Dataset Information

Prediction of Immunotherapy Response in Melanoma through Combined Modeling of Neoantigen Burden and Immune-Related Resistance Mechanisms.

ABSTRACT:

Purpose

While immune checkpoint blockade (ICB) has become a pillar of cancer treatment, biomarkers that consistently predict patient response remain elusive due to the complex mechanisms driving immune response to tumors. We hypothesized that a multi-dimensional approach modeling both tumor and immune-related molecular mechanisms would better predict ICB response than simpler mutation-focused biomarkers, such as tumor mutational burden (TMB).

Experimental design

Tumors from a cohort of patients with late-stage melanoma (n = 51) were profiled using an immune-enhanced exome and transcriptome platform. We demonstrate increasing predictive power with deeper modeling of neoantigens and immune-related resistance mechanisms to ICB.

Results

Our neoantigen burden score, which integrates both exome and transcriptome features, more significantly stratified responders and nonresponders (P = 0.016) than TMB alone (P = 0.049). Extension of this model to include immune-related resistance mechanisms affecting the antigen presentation machinery, such as HLA allele-specific LOH, resulted in a composite neoantigen presentation score (NEOPS) that demonstrated further increased association with therapy response (P = 0.002).

Conclusions

NEOPS proved the statistically strongest biomarker compared with all single-gene biomarkers, expression signatures, and TMB biomarkers evaluated in this cohort. Subsequent confirmation of these findings in an independent cohort of patients (n = 110) suggests that NEOPS is a robust, novel biomarker of ICB response in melanoma.

SUBMITTER: Abbott CW

PROVIDER: S-EPMC9401549 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

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Publications

Prediction of Immunotherapy Response in Melanoma through Combined Modeling of Neoantigen Burden and Immune-Related Resistance Mechanisms.

Abbott Charles W CW Boyle Sean M SM Pyke Rachel Marty RM McDaniel Lee D LD Levy Eric E Navarro Fábio C P FCP Mellacheruvu Dattatreya D Zhang Simo V SV Tan Mengyao M Santiago Rose R Rusan Zeid M ZM Milani Pamela P Bartha Gabor G Harris Jason J McClory Rena R Snyder Michael P MP Jang Sekwon S Chen Richard R

Clinical cancer research : an official journal of the American Association for Cancer Research 20210801 15

<h4>Purpose</h4>While immune checkpoint blockade (ICB) has become a pillar of cancer treatment, biomarkers that consistently predict patient response remain elusive due to the complex mechanisms driving immune response to tumors. We hypothesized that a multi-dimensional approach modeling both tumor and immune-related molecular mechanisms would better predict ICB response than simpler mutation-focused biomarkers, such as tumor mutational burden (TMB).<h4>Experimental design</h4>Tumors from a coho ...[more]

PMID: 34341053

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Project description:Background: Melanoma is the most lethal, and one of the most aggressive forms of cutaneous malignancies, which poor response to treatment has always puzzled clinicians. As is known to all, aging and immune microenvironment are two crucial factors impacting melanoma biological progress through the tumor microenvironment (TME). However, reliable biomarkers for predicting melanoma prognosis based on aging and immune microenvironment and therapeutic efficacy of immune checkpoints remain to be determined. Methods: The aging-related genes (ARGs) were obtained from the Human Ageing Genomic Resources and immune-related genes (IRGs) were downloaded from the Immunology database as well as Analysis Portal (ImmPort) database. Next, we initially performed LASSO regression and multivariate Cox regression to identify prognostic ARGs and IRGs in the TCGA and GSE65904 datasets, and firstly constructed a novel comprehensive index of aging and immune (CIAI) signature. Finally, in vitro molecular biology experiments were performed to assess the regulatory role of CNTFR in melanoma cell lines proliferation and migration, macrophage recruitment, and M2 polarization. Results: This novel CIAI signature consisted of 7 genes, including FOXM1, TP63, ARNTL, KIR2DL4, CCL8, SEMA6A, and CNTFR, in which melanoma patients in the high-CIAI group had shorter OS, DSS, and PFI, indicating CIAI model served as an independent prognostic index. Moreover, we found the CIAI score was potentially correlated with immune scores, estimate score, immune cell infiltration level, tumor microenvironment, immunotherapy effect, and drug sensitivity. Finally, CNTFR might function as oncogenes in melanoma cell lines and the silencing of CNTFR reduced macrophage recruitment and M2 polarization. Conclusion: In this study, we have first presented a novel prognostic CIAI model applied to assess immune checkpoint therapy and the efficacy of conventional chemotherapy agents in melanoma patients. Thus providing a new insight for combating melanoma.

Dataset Information

Prediction of Immunotherapy Response in Melanoma through Combined Modeling of Neoantigen Burden and Immune-Related Resistance Mechanisms.

Purpose

Experimental design

Results

Conclusions

Publications

Prediction of Immunotherapy Response in Melanoma through Combined Modeling of Neoantigen Burden and Immune-Related Resistance Mechanisms.

Similar Datasets

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