A dysfunctional T cell gene signature for predicting non-response to PD-1 blockade in non-small cell lung cancer that is suitable for routine clinical diagnostics.


Journal

Clinical cancer research : an official journal of the American Association for Cancer Research
ISSN: 1557-3265
Titre abrégé: Clin Cancer Res
Pays: United States
ID NLM: 9502500

Informations de publication

Date de publication:
13 Dec 2023
Historique:
accepted: 07 12 2023
received: 14 04 2023
revised: 25 07 2023
medline: 13 12 2023
pubmed: 13 12 2023
entrez: 13 12 2023
Statut: aheadofprint

Résumé

Since PD-1 blockade is only effective in a minority of patients with advanced stage NSCLC, biomarkers are needed to guide treatment decisions. Tumor infiltration by PD-1T TILs, a dysfunctional tumor-infiltrating lymphocyte (TIL) pool with tumor-reactive capacity, can be detected by digital quantitative IHC and has been established as a novel predictive biomarker in NSCLC. To facilitate translation of this biomarker to the clinic, we here aimed to develop a robust RNA signature reflecting a tumor's PD-1T TIL status. mRNA expression analysis using Nanostring was performed in baseline tumor samples from 41 advanced stage NSCLC patients treated with nivolumab that were selected based on PD-1T TIL infiltration by IHC. Samples were included as training cohort (n=41) to develop a predictive gene signature. This signature was independently validated in a second cohort (n=42). Primary outcome was disease control at 12 months (DC 12m) and secondary outcome was progression-free and overall survival. Regularized regression analysis yielded a signature using 12 out of 56 differentially expressed genes between PD-1T IHC high tumors from patients with DC 12m and PD-1T IHC low tumors from patients with progressive disease (PD). In the validation cohort 6/6 (100%) patients with DC 12m and 23/36 (64%) with PD were correctly classified with an NPV of 100% and a PPV of 32%. The PD-1T mRNA signature showed a similar high sensitivity and high NPV as the digital IHC quantification of PD-1T TILs. This provides a straightforward approach allowing for easy implementation in a routine diagnostic clinical setting.

Identifiants

pubmed: 38088895
pii: 731719
doi: 10.1158/1078-0432.CCR-23-1061
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Auteurs

Karlijn Hummelink (K)

Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, Netherlands.

Renaud Tissier (R)

Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, Netherlands.

Linda J W Bosch (LJW)

Netherlands Cancer Institute, Amsterdam, Netherlands.

Oscar Krijgsman (O)

The Netherlands Cancer Institute, Amsterdam, Netherlands.

Michel M Van den Heuvel (MM)

Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands.

Francois Goldwasser (F)

Cochin hospital, AP-HP, Paris, France.

Karen Leroy (K)

Universite Paris Cité, AP-HP, Paris, France.

Egbert F Smit (EF)

Leiden University Medical Center, Leiden, Netherlands.

Gerrit A Meijer (GA)

Netherlands Cancer Institute, Amsterdam, Netherlands.

Daniela S Thommen (DS)

Netherlands Cancer Institute, Amsterdam, Netherlands.

Kim Monkhorst (K)

Netherlands Cancer Institute, Amsterdam, Netherlands.

Classifications MeSH