Abstract LB-373: Comprehensive analysis of cancer stemness
Year: 2018
Type: conference-abstract
Abstract: 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, Bożena Kamińska, Joerg Huelsken, Larsson Omberg, Olivier Gevaert, Antonio Colaprico, Patrycja Czerwińska, 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.
Source: Cancer Research
Authors: Tathiane M. Malta, Artem Sokolov, Andrew J. Gentles, Tomasz Burzykowski, Laila Poisson, John Weinstein, Bożena Kamińska, Joerg Huelsken, Larsson Omberg, Olivier Gevaert, Antonio Colaprico, Patrycja Czerwińska, Sylwia Mazurek, Lopa Mishra, Holger Heyn, Alex Krasnitz, Andrew K. Godwin, Alexander J. Lazar, Joshua M. Stuart, Katherine Hoadley, Peter W. Laird, Houtan Noushmehr, Maciej Wiznerowicz
Institutions: Henry Ford Health, Harvard University, Stanford University, Hasselt University, The University of Texas MD Anderson Cancer Center, Instytut Biologii Doświadczalnej im. Marcelego Nenckiego, École Polytechnique Fédérale de Lausanne, Sage Bionetworks, Université Libre de Bruxelles, Poznan University of Medical Sciences, George Washington University, Centre for Genomic Regulation, Centro Nacional de Análisis Genómico, Cold Spring Harbor Laboratory, University of Kansas Medical Center, University of California, Santa Cruz, University of North Carolina at Chapel Hill, Van Andel Institute, International Institute for Molecular Oncology
Language: English
Topic: Cancer Cells and Metastasis
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W2887689053