Tumor mutation burden for predicting immune checkpoint blockade response: the more, the better

Background Recently, the US Food and Drug Administration (FDA) has approved immune checkpoint blockade (ICB) for treating cancer patients with tumor mutation burden (TMB) >10 mutations/megabase (mut/Mb). However, high TMB (TMB-H) defined by >10 mut/Mb fails to predict ICB response acro...

Full description

Saved in:
Bibliographic Details
Main Author: Ming Zheng
Format: Article
Language:English
Published: BMJ Publishing Group 2022-01-01
Series:Journal for ImmunoTherapy of Cancer
Online Access:https://jitc.bmj.com/content/10/1/e003087.full
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1832569919834685440
author Ming Zheng
author_facet Ming Zheng
author_sort Ming Zheng
collection DOAJ
description Background Recently, the US Food and Drug Administration (FDA) has approved immune checkpoint blockade (ICB) for treating cancer patients with tumor mutation burden (TMB) >10 mutations/megabase (mut/Mb). However, high TMB (TMB-H) defined by >10 mut/Mb fails to predict ICB response across different cancer types, which has raised serious concerns on the current FDA approval. Thus, to better implement TMB as a robust biomarker of ICB response, an optimal and generalizable TMB cut-off within and across cancer types must be addressed as soon as possible.Methods Using Morris’s and Kurzrock’s cohorts (n=1662 and 102), we exhaustively tested all possible TMB cut-offs for predicting ICB treatment outcomes in 10 cancer types. The bootstrap method was applied to generate 10,000 randomly resampled cohorts using original cohorts to measure the reproducibility of TMB cut-off. ICB treatment outcomes were analyzed by overall survival, progression-free survival and objective response rate.Results No universally valid TMB cut-off was available for all cancer types. Only in cancer types with higher TMB (category I), such as melanoma, colorectal cancer, bladder cancer, and non-small cell lung cancer, the associations between TMB-H and ICB treatment outcomes were less affected by TMB cut-off selection. Moreover, high TMB (category I) cancer types shared a wide range of TMB cut-offs and a universally optimal TMB cut-off of 13 mut/Mb for predicting favorable ICB outcomes. In contrast, low TMB (category II) cancer types, for which the prognostic associations were sensitive to TMB cut-off selection, showed markedly limited and distinct ranges of significantly favorable TMB cut-offs. Equivalent results were obtained in the analyses of pooled tumors.Conclusions Our finding—the correlation that TMB-H is more robustly associated with favorable ICB treatment outcomes in cancer types with higher TMBs—can be used to predict whether TMB could be a robust predictive biomarker in cancer types for which TMB data are available, but ICB treatment has not been investigated. This theory was tested in cancer of unknown primary successfully. Additionally, the universal TMB cut-off of 13 mut/Mb might reveal a general requirement to trigger the sequential cascade from somatic mutations to an effective antitumor immunity.
format Article
id doaj-art-383af242382444559729fc5562fd8dd1
institution Kabale University
issn 2051-1426
language English
publishDate 2022-01-01
publisher BMJ Publishing Group
record_format Article
series Journal for ImmunoTherapy of Cancer
spelling doaj-art-383af242382444559729fc5562fd8dd12025-02-02T19:00:10ZengBMJ Publishing GroupJournal for ImmunoTherapy of Cancer2051-14262022-01-0110110.1136/jitc-2021-003087Tumor mutation burden for predicting immune checkpoint blockade response: the more, the betterMing Zheng0Institute of Military Cognition and Brain Sciences, Academy of Military Medical Sciences, Beijing, ChinaBackground Recently, the US Food and Drug Administration (FDA) has approved immune checkpoint blockade (ICB) for treating cancer patients with tumor mutation burden (TMB) >10 mutations/megabase (mut/Mb). However, high TMB (TMB-H) defined by >10 mut/Mb fails to predict ICB response across different cancer types, which has raised serious concerns on the current FDA approval. Thus, to better implement TMB as a robust biomarker of ICB response, an optimal and generalizable TMB cut-off within and across cancer types must be addressed as soon as possible.Methods Using Morris’s and Kurzrock’s cohorts (n=1662 and 102), we exhaustively tested all possible TMB cut-offs for predicting ICB treatment outcomes in 10 cancer types. The bootstrap method was applied to generate 10,000 randomly resampled cohorts using original cohorts to measure the reproducibility of TMB cut-off. ICB treatment outcomes were analyzed by overall survival, progression-free survival and objective response rate.Results No universally valid TMB cut-off was available for all cancer types. Only in cancer types with higher TMB (category I), such as melanoma, colorectal cancer, bladder cancer, and non-small cell lung cancer, the associations between TMB-H and ICB treatment outcomes were less affected by TMB cut-off selection. Moreover, high TMB (category I) cancer types shared a wide range of TMB cut-offs and a universally optimal TMB cut-off of 13 mut/Mb for predicting favorable ICB outcomes. In contrast, low TMB (category II) cancer types, for which the prognostic associations were sensitive to TMB cut-off selection, showed markedly limited and distinct ranges of significantly favorable TMB cut-offs. Equivalent results were obtained in the analyses of pooled tumors.Conclusions Our finding—the correlation that TMB-H is more robustly associated with favorable ICB treatment outcomes in cancer types with higher TMBs—can be used to predict whether TMB could be a robust predictive biomarker in cancer types for which TMB data are available, but ICB treatment has not been investigated. This theory was tested in cancer of unknown primary successfully. Additionally, the universal TMB cut-off of 13 mut/Mb might reveal a general requirement to trigger the sequential cascade from somatic mutations to an effective antitumor immunity.https://jitc.bmj.com/content/10/1/e003087.full
spellingShingle Ming Zheng
Tumor mutation burden for predicting immune checkpoint blockade response: the more, the better
Journal for ImmunoTherapy of Cancer
title Tumor mutation burden for predicting immune checkpoint blockade response: the more, the better
title_full Tumor mutation burden for predicting immune checkpoint blockade response: the more, the better
title_fullStr Tumor mutation burden for predicting immune checkpoint blockade response: the more, the better
title_full_unstemmed Tumor mutation burden for predicting immune checkpoint blockade response: the more, the better
title_short Tumor mutation burden for predicting immune checkpoint blockade response: the more, the better
title_sort tumor mutation burden for predicting immune checkpoint blockade response the more the better
url https://jitc.bmj.com/content/10/1/e003087.full
work_keys_str_mv AT mingzheng tumormutationburdenforpredictingimmunecheckpointblockaderesponsethemorethebetter