Large-scale pathogenicity prediction analysis of cancer-associated kinase mutations reveals variability in sensitivity and specificity of computational methods.

BACKGROUND: Mutations in kinases are the most frequent genetic alterations in cancer; however, experimental evidence establishing their cancerous nature is available only for a small fraction of these mutants. AIMS: Predicition analysis of kinome mutations is the primary aim of this study. Further objective is to compare the performance of various softwares in pathogenicity prediction of kinase mutations. MATERIALS AND METHODS: We employed a set of computational tools to predict the pathogenicity of over forty-two thousand mutations and deposited the kinase-wise data in Mendeley database (Estimated Pathogenicity of Kinase Mutants [EPKiMu]). RESULTS: Mutations are more likely to be drivers when being present in the kinase domain (vs. non-kinase domain) and belonging to hotspot residues (vs. non-hotspot residues). We identified that, while predictive tools have low specificity in general, PolyPhen-2 had the best accuracy. Further efforts to combine all four tools by consensus, voting, or other simple methods did not significantly improve accuracy. DISCUSSION: The study provides a large dataset of kinase mutations along with their predicted pathogenicity that can be used as a training set for future studies. Furthermore, a comparative sensitivity and selectivity of commonly used computational tools is presented. CONCLUSION: Primary-structure-based in silico tools identified more cancerous/deleterious mutations in the kinase domains and at the hot spot residues while having higher sensitivity than specificity in detecting deleterious mutations.

Emergence of ERBB2 Mutation as a Biomarker and an Actionable Target in Solid Cancers.

The oncogenic role ERBB2 amplification is well established in breast and gastric cancers. This has led to the development of a well-known portfolio of monoclonal antibodies and kinase inhibitors targeting the ERBB2 kinase. More recently, activating mutations in the ERBB2 gene have been increasingly reported in multiple solid cancers and were shown to play an oncogenic role similar to that of ERBB2 amplification. Thus, ERBB2 mutations define a distinct molecular subtype of solid tumors and serve as actionable targets. However, efforts to target ERBB2 mutation has met with limited clinical success, possibly because of their low frequency, inadequate understanding of the biological activity of these mutations, and difficulty in separating the drivers from the passenger mutations. Given the current impetus to deliver molecularly targeted treatments for cancer, there is an important need to understand the therapeutic potential of ERBB2 mutations. Here we review the distribution of ERBB2 mutations in different tumor types, their potential as a novel biomarker that defines new subsets in many cancers, and current data on preclinical and clinical efforts to target these mutations. IMPLICATIONS FOR PRACTICE: A current trend in oncology is to identify novel genomic drivers of solid tumors and developing precision treatments that target them. ERBB2 amplification is an established therapeutic target in breast and gastric cancers, but efforts to translate this finding to other solid tumors with ERBB2 amplification have not been effective. Recently the focus has turned to targeting activating ERBB2 mutations. The year 2018 marked an important milestone in establishing ERBB2 mutation as an important actionable target in multiple cancer types. There have been several recent preclinical and clinical studies evaluating ERBB2 mutation as a therapeutic target with varying success. With increasing access to next-generation sequencing technologies in the clinic, oncologists are frequently identifying activating ERBB2 mutations in patients with cancer. There is a significant need both from the clinician and bench scientist perspectives to understand the current state of affairs for ERBB2 mutations.