Integrated Proteogenomic Characterization of HBV-Related Hepatocellular Carcinoma

Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be considered instances of domain-specific AI which have been successfully employed for drug discovery and design. This review provides a comprehensive portrayal of these machine learning techniques and of their applications in medicinal chemistry. After introducing the basic principles, alongside some application notes, of the various machine learning algorithms, the current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects. Finally, several challenges and limitations of the current methods are summarized, with a view to potential future directions for AI-assisted drug discovery and design.

Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

Ras is frequently mutated in cancer, however, there is a lack of consensus in the literature regarding the cancer mutation frequency of Ras, with quoted values varying from 10%–30%. This variability is at least in part due to the selective aggregation of data from different databases and the dominant influence of particular cancer types and particular Ras isoforms within these datasets. To provide a more definitive figure for Ras mutation frequency in cancer, we cross-referenced the data in all major publicly accessible cancer mutation databases to determine reliable mutation frequency values for each Ras isoform in all major cancer types. These percentages were then applied to current U.S. cancer incidence statistics to estimate the number of new patients each year that have Ras-mutant cancers. We find that approximately 19% of patients with cancer harbor Ras mutations, equivalent to approximately 3.4 million new cases per year worldwide. We discuss the Ras isoform and mutation-specific trends evident within the datasets that are relevant to current Ras-targeted therapies.

https://cancerres.aacrjournals.org/content/canres/early/2020/03/24/0008-5472.CAN-19-3682.full.pdf

The Frequency of Ras Mutations in Cancer.

The Drug-Gene Interaction Database (DGIdb, www.dgidb.org) is a web resource that provides information on drug-gene interactions and druggable genes from publications, databases, and other web-based sources. Drug, gene, and interaction data are normalized and merged into conceptual groups. The information contained in this resource is available to users through a straightforward search interface, an application programming interface (API), and TSV data downloads. DGIdb 4.0 is the latest major version release of this database. A primary focus of this update was integration with crowdsourced efforts, leveraging the Drug Target Commons for community-contributed interaction data, Wikidata to facilitate term normalization, and export to NDEx for drug-gene interaction network representations. Seven new sources have been added since the last major version release, bringing the total number of sources included to 41. Of the previously aggregated sources, 15 have been updated. DGIdb 4.0 also includes improvements to the process of drug normalization and grouping of imported sources. Other notable updates include the introduction of a more sophisticated Query Score for interaction search results, an updated Interaction Score, the inclusion of interaction directionality, and several additional improvements to search features, data releases, licensing documentation and the application framework.

/pdf/integration-of-the-drug-gene-interaction-database-dgidb-4-0-5dy3jioy3j.pdf

Integration of the Drug-Gene Interaction Database (DGIdb 4.0) with open crowdsource efforts.

A high tumour mutational burden (hypermutation) is observed in some gliomas1–5; however, the mechanisms by which hypermutation develops and whether it predicts the response to immunotherapy are poorly understood. Here we comprehensively analyse the molecular determinants of mutational burden and signatures in 10,294 gliomas. We delineate two main pathways to hypermutation: a de novo pathway associated with constitutional defects in DNA polymerase and mismatch repair (MMR) genes, and a more common post-treatment pathway, associated with acquired resistance driven by MMR defects in chemotherapy-sensitive gliomas that recur after treatment with the chemotherapy drug temozolomide. Experimentally, the mutational signature of post-treatment hypermutated gliomas was recapitulated by temozolomide-induced damage in cells with MMR deficiency. MMR-deficient gliomas were characterized by a lack of prominent T cell infiltrates, extensive intratumoral heterogeneity, poor patient survival and a low rate of response to PD-1 blockade. Moreover, although bulk analyses did not detect microsatellite instability in MMR-deficient gliomas, single-cell whole-genome sequencing analysis of post-treatment hypermutated glioma cells identified microsatellite mutations. These results show that chemotherapy can drive the acquisition of hypermutated populations without promoting a response to PD-1 blockade and supports the diagnostic use of mutational burden and signatures in cancer. Temozolomide therapy seems to lead to mismatch repair deficiency and hypermutation in gliomas, but not to an increase in response to immunotherapy.

Mechanisms and therapeutic implications of hypermutation in gliomas.

COSMIC, the Catalogue Of Somatic Mutations In Cancer (https://cancer.sanger.ac.uk) is the most detailed and comprehensive resource for exploring the effect of somatic mutations in human cancer. The latest release, COSMIC v86 (August 2018), includes almost 6 million coding mutations across 1.4 million tumour samples, curated from over 26 000 publications. In addition to coding mutations, COSMIC covers all the genetic mechanisms by which somatic mutations promote cancer, including non-coding mutations, gene fusions, copy-number variants and drug-resistance mutations. COSMIC is primarily hand-curated, ensuring quality, accuracy and descriptive data capture. Building on our manual curation processes, we are introducing new initiatives that allow us to prioritize key genes and diseases, and to react more quickly and comprehensively to new findings in the literature. Alongside improvements to the public website and data-download systems, new functionality in COSMIC-3D allows exploration of mutations within three-dimensional protein structures, their protein structural and functional impacts, and implications for druggability. In parallel with COSMIC's deep and broad variant coverage, the Cancer Gene Census (CGC) describes a curated catalogue of genes driving every form of human cancer. Currently describing 719 genes, the CGC has recently introduced functional descriptions of how each gene drives disease, summarized into the 10 cancer Hallmarks.

/pdf/cosmic-the-catalogue-of-somatic-mutations-in-cancer-351kesiecq.pdf

Kate Noble

Papers

COSMIC: the Catalogue Of Somatic Mutations In Cancer