Riester Markus, Wu Hua-Jun, Zehir Ahmet, Gönen Mithat, Moreira Andre L, Downey Robert J, Michor Franziska
Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, and Department of Biostatistics, Harvard School of Public Health, Boston, MA, United States of America.
Cell Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY United States of America.
PLoS One. 2017 Mar 23;12(3):e0173589. doi: 10.1371/journal.pone.0173589. eCollection 2017.
The degree of histologic cellular differentiation of a cancer has been associated with prognosis but is subjectively assessed. We hypothesized that information about tumor differentiation of individual cancers could be derived objectively from cancer gene expression data, and would allow creation of a cancer phylogenetic framework that would correlate with clinical, histologic and molecular characteristics of the cancers, as well as predict prognosis. Here we utilized mRNA expression data from 4,413 patient samples with 7 diverse cancer histologies to explore the utility of ordering samples by their distance in gene expression from that of stem cells. A differentiation baseline was obtained by including expression data of human embryonic stem cells (hESC) and human mesenchymal stem cells (hMSC) for solid tumors, and of hESC and CD34+ cells for liquid tumors. We found that the correlation distance (the degree of similarity) between the gene expression profile of a tumor sample and that of stem cells orients cancers in a clinically coherent fashion. For all histologies analyzed (including carcinomas, sarcomas, and hematologic malignancies), patients with cancers with gene expression patterns most similar to that of stem cells had poorer overall survival. We also found that the genes in all undifferentiated cancers of diverse histologies that were most differentially expressed were associated with up-regulation of specific oncogenes and down-regulation of specific tumor suppressor genes. Thus, a stem cell-oriented phylogeny of cancers allows for the derivation of a novel cancer gene expression signature found in all undifferentiated forms of diverse cancer histologies, that is competitive in predicting overall survival in cancer patients compared to previously published prediction models, and is coherent in that gene expression was associated with up-regulation of specific oncogenes and down-regulation of specific tumor suppressor genes associated with regulation of the multicellular state.
癌症的组织学细胞分化程度与预后相关,但评估具有主观性。我们假设,个体癌症的肿瘤分化信息可以从癌症基因表达数据中客观推导出来,并且能够创建一个与癌症的临床、组织学和分子特征相关联的癌症系统发育框架,同时还能预测预后。在此,我们利用来自4413例患者样本、涵盖7种不同癌症组织学类型的mRNA表达数据,通过样本与干细胞基因表达的距离排序来探索其效用。对于实体瘤,纳入人类胚胎干细胞(hESC)和人间充质干细胞(hMSC)的表达数据,对于液体肿瘤,则纳入hESC和CD34+细胞的表达数据,以此获得分化基线。我们发现,肿瘤样本的基因表达谱与干细胞基因表达谱之间的相关距离(相似程度)以临床连贯的方式对癌症进行了定向。对于所有分析的组织学类型(包括癌、肉瘤和血液系统恶性肿瘤),基因表达模式与干细胞最相似的癌症患者总体生存率较差。我们还发现,所有不同组织学类型的未分化癌症中差异表达最显著的基因与特定癌基因的上调和特定抑癌基因的下调相关。因此,以干细胞为导向的癌症系统发育能够推导出一种在所有不同癌症组织学类型的未分化形式中均存在的新型癌症基因表达特征,与先前发表的预测模型相比,该特征在预测癌症患者总体生存率方面具有竞争力,并且在基因表达与特定癌基因上调以及与多细胞状态调控相关的特定抑癌基因下调相关联方面具有连贯性。