The transcriptional and epigenetic landscape imposes constraints on the self-renewal capacity and lineage specification potential of both naive and primed mouse embryonic stem cells (mESCs). CRISPR/Cas9-based functional screening coupled with single-cell RNA-seq (CROP-seq) establishes relationships between gRNA-mediated knockout genotype and transcriptome phenotype, providing a powerful tool to dissect gene regulatory networks. Here, we employed CROP-seq to investigate the epigenetic regulation governing the pluripotency network in mESCs. This highly sensitive method identified key genes essential for the acquisition and exit from pluripotency, and revealed a novel role for H3K36me2 in modulating DNA methylation through regulating the expression of Dnmt1 and Dnmt3a. Specifically, loss of Nsd1-mediated H3K36me2 delayed naive state exit, whereas Ezh2 deficiency accelerated primed entry. Collectively, our findings identify an epigenetic regulatory network critical for determining mESCs' pluripotent state transitions.

Immune escape during the ductal carcinoma in situ (DCIS)-to-invasive breast cancer (IBC) transition shapes tumor evolution. Through transcriptomic mapping of the immune landscapes of normal breast, DCIS, and IBC from large patient cohorts, we identified T and myeloid cells as the primary distinguishing features between DCIS and IBC. We discovered cycling regulatory T cells (cycTreg) as an orchestrator of immunosuppression in IBC. cycTreg frequency predicts cytotoxic CD8+, TCR diversity, disease-specific survival in IBC, and recurrence in DCIS. In a rat model of breast cancer, we demonstrated that cycTreg act as precursors to mature Treg and are inducible by tumor-localized type 2 dendritic cells. Profiling of tumors subjected to αOX40 and αPD-L1 therapies revealed an IL-33-mediated fibroblast-cycTreg signaling loop, the disruption of which enhances intratumoral antigen-experienced CD8+ effectors and systemic immunosurveillance. Our study defines cycTreg as critical inducers of immune escape and promising immuno-oncology targets in breast cancer.

Supplementary Data from Hippo Signaling Pathway Regulates Cancer Cell–Intrinsic MHC-II Expression

INTRODUCTION:The occurrence of liver cancer in China is primarily attributed to chronic hepatitis B virus (HBV) infection. HBV X protein (HBx) has emerged as a significant carcinogenic driver in HBV-related liver cancer. However, the underlying mechanism by which HBx contributes to liver cancer development is not fully understood. METHODS:This study investigated HBx's role in regulating the tumor-suppressor gene RASSF1A. Firstly, the RASSF1A plasmid was constructed using a luciferase reporter system. The dual luciferase assay system detected HBx's effect on RASSF1A promoter activity. Western blotting and quantitative PCR methods measured HBx's impact on RASSF1A protein and mRNA expression. Chip was used to test the binding of HBx and SP1. CCK8, transwell, flow cytometry were used to detect the effect of RASSF1A on HCC proliferation. Methylation-specific PCR analyzed HBx's effect on RASSF1A methylation. RESULTS:Our results show that HBx significantly enhances RASSF1A promoter activity in an SP1 binding site-dependent manner. When only one SP1 binding site remained, HBx's effect was abolished. RASSF1A can inhibit HCC proliferation. Both mRNA and protein expression levels of RASSF1A were lower in HBx-expressing THLE-2 cells than in control cells, correlating with higher RASSF1A promoter methylation. CONCLUSION:These findings suggest HBx enhances RASSF1A promoter activity and upregulates transcription via SP1, potentially preceding RASSF1A promoter methylation. This study provides new insights into HBx's regulation of the tumor suppressor gene RASSF1A in HBV-related liver cancer.

Radiotherapy is the cornerstone of treatment for cervical cancer, yet the variability of patient response demands a deeper understanding of the molecular determinants of radioresistance. In this study, we investigated the molecular and cellular mechanisms of radioresistance in cervical cancer through a comprehensive multi-omics and machine learning approach. We downloaded and processed transcriptome sequencing, methylation and single-cell sequencing data from the TCGA and GEO databases. Differential gene and methylation analyses were performed to identify radioresistance-related markers. Single-cell data were processed using Seurat and annotated using CellTypist. Prognostic models were constructed and validated through downscaling, cell scoring, trajectory analysis and machine learning. Additionally, immune infiltration and drug sensitivity analyses were conducted. The differential analysis identified 845 up-regulated and 460 down-regulated genes associated with radioresistance. The methylation analysis identified 3042 down-regulated and 158 up-regulated gene loci. Single-cell sequencing revealed 43,475 cells and 13 cell types, with aneuploid cells predominantly present in epithelial cells. Cell scoring highlighted dispersed immune cells, with monocytes, ILCs, and T cells being the most relevant to radiotherapy resistance. The machine learning approach constructed a robust prognostic model using Cox regression and validated it on multiple datasets. The prognostic model demonstrated good predictive ability in assessing radiotherapy efficacy and immune infiltration. Drug screening identified several potential therapeutic candidates with high sensitivity for high-risk patients. This study provides a comprehensive multi-omics analysis and machine learning framework for identifying and validating molecular markers and prognostic models associated with radioresistance in cervical cancer, providing insights for personalized treatment strategies.

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.

RNA-sequencing (RNA-seq) has become an increasingly cost-effective technique for molecular profiling and immune characterization of tumors. In the past decade, many computational tools have been developed to characterize tumor immunity from gene expression data. However, the analysis of large-scale RNA-seq data requires bioinformatics proficiency, large computational resources and cancer genomics and immunology knowledge. In this tutorial, we provide an overview of computational analysis of bulk RNA-seq data for immune characterization of tumors and introduce commonly used computational tools with relevance to cancer immunology and immunotherapy. These tools have diverse functions such as evaluation of expression signatures, estimation of immune infiltration, inference of the immune repertoire, prediction of immunotherapy response, neoantigen detection and microbiome quantification. We describe the RNA-seq IMmune Analysis (RIMA) pipeline integrating many of these tools to streamline RNA-seq analysis. We also developed a comprehensive and user-friendly guide in the form of a GitBook with text and video demos to assist users in analyzing bulk RNA-seq data for immune characterization at both individual sample and cohort levels by using RIMA.

The challenge of eradicating leukemia in patients with acute myelogenous leukemia (AML) after initial cytoreduction has motivated modern efforts to combine synergistic active modalities including immunotherapy. Recently, the ETCTN/CTEP 10026 study tested the combination of the DNA methyltransferase inhibitor decitabine together with the immune checkpoint inhibitor ipilimumab for AML/myelodysplastic syndrome (MDS) either after allogeneic hematopoietic stem cell transplantation (HSCT) or in the HSCT-naive setting. Integrative transcriptome-based analysis of 304 961 individual marrow-infiltrating cells for 18 of 48 subjects treated on study revealed the strong association of response with a high baseline ratio of T to AML cells. Clinical responses were predominantly driven by decitabine-induced cytoreduction. Evidence of immune activation was only apparent after ipilimumab exposure, which altered CD4(+) T-cell gene expression, in line with ongoing T-cell differentiation and increased frequency of marrow-infiltrating regulatory T cells. For post-HSCT samples, relapse could be attributed to insufficient clearing of malignant clones in progenitor cell populations. In contrast to AML/MDS bone marrow, the transcriptomes of leukemia cutis samples from patients with durable remission after ipilimumab monotherapy showed evidence of increased infiltration with antigen-experienced resident memory T cells and higher expression of CTLA-4 and FOXP3. Altogether, activity of combined decitabine and ipilimumab is impacted by cellular expression states within the microenvironmental niche of leukemic cells. The inadequate elimination of leukemic progenitors mandates urgent development of novel approaches for targeting these cell populations to generate long-lasting responses. This trial was registered at www.clinicaltrials.gov as #NCT02890329.

Treatment of patients with advanced myelodysplastic syndrome (MDS) or acute myeloid leukemia (AML) ineligible for intensive chemotherapy currently is mostly non-curative and therapeutic options for relapse after allogeneic hematopoietic stem cell transplantation (HSCT) rarely induce durable remissions. Novel strategies to address disease relapse are thus needed. Immune checkpoint blockade with CTLA-4 antibodies (ipilimumab) is an emerging concept that demonstrated potent clinical activity in relapsed leukemia cutis post-HSCT. Hypothesizing synergism with therapeutic hypomethylation, the ETCTN/CTEP study 10026 evaluated the safety and efficacy of combination decitabine and ipilimumab treatment in transplant-naïve AML/MDS and post-HSCT AML relapse (NCT02890329). Clinical activity has been encouraging with an overall response rate of 20% (post-HSCT) and 52% (transplant-naïve), however most responses lasted for <6 months, particularly among transplant ineligible patients. To systematically identify determinants of response and resistance, we analyzed bone marrow samples from study participants using bulk and single cell RNA sequencing (scRNA-seq), targeted sequencing, and by quantification of plasma analytes. We obtained 304,961 scRNA-seq profiles from 64 serial bone marrow samples (18 patients with 2-5 samples each). To ensure comparable cell annotation across all samples, we mapped cells to a healthy bone marrow reference. While analysis of screening samples did not reveal differences between 10 responders and 8 non-responders in the composition or transcriptional state of T, NK and predominantly malignant myeloid cells, we observed a higher ratio of T/NK to myeloid cells (2.9 vs. 0.1, p = 0.027) in patients achieving complete remission. For the 8 post-transplant patients, we assessed donor engraftment across cell subsets following deconvolution of donor vs. recipient-derived single cells using expressed single nucleotide polymorphisms (souporcell). While 7-73% myeloid cells in responders were donor-derived, myeloid donor chimerism was <7% in non-responders. This was consistent with analyses of bulk RNA and targeted sequencing, which revealed higher expression of proliferation-associated pathways in non-responders and lower median variant allele frequency of recurrent somatic mutations in responders (14.9% vs. 29.4%, p = 0.029). Together, these results indicated lower disease burden in responders at study entry. Leveraging the study design of a single priming cycle of decitabine preceding combination treatment with ipilimumab, we dissected the pharmacodynamics of decitabine and ipilimumab. Focusing on decitabine, we compared cell type-specific scRNA-seq profiles before and after priming. Gene expression changes were detectable in myeloid subsets with enrichment in pathways such as protein translation, metabolism, and apoptosis. Similarly, plasma analyses showed an increase of soluble IL-8, which is mainly expressed by myeloid cells. Following addition of ipilimumab, gene expression changes were detectable in CD4+ T cells, consistent with T cell differentiation, activation and adhesion, and an increase in regulatory T cells, which we confirmed with staining of CD3 and FOXP3 in bone marrow core biopsies of study participants. Together, decitabine and ipilimumab preferentially acted on myeloid and CD4+ T cells, respectively. Finally, we addressed resistance mechanisms. Clinical responses lacked depth as somatic mutations remained detectable in responders, and recipient-derived single cells demonstrated persistence of leukemic clones among progenitor populations, in line with short remission intervals. Previously acquired bulk RNA-seq data from leukemia cutis cases with durable remission after ipilimumab monotherapy showed transcriptomic evidence of increased infiltration with antigen-experienced resident memory T cells and higher expression of CTLA-4 and FOXP3 than in AML/MDS bone marrow. Together, activity of ipilimumab in marrow involved AML/MDS may be limited by the phenotype of bone marrow-infiltrating T cells. Our studies suggest activity of combined decitabine and ipilimumab is impacted by cellular expression states within the microenvironmental niche of leukemia cells. The inadequate elimination of leukemic progenitors motivates to develop novel immunologic approaches for targeting these cell populations.

Abstract 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.

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.

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.

We present scMAGeCK, a computational framework to identify genomic elements associated with multiple expression-based phenotypes in CRISPR/Cas9 functional screening that uses single-cell RNA-seq as readout. scMAGeCK outperforms existing methods, identifies genes and enhancers with known and novel functions in cell proliferation, and enables an unbiased construction of genotype-phenotype network. Single-cell CRISPR screening on mouse embryonic stem cells identifies key genes associated with different pluripotency states. Applying scMAGeCK on multiple datasets, we identify key factors that improve the power of single-cell CRISPR screening. Collectively, scMAGeCK is a novel tool to study genotype-phenotype relationships at a single-cell level.

The CRISPR JournalVol. 2, No. 3 First CutsCRISPR Screening “Big Data” Informs Novel Therapeutic SolutionsSitong Chen, Lin Yang, and Wei LiSitong ChenCenter for Genetic Medicine Research, Children's National Medical Center, Washington, DC.Department of Biochemistry and Molecular Medicine, George Washington University, Washington, DC.*These authors contributed equally to this work.Search for more papers by this author, Lin YangCenter for Genetic Medicine Research, Children's National Medical Center, Washington, DC.Department of Biochemistry and Molecular Medicine, George Washington University, Washington, DC.*These authors contributed equally to this work.Search for more papers by this author, and Wei LiAddress correspondence to: Wei Li, Center for Genetic Medicine Research, Children's National Medical Center, 111 Michigan Ave NW, Washington, DC 20010, E-mail Address: wli2@childrensnational.orgCenter for Genetic Medicine Research, Children's National Medical Center, Washington, DC.Department of Genomics and Precision Medicine, George Washington University, Washington, DC.Search for more papers by this authorPublished Online:21 Jun 2019https://doi.org/10.1089/crispr.2019.29062.schAboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 2Issue 3Jun 2019 InformationCopyright 2019, Mary Ann Liebert, Inc., publishersTo cite this article:Sitong Chen, Lin Yang, and Wei Li.CRISPR Screening “Big Data” Informs Novel Therapeutic Solutions.The CRISPR Journal.Jun 2019.152-154.http://doi.org/10.1089/crispr.2019.29062.schPublished in Volume: 2 Issue 3: June 21, 2019PDF download

Genome-Wide CRISPR Screens Over Hundreds of Cell Lines Performed by Investigators at the Wellcome Sanger Institute Identified Novel Drug Targets