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Published on: February 8, 2018
Integrative Tumor and Immune Cell Multi-omic Analyses Predict Response to Immune Checkpoint Blockade in Melanoma
Valsamo Anagnostou1,2, Daniel C Bruhm1, Noushin Niknafs1
1The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.
Abstract:
In this study, we incorporate analyses of genome-wide sequence and structural alterations with pre- and on-therapy transcriptomic and T cell repertoire features in immunotherapy-naive melanoma patients treated with immune checkpoint blockade. Although tumor mutation burden is associated with improved treatment response, the mutation frequency in expressed genes is superior in predicting outcome. Increased T cell density in baseline tumors and dynamic changes in regression or expansion of the T cell repertoire during therapy distinguish responders from non-responders. Transcriptome analyses reveal an increased abundance of B cell subsets in tumors from responders and patterns of molecular response related to expressed mutation elimination or retention that reflect clinical outcome. High-dimensional genomic, transcriptomic, and immune repertoire data were integrated into a multi-modal predictor of response. These findings identify genomic and transcriptomic characteristics of tumors and immune cells that predict response to immune checkpoint blockade and highlight the importance of pre-existing T and B cell immunity in therapeutic outcomes.
Insights
Predicting melanoma treatment response to immune checkpoint blockade is improved by analyzing gene mutations and T-cell activity. Pre-existing T and B cell immunity significantly impacts therapeutic success.
Area of Science:
- Oncology
- Immunology
- Genomics
Background:
- Immune checkpoint blockade (ICB) therapy has revolutionized melanoma treatment.
- Predicting patient response to ICB remains a challenge, necessitating deeper understanding of underlying biological mechanisms.
Purpose of the Study:
- To integrate multi-omic data for predicting response to ICB in immunotherapy-naive melanoma patients.
- To identify genomic, transcriptomic, and immune features associated with treatment outcomes.
Main Methods:
- Analysis of genome-wide sequence and structural alterations.
- Assessment of pre- and on-therapy transcriptomic and T-cell repertoire features.
- Integration of high-dimensional genomic, transcriptomic, and immune repertoire data into a predictive model.
Main Results:
- Mutation frequency in expressed genes is a superior predictor of outcome compared to tumor mutation burden.
- Increased T-cell density and dynamic T-cell repertoire changes during therapy differentiate responders from non-responders.
- Responders show increased B-cell subsets and distinct patterns of gene mutation elimination/retention.
Conclusions:
- Genomic and transcriptomic profiles of tumors and immune cells predict ICB response in melanoma.
- Pre-existing T and B cell immunity plays a crucial role in therapeutic outcomes.
- A multi-modal approach integrating diverse data types enhances prediction accuracy.
