MUC16 and breast cancer: Both of these approaches identified driver gene mutations in breast cancer-associated genes, such as MUC16, NF1, and BRCA2, suggesting that using different predictive computational tools improves the sensitivity and specificity in identifying cancer somatic mutations (Figure 2c).