About

Marina Chiara Garassino, MD, is a Professor of Medicine and Director of Thoracic Programs in the Section of Hematology/Oncology at the University of Chicago. She is an internationally recognized thoracic medical oncologist specializing in the care of patients with non–small cell lung cancer (NSCLC), small cell lung cancer (SCLC), mesothelioma, and thymic malignancies. Dr. Garassino received her medical degree and completed her oncology training at the Università degli Studi di Milano, followed by clinical fellowships at San Raffaele Scientific Institute, the Istituto Nazionale dei Tumori (INT) in Milan, and Christie’s Hospital in Manchester (UK). Before joining UChicago in 2021, she served as Chief of the Medical Thoracic Oncology Unit at INT Milan, one of Europe’s leading comprehensive cancer centers. She also holds a Master’s in Oncology Management from Bocconi University, reflecting her longstanding interest in redesigning cancer care pathways. Her research has helped reshape global standards of care in lung cancer, including leading pivotal clinical trials such as KEYNOTE-189, PACIFIC, and KEYNOTE-671, which have established chemo-immunotherapy as a standard treatment for metastatic NSCLC and integrated immunotherapy into earlier disease stages. Dr. Garassino has contributed to the development of targeted therapies, authored over 350 peer-reviewed publications, and is at the forefront of AI-driven and multimodal research in thoracic oncology. She co-leads international efforts like I3LUNG to develop AI tools for personalized immunotherapy decisions and has contributed to innovative applications of deep learning in rare tumors such as thymic epithelial malignancies. During the COVID-19 pandemic, she founded and led TERAVOLT, a global registry examining outcomes of patients with thoracic cancers and COVID-19. She is a Fellow of ESMO, a “The Lancet” Commissioner, and the founder and honorary president of Women for Oncology Italy, as well as co-Chair for Women in Thoracic Oncology.

Research topics

• Medicine
• Internal medicine
• Oncology
• Biology
• Pathology
• Surgery
• Environmental health
• Intensive care medicine
• Chemistry
• Family medicine
• Radiology
• Cancer research

Selected publications

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

  npj Precision Oncology · 2026-01-29 · 1 citations

  articleOpen access

  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.

  PublisherDOI

• Additional file 2 of Network analysis predicts pembrolizumab response in advanced NSCLC with PD-L1 < 50%

  Figshare · 2026-03-26

  articleOpen access

  Additional file 2.

  PublisherDOI

• Network analysis predicts pembrolizumab response in advanced NSCLC with PD-L1 < 50%

  Cancer Cell International · 2026-03-26

  articleOpen access

  The efficacy of single-agent immune checkpoint inhibitors as a first-line treatment for advanced non-small cell lung cancer (aNSCLC) patients with PD-L1 Tumor Proportion Score (TPS) < 50% remains variable. Network analysis is promising in addressing tumor biology and behavior, potentially predicting therapeutic response. This study, based on the PEOPLE trial (NCT03447678) data, explores network analysis for predictive biomarker discovery in immunotherapy response. Utilizing circulating immune profiling (CIP) and gene expression profiling (GEP), key immune cells and gene interactions were identified. Our findings confirm the central role of natural killer (NK) cells, with elevated baseline levels associated with a favorable response. Differential co-expression network (DCN) analysis of GEP identified 23 hub genes, with enrichment analysis linking CD48 to immune-related processes. Patient similarity network (PSN) analysis identified two patient clusters with significantly different survival outcomes. The integrated model outperformed single-layer approaches, supporting the added value of combining GEP and CIP data. Despite limitations such as a non-randomized design and small sample size, the study’s innovative network approach provides valuable insights. The results suggest that baseline NK cell subsets and specific gene evaluations could guide personalized treatment strategies, optimizing the use of pembrolizumab in aNSCLC patients with PD-L1 TPS < 50%.

  PublisherDOI

• Probing Cellular Activity Via Charge‐Sensitive Quantum Nanoprobes

  Advanced Materials · 2026-02-04 · 1 citations

  articleOpen access

  Nitrogen-vacancy (NV) based quantum sensors hold great potential for real-time single-cell sensing with far-reaching applications in fundamental biology and medical diagnostics. Although highly sensitive, the mapping of quantum measurements onto cellular physiological states has remained an exceptional challenge. Here, we introduce a novel quantum sensing modality capable of detecting changes in cellular activity. Our approach is based on the detection of environment-induced charge depletion within an individual particle that, owing to a previously unaccounted transverse dipole term, induces systematic shifts in the zero-field splitting (ZFS). Importantly, these charge-induced shifts serve as a reliable indicator for lipopolysaccharide (LPS)-mediated inflammatory response in macrophages. Furthermore, we demonstrate that surface modification of our diamond nanoprobes effectively suppresses these environment-induced ZFS shifts, providing an important tool for differentiating electrostatic shifts caused by the environment from other unrelated effects, such as temperature variations. Notably, this surface modification also leads to significant reductions in particle-induced toxicity and inflammation. Our findings shed light on systematic drifts and sensitivity limits of NV spectroscopy in a biological environment with ramifications for the critical discussion surrounding single-cell thermogenesis. Notably, this work establishes the foundation for a novel sensing modality capable of probing complex cellular processes through straightforward physical measurements.

  PublisherOA PDFDOI

• ENTRÉE Lung Platform Trial Sub-study 1: Phase II Randomized Efficacy and Safety Analysis of Feladilimab Plus Docetaxel Versus Docetaxel Monotherapy in Advanced/Recurrent NSCLC

  Clinical Lung Cancer · 2026-04-01

  article1st authorCorresponding
  PublisherDOI

• I3LUNG: Clinical Validation of a Multimodal AI Tool to Support Immunotherapy Decisions in NSCLC

  medRxiv · 2026-01-22

  articleSenior author

  Abstract Despite a decade of immunotherapy, treatment selection in non-small cell lung cancer (NSCLC) still relies on subgroup analyses and clinical scores. I3LUNG ( NCT05537922 ) is currently the largest international, real-world, multimodal, artificial intelligence (AI)-based trial, enrolling 2365 patients. We integrated real-world clinical data (RWD), computed tomography (CT) images, digital pathology (DP), and genomics (G) into machine learning early-fusion (MLEF) and deep-learning intermediate-fusion (DLIF) models. MLEF achieved consistent performance across outcomes (AUC≈0.74), with improved results in first-line patients (AUC up to 0.82). Multimodal models outperformed RWD in clinical-specific subgroups (AUCs up to 0.86). In the test set, AI models surpassed PD-L1, ECOG PS, NLR, LDH (all with p &lt;0.01) and the LIPI score. The clinical usability study showed that expert and non-expert physicians could improve their prediction with the explainable AI (XAI) tool. The I3LUNG tool emerges as a clinically relevant decision-support system and is currently under prospective validation in &gt;2,000 patients.

  PublisherDOI

• Additional file 1 of Network analysis predicts pembrolizumab response in advanced NSCLC with PD-L1 &lt; 50%

  Figshare · 2026-03-26

  articleOpen access

  Additional file 1.

  PublisherDOI

• Additional file 1 of Network analysis predicts pembrolizumab response in advanced NSCLC with PD-L1 &lt; 50%

  Figshare · 2026-03-26

  articleOpen access

  Additional file 1.

  PublisherDOI

• Additional file 2 of Network analysis predicts pembrolizumab response in advanced NSCLC with PD-L1 &lt; 50%

  Figshare · 2026-03-26

  articleOpen access

  Additional file 2.

  PublisherDOI

• Radiotherapy patterns and factors associated with pneumonitis in PACIFIC-R, a real-world study of patients with unresectable stage III non-small-cell lung cancer treated with durvalumab after chemoradiotherapy

  Clinical and Translational Radiation Oncology · 2026-02-24

  articleOpen access

  Background and purpose: Consolidation durvalumab, standard-of-care treatment for patients with unresectable stage III non-small-cell lung cancer (NSCLC) and no disease progression after chemoradiotherapy, may be associated with pneumonitis. We performed exploratory analyses of radiotherapy patterns and potential risk factors for symptomatic pneumonitis (SP) in PACIFIC-R. Materials and methods: PACIFIC-R is an ongoing, international, observational study based on medical chart data for patients in the durvalumab early access program. Patients with no missing data for candidate SP risk factors were included. SP was defined as a pneumonitis event (any cause) of grade ≥ 2 or requiring corticosteroids. Multivariable logistic regression identified variables associated with SP during durvalumab treatment; sensitivity analyses used a Cox proportional hazards model to account for time to SP. Results: Analyses included 268 patients; most received concurrent (86.6%) versus sequential (13.4%) chemoradiotherapy and intensity-modulated (IMRT; 66.8%) versus three-dimensional conformal (25.7%) radiotherapy, with between-country differences. Patients receiving IMRT were older, more frequently had nonsquamous histology, and had larger tumors. Fifty-two patients (19.4%) had SP during durvalumab treatment. Higher mean lung radiotherapy dose (log transformed) and prior chronic obstructive pulmonary disease (COPD) were associated with higher SP risk, with odds ratios (95% confidence interval [CI]) of 4.73 (1.66-15.07) and 2.60 (1.21-5.65), respectively, and hazard ratios (95% CI) of 3.28 (1.54-6.95) and 1.93 (1.04-3.56), respectively. Conclusions: Concurrent chemoradiotherapy and IMRT were the most common radiotherapy approaches. Higher mean lung radiotherapy dose and prior COPD were associated with higher SP risk during consolidation durvalumab for unresectable stage III NSCLC.

  PublisherOA PDFDOI

Frequent coauthors

• Claudia Proto

  Fondazione IRCCS Istituto Nazionale dei Tumori

  232 shared

• Filippo de Braud

  University of Milan

  214 shared

• Giuseppe Lo Russo

  Fondazione IRCCS Istituto Nazionale dei Tumori

  210 shared

• Diego Signorelli

  Azienda Socio Sanitaria Territoriale Grande Ospedale Metropolitano Niguarda

  204 shared

• Monica Ganzinelli

  Fondazione IRCCS Istituto Nazionale dei Tumori

  187 shared

• Roberto Ferrara

  Vita-Salute San Raffaele University

  184 shared

• Luis Paz‐Ares

  157 shared

• Valter Torri

  Mario Negri Institute for Pharmacological Research

  156 shared

Education

• M.D.

  Università degli Studi di Milano

• Other, Oncology Management

  Bocconi University

Awards & honors

• Fellow of ESMO
• The Lancet Commissioner
• Founder and honorary president of Women for Oncology Italy
• Co-Chair for Women in Thoracic Oncology