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Immunotherapy has emerged as a crucial strategy to combat cancer by "reprogramming" a patient's own immune system. Although immunotherapy is typically reserved for patients with a high mutational burden, neoantigens produced from posttranscriptional regulation may provide an untapped reservoir of common immunogenic targets for new targeted therapies. To comprehensively define tumor-specific and likely immunogenic neoantigens from patient RNA-Seq, we developed Splicing Neo Antigen Finder (SNAF), an easy-to-use and open-source computational workflow to predict splicing-derived immunogenic MHC-bound peptides (T cell antigen) and unannotated transmembrane proteins with altered extracellular epitopes (B cell antigen). This workflow uses a highly accurate deep learning strategy for immunogenicity prediction (DeepImmuno) in conjunction with new algorithms to rank the tumor specificity of neoantigens (BayesTS) and to predict regulators of mis-splicing (RNA-SPRINT). T cell antigens from SNAF were frequently evidenced as HLA-presented peptides from mass spectrometry (MS) and predict response to immunotherapy in melanoma. Splicing neoantigen burden was attributed to coordinated splicing factor dysregulation. Shared splicing neoantigens were found in up to 90% of patients with melanoma, correlated to overall survival in multiple cancer cohorts, induced T cell reactivity, and were characterized by distinct cells of origin and amino acid preferences. In addition to T cell neoantigens, our B cell focused pipeline (SNAF-B) identified a new class of tumor-specific extracellular neoepitopes, which we termed ExNeoEpitopes. ExNeoEpitope full-length mRNA predictions were tumor specific and were validated using long-read isoform sequencing and in vitro transmembrane localization assays. Therefore, our systematic identification of splicing neoantigens revealed potential shared targets for therapy in heterogeneous cancers.

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Motivation: The rapid proliferation of single-cell RNA-sequencing (scRNA-Seq) technologies has spurred the development of diverse computational approaches to detect transcriptionally coherent populations. While the complexity of the algorithms for detecting heterogeneity has increased, most require significant user-tuning, are heavily reliant on dimension reduction techniques and are not scalable to ultra-large datasets. We previously described a multi-step algorithm, Iterative Clustering and Guide-gene Selection (ICGS), which applies intra-gene correlation and hybrid clustering to uniquely resolve novel transcriptionally coherent cell populations from an intuitive graphical user interface., Results: We describe a new iteration of ICGS that outperforms state-of-the-art scRNA-Seq detection workflows when applied to well-established benchmarks. This approach combines multiple complementary subtype detection methods (HOPACH, sparse non-negative matrix factorization, cluster 'fitness', support vector machine) to resolve rare and common cell-states, while minimizing differences due to donor or batch effects. Using data from multiple cell atlases, we show that the PageRank algorithm effectively downsamples ultra-large scRNA-Seq datasets, without losing extremely rare or transcriptionally similar yet distinct cell types and while recovering novel transcriptionally distinct cell populations. We believe this new approach holds tremendous promise in reproducibly resolving hidden cell populations in complex datasets., Availability and Implementation: ICGS2 is implemented in Python. The source code and documentation are available at http://altanalyze.org., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2020. Published by Oxford University Press.)

Despite growing awareness of the biologic features underlying MLL-rearranged leukemia, targeted therapies for this leukemia have remained elusive and clinical outcomes remain dismal. MBNL1, a protein involved in alternative splicing, is consistently overexpressed in MLL-rearranged leukemias. We found that MBNL1 loss significantly impairs propagation of murine and human MLL-rearranged leukemia in vitro and in vivo. Through transcriptomic profiling of our experimental systems, we show that in leukemic cells, MBNL1 regulates alternative splicing (predominantly intron exclusion) of several genes including those essential for MLL-rearranged leukemogenesis, such as DOT1L and SETD1A. We finally show that selective leukemic cell death is achievable with a small molecule inhibitor of MBNL1. These findings provide the basis for a new therapeutic target in MLL-rearranged leukemia and act as further validation of a burgeoning paradigm in targeted therapy, namely the disruption of cancer-specific splicing programs through the targeting of selectively essential RNA binding proteins.

Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) have become a powerful tool for human disease modeling and therapeutic testing. However, their use remains limited by their immaturity and heterogeneity. To characterize the source of this heterogeneity, we applied complementary single-cell RNA-seq and bulk RNA-seq technologies over time during hiPSC cardiac differentiation and in the adult heart. Using integrated transcriptomic and splicing analysis, more than half a dozen distinct single-cell populations were observed, several of which were coincident at a single time-point, day 30 of differentiation. To dissect the role of distinct cardiac transcriptional regulators associated with each cell population, we systematically tested the effect of a gain or loss of three transcription factors (NR2F2, TBX5, and HEY2), using CRISPR genome editing and ChIP-seq, in conjunction with patch clamp, calcium imaging, and CyTOF analysis. These targets, data, and integrative genomics analysis methods provide a powerful platform for understanding in vitro cellular heterogeneity.

The identification and targeting of key molecular drivers of melanoma and breast and lung cancer have substantially improved their therapy. However, subtypes of each of these three common, lethal solid tumors lack identified molecular drivers, and are thus not amenable to targeted therapies. Here we show that pleckstrin homology domain-interacting protein (PHIP) promotes the progression of these "driver-negative" tumors. Suppression of PHIP expression significantly inhibited both tumor cell proliferation and invasion, coordinately suppressing phosphorylated AKT, cyclin D1, and talin1 expression in all three tumor types. Furthermore, PHIP's targetable bromodomain is functional, as it specifically binds the histone modification H4K91ac. Analysis of TCGA profiling efforts revealed PHIP overexpression in triple-negative and basal-like breast cancer, as well as in the bronchioid subtype of nonsmall cell lung cancer. These results identify a role for PHIP in the progression of melanoma and breast and lung cancer subtypes lacking identified targeted therapies. The use of selective, anti-PHIP bromodomain inhibitors may thus yield a broad-based, molecularly targeted therapy against currently nontargetable tumors., Competing Interests: The authors declare no conflict of interest.

The developing kidney provides a useful model for study of the principles of organogenesis. In this report we use three independent platforms, Drop-Seq, Chromium 10x Genomics and Fluidigm C1, to carry out single cell RNA-Seq (scRNA-Seq) analysis of the E14.5 mouse kidney. Using the software AltAnalyze, in conjunction with the unsupervised approach ICGS, we were unable to identify and confirm the presence of 16 distinct cell populations during this stage of active nephrogenesis. Using a novel integrative supervised computational strategy, we were able to successfully harmonize and compare the cell profiles across all three technological platforms. Analysis of possible cross compartment receptor/ligand interactions identified the nephrogenic zone stroma as a source of GDNF. This was unexpected because the cap mesenchyme nephron progenitors had been thought to be the sole source of GDNF, which is a key driver of branching morphogenesis of the collecting duct system. The expression of Gdnf by stromal cells was validated in several ways, including Gdnf in situ hybridization combined with immunohistochemistry for SIX2, and marker of nephron progenitors, and MEIS1, a marker of stromal cells. Finally, the single cell gene expression profiles generated in this study confirmed and extended previous work showing the presence of multilineage priming during kidney development. Nephron progenitors showed stochastic expression of genes associated with multiple potential differentiation lineages., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Many reprogramming methods can generate human induced pluripotent stem cells (hiPSCs) that closely resemble human embryonic stem cells (hESCs). This has led to assessments of how similar hiPSCs are to hESCs, by evaluating differences in gene expression, epigenetic marks and differentiation potential. However, all previous studies were performed using hiPSCs acquired from different laboratories, passage numbers, culturing conditions, genetic backgrounds and reprogramming methods, all of which may contribute to the reported differences. Here, by using high-throughput sequencing under standardized cell culturing conditions and passage number, we compare the epigenetic signatures (H3K4me3, H3K27me3 and HDAC2 ChIP-seq profiles) and transcriptome differences (by RNA-seq) of hiPSCs generated from the same primary fibroblast population by using six different reprogramming methods. We found that the reprogramming method impacts the resulting transcriptome and that all hiPSC lines could terminally differentiate, regardless of the reprogramming method. Moreover, by comparing the differences between the hiPSC and hESC lines, we observed a significant proportion of differentially expressed genes that could be attributed to polycomb repressive complex targets., Competing Interests: Competing interests The authors declare no competing financial interests.

Delineating hierarchical cellular states, including rare intermediates and the networks of regulatory genes that orchestrate cell-type specification, are continuing challenges for developmental biology. Single-cell RNA sequencing is greatly accelerating such research, given its power to provide comprehensive descriptions of genomic states and their presumptive regulators. Haematopoietic multipotential progenitor cells, as well as bipotential intermediates, manifest mixed-lineage patterns of gene expression at a single-cell level. Such mixed-lineage states may reflect the molecular priming of different developmental potentials by co-expressed alternative-lineage determinants, namely transcription factors. Although a bistable gene regulatory network has been proposed to regulate the specification of either neutrophils or macrophages, the nature of the transition states manifested in vivo, and the underlying dynamics of the cell-fate determinants, have remained elusive. Here we use single-cell RNA sequencing coupled with a new analytic tool, iterative clustering and guide-gene selection, and clonogenic assays to delineate hierarchical genomic and regulatory states that culminate in neutrophil or macrophage specification in mice. We show that this analysis captured prevalent mixed-lineage intermediates that manifested concurrent expression of haematopoietic stem cell/progenitor and myeloid progenitor cell genes. It also revealed rare metastable intermediates that had collapsed the haematopoietic stem cell/progenitor gene expression programme, instead expressing low levels of the myeloid determinants, Irf8 and Gfi1 (refs 9, 10, 11, 12, 13). Genetic perturbations and chromatin immunoprecipitation followed by sequencing revealed Irf8 and Gfi1 as key components of counteracting myeloid-gene-regulatory networks. Combined loss of these two determinants 'trapped' the metastable intermediate. We propose that mixed-lineage states are obligatory during cell-fate specification, manifest differing frequencies because of their dynamic instability and are dictated by counteracting gene-regulatory networks.

Unlabelled: Cytogenetically normal acute myeloid leukemia (CN-AML) represents nearly 50% of human AML. Co-occurring mutations in the de novo DNA methyltransferase DNMT3A and the FMS related tyrosine kinase 3 (FLT3) are common in CN-AML and confer a poorer prognosis. We demonstrate that mice with Flt3-internal tandem duplication (Flt3(ITD)) and inducible deletion of Dnmt3a spontaneously develop a rapidly lethal, completely penetrant, and transplantable AML of normal karyotype. AML cells retain a single Dnmt3a floxed allele, revealing the oncogenic potential of Dnmt3a haploinsufficiency. FLT3(ITD)/DNMT3A-mutant primary human and murine AML exhibit a similar pattern of global DNA methylation associated with changes in the expression of nearby genes. In the murine model, rescuing Dnmt3a expression was accompanied by DNA remethylation and loss of clonogenic potential, suggesting that Dnmt3a-mutant oncogenic effects are reversible. Dissection of the cellular architecture of the AML model using single-cell assays, including single-cell RNA sequencing, identified clonogenic subpopulations that express genes sensitive to the methylation of nearby genomic loci and responsive to DNMT3A levels. Thus, Dnmt3a haploinsufficiency transforms Flt3(ITD) myeloproliferative disease by modulating methylation-sensitive gene expression within a clonogenic AML subpopulation., Significance: DNMT3A haploinsufficiency results in reversible epigenetic alterations that transform FLT3(ITD)-mutant myeloproliferative neoplasm into AML. Cancer Discov; 6(5); 501-15. ©2016 AACR.This article is highlighted in the In This Issue feature, p. 461., (©2016 American Association for Cancer Research.)