What are the key considerations in tumor genomics analysis?

Tumor genomics presents unique challenges: 1) Tumor heterogeneity - subclonal populations require high sequencing depth (>100x); clonal architecture reconstruction (PyClone, FACETS). 2) Somatic vs germline - distinguish somatic mutations using matched normal tissue; specialized callers (Mutect2, Strelka2, VarScan2). 3) Tumor purity and ploidy - estimate using copy number tools (ASCAT, ABSOLUTE); affects variant allele frequency interpretation. 4) Driver identification - distinguish drivers from passengers using databases (COSMIC, OncoKB), prediction tools (CHASMplus, PolyPhen), and statistical methods (MutSig, dNdScv for significantly mutated genes). 5) Copy number alterations - GISTIC identifies recurrent CNAs; integrate with expression. 6) Mutational signatures - decompose mutation patterns to infer etiologies (COSMIC signatures, SigProfiler). 7) Neoantigen prediction - identify immunogenic mutations for immunotherapy. 8) Liquid biopsy - ctDNA detection requires specialized sensitive methods.