Biology subjects

Griffith, O. L.

Publications and source records attributed to Griffith, O. L..

At least 19 recordsLinked to original sources

bioRxiv2026

Refinement of the Pathogenic T Cell Receptor Motif Suggests Type 17 CD8+ T Cells Initiate HLA-B*27-associated Autoimmunity

Human leukocyte antigen B*27 (HLA-B*27) is a major risk factor for autoimmune diseases such as axial spondyloarthritis (axSpA), acute anterior uveitis (AAU), and psoriatic arthritis (PsA). HLA-B*27 presents bacterial and self-antigens to disease-associated CD8+ T cells, however, how this leads to a pathogenic IL-17-mediated disease remains unclear. We performed mutational experiments in an established disease-associated TCR and demonstrated that the pathogenic motif encompasses a wider range of TCR sequences than previously appreciated. We next leveraged computational pairing of alpha/beta TCR sequencing (TCR-seq) to demonstrate that the broadened pathogenic motif distinguishes axSpA and AAU patients from healthy controls. Paired single-cell RNA sequencing and single-cell TCR sequencing datasets demonstrated that the pathogenic cells have a distinct transcriptional signature. We find a coupling of pathogenic TCRs to this transcriptional signature in public datasets from axSpA and PsA joint fluid and AAU ocular fluid. Finally, we detect the pathogenic CD8+ T cells in the gut and demonstrate that they express a Type 17 program. Our findings suggest that HLA-B*27+ axSpA, AAU, and PsA are initiated by HLA-B*27-restricted pathogenic CD8+ T cells that undergo Type 17 differentiation in response to intestinal antigens.

immunology↗

bioRxiv2026

Personalized Neoantigen Vaccines Synergize with Immune Checkpoint Therapy and CD8-Targeted Cytokines to Control B-Cell Lymphoma

Personalized neoantigen (neoAg) vaccines have shown clinical promise in solid tumors1-8, yet their efficacy and mechanism of action in hematopoietic malignancies remain poorly defined9-11. Herein, we establish an immunocompetent syngeneic A20 B-cell lymphoma platform to test the efficacy of neoAg vaccines used either as mono- or combinatorial therapies with other immunotherapies12-17. Whereas subcutaneous A20 tumors were refractory to single-agent PD-1 or CTLA4 therapy, they were eradicated in a T cell-dependent manner in 90% of syngeneic hosts treated with dual immune checkpoint therapy (dual ICT, i.e., PD-1 + CTLA4). By mapping antigen specificity of dual-ICT-elicited T cells, we identified and validated dominant endogenous A20 MHC-I and MHC-II neoantigens and designed therapeutic synthetic long peptide (SLP) vaccines containing these neoepitopes. This vaccine (A20 neoVAX) promoted robust neoAg-specific CD4{square} and CD8{square} T cell responses in naive syngeneic BALB/c mice and induced tumor rejection in [~]70% of subcutaneous tumor-bearing mice. In addition, nearly all mice rejected their subcutaneous A20 tumors when A20 neoVAX was combined with PD-1. To render the results of this study more physiologic, we developed a systemic A20 lymphoma model and found that dual ICT failed to control tumor progression and A20 neoVAX delayed tumor progression and prolonged animal survival but did not induce tumor rejection. In contrast, A20 neoVAX plus dual ICT achieved durable systemic tumor elimination. Mechanistically, the combination of A20 neoVAX plus dual ICT amplified priming of A20 neoAg-specific T cells, prevented T cell dysfunction, sustained the cytotoxic capacity of tumor-specific CD8+ T cells, and induced Th1-skewing of CD4+ T cells in tumor and peripheral compartments. To increase the clinical relevance of these findings and to minimize potential adverse events in tumor-bearing, therapeutically treated individuals, we substituted CD8-targeted cytokine muteins (CD8-IL2 or CD8-IL21) for CTLA4. These agents represent genetically modified forms of IL-2 or IL-21 that selectively stimulate CD8+ T cells but have significantly reduced capacity to activate chronic inflammation and immunosuppressive functions of other immune cells. Whereas mice bearing systemic A20 lymphoma treated with either nothing, A20 neoVAX, or A20 neoVAX + CD8-IL2 failed to control tumor outgrowth, 66.7% of tumor-bearing mice treated with A20 neoVAX + CD8-IL2 + PD-1 rejected their tumors. In similar experiments in which CD8-IL21 was substituted for CD8-IL2, tumor clearance was also observed in two-thirds of A20-bearing mice but now rejection occurred in the absence of PD1. Together, these data define a framework for optimal personalized neoAg vaccination in B-lymphoma and demonstrate that neoAg vaccines can safely synergize with CD8+ T cell-selective immunotherapies to prevent T-cell dysfunction and generate durable systemic anti-tumor immunity.

immunology↗

bioRxiv2026

Searching the Druggable Genome using Large Language Models

SummaryThe druggable genome encompasses the genes that are known or predicted to interact with drugs. The Drug-Gene Interaction Database (DGIdb) provides an integrated resource for discovering and contextualizing these interactions, supporting a broad range of research and clinical applications. DGIdb is currently accessed through structured web interfaces and API calls, requiring users to translate natural-language questions into database-specific query patterns. To allow for the use of DGIdb through natural language, we developed the DGIdb Model Context Protocol (MCP) server, which allows large language models (LLMs) access to up-to-date information through the DGIdb API. We demonstrate that the MCP server greatly enhances an LLMs ability to answer questions requiring accurate, up-to-date biomedical knowledge drawn from structured external resources. Availability and implementationThe DGIdb MCP server is detailed at https://github.com/griffithlab/dgidb-mcp-server and includes instructions for accessing the server through the Claude desktop app.

bioinformatics↗

bioRxiv2026

dgiLIT: A Method for Prioritization and AI Curation of Drug-Gene Interactions

IMPORTANCEThe Drug-Gene Interaction Database (DGIdb) has a long history of driving hypothesis generation for biomedical research through the careful curation of drug-gene interaction data from primary and secondary sources with supporting literature. Recent advances in large-language model (LLM) and artificial intelligence (AI) technologies have enabled new paradigms for knowledge extraction and biocuration. The accelerating growth of biomedical literature presents a significant challenge for maintaining up-to-date interaction data. With more than 38 million citations indexed in PubMed alone, new strategies must evolve to identify and incorporate new interaction data into DGIdb. OBJECTIVEIdentify new cost-effective AI curation strategies for incorporating new drug-gene interactions into DGIdb. METHODSWe present a methodology that leverages deterministic natural language processing techniques, existing harmonization frameworks, and AI-assisted curation to systematically narrow the literature space and identify new drug-gene interactions from published studies for inclusion in DGIdb. RESULTSWe demonstrate the use of lemmatization to prioritize a set of 100 abstracts containing high amounts of interaction words for downstream AI curation. From our set of abstracts, we were then able to identify 137 drug-gene interactions via an AI curation task, with 121 (88.3%) of these interactions being completely novel to DGIdb. A human expert evaluator reviewed this interaction set and was able to validate 134 of 137 (97.8%) interactions as being valid based on the text provided. CONCLUSIONTaken together, our results highlight a promising, cost-effective method of ingesting new interactions into DGIdb.

bioinformatics↗

bioRxiv2025

CIViC MCP: Integrating Large Language Models with the Clinical Interpretations of Variants in Cancer

SummaryThe Clinical Interpretation of Variants in Cancer (CIViC) knowledgebase provides a community-driven, open-source platform for discussing the biological and clinical significance of molecular variants in cancer. To enable users to make complex connections between CIViC information, we developed the CIViC Model Context Protocol (MCP) server, which allows users to interface with the CIViC API through natural language via large language models (LLMs), facilitating the rapid summarization of expertly curated cancer variant interpretations. Availability and implementationThe CIViC MCP server is detailed at https://github.com/griffithlab/civic-mcp-server. The repository includes instructions for accessing the server through the Claude desktop app (our recommended approach; Supplementary Figure 1) and hosting it locally with GPT-5, as well as a Python script for directly querying the MCP server. We also provide an MCP-supported Chatbot for CIViC users at https://civicdb.org/mcp-chat. Supplemental informationSupplementary data are available at Bioinformatics Advances online.

cancer biology↗

bioRxiv2025

Clonal dynamics of monozygotic twinning in early human embryogenesis

Monozygotic twins are derived from the split of a single zygote early in embryogenesis. Although it was hypothesized that the timing of twining is overall associated with fetal membrane configuration of twins, i.e., chorionicity and amnionicity, our understanding of early embryonic clonal dynamics underlying human twinning is limited. Here we explored the segregations of early embryonic lineages in 7 dichorionic diamniotic (DCDA), 7 monochorionic diamniotic (MCDA), 8 monochorionic monoamniotic (MCMA) monozygotic twins, and 1 dichorionic triamniotic (DCTA) monozygotic triplets, using post-zygotic early embryonic mutations (EEMs) as endogenous lineage barcodes. Patterns of the early lineage distributions among monozygotic twins revealed three apparent clonal categories, referred to as para-identical, sub-identical, and full-identical twins, which largely correlated with the amnionicity of the twins. Rather, despite conventional wisdom, chorionicity was not substantially associated with early clonal compositions, but with blood exchanges in utero. In sub-identical twins, where one co-twin was clonally a part of the other, our data suggested that the foundation of the latter co-twin was established after acquisition of a median of 6 additional post-zygotic mutations (range: 2-13), corresponding to [~]5 early cell divisions. Additional in-depth analysis on the matched placenta from an MCDA twin suggested that separation of two co-twins can precede the separation of the placenta and embryonic proper, and a single chorion can be formed even with multiclonal origin. Our findings provide insights into the clonal dynamics, twinning processes, and cell fate decisions in early human embryogenesis.

genomics↗

bioRxiv2025

Evaluating Language Models for Biomedical Fact-Checking: A Benchmark Dataset for Cancer Variant Interpretation Verification

Accurate interpretation of genomic variants is critical for precision oncology but remains slow and dependent on specialized expertise. Public knowledgebases such as the Clinical Interpretation of Variants in Cancer (CIViC) help by curating literature-backed variant interpretations in a structured form, yet verification and review have become major bottlenecks. Large language models (LLMs) offer a potential mechanism for accelerating biomedical claim verification, but their rapid turnover, variable availability, and known risks of unsupported reasoning require standardized and reproducible evaluation before integration into curation workflows. To address this, we developed CIViC-Fact, an expert-curated, full-text benchmark and evaluation framework. Domain experts linked structured cancer-variant claims to sentence-level evidence from source publications, including evidence from full-text articles, tables, and non-abstract sections that are commonly omitted from existing biomedical question-answering and scientific fact-checking datasets. Claim-verification reference labels were derived from CIViC records, revision histories and controlled data augmentation. A major finding of CIViC-Fact is that abstracts are insufficient for realistic biomedical claim verification. In the evaluated development subset of text-verifiable entries with full-text access, fewer than 30% could be fully validated from the abstract alone, highlighting the importance of full-text evaluation for biomedical curation. Upon the application of our fact-checking pipeline to newly submitted CIViC entries, after excluding entries requiring supplementary material or images for validation, automated retrieval successfully identified appropriate evidence for most cases (93%), supporting low-incremental-effort evaluation of future systems. Fine-tuning improved agreement with CIViC-Fact reference labels on the static benchmark, but larger general-purpose models performed better on a heterogeneous post-cutoff cohort. These findings support CIViC-Fact primarily as a reproducible framework for comparing evolving retrieval and verification systems rather than as validation of a single deployment-ready model. These findings suggest that, in a rapidly changing model landscape, the durable contribution is not a single optimized model but a reproducible benchmark framework that enables continual testing, model substitution, and lightweight updating through small high-quality few-shot exemplar sets.

bioinformatics↗

bioRxiv2023

Whole-genome sequences reveal zygotic composition in chimeric twins

The monochorionic placenta in dizygotic twins allows in utero exchange of embryonic cells, resulting in chimerism in the twins. In practice, this chimerism is incidentally identified on mixed ABO blood types or in the presence of cells with a discordant sex chromosome. Here, we applied whole-genome sequencing to one triplet and one twin families to precisely understand their zygotic compositions, using millions of genomic variants as barcodes of zygotic origins. Peripheral blood showed asymmetrical contributions from two sister zygotes, where one of the zygotes was the major clone in both twins. Single-cell RNA sequencing of peripheral blood tissues further showed differential contributions from the two sister zygotes across blood cell types. In contrast, buccal tissues were pure in genetic composition, suggesting that in utero cellular exchanges were confined to the blood tissues. Our study illustrates the cellular history of twinning during human development, which is critical for managing the health of chimeric individuals in the era of genomic medicine.

genomics↗

bioRxiv2023

Endothelial cells are a key target of IFN-g during response to combined PD-1/CTLA-4 ICB treatment in a mouse model of bladder cancer

To explore mechanisms of response to combined PD-1/CTLA-4 immune checkpoint blockade (ICB) treatment in individual cell types, we generated scRNA-seq using a mouse model of invasive urothelial carcinoma with three conditions: untreated tumor, treated tumor, and tumor treated after CD4+ T cell depletion. After classifying tumor cells based on detection of somatic variants and assigning non-tumor cell types using SingleR, we performed differential expression analysis, overrepresentation analysis, and gene set enrichment analysis (GSEA) within each cell type. GSEA revealed that endothelial cells were enriched for upregulated IFN-g response genes when comparing treated cells to both untreated cells and cells treated after CD4+ T cell depletion. Functional analysis showed that knocking out IFNgR1 in endothelial cells inhibited treatment response. Together, these results indicated that IFN-g signaling in endothelial cells is a key mediator of ICB induced anti-tumor activity.

cancer biology↗

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↗

bioRxiv2021

Single Cell T Cell Receptor Repertoire Profiling for Dogs

BackgroundSpontaneous cancers in companion dogs are increasingly recognized as robust models of human disease. This recognition has led to translational clinical trials in companion dogs with osteosarcoma, lymphoma, melanoma, squamous cell carcinoma, and soft tissue sarcoma. The ability to precisely track tumor-specific immune responses in such clinical trials would benefit from reagents to perform species-specific single cell T cell receptor sequencing (scTCRseq). This technology defines clones of T cells reacting to immune interventions and can help identify the specific epitope of response. Single cell gene expression data give insights into the activity and polarization of the T cell. To date, scTCRseq has not been demonstrated for canine samples. MethodsSamples from two responding dogs in a trial of an autologous deglycosylated melanoma vaccine were selected to demonstrate applicability of scTCRseq in a cancer immunotherapy setting. A single-cell suspension of cryopreserved peripheral blood mononuclear cells (PBMC) was prepared for 10X single cell sequencing. Full length 10X cDNA was amplified using a custom-designed nested PCR of the alpha/beta V(D)J region. A library made from this enriched product (scTCRseq) and a 10X gene expression (GEX) library (scRNAseq) were sequenced on the NovaSeq 6000. Results1,850-2,172 estimated V(D)J-expressing cells yielded 87-103.7 million reads with 73.8%-75.8% mapped to a V(D)J gene (beta/alpha chains ratio 1.5:1). 43 TRAJ, 29 TRAV, 12 TRBJ, and 22 TRBV gene segments were observed representing 72.9%, 51.8%, 100%, and 62.9% of all known V and J gene segments respectively. A large diversity of clonotypes was captured with 966-1,253 TRA/TRB clonotypes identified. Both dogs also exhibited a small number of highly abundant T cell clonotypes suggesting the presence of an anti-tumor T cell population. GEX enriched libraries successfully defined large clusters of CD8+ and CD4+ T cells that overlapped with V(D)J-expressing cells. DiscussionThe developed reagents successfully generated scTCRseq data, for the first time, which allowed the T cell repertoire to be surveyed in dogs responding to anti-tumor immunotherapy. These reagents will allow longitudinal tracking of anti-tumor T cell dynamics in canine cancer immunotherapy trials.

genomics↗

bioRxiv2021

p53 mutations exhibit sex specific gain-of-function activity in gliomagenesis

The tumor suppressor TP53 is the most frequently mutated gene in cancer. Most TP53 mutations are missense mutations in the DNA-binding domain, which in addition to loss of canonical p53 activity, frequently confer gain-of-function (GOF) aberrant transcriptional activity through mutant p53 localization to non-canonical genes. GOF phenotypes differ by mutation and cell identity and are reported to include increased proliferation, migration, metabolic reprogramming, and therapy resistance. We found that several recurring p53 mutations exhibit a sex-bias in patients with glioblastoma (GBM). In vitro and in vivo analysis of three mutations, p53R172H, p53Y202C, and p53Y217C revealed sex differences in each mutations ability to transform primary mouse astrocytes. p53R172H exhibited a far greater ability to transform female astrocytes than males, p53Y202C transformed both male and female astrocytes with a small male bias, and p53Y217C only exhibited GOF transformation effects in male astrocytes. These phenotypic differences reflect an interaction between sex and GOF mutation to drive unique gene expression patterns in cancer pathways. We found that mutant p53 exhibits sex and mutation specific aberrant genomic localization to the transcriptional start sites of upregulated genes, whose promoter regions were enriched for different sets of transcription factor DNA-binding motifs. Together, our data establish a novel paradigm for sex specific mutant p53 GOF activity in GBM with implications for all cancer.

cancer biology↗

bioRxiv2021

Comparative Multiomic Analysis Reveals Low T Cell Infiltration as the Primary Feature of Tobacco Use in HPV(+) Oropharyngeal Cancer

PurposeTobacco use is an independent adverse prognostic feature in human papillomavirus (HPV)-associated oropharyngeal squamous cell carcinoma (OPSCC). Despite this, the biologic features associated with tobacco use have not been systematically investigated in this population. We sought to characterize the genomic and immunologic features of HPV(+) OPSCC associated with tobacco use and adverse oncologic outcomes. Experimental DesignWhole exome sequencing of 47 primary HPV(+) OPSCC tumors was performed to investigate mutational differences associated with tobacco exposure. To characterize the tumor immune microenvironment (TIME), targeted mRNA hybridization was performed and immunohistochemical (IHC) staining was used to validate these findings. ResultsLow expression of transcripts in a T cell-inflamed gene expression profile (TGEP) was associated with tobacco use at the time of diagnosis and lower overall and disease-free survival. Tobacco use was associated with an increased proportion of T>C substitutions and a lower proportion of mutational signatures typically observed in HPV(+) OPSCC tumors, but was not associated with increases in mutational burden or the rate of recurrent oncogenic mutations. ConclusionsIn HPV(+) OPSCC, low T cell infiltration of primary tumors is associated with current tobacco use and worse oncologic outcomes. Rather than an increased mutational burden, tobaccos primary and clinically relevant association is immunosuppression of the primary TIME. An objective clinical assay like the TGEP, which quantifies immune infiltration of the primary TIME, may have value for HPV(+) OPSCC risk stratification in future clinical trials.

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↗

bioRxiv2019

The CIViC knowledge model and standard operating procedures for curation and clinical interpretation of variants in cancer

Manually curated variant knowledgebases and their associated knowledge models are serving an increasingly important role in distributing and interpreting variants in cancer. These knowledgebases vary in their level of public accessibility, and the complexity of the models used to capture clinical knowledge. CIViC (Clinical Interpretations of Variants in Cancer - www.civicdb.org) is a fully open, free-to-use cancer variant interpretation knowledgebase that incorporates highly detailed curation of evidence obtained from peer-reviewed publications. Currently, the CIViC knowledge model consists of four main components: Genes, Variants, Evidence Items, and Assertions. Each component has an associated knowledge model and methods for curation. Gene and Variant data contextualize the genomic region(s) involved in the clinical statement. Evidence Items provide structured associations between variants and their clinically predictive/therapeutic, prognostic, diagnostic, predisposing, and functional implications. Finally, CIViC Assertions summarize collections of CIViC Evidence Items for a specific Disease, Variant, and Clinical Significance with incorporation of clinical and technical guidelines. Here we present the CIViC knowledge model, curation standard operating procedures, and detailed examples to support community-driven curation of cancer variants.

bioinformatics↗

bioRxiv2019

Stool-derived eukaryotic RNA biomarkers for detection of high-risk adenomas

Background and aimsColorectal cancer (CRC) is the second leading cause of cancer related deaths in the United States. Mortality is largely attributable to low patient compliance with screening and a subsequent high frequency of late-stage diagnoses. Noninvasive methods, such as stool- or blood-based diagnostics could improve patient compliance, however, existing techniques cannot adequately detect high-risk adenomas (HRAs) and early-stage CRC. MethodsHere we apply cancer profiling using amplicon sequencing of stool-derived eukaryotic RNA for 275 patients undergoing prospective CRC screening. A training set of 154 samples was used to build a random forest model that included 4 feature types (differentially expressed amplicons, total RNA expression, demographic information, and fecal immunochemical test results). An independent hold out test set of 121 patients was used to assess model performance. ResultsWhen applied to the 121-patient hold out test set, the model attained a receiver operating characteristic (ROC) area under the curve (AUC) of 0.94 for CRC and a ROC AUC of 0.87 for CRC and HRAs. In aggregate, the model achieved a 91 % sensitivity for CRC and a 73% sensitivity for HRAs at an 89% specificity for all other findings (medium-risk adenomas, low-risk adenomas, benign polyps, and no findings on a colonoscopy). ConclusionCollectively, these results indicate that in addition to early CRC detection, stool-derived biomarkers can accurately and noninvasively identify HRAs, which could be harnessed to prevent CRC development for asymptomatic, average-risk patients.

cancer biology↗