Immunotherapy Response Prediction

Predict patient response to immune checkpoint inhibitors (ICIs) using multi-biomarker integration. Transforms a patient tumor profile (cancer type + mutations + biomarkers) into a quantitative ICI Response Score with drug-specific recommendations, resistance risk assessment, and monitoring plan.

Reasoning Before Searching

Not all tumors respond to checkpoint inhibitors. Reason through the biology before running tools:

• TMB (tumor mutational burden): More somatic mutations produce more neoantigens, which are recognized by T cells. High TMB (>=10 mut/Mb, FDA-approved threshold for pembrolizumab) generally predicts better response — but this varies by cancer type (e.g., RCC responds despite low TMB).
• MSI-H (microsatellite instability-high): Caused by defective DNA mismatch repair (MMR). MSI-H tumors have very high TMB and are pan-cancer approved for pembrolizumab. Check MLH1, MSH2, MSH6, PMS2 mutations.
• PD-L1 expression: The direct target of pembrolizumab/atezolizumab. High PD-L1 (TPS >=50% or CPS >=10 depending on cancer) predicts response in some cancers (NSCLC) but not all (melanoma, where TMB is more predictive).
• Resistance factors are equally important: STK11, KEAP1, JAK1/2 loss, B2M mutations can render an otherwise TMB-high tumor non-responsive.

Before calling any tool, determine which biomarkers are available for this patient and which are unknown. This determines which phases can be scored with data vs. must use cancer-type priors. Do not default to "moderate" for unknowns — flag them explicitly as missing.

LOOK UP DON'T GUESS: Never assume FDA approval for a biomarker-ICI combination — always verify with fda_pharmacogenomic_biomarkers or FDA_get_indications_by_drug_name. Cancer-specific thresholds differ from pan-cancer approvals.

KEY PRINCIPLES:

1. Report-first approach - Create report file FIRST, then populate progressively