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Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
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Recurrent somatic mutations as predictors of immunotherapy response
Zoran Z Gajic1,2,3, Aditya Deshpande1,4,5, Mateusz Legut1,2,3
1New York Genome Center, New York, NY, 10013, USA.
Nature Communications
|July 8, 2022
Summary
Identifying predictive biomarkers for immune checkpoint blockade (ICB) is crucial for cancer immunotherapy. Researchers developed the Cancer Immunotherapy Response CLassifiEr (CIRCLE) using mutation data, improving prediction accuracy over tumor mutational burden alone.
Area of Science:
- Oncology
- Immunology
- Genetics
Background:
- Immune checkpoint blockade (ICB) offers a promising cancer treatment modality.
- Variable patient responses to ICB highlight the need for predictive biomarkers.
- Accurate prediction of ICB response is essential for optimizing patient outcomes.
Purpose of the Study:
- To identify genes and pathways that predict response to ICB therapy.
- To develop a novel classifier for predicting ICB treatment efficacy.
- To improve upon existing methods for predicting cancer immunotherapy response.
Main Methods:
- Analysis of six whole exome sequencing cohorts with matched clinical outcomes.
- Identification of significantly mutated genes and pathways after covariate correction.
- Development and validation of the Cancer Immunotherapy Response CLassifiEr (CIRCLE).
Main Results:
- Identified BCLAF1, KRAS, BRAF, and TP53 mutations as predictors of ICB response.
- Highlighted MAPK signaling, p53-associated, and immunomodulatory pathways as predictive.
- CIRCLE demonstrated superior prediction accuracy compared to tumor mutational burden alone, with increased sensitivity and specificity.
Conclusions:
- Recurrently mutated cancer genes can serve as robust biomarkers for ICB response.
- The CIRCLE classifier offers improved prognostic capability for cancer immunotherapy.
- This approach paves the way for enhanced personalized treatment strategies in oncology.
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