[Therapeutic Targets by Pathology AI and Spatial Transcriptomics in Breast Cancer].

Authors

Maki Tanioka

Published in

Gan to kagaku ryoho. Cancer & chemotherapy. Volume 53. Issue 6. Pages 379-384.

Abstract

Recent advances in pathology foundation models have markedly improved the accuracy and generalizability of histopathological image analysis in breast cancer. However, the mechanisms of resistance to CDK4/6 inhibitors in hormone receptor-positive, HER2-negative advanced breast cancer remain incompletely understood. This article outlines a strategy to identify therapeutic targets by integrating pathology AI with spatial transcriptomics. We developed AI-directed spatial transcriptomics (AID-ST), a framework that compares gene expression profiles between drug-sensitive and drug-resistant regions identified by pathology AI. In a preliminary analysis of clinical breast cancer specimens, this approach suggested that KRAS pathway activation is a major driver of resistance, accompanied in part by Polycomb dysregulation, RB loss, PI3K pathway alteration, and acquisition of stem-like features. Additional spatial analyses of paired pre- and post-treatment specimens supported these findings and further suggested a role for the tumor microenvironment, including EMT- and IL6/JAK/STAT3-related changes, in promoting resistant phenotypes. These results indicate that integrating pathology AI with spatial transcriptomics may enable systematic classification of resistance subtypes and prioritization of actionable therapeutic targets in breast cancer.

PMID:
42551938
Bibliographic data and abstract were imported from PubMed on 05 Aug 2026.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.