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Blastic plasmacytoid dendritic cell neoplasm (BPDCN) is an aggressive leukemia of plasmacytoid dendritic cells (pDC). BPDCN occurs at least three times more frequently in men than in women, but the reasons for this sex bias are unknown. Here, studying genomics of primary BPDCN and modeling disease-associated mutations, we link acquired alterations in RNA splicing to abnormal pDC development and inflammatory response through Toll-like receptors. Loss-of-function mutations in ZRSR2, an X chromosome gene encoding a splicing factor, are enriched in BPDCN, and nearly all mutations occur in males. ZRSR2 mutation impairs pDC activation and apoptosis after inflammatory stimuli, associated with intron retention and inability to upregulate the transcription factor IRF7. In vivo, BPDCN-associated mutations promote pDC expansion and signatures of decreased activation. These data support a model in which male-biased mutations in hematopoietic progenitors alter pDC function and confer protection from apoptosis, which may impair immunity and predispose to leukemic transformation. SIGNIFICANCE: Sex bias in cancer is well recognized, but the underlying mechanisms are incompletely defined. We connect X chromosome mutations in ZRSR2 to an extremely male-predominant leukemia. Aberrant RNA splicing induced by ZRSR2 mutation impairs dendritic cell inflammatory signaling, interferon production, and apoptosis, revealing a sex- and lineage-related tumor suppressor pathway.This article is highlighted in the In This Issue feature, p. 275.

Large panels of comprehensively characterized human cancer models, including the Cancer Cell Line Encyclopedia (CCLE), have provided a rigorous framework with which to study genetic variants, candidate targets, and small-molecule and biological therapeutics and to identify new marker-driven cancer dependencies. To improve our understanding of the molecular features that contribute to cancer phenotypes, including drug responses, here we have expanded the characterizations of cancer cell lines to include genetic, RNA splicing, DNA methylation, histone H3 modification, microRNA expression and reverse-phase protein array data for 1,072 cell lines from individuals of various lineages and ethnicities. Integration of these data with functional characterizations such as drug-sensitivity, short hairpin RNA knockdown and CRISPR-Cas9 knockout data reveals potential targets for cancer drugs and associated biomarkers. Together, this dataset and an accompanying public data portal provide a resource for the acceleration of cancer research using model cancer cell lines.

The systematic sequencing of the cancer genome has led to the identification of numerous genetic alterations in cancer. However, a deeper understanding of the functional consequences of these alterations is necessary to guide appropriate therapeutic strategies. Here, we describe Onco-GPS (OncoGenic Positioning System), a data-driven analysis framework to organize individual tumor samples with shared oncogenic alterations onto a reference map defined by their underlying cellular states. We applied the methodology to the RAS pathway and identified nine distinct components that reflect transcriptional activities downstream of RAS and defined several functional states associated with patterns of transcriptional component activation that associates with genomic hallmarks and response to genetic and pharmacological perturbations. These results show that the Onco-GPS is an effective approach to explore the complex landscape of oncogenic cellular states across cancers, and an analytic framework to summarize knowledge, establish relationships, and generate more effective disease models for research or as part of individualized precision medicine paradigms.

Systematic efforts to sequence the cancer genome have identified large numbers of mutations and copy number alterations in human cancers. However, elucidating the functional consequences of these variants, and their interactions to drive or maintain oncogenic states, remains a challenge in cancer research. We developed REVEALER, a computational method that identifies combinations of mutually exclusive genomic alterations correlated with functional phenotypes, such as the activation or gene dependency of oncogenic pathways or sensitivity to a drug treatment. We used REVEALER to uncover complementary genomic alterations associated with the transcriptional activation of β-catenin and NRF2, MEK-inhibitor sensitivity, and KRAS dependency. REVEALER successfully identified both known and new associations, demonstrating the power of combining functional profiles with extensive characterization of genomic alterations in cancer genomes.

The Molecular Signatures Database (MSigDB) is one of the most widely used and comprehensive databases of gene sets for performing gene set enrichment analysis. Since its creation, MSigDB has grown beyond its roots in metabolic disease and cancer to include >10,000 gene sets. These better represent a wider range of biological processes and diseases, but the utility of the database is reduced by increased redundancy across, and heterogeneity within, gene sets. To address this challenge, here we use a combination of automated approaches and expert curation to develop a collection of "hallmark" gene sets as part of MSigDB. Each hallmark in this collection consists of a "refined" gene set, derived from multiple "founder" sets, that conveys a specific biological state or process and displays coherent expression. The hallmarks effectively summarize most of the relevant information of the original founder sets and, by reducing both variation and redundancy, provide more refined and concise inputs for gene set enrichment analysis.

The interleukin-3 receptor [alpha] subunit, CD123, is expressed in many hematologic malignancies including acute myeloid leukemia (AML) and blastic plasmacytoid dendritic cell neoplasm (BPDCN). Tagraxofusp (SL-401) is a CD123-targeted therapy consisting of interleukin-3 fused to a truncated diphtheria toxin payload. Factors influencing response to tagraxofusp other than CD123 expression are largely unknown. We interrogated tagraxofusp resistance in patients and experimental models and found that it was not associated with CD123 loss. Rather, resistant AML and BPDCN cells frequently acquired deficiencies in the diphthamide synthesis pathway, impairing tagraxofusp's ability to ADP- ribosylate cellular targets. Expression of DPH1, encoding a diphthamide pathway enzyme, was reduced by DNA CpG methylation in resistant cells. Treatment with the DNA methyltransferase inhibitor azacitidine restored DPH1 expression and tagraxofusp sensitivity. We also developed a drug-dependent ADP-ribosylation assay in primary cells that correlated with tagraxofusp activity and may represent an additional novel biomarker. As predicted by these results and our observation that resistance also increased mitochondrial apoptotic priming, we found that the combination of tagraxofusp and azacitidine was effective in patient-derived xenografts treated in vivo. These data have important implications for clinical use of tagraxofusp and led to a phase 1 study combining tagraxofusp and azacitidine in myeloid malignancies., Introduction CD123, or the interleukin-3 (IL-3) receptor (IL-3R) [alpha] chain, is a cell-surface marker associated with a variety of hematologic malignancies and is often enriched on tumor cells compared with [...]

Synthetic lethality--an interaction between two genetic events through which the co-occurrence of these two genetic events leads to cell death, but each event alone does not--can be exploited for cancer therapeutics.sup.1. DNA repair processes represent attractive synthetic lethal targets, because many cancers exhibit an impairment of a DNA repair pathway, which can lead to dependence on specific repair proteins.sup.2. The success of poly(ADP-ribose) polymerase 1 (PARP-1) inhibitors in cancers with deficiencies in homologous recombination highlights the potential of this approach.sup.3. Hypothesizing that other DNA repair defects would give rise to synthetic lethal relationships, we queried dependencies in cancers with microsatellite instability (MSI), which results from deficient DNA mismatch repair. Here we analysed data from large-scale silencing screens using CRISPR-Cas9-mediated knockout and RNA interference, and found that the RecQ DNA helicase WRN was selectively essential in MSI models in vitro and in vivo, yet dispensable in models of cancers that are microsatellite stable. Depletion of WRN induced double-stranded DNA breaks and promoted apoptosis and cell cycle arrest selectively in MSI models. MSI cancer models required the helicase activity of WRN, but not its exonuclease activity. These findings show that WRN is a synthetic lethal vulnerability and promising drug target for MSI cancers. Depletion of the DNA helicase WRN induced double-stranded DNA breaks, and promoted apoptosis and cell cycle arrest selectively in cancers with microsatellite instability, indicating that WRN is a promising drug target for the treatment of these cancers., Author(s): Edmond M. Chan [sup.1] [sup.2] , Tsukasa Shibue [sup.1] , James M. McFarland [sup.1] , Benjamin Gaeta [sup.1] , Mahmoud Ghandi [sup.1] , Nancy Dumont [sup.1] , Alfredo Gonzalez [...]

With the advances in sequencing technologies, a huge amount of biological data is extracted nowadays. Analyzing this amount of data is beyond the ability of human beings, creating a splendid opportunity for machine learning methods to grow. The methods, however, are practical only when the sequences are converted into feature vectors. Many tools target this task including iLearnPlus, a Python-based tool which supports a rich set of features. In this paper, we propose a holistic tool that extracts features from biological sequences (i.e. DNA, RNA and Protein). These features are the inputs to machine learning models that predict properties, structures or functions of the input sequences. Our tool not only supports all features in iLearnPlus but also 30 additional features which exist in the literature. Moreover, our tool is based on R language which makes an alternative for bioinformaticians to transform sequences into feature vectors. We have compared the conversion time of our tool with that of iLearnPlus: we transform the sequences much faster. We convert small nucleotides by a median of 2.8X faster, while we outperform iLearnPlus by a median of 6.3X for large sequences. Finally, in amino acids, our tool achieves a median speedup of 23.9X. [ABSTRACT FROM AUTHOR]