Immunotherapy treatments such as antibodies targeting cytotoxic T lymphocyte-associated antigen 4 (CTLA-4), programmed cell death protein 1 (PD-1) and PD-1 ligand1 (PD-L1) have shown promise in reactivating weakened immune cells to fight cancer. While these immunotherapies have had a dramatic impact in some cancer patients, the positive results only appear in a fraction of cases. The cost of treatment and potential for immune-related adverse events make it imperative that doctors have protocols to identify patient populations with an increased likelihood of successful outcomes with immunotherapy. This has led to a search for predictive biomarkers that may allow identification of such patients. Some have turned to artificial intelligence (AI) to scour data to identify common biomarkers or other covariates in patients successfully treated with immunotherapy.
AI has the capability of reviewing a staggering amount of patient data to identify recurring patterns of shared predictive factors that would elude unaided human capacity.
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