NeoMutate: an ensemble machine learning framework for the prediction of somatic mutations in cancer

• Main Authors: Irantzu Anzar (Author), Angelina Sverchkova (Author), Richard Stratford (Author), Trevor Clancy (Author)
• Format: Book
• Published: BMC, 2019-05-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 10.1186/s12920-019-0508-5; 1755-8794
• Keywords: Somatic variant detection; Machine learning; Cancer genomics; Precision medicine; Internal medicine; RC31-1245; Genetics; QH426-470
• Source: BMC Medical Genomics, Vol 12, Iss 1, Pp 1-14 (2019)

Abstract Background The accurate screening of tumor genomic landscapes for somatic mutations using high-throughput sequencing involves a crucial step in precise clinical diagnosis and targeted therapy. However, the complex inherent features of cancer tissue, especially, tumor genetic intra-heterogeneity coupled with the problem of sequencing and alignment artifacts, makes somatic variant calling a challenging task. Current variant filtering strategies, such as rule-based filtering and consensus voting of different algorithms, have previously helped to increase specificity, although comes at the cost of sensitivity. Methods In light of this, we have developed the NeoMutate framework which incorporates 7 supervised machine learning (ML) algorithms to exploit the strengths of multiple variant callers, using a non-redundant set of biological and sequence features. We benchmarked NeoMutate by simulating more than 10,000 bona fide cancer-related mutations into three well-characterized Genome in a Bottle (GIAB) reference samples. Results A robust and exhaustive evaluation of NeoMutate's performance based on 5-fold cross validation experiments, in addition to 3 independent tests, demonstrated a substantially improved variant detection accuracy compared to any of its individual composite variant callers and consensus calling of multiple tools. Conclusions We show here that integrating multiple tools in an ensemble ML layer optimizes somatic variant detection rates, leading to a potentially improved variant selection framework for the diagnosis and treatment of cancer.