Dendritic cells (DC) are professional antigen presenting cells that develop from hematopoietic stem cells through successive steps of lineage commitment and differentiation. Multipotent progenitors (MPP) are committed to DC restricted common DC progenitors (CDP), which differentiate into specific DC subsets, classical DC (cDC) and plasmacytoid DC (pDC). To determine epigenetic states and regulatory circuitries during DC differentiation, we measured consecutive changes of genome-wide gene expression, histone modification and transcription factor occupancy during the sequel MPP-CDP-cDC/pDC. Specific histone marks in CDP reveal a DC-primed epigenetic signature, which is maintained and reinforced during DC differentiation. Epigenetic marks and transcription factor PU.1 occupancy increasingly coincide upon DC differentiation. By integrating PU.1 occupancy and gene expression we devised a transcription factor regulatory circuitry for DC commitment and subset specification. The circuitry provides the transcription factor hierarchy that drives the sequel MPP-CDP-cDC/pDC, including Irf4, Irf8, Tcf4, Spib and Stat factors. The circuitry also includes feedback loops inferred for individual or multiple factors, which stabilize distinct stages of DC development and DC subsets. In summary, here we describe the basic regulatory circuitry of transcription factors that drives DC development.

PURPOSE Circulating cell-free tumor DNA (ctDNA) reflects the heterogeneous spectrum of tumor-specific mutations, especially in systemic disease. We validated plasma-based assays that allow the dynamic quantitative detection of ctDNA as a prognostic biomarker for tumor load and prediction of therapy response in melanoma. MATERIALS and METHODS We analyzed plasma-derived ctDNA from a large training cohort (n = 96) of patients with advanced-stage melanoma, with assays for the BRAFV600E and NRASQ61 driver mutations as well as TERTC250T and TERTC228T promoter mutations. An independent patient cohort (n = 35) was used to validate the utility of ctDNA monitoring under mitogen-activated protein kinase–targeted or immune checkpoint therapies. RESULTS Elevated plasma ctDNA level at baseline was an independent prognostic factor of disease progression when compared with serum S100 and lactate dehydrogenase levels in multivariable analyses (hazard ratio [HR], 7.43; 95% CI, 1.01 to 55.19; P = .05). The change in ctDNA levels during therapy correlated with treatment response, where increasing ctDNA was predictive for shorter progression-free survival (eg, for BRAFV600E ctDNA, HR, 3.70; 95% CI, 1.86 to 7.34; P < .001). Increasing ctDNA levels predicted disease progression significantly earlier than did routine radiologic scans ( P < .05), with a mean lead time of 3.5 months. NRAS-mutant ctDNA was detected in a significant proportion of patients with BRAF-mutant tumors under therapy, but unexpectedly also at baseline. In vitro sensitivity studies suggested that this represents higher-than-expected intratumoral heterogeneity. The detection of NRASQ61 ctDNA in baseline samples of patients with BRAFV600E mutation who were treated with mitogen-activated protein kinase inhibitors significantly correlated with shorter progression-free survival (HR, 3.18; 95% CI, 1.31 to 7.68; P = .03) and shorter overall survival (HR, 4.08; 95% CI, 1.57 to 10.58; P = .01). CONCLUSION Our results show the potential role of ctDNA measurement as a sensitive monitoring and prediction tool for the early assessment of disease progression and therapeutic response in patients with metastatic melanoma.

We propose an One-stage DIffereNtial peak caller (ODIN); an Hidden Markov Model-based approach to detect and analyze differential peaks (DPs) in pairs of ChIP-seq data. ODIN performs genomic signal processing, peak calling and p-value calculation in an integrated framework. We also propose an evaluation methodology to compare ODIN with competing methods. The evaluation method is based on the association of DPs with expression changes in the same cellular conditions. Our empirical study based on several ChIP-seq experiments from transcription factors, histone modifications and simulated data shows that ODIN outperforms considered competing methods in most scenarios.