Background: It is well known that the development of cancer is caused by the accumulation of somatic mutations within the genome. For oncogenes specifically, current research suggests that there is a small set of "driver" mutations that are primarily responsible for tumorigenesis. Further, due to some recent pharmacological successes in treating these driver mutations and their resulting tumors, a variety of methods have been developed to identify potential driver mutations using methods such as machine learning and mutational clustering. We propose a novel methodology that increases our power to identify mutational clusters by taking into account protein tertiary structure via a graph theoretical approach.
Results: We have designed and implemented GraphPAC (Graph Protein Amino Acid Clustering) to identify mutational clustering while considering protein spatial structure. Using GraphPAC, we are able to detect novel clusters in proteins that are known to exhibit mutation clustering as well as identify clusters in proteins without evidence of prior clustering based on current methods. Specifically, by utilizing the spatial information available in the Protein Data Bank (PDB) along with the mutational data in the Catalogue of Somatic Mutations in Cancer (COSMIC), GraphPAC identifies new mutational clusters in well known oncogenes such as EGFR and KRAS. Further, by utilizing graph theory to account for the tertiary structure, GraphPAC identifies clusters in DPP4, NRP1 and other proteins not identified by existing methods. The R package is available at: http://bioconductor.org/packages/release/bioc/html/GraphPAC.html
Conclusion: GraphPAC provides an alternative to iPAC and an extension to current methodology when identifying potential activating driver mutations by utilizing a graph theoretic approach when considering protein tertiary structure.
Learning the statistics and landscape of somatic mutation-induced insertions and deletions in antibodies
Laboratoire de physique de l’École normale supérieure, CNRS, PSL University, Sorbonne Université, and Université de Paris（巴黎高等师范学院物理实验室，法国国家科学研究中心，巴黎文理研究大学，索邦大学，巴黎大学）；Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Roma I（意大利国家核物理研究所罗马一分所）
Deciphering the Language of Nature: A transformer-based language model for deleterious mutations in proteins
Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia（费城儿童医院雷蒙德·G·佩雷尔曼细胞与分子治疗中心）；Palisades Charter High School（帕利塞德特许高中）；Massachusetts Institute of Technology（麻省理工学院）；Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）
Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data
Stanford University（斯坦福大学）；Università degli Studi di Milano-Bicocca（米兰比可卡大学）；Institute of Molecular Bioimaging and Physiology of the Italian National Research Council (IBFM-CNR)（意大利国家研究委员会分子生物成像与生理学研究所（IBFM-CNR））；Milan Center for Neuroscience（米兰神经科学中心）；The Institute of Cancer Research（癌症研究所）
