Supplementary Table S2 from Deep Learning Predicts Subtype Heterogeneity and Outcomes in Luminal A Breast Cancer Using Routinely Stained Whole-Slide Images

Kurian, Nikhil Cherian; Gann, Peter H.; Kumar, Neeraj; McGregor, Stephanie M.; Verma, Ruchika; Sethi, Amit

doi:10.1158/2767-9764.28286730

Supplementary Table S2 from Deep Learning Predicts Subtype Heterogeneity and Outcomes in Luminal A Breast Cancer Using Routinely Stained Whole-Slide Images

journal contribution

posted on 2025-01-27, 11:20 authored by Nikhil Cherian Kurian, Peter H. Gann, Neeraj Kumar, Stephanie M. McGregor, Ruchika Verma, Amit Sethi

Supplementary Table S2. Clinical and molecular features of PAM50 Luminal A breast cancers in the independent test set (n = 230) according to degree of adherence of transcriptomic profile to the LumA subtype by semi-supervised noon-negative matrix factorization (ssNMF).

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ARTICLE ABSTRACT

A deep learning model, trained using transcriptomic data, inexpensively quantifies and fine-maps ITH due to subtype admixture in routine images of LumA breast cancer, the most favorable subtype. This new approach could facilitate exploration of the mechanisms behind such heterogeneity and its impact on selection of therapy for individual patients.