Padma Sheila Rajagopal

Keywords digital pathology artificial intelligence deep learning breast cancer spatial transcriptomics spatial biomarkers histopathology treatment response prediction HER2 tumor microenvironment Get full text access Log in, subscribe or purchase for full access. References 1. Wu, S.Z. ∙ Al-Eryani, G. ∙ Roden, D.L. ... A single-cell and spatially resolved atlas of human breast cancers Nat. Genet. 2021; 53:1334-1347 2. Wang, X. ∙ Wang, Z. ∙ Liao, Q. ...

Abstract Prostate cancer (PrCa) incidence and severity vary across ancestries; men of African ancestry (AA) are more likely to be diagnosed and die from PrCa than those of European ancestry (EA). Current polygenic risk scores, even from multi-ancestry GWAS, do not fully capture population-specific genetic mechanisms, especially those mediated by non-coding regulatory single nucleotide polymorphisms (SNPs).

Abstract We determined triple-negative breast cancer (TNBC) subtypes, genetic ancestry, and immune features in a cohort of self-reported Black females with TNBC diagnosed at or below age 50. Among 104 tumors, 34.6% were basal-like 1 (BL1), 17.3% basal-like 2 (BL2), 9.6% luminal androgen receptor (LAR), 26.9% mesenchymal (M), and 11.5% unsubtyped (UNS). Subtypes resembled those seen in Europeans or East Asians, with less LAR (9.6% vs. 14.6–24.4%) and more UNS (11.5% vs. 0–7.5%).