APOLLO11: a bio-data-driven model for clinical and translational research in lung cancer

Identifying predictive and resistance biomarkers remains one of the most relevant unmet needs in clinical cancer research. Artificial Intelligence (AI) represents a powerful tool to develop predictive algorithms tailored to individual patients. Thanks to its ability to process large quantities of heterogeneous, patient-level information, the AI-based approach is progressively fostering the growth of a data-driven paradigm to complement traditional, hypothesis-driven clinical research. However, the development of reliable AI models requires access to large, high-quality, and continuously updated datasets. Despite this necessity, no infrastructure currently exists to enable federated, multi-omic, standardized, prospective, and large-scale collection and analysis of real-world clinical and biological data in the context of lung cancer. We established the APOLLO11 consortium, a distributed, nationwide, updated Italian lung cancer network designed to build a decentralized, long-term, population-based, real-world data repository and a multilevel biobank, locally stored and centrally annotated. This strategy seeks to lay the foundation for the clinical implementation of data-driven research, ultimately advancing precision oncology.

Authors

Institutions

Publication Details

Journal
npj Precision Oncology
Published
2026-01-29
DOI
https://doi.org/10.1038/s41698-026-01295-3
Citations
1
Primary Topic
Lung Cancer Treatments and Mutations
Type
article
Field-Weighted Citation Impact
36.44
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

APOLLO11: a bio-data-driven model for clinical and translational research in lung cancer

M. Russano, Federica Buzzacchino, Andra Diana Dumitrascu, Giulia Galli et al.
1 citations
npj Precision Oncology
Lung Cancer Treatments and Mutations
36.44
article

APOLLO11: a bio-data-driven model for clinical and translational research in lung cancer

M. Russano, Federica Buzzacchino, Andra Diana Dumitrascu, Giulia Galli, A. Spagnoletti, Maria Silvia Cona, Claudia Bareggi, Luca Toschi, Margherita Ruggirello, Luca Agnelli, Mario Occhipinti, Stefania Gori, Elio Gregory Pizzutilo, Chiara Cavalli, Sabina Sangaletti, Concetta Licciardello, Federica Corso, Daniela Miliziano, Laura Mazzeo, Grisanti Salvatore, Monica Ganzinelli, Rebecca L. Romano, Giovanni Apolone, Marta Brambilla, V. Miskovic, Matteo Brighenti, Roberta Serino, C. Agosta, Giuseppe Viscardi, Noemi Salmistraro, Claudia Giani, Silvia Taverna, Paolo Ambrosini, C Genova, Alessandro Leonetti, Daniele Lorenzini, Elisa Sottotetti, Francesco Verderame, Alfonso Marchianò, Giulia Corrao, Emanuela Zito, Maria Gemelli, Giovanni Scoazec, A. Ferrarin, Antonella Panzardi, Emanuele Claudio Mingo, Cecilia Silvestri, Michele Aieta, Giulia Barletta, Filippo G. De Braud, A. Zec, Rosa Maria Di Mauro, Rita Emili, Giorgia Di Liberti, Arsela Prelaj, Margherita Favali, C. Bonalume, B. Guirges, Paolo Baili, Giulio Metro, Rita Chiari, Teresa Beninato, Claudia Proto, Moreno Marino, Federica Bertolini, Alessandra Pedrocchi, Federica Biello, Carminia Maria Della Corte, Chiara Catania, Fabrizio Citarella, Alberto Servetto, Francesco Trovò, Giorgia Corbo, Chiara Bennati, Alessandro Guidi, Lorenzo Antonuzzo, Emanuele Vita, Giancarlo Pruneri, Andrea Vingiani, Saverio Cinieri, Antonino Belfiore, Marianna Macerelli, Piergiorgio Solli, Gianpaolo Spinelli, Nicla La Verde, Luigi Lacriola, Ghazal Farhikhteh, Andrea Riccardo Filippi, Roberto Ferrara, Rossana Berardi, Alfredo Tartarone, Michele Borraccino, Marco Meazza Prina, the APOLLO11 study group, Leonardo Provenzano, Alessio Cortellini, Diego Signorelli, Luca Invernizzi, Antonio Lugini, Cesare Fattoi, Giuseppe Lo Russo, Marina Chiara Garassino
article en
1 citations

Abstract

Identifying predictive and resistance biomarkers remains one of the most relevant unmet needs in clinical cancer research. Artificial Intelligence (AI) represents a powerful tool to develop predictive algorithms tailored to individual patients. Thanks to its ability to process large quantities of heterogeneous, patient-level information, the AI-based approach is progressively fostering the growth of a data-driven paradigm to complement traditional, hypothesis-driven clinical research. However, the development of reliable AI models requires access to large, high-quality, and continuously updated datasets. Despite this necessity, no infrastructure currently exists to enable federated, multi-omic, standardized, prospective, and large-scale collection and analysis of real-world clinical and biological data in the context of lung cancer. We established the APOLLO11 consortium, a distributed, nationwide, updated Italian lung cancer network designed to build a decentralized, long-term, population-based, real-world data repository and a multilevel biobank, locally stored and centrally annotated. This strategy seeks to lay the foundation for the clinical implementation of data-driven research, ultimately advancing precision oncology.

npj Precision OncologyVol. 10(1)
Marche Polytechnic University (IT), Università degli Studi del Piemonte Orientale “Amedeo Avogadro” (IT), Vita-Salute San Raffaele University (IT), Università Campus Bio-Medico (IT), University of Milan (IT), University of Perugia (IT), Hammersmith Hospital (GB), University of Chicago Medical Center (US), Centro di Riferimento Oncologico della Basilicata (IT), Azienda Socio Sanitaria Territoriale Grande Ospedale Metropolitano Niguarda (IT), MultiMedica (IT), Ospedale Sacro Cuore Don Calabria (IT), Ospedale A. Perrino (IT), Ospedali Riuniti di Ancona (IT), Azienda Unità Sanitaria Locale Della Romagna (IT), Azienda Ospedaliera S.Giuseppe Moscati (IT), Azienda Ospedaliero Universitaria Maggiore della Carita (IT), Humanitas Gavazzeni (IT), Ospedale Santa Maria della Misericordia di Udine (IT), University of Campania "Luigi Vanvitelli" (IT), Ospedale Monaldi (IT), Campus Bio Medico University Hospital (IT), Azienda Ospedaliera Ospedale Maggiore (IT), Ospedale Policlinico San Martino (IT), Policlinico San Matteo Fondazione (IT), Istituti di Ricovero e Cura a Carattere Scientifico (IT), Fondazione IRCCS Istituto Nazionale dei Tumori (IT), ASST Fatebenefratelli Sacco (IT), IRCCS Humanitas Research Hospital (IT), Istituti Ospitalieri di Cremona (IT), Azienda Socio Sanitaria Territoriale di Cremona, Imperial College London (GB), University of Naples Federico II (IT), University of Genoa (IT), Politecnico di Milano (IT)
Industry, innovation and infrastructure
Openalex Percentile: Top 1%
Lung Cancer Treatments and Mutations
36.44
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.