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Biological Pathway-Derived TMB Robustly Predicts the Outcome of Immune Checkpoint Blockade Therapy
Ya-Ru Miao1, Chun-Jie Liu1, Hui Hu1
1Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.
Abstract:
Although immune checkpoint blockade (ICB) therapies have achieved great progress, the patient response varies among cancers. In this study, we analyzed the potential genomic indicators contributing to ICB therapy response. The results showed that high tumor mutation burden (TMB) failed to predict response in anti-PD1 treated melanoma. SERPINB3 was the most significant response-related gene in melanoma and mutations in either SERPINB3 or PEG3 can serve as an independent risk factor in melanoma. Some recurrent mutations in CSMD3 were only in responders or non-responders, indicating their diverse impacts on patient response. Enrichment scores (ES) of gene mutations in 12 biological pathways were significantly higher in responders or non-responders. Next, the P-TMB calculated from genes in these pathways was significantly related to patient response with prediction AUC 0.74-0.82 in all collected datasets. In conclusion, our work provides new insights into the application of TMB in predicting patient response, which will benefit to immunotherapy research.
Insights
High tumor mutation burden (TMB) does not predict immunotherapy response in melanoma. Specific gene mutations and pathway analysis offer better prediction for anti-PD1 therapy success.
Area of Science:
- Oncology
- Genomics
- Immunotherapy
Background:
- Immune checkpoint blockade (ICB) therapies show variable patient responses across cancers.
- Predicting response to ICB therapies remains a challenge in clinical oncology.
Purpose of the Study:
- To identify genomic indicators that predict response to ICB therapy, specifically anti-PD1 treatment in melanoma.
- To evaluate the predictive power of tumor mutation burden (TMB) and novel gene/pathway signatures.
Main Methods:
- Analysis of genomic data from melanoma patients treated with anti-PD1 therapy.
- Assessment of tumor mutation burden (TMB) and individual gene mutations (e.g., SERPINB3, PEG3, CSMD3).
- Calculation of pathway enrichment scores (ES) and a novel pathway-based TMB (P-TMB) for predictive modeling.
Main Results:
- High TMB did not reliably predict anti-PD1 response in melanoma.
- SERPINB3 mutations were significantly associated with treatment response and identified as a risk factor.
- Mutations in PEG3 and CSMD3 also showed potential as predictive or risk factors.
- Pathway enrichment scores and P-TMB demonstrated significant correlation with patient response, achieving AUCs of 0.74-0.82.
Conclusions:
- Novel genomic markers, including specific gene mutations and pathway-based TMB, offer improved prediction of ICB therapy response in melanoma.
- These findings provide new insights into applying genomic data for personalized immunotherapy strategies.
