Gene-network analysis predicts clinical response to immunotherapy in patients affected by NSCLC

Federico Cucchiara; Stefania Crucitta; Iacopo Petrini; Diego de Miguel Perez; Martina Ruglioni; Eleonora Pardini; Christian Rolfo; Romano Danesi; Marzia Del Re

doi:10.1016/j.lungcan.2023.107308

Gene-network analysis predicts clinical response to immunotherapy in patients affected by NSCLC

Lung Cancer. 2023 Sep:183:107308. doi: 10.1016/j.lungcan.2023.107308. Epub 2023 Jul 16.

Authors

Federico Cucchiara¹, Stefania Crucitta¹, Iacopo Petrini², Diego de Miguel Perez³, Martina Ruglioni¹, Eleonora Pardini⁴, Christian Rolfo⁵, Romano Danesi⁶, Marzia Del Re⁷

Affiliations

¹ Unit of Clinical Pharmacology and Pharmacogenetics, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy.
² Cardiothoracic and Vascular Department, University of Pisa, Pisa, Italy; Unit of General Pathology, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy.
³ Center for Thoracic Oncology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States.
⁴ Unit of General Pathology, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy.
⁵ Center for Thoracic Oncology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States. Electronic address: christian.rolfo@mssm.edu.
⁶ Unit of Clinical Pharmacology and Pharmacogenetics, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy. Electronic address: romano.danesi@unipi.it.
⁷ Unit of Clinical Pharmacology and Pharmacogenetics, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy; Center for Thoracic Oncology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States.

PMID: 37473500
DOI: 10.1016/j.lungcan.2023.107308

Abstract

Objectives: Predictive biomarkers of response to immune checkpoint inhibitors (ICIs) have been extensively studied in non-small cell lung cancer (NSCLC) with controversial results. Recently, gene-network analysis emerged as a new tool to address tumor biology and behavior, representing a potential tool to evaluate response to therapies.

Methods: Clinical data and genetic profiles of 644 advanced NSCLCs were retrieved from cBioPortal and the Cancer Genome Atlas (TCGA); 243 ICI-treated NSCLCs were used to identify an immunotherapy response signatures via mutated gene network analysis and K-means unsupervised clustering. Signatures predictive values were tested in an external dataset of 242 cases and assessed versus a control group of 159 NSCLCs treated with standard chemotherapy.

Results: At least two mutations in the coding sequence of genes belonging to the chromatin remodelling pathway (A signature), and/or at least two mutations of genes involved in cell-to-cell signalling pathways (B signature), showed positive prediction in ICI-treated advanced NSCLC. Signatures performed best when combined for patients undergoing first-line immunotherapy, and for those receiving combined ICIs.

Conclusions: Alterations in genes related to chromatin remodelling complexes and cell-to-cell crosstalk may force dysfunctional immune evasion, explaining susceptibility to immunotherapy. Therefore, exploring mutated gene networks could be valuable for determining essential biological interactions, contributing to treatment personalization.

Keywords: Biomarkers; Co-occurring mutations; Gene network analysis; Immunotherapy; NSCLC.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Carcinoma, Non-Small-Cell Lung* / drug therapy
Carcinoma, Non-Small-Cell Lung* / genetics
Gene Regulatory Networks
Humans
Immune Checkpoint Inhibitors
Immunotherapy
Lung Neoplasms* / drug therapy
Lung Neoplasms* / genetics

Substances

Immune Checkpoint Inhibitors