Distance in cancer gene expression from stem cells predicts patient survival.
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.
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.
Markus
M
Riester
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.
Hua-Jun
HJ
Wu
Cell Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY United States of America.
Ahmet
A
Zehir
Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY United States of America.
Mithat
M
Gönen
Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY United States of America.
Andre L
AL
Moreira
Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY United States of America.
Robert J
RJ
Downey
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.
Franziska
F
Michor
D002454:Cell Differentiation / Q000235:genetics; D015536:Down-Regulation / Q000235:genetics; D015972:Gene Expression Regulation, Neoplastic / Q000235:genetics*; D006801:Humans; D009369:Neoplasms / Q000235:genetics*; D009857:Oncogenes / Q000235:genetics*; D013234:Stem Cells / Q000378:metabolism*; D059467:Transcriptome / Q000235:genetics*; D015854:Up-Regulation / Q000235:genetics