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Liu, Jianfang,Lichtenberg, Tara,Hoadley, Katherine A.,Poisson, Laila M.,Lazar, Alexander J.,Cherniack, Andrew D.,Kovatich, Albert J.,Benz, Christopher C.,Levine, Douglas A.,Lee, Adrian V.,Omberg, Lars Elsevier 2018 Cell Vol.173 No.2
<P><B>Summary</B></P> <P>For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Generation of TCGA Clinical Data Resource for 11,160 patients over 33 cancer types </LI> <LI> Analysis of clinical outcome endpoints with usage recommendations for each cancer </LI> <LI> Demonstration of data validity and utility for large-scale translational research </LI> </UL> </P> <P><B>Graphical Abstract</B></P> <P>[DISPLAY OMISSION]</P>
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Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation
Malta, Tathiane M.,Sokolov, Artem,Gentles, Andrew J.,Burzykowski, Tomasz,Poisson, Laila,Weinstein, John N.,Kamiń,ska, Boż,ena,Huelsken, Joerg,Omberg, Larsson,Gevaert, Olivier,Colaprico, Anto Elsevier 2018 Cell Vol.173 No.2
<P><B>Summary</B></P> <P>Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem-cell-like features. Here, we provide novel stemness indices for assessing the degree of oncogenic dedifferentiation. We used an innovative one-class logistic regression (OCLR) machine-learning algorithm to extract transcriptomic and epigenetic feature sets derived from non-transformed pluripotent stem cells and their differentiated progeny. Using OCLR, we were able to identify previously undiscovered biological mechanisms associated with the dedifferentiated oncogenic state. Analyses of the tumor microenvironment revealed unanticipated correlation of cancer stemness with immune checkpoint expression and infiltrating immune cells. We found that the dedifferentiated oncogenic phenotype was generally most prominent in metastatic tumors. Application of our stemness indices to single-cell data revealed patterns of intra-tumor molecular heterogeneity. Finally, the indices allowed for the identification of novel targets and possible targeted therapies aimed at tumor differentiation.</P> <P><B>Video Abstract</B></P> <P>Display Omitted</P> <P><B>Highlights</B></P> <P> <UL> <LI> Epigenetic and expression-based stemness indices measure oncogenic dedifferentiation </LI> <LI> Immune microenvironment content and PD-L1 levels associate with stemness indices </LI> <LI> Stemness index is increased in metastatic tumors and reveals intratumor heterogeneity </LI> <LI> Applying stemness indices reveals potential drug targets for anti-cancer therapies </LI> </UL> </P> <P><B>Graphical Abstract</B></P> <P>[DISPLAY OMISSION]</P>
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