Biomedical subjects

Obi L Griffith

Publications and source records attributed to Obi L Griffith.

6 recordsLinked to original sources

PubMed2026

Genomic and transcriptomic features of relapsed small cell lung cancer.

BACKGROUND: Relapsed small cell lung cancer is characterized by treatment resistance and poor outcomes. Genomic and transcriptomic alterations in relapsed SCLC have not been characterized well. We comprehensively profiled relapsed SCLC samples along with patient-matched treatment-naive samples, when available, using whole-exome (WES), whole-genome (WGS), and RNA-sequencing (RNA-seq) to describe the molecular landscape of relapsed SCLC. Our goal is to identify potential novel pathways for additional functional validation and eventually novel therapeutic options. METHODS: We analyzed 54 relapsed and 27 treatment-naive SCLC samples using WES (with 26 patient-matched paired samples). A subset of the samples was also analyzed by WGS (n=28) and RNA-seq (n=31). Differences in mutational signatures, gene expression, structural variants, splicing, and neoantigen profiles at diagnosis and relapse were investigated. RESULTS: Relapsed SCLC samples demonstrated mutation signatures characteristic of platinum and APOBEC mutagenesis. Furthermore, these samples were characterized by MYC, MYCL and MYCN amplifications. Both treatment-naive and relapsed SCLC samples showed high prevalence of mutation-associated neoantigens (median= 86 in treatment-naive and 90 in relapsed SCLC; p=0.8) and TP53 was the most frequently altered gene to result in a neoantigen (48% of analyzed samples). Potential mechanisms of immune evasion, including amplification of CD24, overexpression of IDO1, increased M2 macrophage presence, and upregulation of HLA-E were also observed in relapse samples. Differences in alternative splicing patterns were observed between treatment-naive and relapsed small cell samples. Retained intron events were significantly enriched in treatment-naive samples and affected genes involved in DNA repair, metabolism, and WNT and MYC pathways. CONCLUSIONS: This study highlights the genomic and transcriptomic features of relapsed SCLC. These samples were characterized by genomic instability, WNT and MYC dysregulation, and splicing aberrations. Additional studies targeting the splicing machinery, WNT signaling, and immune evasion pathways could identify novel therapeutic vulnerabilities in SCLC.

Journal Article↗

PubMed2026

Synthetic long peptide and DNA personalized cancer vaccines induce robust neoantigen-specific T cell responses in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is unresponsive to standard immunotherapies despite harboring cancer neoantigens capable of eliciting T cell responses. We completed two phase 1 clinical trials (NCT03956056 and NCT03122106) evaluating safety and immunogenicity of synthetic long peptide (SLP) and DNA personalized cancer vaccines (PCVs). PCVs were administered after resection and adjuvant chemotherapy. Tumor/normal whole-exome sequencing, RNA sequencing, and pVACtools were used to identify and prioritize candidate PCV neoantigens. PCVs were well tolerated without any grade ≥3 adverse events. Neoantigen-specific responses were demonstrated by interferon-γ enzyme-linked immunospot and intracellular cytokine staining. Expanded T cell receptor clonotypes were sequenced and transduced into autologous peripheral blood mononuclear cells to confirm neoantigen specificity. When compared with a contemporaneous institutional propensity-matched cohort, PCV patients demonstrated a trend toward prolonged median overall survival (4.4 versus 3.5 years, log-rank P = 0.23). Overall, PDAC PCVs are safe and feasible and elicit polyclonal T cell responses, linking prioritized cancer neoantigens to functional antitumor immunity.

Humans↗

PubMed2026

Searching the druggable genome using large language models.

SUMMARY: The 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 improves an LLM's ability to answer questions requiring accurate, up-to-date biomedical knowledge drawn from structured external resources. AVAILABILITY AND IMPLEMENTATION: The DGIdb MCP server is detailed at https://github.com/dgidb/dgidb-mcp-server and includes instructions for accessing the server through the Claude desktop app.

Large Language Models↗

PubMed2026

Searching the Druggable Genome using Large Language Models.

SUMMARY: The 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 LLM's ability to answer questions requiring accurate, up-to-date biomedical knowledge drawn from structured external resources. AVAILABILITY AND IMPLEMENTATION: The 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.

Journal Article↗

PubMed2025

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. To address this, we developed CIViC-Fact, a benchmark dataset and pipeline for testing automated systems that verify the accuracy of cancer variant claims. CIViC-Fact links structured claims to sentence-level supporting or refuting evidence from full-text articles, and includes expert annotations and explanations. We evaluated multiple language models. Proprietary models performed well without training, but a smaller open-source model, fine-tuned on CIViC-Fact, achieved the highest accuracy (89%). Applying our fact-checking pipeline to real CIViC entries showed that reviewing less than 20% of content, focusing on flagged entries, would be sufficient to catch over half of all errors. This AI-assisted triage greatly accelerates the review process without replacing or reducing expert insight, ensuring that existing careful oversight remains in place while curators can work more efficiently. CIViC-Fact provides a realistic, high-consequence framework for biomedical fact-checking and a path toward more rigorous and efficient knowledgebase curation.

Journal Article↗

PubMed2024

Whole-genome sequences reveal zygotic composition in chimeric twins.

While most dizygotic twins have a dichorionic placenta, rare cases of dizygotic twins with a monochorionic placenta have been reported. 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 in 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 family 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.

Humans↗