Adenocarcinoma of Lung, Cancer, Lung Cancer, NSCLC, Squamous Cell Lung Cancer
Conditions
Keywords
#NSCLC, #Immunotherapy, #Targeted Therapy, #Artificial Intelligence
Brief summary
APOLLO 11 main aim is to build a strong Italian long-lasting lung cancer network (in around 48 Italian centres) on real world data and translational research by creating a decentralized long-term national database (settle locally in each centre) and a virtual multilevel biobank in each centre. Besides, APOLLO 11 will take advantage of the translational research joint effort with the credo unity is strength.
Detailed description
With the advent of several innovative therapies (target therapies, immunotherapy (IO) and/or next generation therapies), the identification of prognostic/predictive and/or resistance biomarkers represents the main goals of translational research in advanced lung cancer (aLC). In particular, with the arrival of IO the treatment landscape of advanced Non-Small Cell Lung Cancer (NSCLC) patients (pts), have radically changed prolonging significantly the overall survival (OS). The role of Programmed Death Ligand 1 (PD-L1) remains undefined and to date no other biomarker are used to select patients for IO. Artificial Intelligence (AI) frameworks, which synthesize and correlate information from different data sources, is a potentially highly efficient instrument to construct algorithms reinforcing the individualized prediction. APOLLO 11 main aim is to build a strong Italian long-lasting lung cancer network (in around 48 Italian centres) on real world data and translational research by creating a decentralized long-term national database and a virtual multilevel biobank. This will allow to avoid worthless dispersion of single institution data, which often remaine unused and inconclusive due to limited number of patients and low statistical power. Besides, APOLLO 11 will take advantage of the translational research joint effort with the credo unity is strength. The multilevel structure is designed also for smallest centres of give them possibility to contribute to biobank looking at their internal facilities. APOLLO 11 have the goal to establish the consortium by fully activating at least 20 of the 48 involved centres able to collect data within the database and to guarantee the activation ready for at least 10 biobanks able to collect samples (tumour tissue, blood, stool and urine). Moreover, the key scientific aim of the project is to provide answers about aNSCLC pts treated with IO. In particular, try to find a software biomarker able to better predict IO selection compare to standard of care single biomarker (e.g., PD-L1, TMB) using artificial intelligence/machine/deep learning (AI/DL/ML) methodologies. Real world (demographic, histology, PD-L1, molecular, treatment and outcomes information etc), radiomics and where available (from previous researches) multiomic data from all the activated centres will be collected. Furthermore, single-cell RNA sequencing (scRNA seq) analysis will be carry out. Finally, AI/ML/DL will be used to integrate the multisource data, analyse them and develop predictive algorithms of IO efficacy in NSCLC pts. Explainable trustworthy AI (XAI) methodology will be used in order to better understand black box for explaining clinical and biological insight.
Interventions
None listed
Sponsors
Politecnico di Milano
Ospedale Policlinico San Martino
Insieme per i Pazienti di Oncologia Polmonare Onlus (IPOP)
ASST Grande Ospedale Metropolitano Niguarda
University of Campania Luigi Vanvitelli
Azienda Ospedaliera dei Colli
Fondazione IRCCS Policlinico San Matteo di Pavia
Azienda Ospedaliera S. Maria della Misericordia
Azienda Ospedaliera Sant'Andrea
Azienda Ospedaliero Universitaria, Santa Maria della Misericordia di Udine, Italy
IRCCS Sacro Cuore Don Calabria di Negrar
Azienda Ospedaliero, Universitaria Ospedali Riuniti
Istituto Clinico Humanitas
Azienda Unità Sanitaria Locale della Romagna
Istituti Ospitalieri di Cremona
Fondazione IRCCS Istituto Nazionale dei Tumori, Milano
Study design
Observational model
COHORT
Time perspective
OTHER
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
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).
Exclusion criteria
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Response Rate | About 8 weeks (first radiological evaluation) | Prediction of response to innovative therapies in NSCLC patients |
| Progression-Free Survival | From data of enrollment until the date of first documented progression or date of death from any cause, whichever come first, assessed up to 120 months | Prediction of Progression-Free Survival in NSCLC patients treated with innovative therapies |
| Overall Survival | From data of enrollment until the date of death from any cause, assessed up to 120 months | Prediction of Overall Survival in NSCLC patients treated with innovative therapies |
Countries
Italy
Contacts
Primary ContactArsela Prelaj, M.D.
Outcome results
None listed