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With advances in high-throughput technology, molecular disease subtyping by high-dimensional omics data has been recognized as an effective approach for identifying subtypes of complex diseases with distinct disease mechanisms and prognoses. Conventional cluster analysis takes omics data as input and generates patient clusters with similar gene expression pattern. The omics data, however, usually contain multifaceted cluster structures that can be defined by different sets of genes. If the gene set associated with irrelevant clinical variables (e.g., sex or age) dominates the clustering process, the resulting clusters may not capture clinically meaningful disease subtypes. This motivates the development of a clustering framework with guidance from a prespecified disease outcome, such as lung function measurement or survival, in this paper. We propose two disease subtyping methods by omics data with outcome guidance using a generative model or a weighted joint likelihood. Both methods connect an outcome association model and a disease subtyping model by a latent variable of cluster labels. Compared to the generative model, weighted joint likelihood contains a data-driven weight parameter to balance the likelihood contributions from outcome association and gene cluster separation, which improves generalizability in independent validation but requires heavier computing. Extensive simulations and two real applications in lung disease and triple-negative breast cancer demonstrate superior disease subtyping performance of the outcome-guided clustering methods in terms of disease subtyping accuracy, gene selection and outcome association. Unlike existing clustering methods, the outcome-guided disease subtyping framework creates a new precision medicine paradigm to directly identify patient subgroups with clinical association. 

Abstract Triple-negative breast cancer (TNBC) is traditionally considered a glycolytic tumor with a poor prognosis while lacking targeted therapies. Here we show that high expression of dihydrolipoamide S-succinyltransferase (DLST), a tricarboxylic acid (TCA) cycle enzyme, predicts poor overall and recurrence-free survival among TNBC patients. DLST depletion suppresses growth and induces death in subsets of human TNBC cell lines, which are capable of utilizing glutamine anaplerosis. Metabolomics profiling reveals significant changes in the TCA cycle and reactive oxygen species (ROS) related pathways for sensitive but not resistant TNBC cells. Consequently, DLST depletion in sensitive TNBC cells increases ROS levels while N-acetyl-L-cysteine partially rescues cell growth. Importantly, suppression of the TCA cycle through DLST depletion or CPI-613, a drug currently in clinical trials for treating other cancers, decreases the burden and invasion of these TNBC. Together, our data demonstrate differential TCA-cycle usage in TNBC and provide therapeutic implications for the DLST-dependent subsets. 

Summary Evolutionarily conserved DEK domain‐containing proteins have been implicated in multiple chromatin‐related processes, mRNA splicing and transcriptional regulation in eukaryotes.Here, we show that two DEK proteins, DEK3 and DEK4, control the floral transition inArabidopsis. DEK3 and DEK4 directly associate with chromatin of related flowering repressors,FLOWERING LOCUS C(FLC), and its two homologs,MADS AFFECTING FLOWERING4(MAF4) andMAF5, to promote their expression.The binding of DEK3 and DEK4 to a histone octamerin vivoaffects histone modifications atFLC,MAF4andMAF5loci. In addition, DEK3 and DEK4 interact with RNA polymerase II and promote the association of RNA polymerase II withFLC,MAF4andMAF5chromatin to promote their expression.Our results show that DEK3 and DEK4 directly interact with chromatin to facilitate the transcription of key flowering repressors and thus prevent precocious flowering inArabidopsis.