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Novel tumor mutation score versus tumor mutation burden in predicting survival after immunotherapy in pan-cancer patients from the MSK-IMPACT cohort.

ABSTRACT: BackgroundTumor mutation burden (TMB) may predict the immune checkpoint inhibitor (ICI) response. The TMB calculation includes all nonsynonymous somatic mutations, but not all mutations are favorable, and the efficiency of TMB is attenuated by including adverse mutations. Moreover, no universal cutoff value of a high TMB hinders its application in practice.MethodsTumor mutation score (TMS), defined as the number of genes with nonsynonymous somatic mutations, TMS55, defined as the TMS of 55 favorable prognostic genes, and TMB were calculated and compared in 1,661 advanced cancer patients treated with ICIs and 3,840 matched advanced cancer patients not treated with ICIs among ten cancer types.ResultsTMS55 was significantly associated with TMB. In 1,661 ICI-treated patients, 55 genes were significantly associated with prolonged overall survival (OS), and a high TMS55 (TMS55 >5) was associated with a smaller hazard ratio (HR) and P value than a high TMB (highest 20% in each histology group) in predicting OS. The C-index of TMS55 was significantly higher than that of TMB (TMS55 0.65 vs. TMB 0.54, P<0.001). Moreover, TMS55 was significantly associated with improved survival in more tumor types than TMB, especially in non-small cell lung cancer (NSCLC), melanoma, bladder cancer and colorectal cancer. In 3,840 non-ICI-treated patients, a high TMS55 and TMB predicted poor OS.ConclusionsThe novel TMS55 might be better than TMB as a biomarker for patients treated with ICIs. The easy calculation and universal cutoff value of TMS55 will not be affected across platforms and is feasible in clinical settings, which may greatly promote its application in the clinic with further validation.

SUBMITTER: Li Y

PROVIDER: S-EPMC7210182 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Project description:Background:Treatment with immune checkpoint blockade (ICB) with agents such as anti-programmed cell death protein 1 (PD-1), anti-programmed death-ligand 1 (PD-L1), and/or anti-cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) can result in impressive response rates and durable disease remission but only in a subset of patients with cancer. Expression of PD-L1 has demonstrated utility in selecting patients for response to ICB and has proven to be an important biomarker for patient selection. Tumor mutation burden (TMB) is emerging as a potential biomarker. However, refinement of interpretation and contextualization is required. Materials and methods:In this review, we outline the evolution of TMB as a biomarker in oncology, delineate how TMB can be applied in the clinic, discuss current limitations as a diagnostic test, and highlight mechanistic insights unveiled by the study of TMB. We review available data to date studying TMB as a biomarker for response to ICB by tumor type, focusing on studies proposing a threshold for TMB as a predictive biomarker for ICB activity. Results:High TMB consistently selects for benefit with ICB therapy. In lung, bladder and head and neck cancers, the current predictive TMB thresholds proposed approximate 200 non-synonymous somatic mutations by whole exome sequencing (WES). PD-L1 expression influences response to ICB in high TMB tumors with single agent PD-(L)1 antibodies; however, response may not be dependent on PD-L1 expression in the setting of anti-CTLA4 or anti-PD-1/CTLA-4 combination therapy. Disease-specific TMB thresholds for effective prediction of response in various other malignancies are not well established. Conclusions:TMB, in concert with PD-L1 expression, has been demonstrated to be a useful biomarker for ICB selection across some cancer types; however, further prospective validation studies are required. TMB determination by selected targeted panels has been correlated with WES. Calibration and harmonization will be required for optimal utility and alignment across all platforms currently used internationally. Key challenges will need to be addressed before broader use in different tumor types.

Project description:BackgroundUCEC is the most common gynecological malignancy in many countries, and its mechanism of occurrence and development is related to tumor mutation burden (TMB) and immune cell infiltration. Therefore, it is necessary to systematically explore the TMB-related gene profile in immune cells to improve the prognosis of UCEC.MethodsWe integrated TMB-related genes with basic clinical information of UCEC patients based on TCGA dataset. Differentially expressed genes (DEGs) were selected through differential expression screening, PPI, and enrichment analysis. Additionally, we analyzed the components of immune cell infiltration of the DEGs to obtain the differential immunity-related genes. A single factor and multifactor Cox regression analyses were conducted to establish new prognostic indicators of OS and DFS based on TMB-related immune genes. To further study the correlation between survival and immune cell infiltration, a Cox model based on these immune infiltration compositions was built. Using the clinical variables, we established nomograms for OS and DFS.Results393 DEGs were significantly associated with clinical outcomes and the immune component in patients with UCEC. Gene Ontology (GO) and Kyoto Encyclopedia of Genes, Genomes (KEGG) pathway and protein-protein interaction network (PPI) analyses revealed the role of these genes and information on related pathways. Then, two prognostic models were established based on the differential immune genes for OS (GFAP and MX2) and DFS (MX2, GFAP, IGHM, FGF20, and TRAV21). In DFS, the differential immune genes were related to CD4+?T cell, CD8+?T cell, macrophage, and neutrophil (all P?<?0.05). B cell and CD8+?T cell were independent prognostic factors from among the immune cell elements in UCEC. Finally, the risk scores of these models were combined with the clinical elements-based nomogram models, and the AUC values were all over 0.7.ConclusionsOur results identified several clinically significant differential immune genes and established relevant prognostic models, providing a basis for the molecular analysis of TMB and immune cells in UCEC, and identified potential prognostic and immune-related genes for UCEC. We added clinical related conditions for further analysis to confirm the identity of the genes and clinical elements-based models.

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Novel tumor mutation score versus tumor mutation burden in predicting survival after immunotherapy in pan-cancer patients from the MSK-IMPACT cohort.

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