Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.

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

Molecular characterization and clinical relevance of metabolic expression subtypes in human cancers.

Metabolic reprogramming provides critical information for clinical oncology. Using molecular data of 9,125 patient samples from The Cancer Genome Atlas, we identified tumor subtypes in 33 cancer types based on mRNA expression patterns of seven major metabolic processes and assessed their clinical relevance. Our metabolic expression subtypes correlated extensively with clinical outcome: subtypes with upregulated carbohydrate, nucleotide, and vitamin/cofactor metabolism most consistently correlated with worse prognosis, whereas subtypes with upregulated lipid metabolism showed the opposite. Metabolic subtypes correlated with diverse somatic drivers but exhibited effects convergent on cancer hallmark pathways and were modulated by highly recurrent master regulators across cancer types. As a proof-of-concept example, we demonstrated that knockdown of SNAI1 or RUNX1—master regulators of carbohydrate metabolic subtypes-modulates metabolic activity and drug sensitivity. Our study provides a system-level view of metabolic heterogeneity within and across cancer types and identifies pathway cross-talk, suggesting related prognostic, therapeutic, and predictive utility

A partial african ancestry for the Creole cattle populations of the Caribbean

Seventy-eight cattle samples from three Creole Caribbean islands and one Brazilian breed were analyzed for sequence variation in the hypervariable segment of the mitochondrial DNA control region. Seventy-three samples displayed Bos taurus haplotypes, and five samples exhibited haplotypes that were of Bos indicus ancestry. Phylogenetic analysis revealed that all sampled B. taurus sequences fell into two distinct clusters with separate African and European origins. European sequences were encountered in each population; however, the distribution of African haplotypes was uneven, with the highest proportion of African influence found in the Guadeloupe Creole. The reduced levels of African haplotypic variation within the Caribbean and Brazilian are consistent with prior founder effects. Additionally, genetic variation at three microsatellite loci illustrated African influence uniquely in the Guadeloupe Creole. Collectively, the data suggest that this African influence is, at least in part, attributable to the historical importation of African cattle to the Americas. Furthermore, alleles of B. indicus ancestry were detected at appreciable frequencies in all Caribbean Creole populations and may reflect zebu introgressions from either West Africa or the Indian subcontinent. (Résumé d'auteur