DNA Damage Response Alterations and Immune Checkpoint Blockade Outcomes Across Multiple Cancers

Area of Science:

• Genomics
• Cancer Biology
• Immunotherapy

Background:

• Alterations in DNA damage response (DDR) pathways are increasingly recognized for their role in predicting patient responses to cancer immunotherapies.
• However, the pan-cancer utility of DDR alterations as biomarkers for immune checkpoint blockade (ICB) efficacy is not well-established.

Purpose of the Study:

• To comprehensively characterize DDR mutational landscapes across various cancer types.
• To evaluate the predictive capability of DDR alterations for ICB treatment outcomes.

Main Methods:

• Integrated multiomics data from The Cancer Genome Atlas (TCGA) and independent ICB-treated cohorts.
• Employed unsupervised clustering and machine learning to stratify patients based on DDR mutation patterns.
• Conducted survival analyses and multivariable Cox models, assessing independence from tumor mutational burden (TMB).
• Utilized bioinformatic analyses to explore immune-related features associated with DDR-defined subtypes.

Main Results:

• Pan-cancer DDR clustering showed limited prognostic value outside of ICB treatment.
• Four DDR mutation-based subtypes significantly predicted overall survival in ICB-treated melanoma, non-small cell lung cancer, and gastrointestinal cancers.
• These DDR subtypes demonstrated treatment-specific predictive relevance, not predicting survival in non-ICB cohorts.
• A DDR-defined high-risk subgroup in melanoma showed poor survival independent of TMB, which lacked independent predictive value.
• DDR subtypes correlated with distinct transcriptional programs in immune signaling and DNA damage repair.

Conclusions:

• DDR mutational landscapes serve as context-dependent biomarkers for ICB efficacy.
• A DDR-based machine learning model predicts ICB outcomes, offering advantages over TMB.
• Supports a framework for developing context-aware biomarkers in precision oncology.