Biology subjects

Cotto, K. C.

Publications and source records attributed to Cotto, K. C..

6 recordsLinked to original sources

bioRxiv2021

Genomic and transcriptomic somatic alterations of hepatocellular carcinoma in non-cirrhotic livers

BackgroundLiver cancer is the second leading cause of cancer-related deaths worldwide. Hepatocellular carcinoma (HCC) risk factors include chronic hepatitis, cirrhosis, and alcohol abuse, whereby tumorigenesis is induced through inflammation and subsequent fibrotic response. However, a subset of HCC arises in non-cirrhotic livers. We characterized the genomic and transcriptomic landscape of non-cirrhotic HCC to identify features underlying the diseases development and progression. MethodsWhole genome and transcriptome sequencing was performed on 30 surgically resectable tumors comprised of primarily of non-cirrhotic HCC and adjacent normal tissue. Using somatic variants, capture reagents were created and employed on an additional 87 cases of mixed cirrhotic/non-cirrhotic HCC. Cases were analyzed to identify viral integrations, single nucleotide variants (SNVs), insertions and deletions (INDELS), copy number variants, loss of heterozygosity, gene fusions, structural variants, and differential gene expression. ResultsWe detected 3,750 SNVs/INDELS and extensive CNVs and expression changes. Recurrent TERT promoter mutations occurred in >52% of non-cirrhotic discovery samples. Frequently mutated genes included TP53, CTNNB1, and APOB. Cytochrome P450 mediated metabolism was significantly downregulated. Structural variants were observed at MACROD2, WDPCP and NCKAP5 in >20% of samples. Furthermore, NR1H4 fusions involving gene partners EWSR1, GNPTAB, and FNIP1 were detected and validated in 2 non-cirrhotic samples. ConclusionGenomic analysis can elucidate mechanisms that may contribute to non-cirrhotic HCC tumorigenesis. The comparable mutational landscape between cirrhotic and non-cirrhotic HCC supports previous work suggesting a convergence at the genomic level during disease progression. It is therefore possible genomic-based treatments can be applied to both HCC subtypes with progressed disease. HighlightsO_LINon-cirrhotic HCC genomically resembles cirrhotic HCC C_LIO_LIComprehensive genome- and transcriptome-wide profiling allows detection of novel structural variants, fusions, and undiagnosed viral infections C_LIO_LINR1H4 fusions may represent a novel mechanism for tumorigenesis in HCC C_LIO_LINon-cirrhotic HCC is characterized by genotoxic mutational signatures and dysregulated liver metabolism C_LIO_LIClinical history and comprehensive omic profiling incompletely explain underlying etiologies for non-cirrhotic HCC highlighting the need for further research C_LI Short DescriptionThis study characterizes the genomic landscape of hepatocellular carcinomas (HCCs) in non-cirrhotic livers. Using 117 HCCs tumor/normal pairs, we identified 3,750 SNVs/INDELS with high variant frequency in TERT, TP53, CTNNB1, and APOB. CYP450 was significantly downregulated and many structural variants were observed. This characterization could assist in elucidating non-cirrhotic HCC tumorigenesis.

cancer biology↗

bioRxiv2020

Accurate neoantigen prediction depends on mutation position relative to patient allele-specific MHC anchor location

Neoantigens are novel peptide sequences resulting from sources such as somatic mutations in tumors. Upon loading onto major histocompatibility complex (MHC) molecules, they can trigger recognition by T cells. Accurate neoantigen identification is thus critical for both designing cancer vaccines and predicting response to immunotherapies. Neoantigen identification and prioritization relies on correctly predicting whether the presenting peptide sequence can successfully induce an immune response. As the majority of somatic mutations are single nucleotide variants, changes between wildtype and mutated peptides are typically subtle and require cautious interpretation. A potentially underappreciated variable in neoantigen-prediction pipelines is the mutation position within the peptide relative to its anchor positions for the patients specific MHC molecules. While a subset of peptide positions are presented to the T-cell receptor for recognition, others are responsible for anchoring to the MHC, making these positional considerations critical for predicting T-cell responses. We computationally predicted high probability anchor positions for different peptide lengths for 328 common HLA alleles and identified unique anchoring patterns among them. Analysis of 923 tumor samples shows that 6-38% of neoantigen candidates are potentially misclassified and can be rescued using allelespecific knowledge of anchor positions. A subset of anchor results were orthogonally validated using protein crystallography structures. Representative anchor trends were experimentally validated using peptide-MHC stability assays and competition binding assays. By incorporating our anchor prediction results into neoantigen prediction pipelines, we hope to formalize, streamline and improve the identification process for relevant clinical studies. One Sentence SummaryNeoantigen prediction accuracy is significantly influenced by the mutation position within the neoantigen and its relative position to the patients allele-specific MHC anchor locations.

bioinformatics↗

bioRxiv2020

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

The Drug-Gene Interaction Database (DGIdb, www.dgidb.org) is a web resource that provides information on drug-gene interactions and druggable genes from various sources including publications, databases, and other web-based sources in one resource. These drug, gene, and interaction claims are normalized and grouped to identify aliases, merge concepts, and reduce redundancy. 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 update of this database. Seven new sources have been added, 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 further development of automatic jobs for routine data updates, more sophisticated query scores for interaction search results, extensive manual curation of interaction source link outs, and the inclusion of interaction directionality. A major focus of this update was integration with crowd-sourced efforts, including leveraging the curation activities of Drug Target Commons, using Wikidata to facilitate term normalization, and integrating into NDEx for producing network representations.

bioinformatics↗

bioRxiv2019

CIViCpy: a Python software development and analysis toolkit for the CIViC knowledgebase

PurposePrecision oncology is dependent upon the matching of tumor variants to relevant knowledge describing the clinical significance of those variants. We recently developed the Clinical Interpretations for Variants in Cancer (CIViC; civicdb.org) crowd-sourced, expert-moderated, open-access knowledgebase, providing a structured framework for evaluating genomic variants of various types (e.g., fusions, SNVs) for their therapeutic, prognostic, predisposing, diagnostic, or functional utility. CIViC has a documented API for accessing CIViC records: Assertions, Evidence, Variants, and Genes. Third-party tools that analyze or access the contents of this knowledgebase programmatically must leverage this API, often reimplementing redundant functionality in the pursuit of common analysis tasks that are beyond the scope of the CIViC web application.\n\nMethodsTo address this limitation, we developed CIViCpy (civicpy.org), a software development kit (SDK) for extracting and analyzing the contents of the CIViC knowledgebase. CIViCpy enables users to query CIViC content as dynamic objects in Python. We assess the viability of CIViCpy as a tool for advancing individualized patient care by using it to systematically match CIViC evidence to observed variants in patient cancer samples.\n\nResultsWe used CIViCpy to evaluate variants from 59,437 sequenced tumors of the AACR Project GENIE dataset. We demonstrate that CIViCpy enables annotation of >1,200 variants per second, resulting in precise variant matches to CIViC level A (professional guideline) or B (clinical trial) evidence for 38.6% of tumors.\n\nConclusionsThe clinical interpretation of genomic variants in cancers requires high-throughput tools for interoperability and analysis of variant interpretation knowledge. These needs are met by CIViCpy, an SDK for downstream applications and rapid analysis. CIViCpy (civicpy.org) is fully documented, open-source, and freely available online.

bioinformatics↗

bioRxiv2018

RegTools: Integrated analysis of genomic and transcriptomic data for discovery of splicing variants in cancer

Somatic mutations within non-coding regions and even exons may have unidentified regulatory consequences that are often overlooked in analysis workflows. Here we present RegTools (www.regtools.org), a computationally efficient, free, and open-source software package designed to integrate somatic variants from genomic data with splice junctions from bulk or single cell transcriptomic data to identify variants that may cause aberrant splicing. RegTools was applied to over 9,000 tumor samples with both tumor DNA and RNA sequence data. We discovered 235,778 events where a splice-associated variant significantly increased the splicing of a particular junction, across 158,200 unique variants and 131,212 unique junctions. To characterize these somatic variants and their associated splice isoforms, we annotated them with the Variant Effect Predictor (VEP), SpliceAI, and Genotype-Tissue Expression (GTEx) junction counts and compared our results to other tools that integrate genomic and transcriptomic data. While many events were corroborated by the aforementioned tools, the flexibility of RegTools also allowed us to identify novel splice-associated variants and previously unreported patterns of splicing disruption in known cancer drivers, such as TP53, CDKN2A, and B2M, as well as in genes not previously considered cancer-relevant.

bioinformatics↗

bioRxiv2017

DGIdb 3.0: a redesign and expansion of the drug-gene interaction database

The Drug-Gene Interaction Database (DGIdb, www.dgidb.org) consolidates, organizes, and presents drug-gene interactions and gene druggability information from papers, databases, and web resources. DGIdb normalizes content from more than thirty disparate sources and allows for user-friendly advanced browsing, searching and filtering for ease of access through an intuitive web user interface, application programming interface (API), and public cloud-based server image. DGIdb v3.0 represents a major update of the database. Nine of the previously included twenty-eight sources were updated. Six new resources were added, bringing the total number of sources to thirty-three. These updates and additions of sources have cumulatively resulted in 56,309 interaction claims. This has also substantially expanded the comprehensive catalogue of druggable genes and antineoplastic drug-gene interactions included in the DGIdb. Along with these content updates, v3.0 has received a major overhaul of its codebase, including an updated user interface, preset interaction search filters, consolidation of interaction information into interaction groups, greatly improved search response times, and upgrading the underlying web application framework. In addition, the expanded API features new endpoints which allow users to extract more detailed information about queried drugs, genes, and drug-gene interactions, including listings of PubMed IDs (PMIDs), interaction type, and other interaction metadata.

bioinformatics↗