APOLLO 11, Consortium of Italian Centers Involved in Treatment of Patients With Lung Cancer Treated With Innovative Therapies: Real World Data and Translational Reaserch
Conditions: NSCLC, Cancer, Lung Cancer, Adenocarcinoma of Lung, Squamous Cell Lung Cancer
Sponsor: Fondazione IRCCS Istituto Nazionale dei Tumori, Milano

  
Observational Study Protocol 
Versione n.  1.0, 23_May_2022 
Firma del PI: Arsela Prelaj 
  
Titolo 
dello 
studio 
APOLLO 11, CONSORTIUM OF LUNG CANCER PATIENTS 
TREATED WITH INNOVATIVE THERAPIES: INTEGRATION OF 
REAL WORLD DATA AND TRANSLATIONAL RESEARCH 

  
Rational 
BACKGROUND 
Lung cancer is the leading cause of cancer-related mortality worldwide. The global 
incidence in 2018 was an estimated 2 million cases with  1.7 million deaths predicted 
(1). A 70% increase in the number of new cases of lung malignancy is expected over the 
next 2 decades (1). There are two major subtypes  of lung cancer: small cell lung cancer 
(SCLC) and non-small cell lung cancer (NSCLC), accounting for approximately 15-20% 
and 80-85% of total cases, respectively (2). Within NSCLC, 45-65% is represented by 
adenocarcinomas, 15-25% by squamous cell carcinomas, 5% by large cell 
neuroendocrine carcinomas, and the remainder by "not otherwise specified" carcinomas 
(3). 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 
(4). 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 better understanding of cell biology has led to the identification of 
specific genetic alterations, known as "driver" alterations, at the basis of tumorigenesis, 
including mutations of EGFR, KRAS, BRAF and MET, and rearrangements of ALK, 
ROS1, RET genes. The use of "target therapies" is now the treatment choice in this 
subgroup of patients. About 15% of NSCLC harbour somatic mutations in EGFR. 
Tyrosine kinase inhibitors (TKIs) such as gefitinib, erlotinib, and afatinib resulted in 
significantly improved progression-free survival (PFS) compared with standard 
chemotherapy and were initially  indicated as first-line therapy (6-13). More recently, 
osimertinib, a 3rd generation drug, has reported, compared with other TKIs, a significant 
advantage in terms of PFS and OS and has become the new first-line standard (14).  In 
light of its superiority over chemotherapy in the AURA3 trial (15), osimertinib also 
represents the second-line standard in patients with T790M-mediated acquired resistance 
to first-line therapy with first- and second-generation EGFR-TKIs (approximately 60% 
of cases). The ALK gene is shown to be rearranged in 5% of NSCLC cases. The first 
agent approved for ALK-positive NSCLC was crizotinib. Following initial disease 
control almost all patients inevitably develop resistance within approximately 12 months 
with disease progression predominantly in the brain (16). Therefore, the development of 
next-generation ALK inhibitors has been encouraged. To date, ceritinib, alectinib, 
brigatinib and lorlatinib have received approval from the Food and Drug Administration 
(FDA) and/or the European Medicines Agency (EMA), and the Agenzia Italiana del 
Farmaco (AIFA), for the treatment of ALK rearranged NSCLC demonstrating an 
enhanced ability to penetrate through the blood-brain barrier. Moreover, in ongoing 
clinical trials othe novel ALK-TKIs have provided promising preliminary data in terms 
of both clinical activity and safety (17).  
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 
then destruction by the immune system. By blocking these inhibitory pathways, which 
physiologically control the immune response, they reactivate and sustain the immune 
system against tumor cells (18). In particular, cytotoxic T-lymphocyte-associated-4 
(CTLA-4) programmed cell death protein 1 (PD-1) are receptors expressed on T cells 
that, by interacting with CD80/CD86 (19) and programmed cell death protein 1 (PD-L1) 
or PD-L2 ligands, respectively (20), are able to promote and foster immune evasion by 
tumor cells. Several ICIs, capable of blocking PD-1/PD-L1 and CTLA-4 inhibitory 

Objectives 
and clinical 
relevance  
  
OBJECTIVES 
Primary objectives: 
1.     To create a network of Italian Lung Cancer Centers (APOLLO 11 
Consortium) through: 
a.     The development of a National Real World distributed database 
assets for lung cancer patients (NSCLC and SCLC) treated with 
INNOVATIVE THERAPIES (immunotherapy, target therapy and 
next generation treatments). 
b.     Building the consortium, harmonizing all the relevant ethical, legal, 
and data protection issues among centers. 
c.     The development of a National “virtual multilevel biobank” in lung 
cancer patients through the creation of real local biobanks stored in 
each participating center. 
d.     The set-up and harmonization of the operative procedures for 
collection, storage and shipment of all the biological specimens 
foreseen. 
  
2.     To identify a specific factor or combinations of factors (i.e. mutations, 
transcript abundances, protein levels and immunological markers) 
influencing response, progression/hyperprogression, treatment resistance, 
survivals in lung cancer patients (NSCLC and SCLC).   
In particular in the first phase, we will focus our analysis in:  
-       Integrating retrospective and prospective multisource data collection of 
advanced NSCLC already treated or candidate to receive ICIs-based 
therapy to assist the creation of a predictive artificial intelligence (AI) 
model to improve response prediction, ultimately leading to better 
cancer patients survival and their Quality of Life. 
-       Characterizing the immune circulating profile by single cell analysis, 
FACS analysis, and TCR sequencing in order to correlate these 
variables with clinical outcome of NSCLC patients receiving first line 
ICIs treatment feeding the predictive machine learning algorithms.  
-       Collecting all the available ICIs baseline CT scans from retrospective 
and prospective aNSCLC patients treated with ICIs to create and 
validate a radiomic signature and use its value to refine the predictive 
algorithms.  
  
  
Further scientific objectives of APOLLO 11: 
1.     To evaluate the association of data on genomics, tumoural  and 
immunological microenvironment with clinical and pathological features of 
lung cancer (NSCLC and SCLC) treated with innovative therapies 
(immunotherapy, target-therapies and next-generation therapies). 
  

2.     To identify how specific factors or combinations of factors (tumoral  
microenviromental and immunological markers) may influence response, 
progression, atypical response (hyperprogression or pseudoprogression) and 
treatment resistance in lung cancer (NSCLC and SCLC).   
3.     To evaluate the prognostic role of these factors or combinations of factors 
in lung cancer (NSCLC and SCLC). 
4.     To assess possible changes over time of specific factors or combinations of 
factors (gene, biomolecular and immunological markers) in relation to the 
clinical evolution of the disease. 
5.     To specifically assess the role of metabolism in influencing tumour biology, 
the biology of the anti-tumour immune response and the activity of cancer 
therapies.  
6.     To evaluate the possible impact of clinical characteristics, comorbidities and 
concomitant therapies on oncological outcomes 
7.     To develop prognostic/predictive models of response/resistance to 
innovative treatments analyzing "big data" through artificial intelligence 
methods such as machine learning and deep learning in order to tailor 
treatments for patients with lung cancer and guide decision-making 
processes.  
8.    To use XAI approach to study decision-making systems (such as traditional 
AI and ML methodologies), with the aim of making them more 
interpretable, transparent, explainable, and reliable for clinicians by 
unfolding the black box of the AI algorithms. 
9.     To compare mutational and gene expression profiles identified in tumour 
tissue with those identified in peripheral blood. 
10.  To identify factors or combinations of factors predictive of response, 
resistance, disease progression in blood, urine, faeces and/or any other 
available biological material in lung cancer (NSCLC and SCLC). 
11.  To define the immunological mechanisms underlying immunorelated 
toxicities or toxicities derived from target therapies and possible biomarkers 
associated with their development and clinical course. 
12.  To define the mechanisms of secondary resistance to specific treatments 
used in lung cancer e.g., in patients with EGFR, ALK, ROS1, RET, BRAF, 
HER2, KRAS, MET exon skipping 14 and other possible driver alterations 
and their relative driver TKIs.  
13.  To identify new potential therapeutic targets at the level of any biological 
material. 
14.  To identify the association between meta-genomic profiles on stool, patients' 
immune phenotype and cancer outcome. 
  
CLINICAL RELEVANCE  
Firstly, creating an Italian network of hospitals with expertise in the treatment of 
advanced LC and facilitating the integration of real-world data and biological samples 

from multiple centers, in line with the credo “union is strength”, will avoid unnecessary 
dispersion of data from single institutions that are often inconclusive, due to the limit 
sample number. This real-world data collection will also help to understand real-world 
outcomes, which frequently are not the same compared to results upcoming from clinical 
trials. With the advent of several innovative therapies (target, ICIs and next-generation 
therapies), there is an increasing attention towards mechanisms potentially involved in 
tumor escape in both NSCLC and SCLC. APOLLO 11 network aims to identify and 
combine different types of markers (clinical, radiologic, genetic, molecular and 
immunological) in different histologies (NSCLC and SCLC)capable of predicting 
response to therapy, toxicity, relapse after curative intervention  or associated with 
primary and  acquired resistance. Moreover, the identification of gene expression, 
biomolecular and immunological profiles may allow an increased personalization of  
lung cancer (NSCLC and SCLC) treatment. The aforementioned analyses/activities will 
be carried out on the collected data in the local databases. Moreover, the sample collected 
and stored in each local biobank will be shared and used to achieve the primary 
objectives. Moreover, biological samples will be accessible over time as soon as 
dedicated funds are available to address the specific objectives described above and 
future research in the light of the evolution of scientific evidence.  

Study 
population 
  
Patients affected by lung cancer will be enrolled according to the following inclusion 
criteria: 
1. Provision of signed and dated written informed consent, when applicable, by the 
patient or legally acceptable representative prior to any mandatory study-specific 
procedures, sampling, and analyses;  
2. Patients must be ≥18 years of age at the time of signing the informed consent;  
3. Histologically or cytologically confirmed lung cancer (NSCLC or SCLC) at any stage 
(I-IV) candidate to receive or already treated with innovative therapies (ICIs, target 
therapies and next generation therapies). 
Patients must not enter the study if any of the following exclusion criteria are fulfilled:  
1.     Patients who are or will be taking other unapproved antineoplastic therapies 
concurrently are not eligible.  
2.     Patients who have not received an oncological systemic innovative therapy 
at any setting  
3.     Patients received only local treatments (e.g., only surgery, or only 
radiotherapy)  
4.     Patients who received only standard chemotherapy are excluded.  
5.     Patients who received treatment therapies before 2010 are excluded.  

  
Methods 
and study 
design 
  
This is a retrospective/prospective multicenter observational study that will enroll lung 
cancer patients (NSCLC and SCLC) treated with at least one innovative systemic 
treatment.  
  
DATA and MATERIAL COLLECTION (Figure 1) 
  
Real world data collection on the local databases (REDcap) 
Retrospective phase: all the involved centers will participate in the collection of clinical 
data that will be stored in the local database of each Center. This phase involves the 
collection of "real world" available jeopardized data (e.g., demographic, 
epidemiological, clinical, biochemical, biological, radiological including also data of 
treatments performed, survival, and toxicity outcomes) related to cases of lung cancer 
(NSCLC stage I - IV and SCLC-LD and ED) treated with innovative therapy (e.g., target 
therapy, immunotherapy and next generation therapies) since January 2010 at the 
moment of ethical committee approval if the patients have already started the innovative 
therapy.  
Prospective phase: each patient with new diagnosis of lung cancer (NSCLC stage I - IV 
and SCLC-LD and ED) candidate to receive an innovative therapy and able to sign the 
informed consent to the processing of personal data can be enrolled in the prospective 
phase. In this phase homogenized data will be collected from the date of the committee 
approval.  
The data will be entered into the local REDCAP database.  
  
Imaging collection: For both retrospectively and prospectively enrolled patients, CT, 
PET and MRI scans will be performed as per standard of care, locally stored and shared, 
with the same code assigned during enrollment, to achieve the described objectives as 
detailed in the data management plan.  The collection of these images will be performed 
at each innovative treatment at first radiologic evaluation, best response and radiological 
progression.  
  
Radiomics analysis: the first analysis will start on ICIs baseline CT scans of aNSCLC-
IO cohort. In the retrospective phase in around 1000 patients and prospectively in around 
200 patients. The CT scans from the participating sites will be de-identified in 
accordance with GDPR standards and encrypted prior to transmission to the central 
server for Radiomics analysis. A dedicated radiologist will analyze CT images and 3D 
tumour targets volume delineation will be manually determined for each patient and 
image segmentation will be performed. The main clusters of patients will be related with 
other collected parameters and the generated radiomics signature will be validated and 
then integrated with AI models. 
  
Biological samples collection for translational research  
The study foresees the annotation of samples already available for standard of care (i.e. 
FFPE tumor tissue) for retrospective enrolled patients in dedicated CRF. In case of 

previous other samples collection (e.g,. blood, feces etc) in this setting it is possible to 
record retrospectively these samples in the dedicated CRF.  
The study foresees the collection, preservation and annotations of biological samples 
(peripheral blood, stool, urine, saliva as per protocol and, when available, tumor tissue 
collected as per standard of care) for prospective enrolled patients.  
Each biological sample collected per protocol will be identified with the same code 
assigned to the patient when enrolled, with a label reporting also date of collection, and 
type of specimens. Moreover, these information and storage references will be entered 
into dedicated CRF.   
  
Tissue collection 
For both retrospective and prospective enrolled patients, cyto-histological tumor tissue 
collected for diagnostic purposes and during an innovative treatment per standard of 
care, will be annotated in the “virtual biobank” dedicated CRF. Two different types of 
storage are foreseen: 1) FFPE tissue sample 2) Frozen tissue, whenever possible.   
  
Blood, urine, saliva, stool Samples Collection 
For patients enrolled in the retrospective phase no blood specimens will be collected if 
the patients have already started the innovative therapy. A patient enrolled in the 
retrospective phase, could be also enrolled in the prospective phase, if he will start a 
subsequent innovative treatment. The same code patient will be assigned.  
  
For patients enrolled in prospective phase, blood, urine, saliva, stool samples will be 
collected during the patient's appointments and therapies already scheduled on site at 
different timepoints for each innovative treatment (when more than one innovative 
therapy is foreseen for a single patient): 1) baseline, when treatment starts, 2) after the 
first cycle, 3) at the first radiological evaluation and 4) at evidence of progressive 
disease. 
  
In particular, a total amount of 32 ml peripheral blood will be collected to obtain the 
following biological specimens for each timepoint.  
Details for pre-analytical procedures are stated in detail in the laboratory manual of 
APOLLO 11 biobank.  
  
  
  
  
  
  
  
  
  
   

  
 
   
Figure 1. Master Protocol long-term scientific objectives of APOLLO 11. 
 
 
Figure 2. Project design of the first analysis of the APOLLO 11 project. 
  

Sample size 
and 
statistical 
analysis 
  
Since the aim of the project is to identify prognostic/predictive factors of response, 
progression or resistance to treatment, for which incidence and prevalence data are not 
available to date, and in light of the multicenter participation in the study, the total 
number of patients who can be enrolled cannot be defined at present. About 48 IRCCS, 
general and academic hospitals from different Italian Regions will be involved in this 
study.. The study cohort for the first analysis will consist of around 1000 retrospective 
and 200 prospective aNSCLC-IO cases. The primary objective of this part is to compare 
the predictive accuracy of ML/DL methods (created using multisource data) with current 
validated biomarkers (e.g., PD-L1 or available scores). 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.  
  
Informed 
consent 
During the visit, after adequate information, the patient may sign an informed consent 
form, in accordance with the provisions of the regulations set out in Legislative Decree 
of 01 March 2012 no. 85, OJ General Series no. 72 of 26-3-2012, for the prospective 
collection of clinical data and conservation of biological samples. As it is impossible to 
ask for consent to process data from patients entered retrospectively, for ethical-
administrative reasons, the study falls within the scope and purposes specified by the 
Guarantor for the Protection of Personal Data (General authorization to process personal 
data for scientific research purposes and General authorization to process genetic data - 
15 December 2016 - Official Gazette no. 303 of 29 December 2016).