Prostate cancer is heterogeneous and patients would benefit from methods that stratify those who are likely to respond to systemic therapy. Here, we employ single-cell assays for transposase-accessible chromatin (ATAC) and RNA sequencing in models of early treatment response and resistance to enzalutamide. In doing so, we identify pre-existing and treatment-persistent cell subpopulations that possess regenerative potential when subjected to treatment. We find distinct chromatin landscapes associated with enzalutamide treatment and resistance that are linked to alternative transcriptional programs. Transcriptional profiles characteristic of persistent cells are able to stratify the treatment response of patients. Ultimately, we show that defining changes in chromatin and gene expression in single-cell populations from pre-clinical models can reveal as yet unrecognized molecular predictors of treatment response. This suggests that the application of single-cell methods with high analytical resolution in pre-clinical models may powerfully inform clinical decision-making.

BackgroundThe differentiation of fibroblast-like pre-adipocytes to lipid-loaded adipocytes is regulated by a network of transcription factors, the most prominent one being the nuclear receptor peroxisome proliferator-activated receptor (PPAR) γ. However, many of the other 47 members of the nuclear receptor superfamily have an impact on adipogenesis, which in human cells has not been investigated in detail.Methodology/Principal FindingsWe analyzed by quantitative PCR all human nuclear receptors at multiple time points during differentiation of SGBS pre-adipocytes. The earliest effect was the down-regulation of the genes RARG, PPARD, REV-ERBA, REV-ERBB, VDR and GR followed by the up-regulation of PPARG, LXRA and AR. These observations are supported with data from 3T3-L1 mouse pre-adipocytes and primary human adipocytes. Investigation of the effects of the individual differentiation mix components in short-term treatments and of their omission from the full mix showed that the expression levels of the early-regulated nuclear receptor genes were most affected by the glucocorticoid receptor (GR) ligand cortisol and the phosophodiesterase inhibitor IBMX. Interestingly, the effects of both compounds converged to repress the genes PPARD, REV-ERBA, REV-ERBB, VDR and GR, whereas cortisol and IBMX showed antagonistic interaction for PPARG, LXRA and AR causing a time lag in their up-regulation. We hypothesize that the well-known auto-repression of GR fine-tunes the detected early responses. Consistently, chromatin immunoprecipitation experiments showed that GR association increased on the transcription start sites of the genes RARG, REV-ERBB, VDR and GR.Conclusions/SignificanceAdipocyte differentiation is a process, in which many members of the nuclear receptor superfamily change their mRNA expression. The actions of cortisol and IBMX converged to repress several nuclear receptors early in differentiation, while up-regulation of other nuclear receptor genes showed a time lag due to antagonisms of the signals. Our results place GR and its ligand cortisol as central regulatory factors controlling early regulatory events in human adipogenesis that precedes the regulation of the later events by PPARG.

Background Tight regulatory loops orchestrate commitment to B cell fate within bone marrow. Genetic lesions in this gene regulatory network underlie the emergence of the most common childhood cancer, acute lymphoblastic leukemia (ALL). The initial genetic hits, including the common translocation that fuses ETV6 and RUNX1 genes, lead to arrested cell differentiation. Here, we aimed to characterize transcription factor activities along the B-lineage differentiation trajectory as a reference to characterize the aberrant cell states present in leukemic bone marrow, and to identify those transcription factors that maintain cancer-specific cell states for more precise therapeutic intervention. Methods We compared normal B-lineage differentiation and in vivo leukemic cell states using single cell RNA-sequencing (scRNA-seq) and several complementary genomics profiles. Based on statistical tools for scRNA-seq, we benchmarked a workflow to resolve transcription factor activities and gene expression distribution changes in healthy bone marrow lymphoid cell states. We compared these to ALL bone marrow at diagnosis and in vivo during chemotherapy, focusing on leukemias carrying the ETV6-RUNX1 fusion. Results We show that lymphoid cell transcription factor activities uncovered from bone marrow scRNA-seq have high correspondence with independent ATAC- and ChIP-seq data. Using this comprehensive reference for regulatory factors coordinating B-lineage differentiation, our analysis of ETV6-RUNX1-positive ALL cases revealed elevated activity of multiple ETS-transcription factors in leukemic cells states, including the leukemia genome-wide association study hit ELK3. The accompanying gene expression changes associated with natural killer cell inactivation and depletion in the leukemic immune microenvironment. Moreover, our results suggest that the abundance of G1 cell cycle state at diagnosis and lack of differentiation-associated regulatory network changes during induction chemotherapy represent features of chemoresistance. To target the leukemic regulatory program and thereby overcome treatment resistance, we show that inhibition of ETS-transcription factors reduced cell viability and resolved pathways contributing to this using scRNA-seq. Conclusions Our data provide a detailed picture of the transcription factor activities characterizing both normal B-lineage differentiation and those acquired in leukemic bone marrow and provide a rational basis for new treatment strategies targeting the immune microenvironment and the active regulatory network in leukemia.

Prostate cancer is profoundly heterogeneous and patients would benefit from methods that stratify clinically indolent from more aggressive forms of the disease. We employed single-cell assay for transposase-accessible chromatin (ATAC) and RNA sequencing in models of early treatment response and resistance to enzalutamide. In doing so, we identified pre-existing and treatment-persistent cell subpopulations that possess transcriptional stem-like features and regenerative potential when subjected to treatment. We found distinct chromatin landscapes associated with enzalutamide treatment and resistance that are linked to alternative transcriptional programs. Transcriptional profiles characteristic of persistent stem-like cells were able to stratify the treatment response of patients. Ultimately, we show that defining changes in chromatin and gene expression in single-cell populations from pre-clinical models can reveal hitherto unrecognized molecular predictors of treatment response. This suggests that high analytical resolution of pre-clinical models may powerfully inform clinical decision-making.

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