Danh-Tai Hoang

Abstract Artificial intelligence (AI) algorithms such as ENLIGHT and DeepPT represent promising approaches to identify predictive biomarkers for immune checkpoint blockade (ICB). Evaluation of ICB response prediction throughout the span of ICB treatment provides a dynamic perspective of a biomarker’s predictive value. A post-hoc analysis of two pan-cancer trials of patients receiving ICB was performed. Samples were available from the pre-ICB, on-ICB and post-progression timepoints.

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 Cell type-specific gene expression from single-cell RNA sequencing (RNA-seq) is valuable for breast cancer precision oncology but available cohorts are still limited due to its high cost. Deconvolution methods infer cell type-specific expression from bulk RNA-seq at a lower cost, yet expenses and processing time of bulk RNA-seq are also non-negligible and limit their application too.