In vivo CRISPR screens identify the E3 ligase Cop1 as a modulator of macrophage infiltration and cancer immunotherapy target.

Despite remarkable clinical efficacy of immune checkpoint blockade (ICB) in cancer treatment, ICB benefits for triple-negative breast cancer (TNBC) remain limited. Through pooled in vivo CRISPR knockout (KO) screens in syngeneic TNBC mouse models, we found that deletion of the E3 ubiquitin ligase Cop1 in cancer cells decreases secretion of macrophage-associated chemokines, reduces tumor macrophage infiltration, enhances anti-tumor immunity, and strengthens ICB response. Transcriptomics, epigenomics, and proteomics analyses revealed that Cop1 functions through proteasomal degradation of the C/ebpδ protein. The Cop1 substrate Trib2 functions as a scaffold linking Cop1 and C/ebpδ, which leads to polyubiquitination of C/ebpδ. In addition, deletion of the E3 ubiquitin ligase Cop1 in cancer cells stabilizes C/ebpδ to suppress expression of macrophage chemoattractant genes. Our integrated approach implicates Cop1 as a target for improving cancer immunotherapy efficacy in TNBC by regulating chemokine secretion and macrophage infiltration in the tumor microenvironment.

Loss of Tankyrase-mediated destruction of 3BP2 is the underlying pathogenic mechanism of cherubism.

Cherubism is an autosomal-dominant syndrome characterized by inflammatory destructive bony lesions resulting in symmetrical deformities of the facial bones. Cherubism is caused by mutations in Sh3bp2, the gene that encodes the adaptor protein 3BP2. Most identified mutations in 3BP2 lie within the peptide sequence RSPPDG. A mouse model of cherubism develops hyperactive bone-remodeling osteoclasts and systemic inflammation characterized by expansion of the myelomonocytic lineage. The mechanism by which cherubism mutations alter 3BP2 function has remained obscure. Here we show that Tankyrase, a member of the poly(ADP-ribose)polymerase (PARP) family, regulates 3BP2 stability through ADP-ribosylation and subsequent ubiquitylation by the E3-ubiquitin ligase RNF146 in osteoclasts. Cherubism mutations uncouple 3BP2 from Tankyrase-mediated protein destruction, which results in its stabilization and subsequent hyperactivation of the SRC, SYK, and VAV signaling pathways.

A community challenge to predict clinical outcomes after immune checkpoint blockade in non-small cell lung cancer.

BACKGROUND: Predictive biomarkers of immune checkpoint inhibitor (ICI) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti-PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC. METHODS: Participants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials. RESULTS: A total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression-based signatures. The best-performing models showed improved predictive power over reference variables, including TMB or PD-L1. CONCLUSIONS: This DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICI clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of ICI efficacy. TRIAL REGISTRATION: CheckMate 026; NCT02041533, registered January 22, 2014. CheckMate 227; NCT02477826, registered June 23, 2015.

TISMO: syngeneic mouse tumor database to model tumor immunity and immunotherapy response.

Syngeneic mouse models are tumors derived from murine cancer cells engrafted on genetically identical mouse strains. They are widely used tools for studying tumor immunity and immunotherapy response in the context of a fully functional murine immune system. Large volumes of syngeneic mouse tumor expression profiles under different immunotherapy treatments have been generated, although a lack of systematic collection and analysis makes data reuse challenging. We present Tumor Immune Syngeneic MOuse (TISMO), a database with an extensive collection of syngeneic mouse model profiles with interactive visualization features. TISMO contains 605 in vitro RNA-seq samples from 49 syngeneic cancer cell lines across 23 cancer types, of which 195 underwent cytokine treatment. TISMO also includes 1518 in vivo RNA-seq samples from 68 syngeneic mouse tumor models across 19 cancer types, of which 832 were from immune checkpoint blockade (ICB) studies. We manually annotated the sample metadata, such as cell line, mouse strain, transplantation site, treatment, and response status, and uniformly processed and quality-controlled the RNA-seq data. Besides data download, TISMO provides interactive web interfaces to investigate whether specific gene expression, pathway enrichment, or immune infiltration level is associated with differential immunotherapy response. TISMO is available at http://tismo.cistrome.org.

Computational modeling of ovarian cancer dynamics suggests optimal strategies for therapy and screening.

High-grade serous tubo-ovarian carcinoma (HGSC) is a major cause of cancer-related death. Treatment is not uniform, with some patients undergoing primary debulking surgery followed by chemotherapy (PDS) and others being treated directly with chemotherapy and only having surgery after three to four cycles (NACT). Which strategy is optimal remains controversial. We developed a mathematical framework that simulates hierarchical or stochastic models of tumor initiation and reproduces the clinical course of HGSC. After estimating parameter values, we infer that most patients harbor chemoresistant HGSC cells at diagnosis and that, if the tumor burden is not too large and complete debulking can be achieved, PDS is superior to NACT due to better depletion of resistant cells. We further predict that earlier diagnosis of primary HGSC, followed by complete debulking, could improve survival, but its benefit in relapsed patients is likely to be limited. These predictions are supported by primary clinical data from multiple cohorts. Our results have clear implications for these key issues in HGSC management.

Pan-ERBB kinase inhibition augments CDK4/6 inhibitor efficacy in oesophageal squamous cell carcinoma.

OBJECTIVE: Oesophageal squamous cell carcinoma (OSCC), like other squamous carcinomas, harbour highly recurrent cell cycle pathway alterations, especially hyperactivation of the CCND1/CDK4/6 axis, raising the potential for use of existing CDK4/6 inhibitors in these cancers. Although CDK4/6 inhibition has shown striking success when combined with endocrine therapy in oestrogen receptor positive breast cancer, CDK4/6 inhibitor palbociclib monotherapy has not revealed evidence of efficacy to date in OSCC clinical studies. Herein, we sought to elucidate the identification of key dependencies in OSCC as a foundation for the selection of targets whose blockade could be combined with CDK4/6 inhibition. DESIGN: We combined large-scale genomic dependency and pharmaceutical screening datasets with preclinical cell line models, to identified potential combination therapies in squamous cell cancer. RESULTS: We identified sensitivity to inhibitors to the ERBB family of receptor kinases, results clearly extending beyond the previously described minority of tumours with EGFR amplification/dependence, specifically finding a subset of OSCCs with dual dependence on ERBB3 and ERBB2. Subsequently. we demonstrated marked efficacy of combined pan-ERBB and CDK4/6 inhibition in vitro and in vivo. Furthermore, we demonstrated that squamous lineage transcription factor KLF5 facilitated activation of ERBBs in OSCC. CONCLUSION: These results provide clear rationale for development of combined ERBB and CDK4/6 inhibition in these cancers and raises the potential for KLF5 expression as a candidate biomarker to guide the use of these agents. These data suggested that by combining existing Food and Drug Administration (FDA)-approved agents, we have the capacity to improve therapy for OSCC and other squamous cancer.

High IL2RA mRNA expression is an independent adverse prognostic biomarker in core binding factor and intermediate-risk acute myeloid leukemia.

BACKGROUND: Elevated protein expressions of CD markers such as IL2RA/CD25, CXCR4/CD184, CD34 and CD56 are associated with adverse prognosis in acute myeloid leukemia (AML). However, the prognostic value of mRNA expressions of these CD markers in AML remains unclear. Through our pilot evaluation, IL2RA mRNA expression appeared to be the best candidate as a prognostic biomarker. Therefore, the aim of this study is to characterize the prognostic value of IL2RA mRNA expression and evaluate its potential to refine prognostification in AML. METHODS: In a cohort of 239 newly diagnosed AML patients, IL2RA mRNA expression were measured by TaqMan realtime quantitative PCR. Morphological, cytogenetics and mutational analyses were also performed. In an intermediate-risk AML cohort with 66 patients, the mRNA expression of prognostic biomarkers (BAALC, CDKN1B, ERG, MECOM/EVI1, FLT3, ID1, IL2RA, MN1 and WT1) were quantified by NanoString technology. A TCGA cohort was analyzed to validate the prognostic value of IL2RA. For statistical analysis, Mann-Whitney U test, Fisher exact test, logistic regression, Kaplan-Meier and Cox regression analyses were used. RESULTS: In AML cohort of 239 patients, high IL2RA mRNA expression independently predicted shorter relapse free survival (RFS, p < 0.001) and overall survival (OS, p < 0.001) irrespective of age, cytogenetics, FLT3-ITD or c-KIT D816V mutational status. In core binding factor (CBF) AML, high IL2RA mRNA expression correlated with FLT3-ITD status (p = 0.023). Multivariable analyses revealed that high IL2RA expression (p = 0.002), along with c-KIT D816V status (p = 0.013) significantly predicted shorter RFS, whereas only high IL2RA mRNA expression (p = 0.014) significantly predicted shorter OS in CBF AML. In intermediate-risk AML in which multiple gene expression markers were tested by NanoString, IL2RA significantly correlated with ID1 (p = 0.006), FLT3 (p = 0.007), CDKN1B (p = 0.033) and ERG (p = 0.030) expressions. IL2RA (p < 0.001) and FLT3 (p = 0.008) expressions remained significant in predicting shorter RFS, whereas ERG (p = 0.008) and IL2RA (p = 0.044) remained significant in predicting shorter OS. Similar analyses in TCGA intermediate-risk AML showed the independent prognostic role of IL2RA in predicting event free survival (p < 0.001) and OS (p < 0.001). CONCLUSIONS: High IL2RA mRNA expression is an independent and adverse prognostic factor in AML and specifically stratifies patients to worse prognosis in both CBF and intermediate-risk AML.

Distinct GAB2 signaling pathways are essential for myeloid and lymphoid transformation and leukemogenesis by BCR-ABL1.

Tyrosine kinase inhibitors (TKIs) directed against BCR-ABL1, the product of the Philadelphia (Ph) chromosome, have revolutionized treatment of patients with chronic myeloid leukemia (CML). However, acquired resistance to TKIs is a significant clinical problem in CML, and TKI therapy is much less effective against Ph(+)B-cell acute lymphoblastic leukemia (B-ALL). BCR-ABL1, via phosphorylated Tyr177, recruits the adapter GRB2-associated binding protein 2 (GAB2) as part of a GRB2/GAB2 complex. We showed previously that GAB2 is essential for BCR-ABL1-evoked myeloid transformation in vitro. Using a genetic strategy and mouse models of CML and B-ALL, we show here that GAB2 is essential for myeloid and lymphoid leukemogenesis by BCR-ABL1. In the mouse model, recipients of BCR-ABL1-transducedGab2(-/-)bone marrow failed to develop CML-like myeloproliferative neoplasia. Leukemogenesis was restored by expression of GAB2 but not by GAB2 mutants lacking binding sites for its effectors phosphatidylinositol 3-kinase (PI3K) or SRC homology 2-containing phosphotyrosine phosphatase 2 (SHP2). GAB2 deficiency also attenuated BCR-ABL1-induced B-ALL, but only the SHP2 binding site was required. The SHP2 and PI3K binding sites were differentially required for signaling downstream of GAB2. Hence, GAB2 transmits critical transforming signals from Tyr177 to PI3K and SHP2 for CML pathogenesis, whereas only the GAB2-SHP2 pathway is essential for lymphoid leukemogenesis. Given that GAB2 is dispensable for normal hematopoiesis, GAB2 and its effectors PI3K and SHP2 represent promising targets for therapy in Ph(+)hematologic neoplasms.

Erk1 and Erk2 are required for maintenance of hematopoietic stem cells and adult hematopoiesis.

Extracellular signal-regulated kinase 1 (Erk1) and Erk2 play crucial roles in cell survival, proliferation, cell adhesion, migration, and differentiation in many tissues. Here, we report that the absence of Erk1 and Erk2 in murine hematopoietic cells leads to bone marrow aplasia, leukopenia, anemia, and early lethality. Mice doubly-deficient in Erk1 and Erk2 show rapid attrition of hematopoietic stem cells and immature progenitors in a cell-autonomous manner. Reconstitution studies show that Erk1 and Erk2 play redundant and kinase-dependent functions in hematopoietic progenitor cells. Moreover, in cells transformed by the oncogenic KRas(G12D) allele, the presence of either Erk1 or Erk2 with intact kinase activity is sufficient to promote cytokine-independent proliferation.

Essential role for Ptpn11 in survival of hematopoietic stem and progenitor cells.

Src homology 2 domain-containing phosphatase 2 (Shp2), encoded by Ptpn11, is a member of the nonreceptor protein-tyrosine phosphatase family, and functions in cell survival, proliferation, migration, and differentiation in many tissues. Here we report that loss of Ptpn11 in murine hematopoietic cells leads to bone marrow aplasia and lethality. Mutant mice show rapid loss of hematopoietic stem cells (HSCs) and immature progenitors of all hematopoietic lineages in a gene dosage-dependent and cell-autonomous manner. Ptpn11-deficient HSCs and progenitors undergo apoptosis concomitant with increased Noxa expression. Mutant HSCs/progenitors also show defective Erk and Akt activation in response to stem cell factor and diminished thrombopoietin-evoked Erk activation. Activated Kras alleviates the Ptpn11 requirement for colony formation by progenitors and cytokine/growth factor responsiveness of HSCs, indicating that Ras is functionally downstream of Shp2 in these cells. Thus, Shp2 plays a critical role in controlling the survival and maintenance of HSCs and immature progenitors in vivo.

MetaTiME integrates single-cell gene expression to characterize the meta-components of the tumor immune microenvironment.

Recent advances in single-cell RNA sequencing have shown heterogeneous cell types and gene expression states in the non-cancerous cells in tumors. The integration of multiple scRNA-seq datasets across tumors can indicate common cell types and states in the tumor microenvironment (TME). We develop a data driven framework, MetaTiME, to overcome the limitations in resolution and consistency that result from manual labelling using known gene markers. Using millions of TME single cells, MetaTiME learns meta-components that encode independent components of gene expression observed across cancer types. The meta-components are biologically interpretable as cell types, cell states, and signaling activities. By projecting onto the MetaTiME space, we provide a tool to annotate cell states and signature continuums for TME scRNA-seq data. Leveraging epigenetics data, MetaTiME reveals critical transcriptional regulators for the cell states. Overall, MetaTiME learns data-driven meta-components that depict cellular states and gene regulators for tumor immunity and cancer immunotherapy.

A Molecular Switch between Mammalian MLL Complexes Dictates Response to Menin-MLL Inhibition.

Menin interacts with oncogenic MLL1-fusion proteins, and small molecules that disrupt these associations are in clinical trials for leukemia treatment. By integrating chromatin-focused and genome-wide CRISPR screens with genetic, pharmacologic, and biochemical approaches, we discovered a conserved molecular switch between the MLL1-Menin and MLL3/4-UTX chromatin-modifying complexes that dictates response to Menin-MLL inhibitors. MLL1-Menin safeguards leukemia survival by impeding the binding of the MLL3/4-UTX complex at a subset of target gene promoters. Disrupting the Menin-MLL1 interaction triggers UTX-dependent transcriptional activation of a tumor-suppressive program that dictates therapeutic responses in murine and human leukemia. Therapeutic reactivation of this program using CDK4/6 inhibitors mitigates treatment resistance in leukemia cells that are insensitive to Menin inhibitors. These findings shed light on novel functions of evolutionarily conserved epigenetic mediators like MLL1-Menin and MLL3/4-UTX and are relevant to understand and target molecular pathways determining therapeutic responses in ongoing clinical trials. SIGNIFICANCE: Menin-MLL inhibitors silence a canonical HOX- and MEIS1-dependent oncogenic gene expression program in leukemia. We discovered a parallel, noncanonical transcriptional program involving tumor suppressor genes that are repressed in Menin-MLL inhibitor-resistant leukemia cells but that can be reactivated upon combinatorial treatment with CDK4/6 inhibitors to augment therapy responses. This article is highlighted in the In This Issue feature, p. 1.

Discovery of Targets for Immune-Metabolic Antitumor Drugs Identifies Estrogen-Related Receptor Alpha.

Drugs that kill tumors through multiple mechanisms have the potential for broad clinical benefits. Here, we first developed an in silico multiomics approach (BipotentR) to find cancer cell-specific regulators that simultaneously modulate tumor immunity and another oncogenic pathway and then used it to identify 38 candidate immune-metabolic regulators. We show the tumor activities of these regulators stratify patients with melanoma by their response to anti-PD-1 using machine learning and deep neural approaches, which improve the predictive power of current biomarkers. The topmost identified regulator, ESRRA, is activated in immunotherapy-resistant tumors. Its inhibition killed tumors by suppressing energy metabolism and activating two immune mechanisms: (i) cytokine induction, causing proinflammatory macrophage polarization, and (ii) antigen-presentation stimulation, recruiting CD8+ T cells into tumors. We also demonstrate a wide utility of BipotentR by applying it to angiogenesis and growth suppressor evasion pathways. BipotentR (http://bipotentr.dfci.harvard.edu/) provides a resource for evaluating patient response and discovering drug targets that act simultaneously through multiple mechanisms. SIGNIFICANCE: BipotentR presents resources for evaluating patient response and identifying targets for drugs that can kill tumors through multiple mechanisms concurrently. Inhibition of the topmost candidate target killed tumors by suppressing energy metabolism and effects on two immune mechanisms. This article is highlighted in the In This Issue feature, p. 517.

Machine learning on syngeneic mouse tumor profiles to model clinical immunotherapy response.

Most patients with cancer are refractory to immune checkpoint blockade (ICB) therapy, and proper patient stratification remains an open question. Primary patient data suffer from high heterogeneity, low accessibility, and lack of proper controls. In contrast, syngeneic mouse tumor models enable controlled experiments with ICB treatments. Using transcriptomic and experimental variables from >700 ICB-treated/control syngeneic mouse tumors, we developed a machine learning framework to model tumor immunity and identify factors influencing ICB response. Projected on human immunotherapy trial data, we found that the model can predict clinical ICB response. We further applied the model to predicting ICB-responsive/resistant cancer types in The Cancer Genome Atlas, which agreed well with existing clinical reports. Last, feature analysis implicated factors associated with ICB response. In summary, our computational framework based on mouse tumor data reliably stratified patients regarding ICB response, informed resistance mechanisms, and has the potential for wide applications in disease treatment studies.

Hippo Signaling Pathway Regulates Cancer Cell-Intrinsic MHC-II Expression.

MHC-II is known to be mainly expressed on the surface of antigen-presenting cells. Evidence suggests MHC-II is also expressed by cancer cells and may be associated with better immunotherapy responses. However, the role and regulation of MHC-II in cancer cells remain unclear. In this study, we leveraged data mining and experimental validation to elucidate the regulation of MHC-II in cancer cells and its role in modulating the response to immunotherapy. We collated an extensive collection of omics data to examine cancer cell-intrinsic MHC-II expression and its association with immunotherapy outcomes. We then tested the functional relevance of cancer cell-intrinsic MHC-II expression using a syngeneic transplantation model. Finally, we performed data mining to identify pathways potentially involved in the regulation of MHC-II expression, and experimentally validated candidate regulators. Analyses of preimmunotherapy clinical samples in the CheckMate 064 trial revealed that cancer cell-intrinsic MHC-II protein was positively correlated with more favorable immunotherapy outcomes. Comprehensive meta-analyses of multiomics data from an exhaustive collection of data revealed that MHC-II is heterogeneously expressed in various solid tumors, and its expression is particularly high in melanoma. Using a syngeneic transplantation model, we further established that melanoma cells with high MHC-II responded better to anti-PD-1 treatment. Data mining followed by experimental validation revealed the Hippo signaling pathway as a potential regulator of melanoma MHC-II expression. In summary, we identified the Hippo signaling pathway as a novel regulator of cancer cell-intrinsic MHC-II expression. These findings suggest modulation of MHC-II in melanoma could potentially improve immunotherapy response.

Machine Learning Modeling of Protein-intrinsic Features Predicts Tractability of Targeted Protein Degradation.

Targeted protein degradation (TPD) has rapidly emerged as a therapeutic modality to eliminate previously undruggable proteins by repurposing the cell's endogenous protein degradation machinery. However, the susceptibility of proteins for targeting by TPD approaches, termed "degradability", is largely unknown. Here, we developed a machine learning model, model-free analysis of protein degradability (MAPD), to predict degradability from features intrinsic to protein targets. MAPD shows accurate performance in predicting kinases that are degradable by TPD compounds [with an area under the precision-recall curve (AUPRC) of 0.759 and an area under the receiver operating characteristic curve (AUROC) of 0.775] and is likely generalizable to independent non-kinase proteins. We found five features with statistical significance to achieve optimal prediction, with ubiquitination potential being the most predictive. By structural modeling, we found that E2-accessible ubiquitination sites, but not lysine residues in general, are particularly associated with kinase degradability. Finally, we extended MAPD predictions to the entire proteome to find 964 disease-causing proteins (including proteins encoded by 278 cancer genes) that may be tractable to TPD drug development.

IKAROS and MENIN coordinate therapeutically actionable leukemogenic gene expression in MLL-r acute myeloid leukemia.

Acute myeloid leukemia (AML) remains difficult to treat and requires new therapeutic approaches. Potent inhibitors of the chromatin-associated protein MENIN have recently entered human clinical trials, opening new therapeutic opportunities for some genetic subtypes of this disease. Using genome-scale functional genetic screens, we identified IKAROS (encoded by IKZF1) as an essential transcription factor in KMT2A (MLL1)-rearranged (MLL-r) AML that maintains leukemogenic gene expression while also repressing pathways for tumor suppression, immune regulation and cellular differentiation. Furthermore, IKAROS displays an unexpected functional cooperativity and extensive chromatin co-occupancy with mixed lineage leukemia (MLL)1-MENIN and the regulator MEIS1 and an extensive hematopoietic transcriptional complex involving homeobox (HOX)A10, MEIS1 and IKAROS. This dependency could be therapeutically exploited by inducing IKAROS protein degradation with immunomodulatory imide drugs (IMiDs). Finally, we demonstrate that combined IKAROS degradation and MENIN inhibition effectively disrupts leukemogenic transcriptional networks, resulting in synergistic killing of leukemia cells and providing a paradigm for improved drug targeting of transcription and an opportunity for rapid clinical translation.

Checkpoint blockade-induced CD8+ T cell differentiation in head and neck cancer responders.

BACKGROUND: Immune checkpoint blockade (ICB) response in recurrent/metastatic head and neck squamous cell carcinoma (HNSCC) is limited to 15%-20% of patients and underpinnings of resistance remain undefined. METHODS: Starting with an anti-PD1 sensitive murine HNSCC cell line, we generated an isogenic anti-PD1 resistant model. Mass cytometry was used to delineate tumor microenvironments of both sensitive parental murine oral carcinoma (MOC1) and resistant MOC1esc1 tumors. To examine heterogeneity and clonal dynamics of tumor infiltrating lymphocytes (TILs), we applied paired single-cell RNA and TCR sequencing in three HNSCC models. RESULTS: Anti-PD1 resistant MOC1esc1 line displayed a conserved cell intrinsic immune evasion signature. Immunoprofiling showed distinct baseline tumor microenvironments of MOC1 and MOC1esc1, as well as the remodeling of immune compartments on ICB in MOC1esc1 tumors. Single cell sequencing analysis identified several CD8 +TIL subsets including Tcf7 +Pd1- (naïve/memory-like), Tcf7 +Pd1+ (progenitor), and Tcf7-Pd1+ (differentiated effector). Mapping TCR shared fractions identified that successful anti-PD1 or anti-CTLA4 therapy-induced higher post-treatment T cell lineage transitions. CONCLUSIONS: These data highlight critical aspects of CD8 +TIL heterogeneity and differentiation and suggest facilitation of CD8 +TIL differentiation as a strategy to improve HNSCC ICB response.

MYC drives aggressive prostate cancer by disrupting transcriptional pause release at androgen receptor targets.

c-MYC (MYC) is a major driver of prostate cancer tumorigenesis and progression. Although MYC is overexpressed in both early and metastatic disease and associated with poor survival, its impact on prostate transcriptional reprogramming remains elusive. We demonstrate that MYC overexpression significantly diminishes the androgen receptor (AR) transcriptional program (the set of genes directly targeted by the AR protein) in luminal prostate cells without altering AR expression. Analyses of clinical specimens reveal that concurrent low AR and high MYC transcriptional programs accelerate prostate cancer progression toward a metastatic, castration-resistant disease. Data integration of single-cell transcriptomics together with ChIP-seq uncover an increase in RNA polymerase II (Pol II) promoter-proximal pausing at AR-dependent genes following MYC overexpression without an accompanying deactivation of AR-bound enhancers. Altogether, our findings suggest that MYC overexpression antagonizes the canonical AR transcriptional program and contributes to prostate tumor initiation and progression by disrupting transcriptional pause release at AR-regulated genes.

CoBRA: Containerized Bioinformatics Workflow for Reproducible ChIP/ATAC-seq Analysis.

Chromatin immunoprecipitation sequencing (ChIP-seq) and the Assay for Transposase-Accessible Chromatin with high-throughput sequencing (ATAC-seq) have become essential technologies to effectively measure protein-DNA interactions and chromatin accessibility. However, there is a need for a scalable and reproducible pipeline that incorporates proper normalization between samples, correction of copy number variations, and integration of new downstream analysis tools. Here we present Containerized Bioinformatics workflow for Reproducible ChIP/ATAC-seq Analysis (CoBRA), a modularized computational workflow which quantifies ChIP-seq and ATAC-seq peak regions and performs unsupervised and supervised analyses. CoBRA provides a comprehensive state-of-the-art ChIP-seq and ATAC-seq analysis pipeline that can be used by scientists with limited computational experience. This enables researchers to gain rapid insight into protein-DNA interactions and chromatin accessibility through sample clustering, differential peak calling, motif enrichment, comparison of sites to a reference database, and pathway analysis. CoBRA is publicly available online at https://bitbucket.org/cfce/cobra.