Tumor mutational burden and immune infiltration as independent predictors of response to neoadjuvant immune checkpoint inhibition in early TNBC in GeparNuevo.
exome sequencing
neoadjuvant immune checkpoint inhibition
triple negative breast cancer
tumor mutational burden
Journal
Annals of oncology : official journal of the European Society for Medical Oncology
ISSN: 1569-8041
Titre abrégé: Ann Oncol
Pays: England
ID NLM: 9007735
Informations de publication
Date de publication:
09 2020
09 2020
Historique:
received:
24
01
2020
revised:
27
04
2020
accepted:
07
05
2020
pubmed:
29
5
2020
medline:
7
1
2021
entrez:
29
5
2020
Statut:
ppublish
Résumé
The predictive value of tumor mutational burden (TMB), alone or in combination with an immune gene expression profile (GEP), for response to neoadjuvant therapy in early triple negative breast cancer (TNBC) is currently not known, either for immune checkpoint blockade (ICB) or conventional chemotherapy. We obtained both whole exome sequencing and RNA-Seq data from pretreatment samples of 149 TNBC of the recent neoadjuvant ICB trial, GeparNuevo. In a predefined analysis, we assessed the predictive value of TMB and a previously developed immune GEP for pathological complete remission (pCR). Median TMB was 1.52 mut/Mb (range 0.02-7.65) and was significantly higher in patients with pCR (median 1.87 versus 1.39; P = 0.005). In multivariate analysis, odds ratios for pCR per mut/Mb were 2.06 [95% confidence intervals (CI) 1.33-3.20, P = 0.001] among all patients, 1.77 (95% CI 1.00-3.13, P = 0.049) in the durvalumab treatment arm, and 2.82 (95% CI 1.21-6.54, P = 0.016) in the placebo treatment arm, respectively. We also found that both continuous TMB and immune GEP (or tumor infiltrating lymphocytes) independently predicted pCR. When we stratified patients in groups based on the upper tertile of TMB and median GEP, we observed a pCR rate of 82% (95% CI 60% to 95%) in the group with both high TMB and GEP in contrast to only 28% (95% CI 16% to 43%) in the group with both low TMB and GEP. TMB and immune GEP add independent value for pCR prediction. Our results recommend further analysis of TMB in combination with immune parameters to individually tailor therapies in breast cancer.
Sections du résumé
BACKGROUND
The predictive value of tumor mutational burden (TMB), alone or in combination with an immune gene expression profile (GEP), for response to neoadjuvant therapy in early triple negative breast cancer (TNBC) is currently not known, either for immune checkpoint blockade (ICB) or conventional chemotherapy.
PATIENTS AND METHODS
We obtained both whole exome sequencing and RNA-Seq data from pretreatment samples of 149 TNBC of the recent neoadjuvant ICB trial, GeparNuevo. In a predefined analysis, we assessed the predictive value of TMB and a previously developed immune GEP for pathological complete remission (pCR).
RESULTS
Median TMB was 1.52 mut/Mb (range 0.02-7.65) and was significantly higher in patients with pCR (median 1.87 versus 1.39; P = 0.005). In multivariate analysis, odds ratios for pCR per mut/Mb were 2.06 [95% confidence intervals (CI) 1.33-3.20, P = 0.001] among all patients, 1.77 (95% CI 1.00-3.13, P = 0.049) in the durvalumab treatment arm, and 2.82 (95% CI 1.21-6.54, P = 0.016) in the placebo treatment arm, respectively. We also found that both continuous TMB and immune GEP (or tumor infiltrating lymphocytes) independently predicted pCR. When we stratified patients in groups based on the upper tertile of TMB and median GEP, we observed a pCR rate of 82% (95% CI 60% to 95%) in the group with both high TMB and GEP in contrast to only 28% (95% CI 16% to 43%) in the group with both low TMB and GEP.
CONCLUSIONS
TMB and immune GEP add independent value for pCR prediction. Our results recommend further analysis of TMB in combination with immune parameters to individually tailor therapies in breast cancer.
Identifiants
pubmed: 32461104
pii: S0923-7534(20)39836-7
doi: 10.1016/j.annonc.2020.05.015
pii:
doi:
Substances chimiques
Biomarkers, Tumor
0
Immune Checkpoint Inhibitors
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1216-1222Informations de copyright
Copyright © 2020 European Society for Medical Oncology. Published by Elsevier Ltd. All rights reserved.
Déclaration de conflit d'intérêts
Disclosure This work was supported by grants and non-financial support from Astra Zeneca and other from Celgene during the conduct of the study, grants from Teva and Vifor, grants and other from AbbVie; Amgen; AstraZeneca; Celgene; Novartis; Pfizer; Roche; and Daiichi-Sankyo, other from Seattle Genetics; PriME/Medscape; Lilly; Samsung; and Eirgenix, and personal fees from Chugai outside the submitted work to SL. In addition, SL has a patent EP18209672 pending. CD reports personal fees from Teva; Novartis; Pfizer; Roche; Amgen; MSD; Celgene; and AstraZeneca; outside the submitted work, CD has patents EP20150702464 issued and EP18209672 pending and is a co-founder of Sividon Diagnostics. KEW had shares from Sividon Diagnostics, received employee inventor remuneration from a patent on the EndoPredict test, and has a patent EP18209672 pending. TK has a patent EP18209672 pending. CH reports personal fees from Roche; Novartis; Lilly; Amgen; AstraZeneca; and Pfizer outside the submitted work. BWH reports being an employee of and owning stock in AstraZeneca. JH reports grants from Celgene; Novartis; Hexal; and personal fees from Lilly; Novartis; Roche; Pfizer; AstraZeneca; MSD; Celgene; Eisai; AbbVie; Hexal; and Daichi outside the submitted work. JB reports personal fees from Amgen; AstraZeneca; Genomic Health; MSD; Myriad; Novartis; Pfizer; Roche; and SonoScape outside the submitted work. FM reports personal fees from Roche; AstraZeneca; Pfizer; Tesaro; Novartis; Amgen; PharmaMar; Genomic Health; CureVac; Eisai; Clovis; and Celgene outside the submitted work. WDS reports personal fees from AstraZeneca outside the submitted work. SW reports that he is an employee and shareholder of AstraZeneca. MvM reports personal fees from Amgen; AstraZeneca; Genomic Health; Novartis; and Lilly outside the submitted work. VM reports grants, personal fees, and other from Roche, personal fees and other from Novartis; Pfizer; and Nektar, and personal fees from Amgen; Astra Zeneca; Daiichi-Sankyo; Eisai; Teva; Tesaro; Hexal; and Novartis outside the submitted work. PAF reports grants from Novartis and BioNtech and personal fees from Novartis; Roche; Pfizer; Celgene; Daiichi-Sankyo; Teva; AstraZeneca; Merck Sharp & Dohme; Myelo Therapeutics; MacroGenics; Eisai; Puma; Cepheid; and Lilly during the conduct of the study. CJ reports personal fees from Roche and Celgene outside the submitted work. MU reports personal fees and non-financial support from Odonate and Puma Biotechnology and personal fees and non-financial support to his institution from Lilly; MSD; Mundipharma; Myriad; Pfizer; Roche; Sanovi; Teva; Novartis; Pierre Fabre; and Clovis outside the submitted work. AS reports grants from Celgene; Roche; AbbVie; and Molecular Partner and personal fees from Roche; AstraZeneca; Celgene; Pfizer; Novartis; MSD; Tesaro; and Lilly outside the submitted work. All remaining authors have declared no conflicts of interest.