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Real-world data (RWD) and real-world evidence (RWE) from heterogeneous data sources has the potential to transform oncology research, especially when coupled with artificial intelligence (AI). We discuss the issues involved in primary data capture and post-hoc AI analysis and propose using AI to support the capture of primary RWD.
Neoantigen immunogenicity prediction is a burgeoning field with vast potential; however, the shortage of high-quality data and biases in current datasets limit model generalizability. Here we discuss some of the pitfalls that may underly this limited performance and propose a path forward.
Owing to high response rates, the Food and Drug Administration has approved both gene- and immune-targeted drugs for tumor-agnostic, genomic biomarker-based indications, for lethal solid and blood cancers. We posit that current data support tissue-agnostic activity as a paradigm, rather than an exception to the rule.
