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Published on: June 12, 2021
Patients deriving long-term benefit from immune checkpoint inhibitors demonstrate conserved patterns of site-specific
1University of Illinois College of Medicine, 840 South Wood Street, 601 CSB, Chicago, IL, 60612, USA. principe@illinois.edu.
Abstract:
Immune checkpoint inhibitors (ICIs) have revolutionized cancer therapy and are now the preferred treatment for several tumor types. Though ICIs have shown remarkable efficacy in several cancer histologies, in many cases providing long-term disease control, not all patients will derive clinical benefit from such approaches. Given the lack of a reliable predictive biomarker for therapeutic responses to ICIs, we conducted a retrospective analysis of publicly available genomic data from a large pan-cancer cohort of patients receiving ICI-based immunotherapy. Consistent with previous results, patients in the combined cohort deriving a long-term survival benefit from ICIs were more likely to have a higher tumor mutational burden (TMB). However, this was not uniform across tumor-types, failing to predict for long-term survivorship in most non-melanoma cancers. Interestingly, long-term survivors in most cancers had conserved patterns of mutations affecting several genes. In melanoma, this included mutations affecting TET1 or PTPRD. In patients with colorectal cancer, mutations affecting TET1, RNF43, NCOA3, LATS1, NOTCH3, or CREBBP were also associated with improved prognosis, as were mutations affecting PTPRD, EPHA7, NTRK3, or ZFHX3 in non-small cell lung cancer, RNF43, LATS1, or CREBBP mutations in bladder cancer, and VHL mutations in renal cell carcinoma patients. Thus, this study identified several genes that may have utility as predictive biomarkers for therapeutic responses in patients receiving ICIs. As many have no known relationship to immunotherapy or ICIs, these genes warrant continued exploration, particularly for cancers in which established biomarkers such as PD-L1 expression or TMB have little predictive value.
Insights
Immune checkpoint inhibitors (ICIs) show promise, but not all patients benefit. This study found specific gene mutations, beyond tumor mutational burden (TMB), may predict ICI therapy response across various cancers.
Area of Science:
- Oncology
- Immunotherapy
- Genomics
Background:
- Immune checkpoint inhibitors (ICIs) have transformed cancer treatment, offering long-term disease control for many patients.
- However, predicting which patients will respond to ICIs remains a challenge due to the lack of reliable biomarkers.
- Tumor mutational burden (TMB) is an established biomarker, but its predictive value varies across cancer types.
Purpose of the Study:
- To identify novel predictive biomarkers for therapeutic response to ICI-based immunotherapy.
- To investigate conserved mutation patterns in long-term survivors across a pan-cancer cohort.
- To explore potential biomarkers in cancers where TMB and PD-L1 expression have limited predictive value.
Main Methods:
- Retrospective analysis of publicly available genomic data from a large pan-cancer cohort of patients receiving ICI therapy.
- Comparison of mutation profiles between patients achieving long-term survival benefit and others.
- Evaluation of specific gene mutations (e.g., TET1, PTPRD, RNF43, LATS1, CREBBP, VHL) associated with improved prognosis.
Main Results:
- Higher tumor mutational burden (TMB) correlated with long-term survival benefit from ICIs in the combined cohort.
- TMB's predictive value was inconsistent across tumor types, particularly in non-melanoma cancers.
- Conserved mutation patterns in specific genes (e.g., TET1, PTPRD, RNF43, LATS1, CREBBP, VHL) were associated with improved outcomes in melanoma, colorectal, non-small cell lung, bladder, and renal cell carcinoma patients.
Conclusions:
- Specific gene mutations may serve as valuable predictive biomarkers for ICI therapy response, complementing TMB and PD-L1.
- These identified genes, including TET1, PTPRD, RNF43, LATS1, CREBBP, and VHL, warrant further investigation.
- Further research is crucial for developing targeted biomarkers, especially for cancers with limited response prediction by current methods.
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