-74. 
8. 
Maemondo M, et al. Gefitinib or chemotherapy for non-smallcell lung cancer with mutated 
EGFR. N Engl J Med 2010; 362: 2380-88. 
9. 
Mitsudomi T, et al. Gefitinib versus cisplatin plus docetaxel in patients with non-small-cell 
lung cancer harbouring mutations of the epidermal growth factor receptor (WJTOG3405): an open 
label, randomised phase 3 trial. Lancet Oncol 2010; 11: 121-128. 
10. 
Rosell R, et al. Erlotinib versus standard chemotherapy as first-line treatment for European 
patients with advanced EGFR mutation-positive nonsmall- cell lung cancer (EURTAC): a 
multicentre, open-label, randomised phase 3 trial. Lancet Oncol 2012; 13: 239-46. 
11. 
 Sequist LV, Yang et al. Phase III study of afatinib or cisplatin plus pemetrexed in patients 
with metastatic lung adenocarcinoma with EGFR mutations. J Clin Oncol 2013; 31: 3327-34. 
12. 
Wu YL, et al. Afatinib versus cisplatin plus gemcitabine for first-line treatment of Asian 
patients with advanced non-small-cell lung cancer harbouring EGFR mutations (LUX-Lung 6): an 
open-label, randomised phase 3 trial. Lancet Oncol 2014; 15: 213-22. 
13. 
 Zhou C, et al. Erlotinib versus chemotherapy as first-line treatment for patients with 
advanced EGFR mutation-positive non-small-cell lung cancer (OPTIMAL, CTONG-0802): a 
multicentre, open-label, randomised, phase 3 study. Lancet Oncol 2011; 12: 735-42. 
14. 
Soria JC, et al. Osimertinib in Untreated EGFR-Mutated Advanced Non–Small-Cell Lung 
Cancer.  N Engl J Med 2018; 378:113-25 
15. 
Mok TS, et al. Osimertinib or platinum–pemetrexed in EGFR T790M–positive lung cancer. 
N Engl J Med 2017;376:629-40. 
16. 
Sullivan I, Planchard D. ALK inhibitors in non-small cell lung cancer: the latest evidence 
and developments. Ther Adv Med Oncol 2016;8:32-47.  
17. 
 Karachaliou N, , et al. Anaplastic lymphoma kinase inhibitors in phase I and phase II 
clinical trials for non-small cell lung cancer. Expert Opin Investig Drugs 2017;26:713-22.  

 
OBSERVATIONAL STUDY 
PROTOCOL 
MOD-PRO-G-03-
DSC-10 
 
rev. n°1 del 22/06/2022 
 
 Pag. 17 di 18 
 
18. 
Pardoll DM. The blockade of immune checkpoints in cancer immunotherapy. Nat Rev 
Cancer 2012;12:252–64.  
19. 
Hathcock KS, et al. Identification of an alternative CTLA-4 ligand costimulatory for T cell 
activation. Science 1993; 262:905–7. 
20. 
Thompson RH, et al. Indicator of tumor aggressiveness and potential therapeutic target. Proc 
Natl Acad Sci U S A 2004;101:17174–9.  
21. 
Borghaei H, et al. Nivolumab versus  docetaxel in advanced nonsquamous non-small-cell 
lung  cancer. N Engl J Med. 2015; 373: 1627–39. 
22. 
Brahmer  J,  et al.  Nivolumab versus docetaxel in advanced squamous-cell  non-small-cell 
lung cancer. New Engl J Med 2015 
23. 
Herbst RS, et al. Pembrolizumab versus  docetaxel for previously treated, PD-L1-positive, 
advanced  non-small-cell lung cancer (KEYNOTE-010): a randomised  controlled trial. Lancet. 
2016; 387: 1540–50. 
24. 
Fehrenbacher  L,  et  al.  Atezolizumab  versus  docetaxel  for  patients  with  previously 
treated non-small-cell lung cancer (POPLAR): a multicentre, open-label, phase 2 randomised 
controlled trial.  Lancet. 2016; 387: 1837–46. 
25. 
Reck M, et al. Pembrolizumab versus Chemotherapy for PD-L1-Positive Non-Small-Cell 
Lung Cancer. N Engl J Med 2016;375:1823–33.  
26. 
Antonia SJ, et al. Durvalumab after Chemoradiotherapy in Stage III Non–Small-Cell Lung 
Cancer. N Engl J Med 2017; 377:1919-29. 
27. Gandhi L, et al. Pembrolizumab plus Chemotherapy in Metastatic Non-Small-Cell Lung Cancer. 
N Engl J Med 2018;378:2078-92. 
28. Paz-Ares L, et al. Durvalumab plus platinum-etoposide versus platinum-etoposide in first-line 
treatment of extensive-stage small-cell lung cancer (CASPIAN): a randomised, controlled, open-
label, phase 3 trial. Lancet 2019;394:1929-39. 
29. 
Brahmer JR, et al: five-yer follow-up from the CA209-003 study of nivolumab in previously 
treated advanced non small cell lung cancer: clinical characteristics of long-term survivors. 
Presented at 2017 AACR Annual meeting Washington, DC, USA, 1-5 April 2017. 
30.    Adashek JJ,  et al. Hyperprogression and Immune Checkpoint Inhibitors: Hype or Progress?   
Oncologist 2020; 25(2):94-98. doi: 10.1634/theoncologist.2019-0636. 
31.   Champiat Set al. Hyperprogressive disease: recognizing a novel pattern to improve patient 
management.  Nat Rev Clin Oncol. 2018;15(12):748-762 
32.  Lo Russo G, et al. Antibody-Fc/FcR Interaction on Macrophages as a Mechanism for 
Hyperprogressive Disease in Non-small Cell Lung Cancer Subsequent to PD-1/PD-L1 Blockade 
Clin Cancer Res 2019; 25(3):989-999. 
33.  Ferrara R, et al. Hyperprogressive Disease in Patients With Advanced Non-Small Cell Lung 
Cancer Treated With PD-1/PD-L1 Inhibitors or With Single-Agent Chemotherapy JAMA Oncol 
2018;4:1543–52.  

 
OBSERVATIONAL STUDY 
PROTOCOL 
MOD-PRO-G-03-
DSC-10 
 
rev. n°1 del 22/06/2022 
 
 Pag. 18 di 18 
 
34. Champiat S, et al. Hyperprogressive Disease Is a New Pattern of Progression in Cancer Patients 
Treated by Anti-PD-1/PD-L1. Clin Cancer Res 2017;23:1920–8. 
35. Sun, Z. et al. Radiomics study for predicting the expression of PD-L1 in non-small cell lung 
cancer based on CT images and clinicopathologic features. J. X-Ray Sci. Technol. J Xray Sci 
Technol. 2020;28(3):449-459. 
36. Polverari, G. et al. 18F-FDG Pet Parameters and Radiomics Features Analysis in Advanced 
Nsclc Treated with Immunotherapy as Predictors of Therapy Response and Survival. Cancers. 
2020;12(5):1163. 
37. Miller, T. Explanation in artificial intelligence: Insights from the social sciences. Artif. Intell. 
2019;267:1–38. 
36. Conforti, F. et al. Cancer immunotherapy efficacy and patients’ sex: a systematic review and 
meta- analysis. Lancet Oncol. 2018;19:737–746. 
37. Triberti, S., Durosini, I. & Pravettoni, G. A “Third Wheel” Effect in Health Decision Making 
Involving Artificial Entities: A Psychological Perspective. Front. Public Health 8, (2020) 
38. Stacey D, Légaré F, Lewis K, Barry MJ, Bennett CL, Eden KB, et al. Decision aids for people 
facing health treatment or screening decisions. Cochrane Database Syst Rev. 2017;4:CD001431 
39. Søndergaard, S. R., Madsen, P. H., Hilberg, O., Jensen, K. M., Olling, K., & Steffensen, K. D. 
(2019). A prospective cohort study of shared decision making in lung cancer diagnostics: impact of 
using a patient decision aid. Patient Education and Counseling, 102(11), 1961-1968. 
40. Hoffman, A. S., Hempstead, A. P., Housten, A. J., Richards, V. F., Lowenstein, L. M., Leal, V. 
B., & Volk, R. J. (2018). Using a patient decision aid video to assess current and former smokers’ 
values about the harms and benefits of lung cancer screening with low-dose computed tomography. 
MDM policy & practice, 3(1), 2381468318769886. 
41. Shay LA, Lafata JE. Where is the evidence? A systematic review of shared decision making and 
patient outcomes. Med Decis Mak. 2014;35:114–31.