Agrawal Piyush, Jain Navami, Gopalan Vishaka, Timon Annan, Singh Arashdeep, Rajagopal Padma S, Hannenhalli Sridhar
Cancer Data Science Lab, National Cancer Institute, NIH, Bethesda, MD, USA.
Stanford University, Stanford, CA, USA.
bioRxiv. 2023 May 23:2023.05.21.541618. doi: 10.1101/2023.05.21.541618.
Breast cancers exhibit substantial transcriptional heterogeneity, posing a significant challenge to the prediction of treatment response and prognostication of outcomes. Especially, translation of TNBC subtypes to the clinic remains a work in progress, in part because of a lack of clear transcriptional signatures distinguishing the subtypes. Our recent network-based approach, PathExt, demonstrates that global transcriptional changes in a disease context are likely mediated by a small number of key genes, and these mediators may better reflect functional or translationally relevant heterogeneity. We apply PathExt to 1059 BRCA tumors and 112 healthy control samples across 4 subtypes to identify frequent, key-mediator genes in each BRCA subtype. Compared to conventional differential expression analysis, PathExt-identified genes (1) exhibit greater concordance across tumors, revealing shared as well as BRCA subtype-specific biological processes, (2) better recapitulate BRCA-associated genes in multiple benchmarks, and (3) exhibit greater dependency scores in BRCA subtype-specific cancer cell lines. Single cell transcriptomes of BRCA subtype tumors reveal a subtype-specific distribution of PathExt-identified genes in multiple cell types from the tumor microenvironment. Application of PathExt to a TNBC chemotherapy response dataset identified TNBC subtype-specific key genes and biological processes associated with resistance. We described putative drugs that target top novel genes potentially mediating drug resistance. Overall, PathExt applied to breast cancer refines previous views of gene expression heterogeneity and identifies potential mediators of TNBC subtypes, including potential therapeutic targets.
乳腺癌表现出显著的转录异质性，这对治疗反应的预测和预后结果的判断构成了重大挑战。特别是，三阴性乳腺癌（TNBC）亚型在临床中的转化仍在进行中，部分原因是缺乏区分这些亚型的明确转录特征。我们最近基于网络的方法PathExt表明，疾病背景下的全局转录变化可能由少数关键基因介导，并且这些介导因子可能更好地反映功能或与翻译相关的异质性。我们将PathExt应用于1059个乳腺癌肿瘤和112个健康对照样本，涵盖4种亚型，以识别每种乳腺癌亚型中常见的关键介导基因。与传统的差异表达分析相比，PathExt识别出的基因：（1）在肿瘤之间表现出更高的一致性，揭示了共同的以及乳腺癌亚型特异性的生物学过程；（2）在多个基准中更好地概括了与乳腺癌相关的基因；（3）在乳腺癌亚型特异性癌细胞系中表现出更高的依赖性评分。乳腺癌亚型肿瘤的单细胞转录组揭示了PathExt识别出的基因在肿瘤微环境的多种细胞类型中的亚型特异性分布。将PathExt应用于TNBC化疗反应数据集，确定了TNBC亚型特异性关键基因以及与耐药性相关的生物学过程。我们描述了针对可能介导耐药性的新型顶级基因的推定药物。总体而言，将PathExt应用于乳腺癌改进了之前对基因表达异质性的看法，并识别出TNBC亚型的潜在介导因子，包括潜在的治疗靶点。
