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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Large Language Models for Translational Cancer Informatics.

Yining Pan¹, Yanfei Wang¹, Guangyu Wang^2,3

¹Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL.

JCO Clinical Cancer Informatics

|October 14, 2025

Summary

Large language models (LLMs) offer powerful tools for analyzing complex cancer data, including single-cell, spatial transcriptomic, and pathology information. Tailoring these models to cancer research will accelerate discoveries in disease mechanisms and therapeutic strategies.

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Area of Science:

Bioinformatics
Computational Biology
Oncology

Background:

Cancer is a leading global cause of death.
High-throughput transcriptomic and pathology data present analytical challenges.
Large language models (LLMs) show promise for biomedical data analysis.

Purpose of the Study:

To review the application of LLMs in cancer research.
To explore LLM use in cellular and pathologic cancer data analysis.
To highlight LLM potential for advancing precision oncology.

Main Methods:

Systematic review of current LLMs (2022-2025).
Focus on LLMs for single-cell RNA sequencing, spatial transcriptomics, and histology images.
Emphasis on relevance to cancer biology and translational research.

Main Results:

LLMs are used for cell annotation, integration, and drug-response prediction in single-cell data.
LLMs aid spatial data integration, imputation, and cell communication analysis.
LLMs assist in cancer subtyping, mutation prediction, and image segmentation in pathology.

Conclusions:

LLMs can significantly advance cancer research with scalable data analysis tools.
Domain-specific fine-tuning is crucial for optimizing LLMs for cancer applications.
Tailored LLMs will enhance biomarker discovery and therapeutic strategy development.