A study published in Nature (2013) highlights the challenges in identifying cancer genes through genome sequencing. As sample sizes increase, the number of putatively significant genes identified by current methods grows significantly, often including implausible candidates like olfactory receptors and large proteins. This issue is attributed to mutational heterogeneity, where mutations vary widely across cancer types and within individual tumors. The study introduces MutSigCV, a novel analytical method that accounts for this heterogeneity, reducing false positives and identifying true cancer genes. The research analyzed exome sequences from 3,083 tumor-normal pairs, revealing substantial variation in mutation frequency and spectrum across cancer types. Mutation frequencies varied by over 1,000-fold, with some cancers, like melanoma and lung cancer, having extremely high mutation rates due to exposure to carcinogens. Mutation spectra also differed, with lung cancers showing C→A mutations, melanomas showing C→T mutations, and certain cancers showing mutations linked to APOBEC enzymes. The study also found that mutation rates correlate with DNA replication timing and gene expression levels. Genes with lower expression and later replication times had higher mutation rates, explaining why some genes, like olfactory receptors and large proteins, were frequently mutated. These findings suggest that accurate identification of cancer genes requires accounting for mutational heterogeneity. MutSigCV was applied to lung cancer data, reducing the list of significantly mutated genes from 450 to 11, including known cancer genes and a novel one, HLA-A, which may be involved in immune evasion. The study emphasizes the need for more comprehensive analyses that consider mutational heterogeneity to accurately identify cancer genes. The results underscore the importance of integrating genomic features like replication timing and expression levels in cancer gene discovery.A study published in Nature (2013) highlights the challenges in identifying cancer genes through genome sequencing. As sample sizes increase, the number of putatively significant genes identified by current methods grows significantly, often including implausible candidates like olfactory receptors and large proteins. This issue is attributed to mutational heterogeneity, where mutations vary widely across cancer types and within individual tumors. The study introduces MutSigCV, a novel analytical method that accounts for this heterogeneity, reducing false positives and identifying true cancer genes. The research analyzed exome sequences from 3,083 tumor-normal pairs, revealing substantial variation in mutation frequency and spectrum across cancer types. Mutation frequencies varied by over 1,000-fold, with some cancers, like melanoma and lung cancer, having extremely high mutation rates due to exposure to carcinogens. Mutation spectra also differed, with lung cancers showing C→A mutations, melanomas showing C→T mutations, and certain cancers showing mutations linked to APOBEC enzymes. The study also found that mutation rates correlate with DNA replication timing and gene expression levels. Genes with lower expression and later replication times had higher mutation rates, explaining why some genes, like olfactory receptors and large proteins, were frequently mutated. These findings suggest that accurate identification of cancer genes requires accounting for mutational heterogeneity. MutSigCV was applied to lung cancer data, reducing the list of significantly mutated genes from 450 to 11, including known cancer genes and a novel one, HLA-A, which may be involved in immune evasion. The study emphasizes the need for more comprehensive analyses that consider mutational heterogeneity to accurately identify cancer genes. The results underscore the importance of integrating genomic features like replication timing and expression levels in cancer gene discovery.