The article discusses the challenges and solutions in identifying cancer genes through comprehensive genome sequencing studies. It highlights that as sample sizes increase, the number of putatively significant genes grows, often including many implausible genes, leading to extensive false positives. The authors identify mutational heterogeneity as the primary cause of this issue and introduce MutSigCV, a novel analytical method that accounts for patient-specific mutation frequencies and gene-specific background mutation rates. By applying MutSigCV to exome sequences from 3,083 tumor-normal pairs, they uncover significant variation in mutation frequency and spectrum within cancer types, providing insights into mutational processes and disease etiology. Additionally, they find that mutation frequency across the genome is strongly correlated with DNA replication timing and transcriptional activity. These findings help eliminate false positives and highlight true cancer genes, such as HLA-A, which may play a role in immune evasion. The study underscores the importance of accurately modeling mutational processes to identify new cancer genes effectively.The article discusses the challenges and solutions in identifying cancer genes through comprehensive genome sequencing studies. It highlights that as sample sizes increase, the number of putatively significant genes grows, often including many implausible genes, leading to extensive false positives. The authors identify mutational heterogeneity as the primary cause of this issue and introduce MutSigCV, a novel analytical method that accounts for patient-specific mutation frequencies and gene-specific background mutation rates. By applying MutSigCV to exome sequences from 3,083 tumor-normal pairs, they uncover significant variation in mutation frequency and spectrum within cancer types, providing insights into mutational processes and disease etiology. Additionally, they find that mutation frequency across the genome is strongly correlated with DNA replication timing and transcriptional activity. These findings help eliminate false positives and highlight true cancer genes, such as HLA-A, which may play a role in immune evasion. The study underscores the importance of accurately modeling mutational processes to identify new cancer genes effectively.