Biological Misinterpretation of Transcriptional Signatures in Tumor Samples Can Unknowingly Undermine Mechanistic Understanding and Faithful Alignment with Preclinical Data.
Biological Misinterpretation of Transcriptional Signatures in Tumor Samples Can Unknowingly Undermine Mechanistic Understanding and Faithful Alignment with Preclinical Data.
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DOI：
10.1158/1078-0432.ccr-22-1102
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发表时间：
2022-09-15
影响因子：
11.5
通讯作者：
Dunne, Philip D.
中科院分区：
文献类型：
--
作者：
Fisher, Natalie C.;Byrne, Ryan M.;Leslie, Holly;Wood, Colin;Legrini, Assya;Cameron, Andrew J.;Ahmaderaghi, Baharak;Corry, Shania M.;Malla, Sudhir B.;Amirkhah, Raheleh;McCooey, Aoife J.;Rogan, Emily;Redmond, Keara L.;Sakhnevych, Svetlana;Domingo, Enric;Jackson, James;Loughrey, Maurice B.;Leedham, Simon;Maughan, Tim;Lawler, Mark;Sansom, Owen J.;Lamrock, Felicity;Koelzer, Viktor H.;Jamieson, Nigel B.;Dunne, Philip D.
精确的机制为基础的基因表达签名（GES）已在适当的体外和体内模型系统，以确定重要的癌症相关的信号转导过程。然而，一些最初开发用于代表特定疾病过程（主要是上皮细胞病灶）的GES正在应用于异质性肿瘤样本，其中签名中的基因表达可能不再是上皮特异性的。因此，在不知不觉中，即使肿瘤间质百分比的微小变化也会直接影响GES，破坏预期的机制信号传导。以结直肠癌为例，我们部署了许多正交分析方法，包括激光捕获显微切割、流式细胞术、批量和多区域活检临床样本、单细胞RNA-Seq和最后的空间转录组学，以全面评估最广泛使用的GES受肿瘤组织中基质含量影响或混淆的可能性。为了补充这项工作，我们生成了一个免费可用的资源ConfoundR; https：//confoundr.qub.ac.uk/，使用户能够同时在结直肠癌、乳腺癌、胰腺癌、卵巢癌和前列腺癌数据集中测试基质对无限数量的基因/特征的影响程度。本文中的研究结果表明，由于广泛的基质影响，GES的含义有可能被误解，这反过来又会破坏临床样本和临床前数据/模型之间的忠实一致性，特别是细胞系和类器官，或肿瘤模型不能完全重现基质和免疫微环境。使用表型设计的GES忠实地对齐疾病的临床前模型的努力必须确保签名本身在应用于临床样品时仍然代表相同的生物学。
Precise mechanism-based gene expression signatures (GESs) have been developed in appropriate in vitro and in vivo model systems, to identify important cancer-related signaling processes. However, some GESs originally developed to represent specific disease processes, primarily with an epithelial cell focus, are being applied to heterogeneous tumor samples where the expression of the genes in the signature may no longer be epithelial-specific. Therefore, unknowingly, even small changes in tumor stroma percentage can directly influence GESs, undermining the intended mechanistic signaling. Using colorectal cancer as an exemplar, we deployed numerous orthogonal profiling methodologies, including laser capture microdissection, flow cytometry, bulk and multiregional biopsy clinical samples, single cell RNA-Seq and finally spatial transcriptomics, to perform a comprehensive assessment of the potential for the most widely used GESs to be influenced, or confounded, by stromal content in tumor tissue. To complement this work, we generated a freely-available resource, ConfoundR; https://confoundr.qub.ac.uk/, that enables users to test the extent of stromal influence on an unlimited number of the genes/signatures simultaneously across colorectal, breast, pancreatic, ovarian and prostate cancer datasets. Findings presented here demonstrate the clear potential for misinterpretation of the meaning of GESs, due to widespread stromal influences, which in-turn can undermine faithful alignment between clinical samples and preclinical data/models, particularly cell lines and organoids, or tumor models not fully recapitulating the stromal and immune microenvironment. Efforts to faithfully align preclinical models of disease using phenotypically-designed GESs must ensure that the signatures themselves remain representative of the same biology when applied to clinical samples.