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). Here, NF1 is linked to cancer.