皮肤黑色素瘤中基于基因表达的肿瘤纯度评估和个体特异性生存工具

Marc Jermaine Pontiveros, J. Diaz, Geoffrey A. Solano
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摘要

皮肤黑色素瘤(SKCM)是一种由黑色素细胞基因突变引起的癌症,是最具侵袭性和致命性的癌症类型,主要影响高加索人群,在亚洲发病率越来越高。肿瘤和病变是高度异质性的,由癌细胞和非癌细胞组成,这种混合物被认为在肿瘤的生长和疾病的进展中起重要作用。这项研究的特点是,该系统能够使用梯度增强机(Gradient Boosting Machines)从RNA-Seq基因表达数据中估计肿瘤纯度,并使用一组临床特征和从训练模型中估计的肿瘤纯度,提供个体特异性生存预测(死亡或疾病进展)。比较了使用整个基因表达集和通过重要性评分选择特征的肿瘤纯度模型的性能。生存模型表明,经过训练的模型的肿瘤纯度估计提供了超过既定临床特征(包括年龄、肿瘤分期和性别)的额外预后信息。提供了使用Cox比例风险的生存模型,允许用户评估和探索模型,以进一步了解过去的历史病例,当前或假设的患者。未来的模型改进和前瞻性复制将证明真正的临床应用是必要的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Gene Expression Based Tumor Purity Estimation and Individual-Specific Survival Tool in Skin Cutaneous Melanoma
Skin Cutaneous Melanoma (SKCM) is a type of cancer that arises from the occurrence of genetic mutations in melanocytes and is the most aggressive and fatal type of cancer affecting mostly the Caucasian population with increasing incidences in Asia. Tumors and lesions are highly heterogeneous comprised of cancerous and non-cancerous cells, and the admixture is thought to have an important role in tumor growth and progression of the disease. This study features a system capable of estimating tumor purity from RNA-Seq gene expression data using Gradient Boosting Machines and providing individual-specific survival prediction (death or progression of the disease) using a set of clinical features and the tumor purity estimate from the trained model. The performance of the models for tumor purity using the entire set of gene expression and selected features by importance scores were compared. The survival models have shown that the tumor purity estimate from the trained model provided additional prognostic information over established clinical features including age, tumor stage, and sex. Survival models using Cox Proportional Hazards are provided to allow users to evaluate and probe the models for further in-sights, whether with past historical cases, current or hypothetical patients. Future model improvements and prospective replication will be necessary to demonstrate true clinical utility.
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