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用于肿瘤类型不可知表型预测的正常组织转录特征。

Normal tissue transcriptional signatures for tumor-type-agnostic phenotype prediction.

机构信息

Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA.

Department of Biomedical Informatics, Stony Brook University, Stony Brook, USA.

出版信息

Sci Rep. 2024 Nov 8;14(1):27230. doi: 10.1038/s41598-024-76625-1.

Abstract

Cancer transcriptional patterns reflect both unique features and shared hallmarks across diverse cancer types, but whether differences in these patterns are sufficient to characterize the full breadth of tumor phenotype heterogeneity remains an open question. We hypothesized that these shared transcriptomic signatures reflect repurposed versions of functional tasks performed by normal tissues. Starting with normal tissue transcriptomic profiles, we use non-negative matrix factorization to derive six distinct transcriptomic phenotypes, called archetypes, which combine to describe both normal tissue patterns and variations across a broad spectrum of malignancies. We show that differential enrichment of these signatures correlates with key tumor characteristics, including overall patient survival and drug sensitivity, independent of clinically actionable DNA alterations. Additionally, we show that in HR+/HER2- breast cancers, metastatic tumors adopt transcriptomic signatures consistent with the invaded tissue. Broadly, our findings suggest that cancer often arrogates normal tissue transcriptomic characteristics as a component of both malignant progression and drug response. This quantitative framework provides a strategy for connecting the diversity of cancer phenotypes and could potentially help manage individual patients.

摘要

癌症转录模式反映了不同癌症类型的独特特征和共同标志,但这些模式的差异是否足以描述肿瘤表型异质性的全貌仍是一个悬而未决的问题。我们假设这些共享的转录组特征反映了正常组织执行的功能任务的重新利用版本。从正常组织转录组图谱开始,我们使用非负矩阵分解来得出六个不同的转录组表型,称为原型,它们结合起来描述了正常组织的模式以及广泛的恶性肿瘤的变化。我们表明,这些特征的差异富集与关键的肿瘤特征相关,包括总体患者生存和药物敏感性,独立于临床上可操作的 DNA 改变。此外,我们还表明,在 HR+/HER2-乳腺癌中,转移性肿瘤采用与侵袭组织一致的转录组特征。总的来说,我们的研究结果表明,癌症经常将正常组织转录组特征作为恶性进展和药物反应的一个组成部分。这个定量框架为连接癌症表型的多样性提供了一种策略,并可能有助于管理个体患者。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae4a/11549333/ff4ed0dd135f/41598_2024_76625_Fig1_HTML.jpg

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