Application of a Neural Network Whole Transcriptome-Based Pan-Cancer Method for Diagnosis of Primary and Metastatic Cancers
Application of a Neural Network Whole Transcriptome-Based Pan-Cancer Method for Diagnosis of Primary and Metastatic Cancers
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DOI：
10.1001/jamanetworkopen.2019.2597
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发表时间：
2019-04-01
影响因子：
13.8
通讯作者：
Jones, Steven J. M.
中科院分区：
文献类型：
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作者：
Grewal, Jasleen K.;Tessier-Cloutier, Basile;Jones, Steven J. M.
重要性一种结合了多种组织类型中所有基因转录状态信息的分子诊断方法，可以增强癌症诊断的信心。目的确定基于整个转录组的泛癌症方法在诊断原发癌和转移癌以及解决复杂诊断中的实际应用。设计、设置和参与者这项横断面诊断研究评估了使用表达监督癌症起源预测(SCOPE)，这是一种使用全转录组RNA测序数据的机器学习方法。对可公开获得的原发癌症数据集进行了培训，包括癌症基因组图谱。从2013年1月1日到2016年3月31日，在不列颠哥伦比亚省温哥华的BC癌症中心，对未经治疗的原发癌症和接受治疗的成年患者的转移癌进行了回顾性测试，测试涵盖了10 822个样本和66个输出类别，代表了未经治疗的原发癌症(n=40)和邻近的正常组织(n=26)。Scope的性能在211例未经治疗的原发间皮瘤癌症和201例耐药转移性癌症上得到了展示。结果共收集了10 688例成人患者样本，分别代表40种未经治疗的原发肿瘤类型和26例邻近正常组织。并非所有数据集都有人口统计数据。在训练数据集中，10244人中有5157人(50.3%)是男性，平均年龄(SD)为58.9(14.5)岁。对211例未经治疗的原发性间皮瘤患者(173例男性，平均年龄64.5[11.3]岁)、201例耐药癌症患者(女性141例[70.1%]，平均年龄55.6[12.9]岁)和15例原发原因不明的癌症患者进行了检测，其中168例为转移癌，33例为原发肿瘤。对126例原发性上皮样间皮瘤的诊断准确率为99%(125/126)。其余85例间皮瘤具有混合病因学(肉瘤样间皮瘤)，并被正确地鉴定为其主要成分的混合物，在解决混合组织学的亚型和发病率方面具有潜在的意义。SCOPE对201例耐药癌的总体平均(SD)准确率为86%(11%)，F-1评分为0.79(0.12)，并与15例与传统病理学不确定的癌症的假定诊断中的12例相匹配。结论这些结果表明，结合多个肿瘤特征的机器学习方法可以更准确地识别癌症状态，并将其与正常细胞区分开来。SCOPE使用正常和肿瘤组织的整个转录本，这项研究的结果表明，它在罕见的癌症类型、原发癌、耐药转移癌和原发癌来源不明的癌症中表现良好。对与Scope的决策最相关的基因进行了检查，其中几个是各自癌症的已知生物标记。在癌症起源未知的情况下，或者当标准的病理评估不确定的情况下，可以应用范围作为一种正交诊断方法。
IMPORTANCE A molecular diagnostic method that incorporates information about the transcriptional status of all genes across multiple tissue types can strengthen confidence in cancer diagnosis.OBJECTIVE To determine the practical use of a whole transcriptome-based pan-cancer method in diagnosing primary and metastatic cancers and resolving complex diagnoses.DESIGN, SETTING, AND PARTICIPANTS This cross-sectional diagnostic study assessed Supervised Cancer Origin Prediction Using Expression (SCOPE), a machine learning method using whole-transcriptome RNA sequencing data. Training was performed on publicly available primary cancer data sets, including The Cancer Genome Atlas. Testing was performed retrospectively on untreated primary cancers and treated metastases from volunteer adult patients at BC Cancer in Vancouver, British Columbia, from January 1, 2013, to March 31, 2016, and testing spanned 10 822 samples and 66 output classes representing untreated primary cancers (n = 40) and adjacent normal tissues (n = 26). SCOPE's performance was demonstrated on 211 untreated primary mesothelioma cancers and 201 treatment-resistant metastatic cancers. Finally, SCOPE was used to identify the putative site of origin in 15 cases with initial presentation as cancers with unknown primary of origin.RESULTS A total of 10 688 adult patient samples representing 40 untreated primary tumor types and 26 adjacent-normal tissues were used for training. Demographic data were not available for all data sets. Among the training data set, 5157 of 10 244 (50.3%) were male and the mean (SD) age was 58.9 (14.5) years. Testing was performed on 211 patients with untreated primary mesothelioma (173 [82.0%] male; mean [SD] age, 64.5 [11.3] years); 201 patients with treatment-resistant cancers (141 [70.1%] female; mean [SD] age, 55.6 [12.9] years); and 15 patients with cancers of unknown primary of origin; among the treatment-resistant cancers, 168 were metastatic, and 33 were the primary presentation. An accuracy rate of 99% was obtained for primary epithelioid mesotheliomas tested (125 of 126). The remaining 85 mesotheliomas had a mixed etiology (sarcomatoid mesotheliomas) and were correctly identified as a mixture of their primary components, with potential implications in resolving subtypes and incidences of mixed histology. SCOPE achieved an overall mean (SD) accuracy rate of 86%(11%) and F-1 score of 0.79 (0.12) on the 201 treatment-resistant cancers and matched 12 of 15 of the putative diagnoses for cancers with indeterminate diagnosis from conventional pathology.CONCLUSIONS AND RELEVANCE These results suggest that machine learning approaches incorporating multiple tumor profiles can more accurately identify the cancerous state and discriminate it from normal cells. SCOPE uses the whole transcriptomes from normal and tumor tissues, and results of this study suggest that it performs well for rare cancer types, primary cancers, treatment-resistant metastatic cancers, and cancers of unknown primary of origin. Genes most relevant in SCOPE's decision making were examined, and several are known biological markers of respective cancers. SCOPE may be applied as an orthogonal diagnostic method in cases where the site of origin of a cancer is unknown, or when standard pathology assessment is inconclusive.