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Biomedical subjects

Joshua McMichael

Publications and source records attributed to Joshua McMichael.

3 recordsLinked to original sources

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

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

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