Back

A novel subtyping method for TNBC with implications for prognosis and therapy

Mesrizadeh, Z.; Mukund, K.; Zabaleta, J.; Valle, L. D.; Tomsic, J.; Neuhausen, S. L.; Ding, Y. C.; Seewaldt, V.; Ochoa, A.; Miele, L.; Subramaniam, S.

2025-07-08 bioinformatics
10.1101/2025.07.04.663242 bioRxiv
Show abstract

The biological heterogeneity of triple-negative breast cancer (TNBC) poses significant challenges for diagnosis, prognosis, and treatment. While prior TNBC subtype classifications exist, they are not widely used clinically. Here, we aimed to subtype TNBC based on transcriptomic profiles using cell type and state heterogeneity in tumor tissue from 250 pre-treatment women (127 African-American and 123 European-American). We identified three major subtypes and three distinct groups exhibiting unique cell-type composition and mechanisms: Subtype-1 immune signaling/T-cell response; Subtype-2 pro-fibrotic and immune desert; Subtype-3 fatty acid and nuclear receptor signaling. Subtype-1 showed potential responsiveness to immunotherapy, while Subtypes-2 and 3 suggested alternative therapeutic targets. In Subtype-3, which contained a patient group with high ESR1, (but not high ER protein expression) we identified putative mutations in the gene that are unique to these patients. This framework provides a path toward personalized TNBC treatment and is accessible through a user-friendly RShiny application for clinical use.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.