The rechallenge benefit score: A clinical decision tool for patients progressing after immunotherapy
Year: 2025
Type: article
Study design: Observational Study
Abstract: BACKGROUND: Most patients with advanced Non-Small-Cell Lung Cancer (NSCLC) receive an immune checkpoint inhibitor (ICI) as first-line treatment. Upon progression, therapeutic options are limited, making ICI rechallenge a relevant strategy. However, identifying patients who may benefit remains challenging. This study presents a predictive score to guide treatment decisions. METHODS: Seventeen routine blood tests performed before the first two ICI cycles were analyzed in 80 NSCLC patients rechallenged across 16 French centers. Using a robust machine learning approach, we identified biological parameters associated with progression-free survival during rechallenge (rPFS) and developed an integrative score able to stratify patients according to rPFS. Its predictive value was assessed in relation to patients treated with chemotherapy after ICI (n = 82) and to their outcomes evaluated during first-line immunotherapy (n = 252). RESULTS: Our findings indicate that clinical characteristics alone are insufficient to reliably identify patients who are likely to respond to ICI rechallenge. We identify the Rechallenge Benefit Score (RBS), which identifies patients likely to achieve longer clinical benefit from ICI rechallenge (C-index 0.79), regardless of clinical characteristics. Median rPFS was significantly longer in patients with low versus high RBS (6.4 and 1.9 months respectively; p < 0.0001, HR=0.31, 95 % CI:0.17-0.55). Notably, the RBS is specific, as it does not predict benefit in Chemotherapy or first-line ICI cohorts. CONCLUSION: We identified and validated the RBS score to select patients likely to benefit from ICI rechallenge. Since clinical features alone are insufficient, our findings may guide treatment strategies for patients who experienced disease progression during prior ICI treatment.
Source: European Journal of Cancer
Authors: Caroline Plazy, Mohamed Boussena, Linh Nguyen Phuong, Jean Baptiste Assié, Emmanuel Grolleau, Valerie Gounant, Chantal Decroisette, Eric Dansin, Catherine Daniel, Etienne Giroux Leprieur, David Planchard, Mariona Riudavets, Anthony Canellas, Youssef Oulkhouir, Maurice Pérol, Anne-Claire Toffart, Sebastien Benzekry, Elisa Gobbini
Institutions: Institut polytechnique de Grenoble, Centre National de la Recherche Scientifique, Centre Hospitalier Universitaire de Grenoble, Translational Innovation in Medicine and Complexity, Université Grenoble Alpes, Institut national de recherche en sciences et technologies du numérique, Inserm, Aix-Marseille Université, Centre Inria d'Université Côte d'Azur, Centre de Recherche en Cancérologie de Marseille, Institut Paoli-Calmettes, COMPO: Méthodes computationnelles pour la prise en charge thérapeutique en oncologie, Hôpital Intercommunal de Créteil, Hospices Civils de Lyon, Hôpital Lyon Sud, Assistance Publique – Hôpitaux de Paris, Hôpital Bichat-Claude-Bernard, Hôpital Tenon, Centre d'Investigation Clinique Bichat, Hôpital Cochin, Centre Oscar Lambret, Centre Hospitalier Annecy Genevois, Institut Curie, Centre Hospitalier Régional Universitaire de Brest, Hôpital Ambroise-Paré, Institut Gustave Roussy, Fondation Gustave Roussy, Sorbonne Université, Centre Hospitalier Universitaire de Caen Normandie, Centre Léon Bérard, Université de Rouen Normandie, UniCancer Group, Centre de Recherche en Cancérologie de Lyon
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $4,270
OpenAlex ID: W4414487869
FWCI:
Cites:
Cited by:
Related to:
