Comprehensive Analysis Of Cancer Stemness

Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem cell-like features. Here, we provide new stemness indices for assessing the degree of oncogenic dedifferentiation. We took advantage of an innovative one-class logistic regression machine learning algorithm (OCLR) to extract transcriptomic and epigenetic feature sets derived from non-transformed pluripotent stem cells and their differentiated progenies. Using OCLR, we were able to sort TCGA tumor samples by stemness phenotype and identify previously undiscovered biological mechanisms associated with the dedifferentiated oncogenic state. Analyses of tumor microenvironment revealed the correlation of cancer stemness with immune checkpoint expression and infiltrating immune system cells not previously anticipated. We have shown the de-differentiated oncogenic phenotype increased in the metastatic tumor that further justify their more aggressive phenotype. Application of our stemness indices reveals features of intra-tumor heterogeneity in molecular profiles obtained from the single-cell analyses. Finally, the machine learning-based indices allowed for the identification of chemical compounds and novel targets for the cancer therapies aiming at tumor differentiation. Our findings provide new prognostic signatures that enable cancer biologists and oncologists to quantify the impact of tumor stemness on outcome across cancer types and may help to pave the way for progress in treatment strategies for cancer patients. Citation Format: Tathiane M. Malta, Artem Sokolov, Andrew J. Gentles, Tomasz Burzykowski, Laila Poisson, John Weinstein, Bozena Kaminska, Joerg Huelsken, Larsson Omberg, Olivier Gevaert, Antonio Colaprico, Patrycja Czerwinska, Sylwia Mazurek, Lopa Mishra, Holger Heyn, Alex Krasnitz, Andrew K. Godwin, Alexander J. Lazar, The Cancer Genome Atlas Research Network, Joshua M. Stuart, Katherine Hoadley, Peter W. Laird, Houtan Noushmehr, Maciej Wiznerowicz. Comprehensive analysis of cancer stemness [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr LB-373.