Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation

By T. Malta, A. Sokolov, Andrew J. Gentles, T. Burzykowski, L. Poisson, John N. Weinstein, Bozena Kaminska, J. Huelsken, L. Omberg, Olivier Gevaert and 13 more. Cell, 2018

1,865 citations recorded by the source corpus.

SUMMARY 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 machine learning algorithm (OCLR) 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 system 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.

Subfields: Cancer Research, Molecular Biology, Pulmonary and Respiratory Medicine

View Paper: https://www.semanticscholar.org/paper/1b9c68de8212a2255b06074bcb94682fed170537

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In Cancer Research

In Molecular Biology

In Pulmonary and Respiratory Medicine

Arul M. Chinnaiyan

Citations in the corpus

JMJD8 Is an M2 Macrophage Biomarker, and It Associates With DNA Damage Repair to Facilitate Stemness Maintenance, Chemoresistance, and Immunosuppression in Pan-Cancer (2022, methodology): “The “differentially methylated probes-based stemness index” (DMPsi) of each cancer type was also obtained from a previous study (31), and their intercorrelation with JMJD8 mRNA expression was investigated.”
New functionalities in the TCGAbiolinks package for the study and integration of cancer data from GDC and GTEx (2019, methodology): “In this example, we integrated the TCGA Lung Adenocarcinoma (LUAD) with the GEO dataset GSE60052 [45] where RNA-seq data are available for 79 samples from Small Cell Lung Cancer (SCLC) tissues and 7 normal controls.”
Integrated Proteogenomic Characterization of Clear Cell Renal Cell Carcinoma (2019, methodology): “, ZiK ) was utilized previously to classify samples (Malta et al., 2018).”
Deep learning with multimodal representation for pancancer prognosis prediction (2019, methodology): “Our main source of data is preprocessed and batch corrected data from the PanCanAtlas TCGA project (Campbell et al. , 2018; Malta et al. , 2018; Weinstein et al. , 2013).”
Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types of Cancer (2018, background): “Two stemness indices (Malta et al., 2018), based on mRNA expression and on DNA methylation data, revealed aggregation of high stemness tumors across distinct regions of the TumorMap (Figures 5C and 5D).”