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整合计算病理学和蛋白质组学以解决肿瘤异质性。

Integrating computational pathology and proteomics to address tumor heterogeneity.

机构信息

Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada.

Princess Margaret Cancer Center, University Health Network, Toronto, ON, Canada.

出版信息

J Pathol. 2022 Jul;257(4):445-453. doi: 10.1002/path.5905. Epub 2022 May 6.

Abstract

Despite numerous advances in our molecular understanding of cancer biology, success in precision medicine trials has remained elusive for many malignancies. Emerging evidence now supports that these challenges are partly driven by proteogenomic discordances across molecular readouts and heterogeneous biology that is spatially distributed across tumors. Here we discuss these key limitations and how integrating the promise of mass-spectrometry-based global proteomics and computational imaging can help prioritize and direct regional sampling to help overcome these important challenges of biologic variation in cancer. © 2022 The Pathological Society of Great Britain and Ireland.

摘要

尽管我们在癌症生物学的分子理解方面取得了许多进展,但许多恶性肿瘤的精准医学试验仍未能取得成功。新出现的证据表明,这些挑战部分是由分子读数和空间分布在肿瘤中的异质生物学之间的蛋白质基因组不和谐引起的。在这里,我们讨论了这些关键限制因素,以及如何整合基于质谱的全局蛋白质组学和计算成像的承诺,以帮助确定优先级并指导区域采样,从而帮助克服癌症中生物学变异性的这些重要挑战。© 2022 英国和爱尔兰病理学会。

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