This brief demonstration explores methods to rank immune oncology targets in support of indication selection and precision oncology. We will apply our suite of digital assets—interrogating mutation, expression, and clinical data, as well as mechanistic drivers of disease—to identify new possibilities for therapeutic intervention.

Mirroring successful examples like Alpelisib (a PI3Kα inhibitor for cancers with PIK3CA mutations), we summarize the case for MUC16 targeting in pancreatic cancer using tools accessible to non-informaticians.

Techniques Highlighted:

• Survey top mutated genes from TCGA, GEO, and other sources, prioritizing those with cell surface distribution.

• Triage by structural features, browsing functional relevance within transmembrane and extracellular domains to flag MUC16 as a candidate.

• Correlate variants with clinical survival, visualizing prognostic impact by tumor type.

• Overlay transcriptomic data with patient survival, highlighting cancers where MUC16 presents high therapeutic potential.

• Systems analysis of MUC16’s mechanistic connections to hallmark oncogenes, modulating pathways involved in neovascularization, immune evasion, and cell cycle dysregulation.

• Use in silico modeling to infer therapeutic effects of MUC16 inhibition, suggesting its potential to disrupt oncogenic circuits and reverse aggressive cancer phenotypes.