Distance in cancer gene expression from stem cells predicts patient survival.

Markus Riester,Andre L. Moreira,Ahmet Zehir,Mithat Gönen,Hua-Jun Wu,Robert J. Downey,Franziska Michor,Tiffany Seagroves

doi:10.1371/journal.pone.0173589

Markus Riester, Andre L. Moreira + Show 6 more

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https://doi.org/10.1371/journal.pone.0173589

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Abstract

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.

Highlights

Signatures based upon the expression levels of subgroups of genes in tumor samples have been explored in an effort to classify tumors and to predict the likelihood of survival of cancer patients [1,2,3,4,5,6]
A differentiation baseline was obtained by including expression data of human embryonic stem cells [27] and human mesenchymal stem cells [27] for solid tumors, and of hESC and CD34+ cells [28] for liquid tumors
Just as histologic determination of levels of differentiation is oriented between the poles of stem cells and fully differentiated cells], we hypothesized that cancer gene expression is best analyzed by orienting it between the poles of the expression of stem cells and of fully differentiated tissue

Summary

Introduction

Signatures based upon the expression levels of subgroups of genes in tumor samples have been explored in an effort to classify tumors and to predict the likelihood of survival of cancer patients [1,2,3,4,5,6] These signatures are usually determined by identifying the subset of differentially expressed genes that stratify a patient cohort of a given histology into those with short versus long survival times A significant limitation of this approach is that signatures need to be identified for each histologic type, as the prognostic benefit of a signature for one cancer type contains very little information about another It is an important goal of the field to identify gene expression-based approaches that reliably predict patient survival for any tumor type. We have not investigated an exhaustive set of stem cell datasets

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