Building digital histology models of transcriptional tumor programs with generative deep learning for pathology-based precision medicine.

IF 10.4 1区 生物学 Q1 GENETICS & HEREDITY
Hanna M Hieromnimon, James Dolezal, Kristina Doytcheva, Frederick M Howard, Sara Kochanny, Zhenyu Zhang, Robert L Grossman, Kevin Tanager, Cindy Wang, Jakob Nikolas Kather, Evgeny Izumchenko, Nicole A Cipriani, Elana J Fertig, Alexander T Pearson, Samantha J Riesenfeld
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引用次数: 0

Abstract

Background: Precision oncology depends on identifying the biological vulnerabilities of a tumor. Molecular assays, like transcriptomics, provide an information-rich view of the tumor that can be leveraged to inform therapeutic selection. However, the costs of such assays can be prohibitive for clinical translation at scale. Histology-based imaging remains a predominant means of diagnosis that is widely accessible. To more broadly leverage limited molecular datasets, models have been trained to use histology to infer the expression of individual genes or pathways, with varying levels of accuracy and explainability.

Methods: Our approach detects expression of transcriptional programs from tumor histology and interprets the image features supporting program detection. Specifically, we used RNA-seq data from squamous cell carcinoma (SCC) patients to infer cohesive expression patterns of multiple genes. Then, we used deep learning techniques to train a computational model to predict the activity levels of the transcriptional programs directly from histology images. We exploited that predictive capability to generate synthetic digital models of the cellular histology of each transcriptional program, using generative adversarial networks to isolate image features supporting specific transcriptional predictions and pathologist review to interpret the images.

Results: Applying our histologically integrated latent space analysis to SCCs revealed sets of genes associated with both pathologist-interpretable image features and clinically relevant processes, including immune response, collagen remodeling, and fibrosis, going beyond predictions of individual molecular features.

Conclusions: Our results demonstrate an approach for discovering clinically interpretable histological features that indicate molecular, potentially treatment-informing, biological processes. These features are detectable in widely available histology slides, allowing a standard microscope to deliver complex, patient-specific molecular information.

利用生成式深度学习为基于病理的精准医学构建转录肿瘤程序的数字组织学模型。
背景:精确肿瘤学依赖于识别肿瘤的生物学脆弱性。分子分析,如转录组学,提供了丰富的肿瘤信息,可以用来指导治疗选择。然而,这种检测的成本对于大规模的临床转化来说可能是令人望而却步的。基于组织学的影像学仍然是一种主要的诊断手段,是广泛可及的。为了更广泛地利用有限的分子数据集,模型已经被训练成使用组织学来推断个体基因或途径的表达,具有不同程度的准确性和可解释性。方法:我们的方法从肿瘤组织学中检测转录程序的表达,并解释支持程序检测的图像特征。具体来说,我们使用来自鳞状细胞癌(SCC)患者的RNA-seq数据来推断多个基因的内聚表达模式。然后,我们使用深度学习技术来训练一个计算模型,以直接从组织学图像中预测转录程序的活动水平。我们利用这种预测能力来生成每个转录程序的细胞组织学的合成数字模型,使用生成对抗网络来分离支持特定转录预测的图像特征和病理学家评论来解释图像。结果:对SCCs进行组织学综合潜伏期分析,揭示了与病理学可解释的图像特征和临床相关过程(包括免疫反应、胶原重塑和纤维化)相关的一系列基因,超出了个体分子特征的预测。结论:我们的结果展示了一种发现临床可解释的组织学特征的方法,这些组织学特征表明分子,潜在的治疗信息,生物学过程。这些特征可以在广泛可用的组织学切片中检测到,允许标准显微镜提供复杂的,患者特异性的分子信息。
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来源期刊
Genome Medicine
Genome Medicine GENETICS & HEREDITY-
CiteScore
20.80
自引率
0.80%
发文量
128
审稿时长
6-12 weeks
期刊介绍: Genome Medicine is an open access journal that publishes outstanding research applying genetics, genomics, and multi-omics to understand, diagnose, and treat disease. Bridging basic science and clinical research, it covers areas such as cancer genomics, immuno-oncology, immunogenomics, infectious disease, microbiome, neurogenomics, systems medicine, clinical genomics, gene therapies, precision medicine, and clinical trials. The journal publishes original research, methods, software, and reviews to serve authors and promote broad interest and importance in the field.
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