Cancer Genomics

Cancer genomics studies how mutations, gene expression, copy-number changes, and other molecular features shape tumor development, patient outcomes, and treatment response. Common approaches include statistical driver and mutation-signature analysis, multi-omics integration, and pathway or regulatory-network models, alongside machine learning on sequencing, pathology images, and scans. Researchers evaluate these methods on resources such as TCGA and clinical cohorts, with tasks including subtype classification, survival prediction, biomarker discovery, and drug response. A central challenge is making predictions that generalize across cancers and incomplete datasets while remaining biologically interpretable and reliable enough for clinical use.