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Oncogenic fusions formed through chromosomal rearrangements are hallmarks of childhood cancer that define cancer subtype, predict outcome, persist through treatment, and can be ideal therapeutic targets. However, mechanistic understanding of the etiology of oncogenic fusions remains elusive. Here we report a comprehensive detection of 272 oncogenic fusion gene pairs by using tumor transcriptome sequencing data from 5190 childhood cancer patients. We identify diverse factors, including translation frame, protein domain, splicing, and gene length, that shape the formation of oncogenic fusions. Our mathematical modeling reveals a strong link between differential selection pressure and clinical outcome in CBFB-MYH11. We discover 4 oncogenic fusions, including RUNX1-RUNX1T1, TCF3-PBX1, CBFA2T3-GLIS2, and KMT2A-AFDN, with promoter-hijacking-like features that may offer alternative strategies for therapeutic targeting. We uncover extensive alternative splicing in oncogenic fusions including KMT2A-MLLT3, KMT2A-MLLT10, C11orf95-RELA, NUP98-NSD1, KMT2A-AFDN and ETV6-RUNX1. We discover neo splice sites in 18 oncogenic fusion gene pairs and demonstrate that such splice sites confer therapeutic vulnerability for etiology-based genome editing. Our study reveals general principles on the etiology of oncogenic fusions in childhood cancer and suggests profound clinical implications including etiology-based risk stratification and genome-editing-based therapeutics.

Relapsed or refractory pediatric acute myeloid leukemia (AML) is associated with poor outcomes and relapse risk prediction approaches have not changed significantly in decades. To build a robust transcriptional risk prediction model for pediatric AML, we perform RNA-sequencing on 1503 primary diagnostic samples. While a 17 gene leukemia stem cell signature (LSC17) is predictive in our aggregated pediatric study population, LSC17 is no longer predictive within established cytogenetic and molecular (cytomolecular) risk groups. Therefore, we identify distinct LSC signatures on the basis of AML cytomolecular subtypes (LSC47) that were more predictive than LSC17. Based on these findings, we build a robust relapse prediction model within a training cohort and then validate it within independent cohorts. Here, we show that LSC47 increases the predictive power of conventional risk stratification and that applying biomarkers in a manner that is informed by cytomolecular profiling outperforms a uniform biomarker approach.

Super enhancers (SE), large genomic elements that activate transcription and drive cell identity, have been found with cancer-specific gene regulation in human cancers. Recent studies reported the importance of understanding the cooperation and function of SE internal components, i.e., the constituent enhancers (CE). However, there are no pan-cancer studies to identify cancer-specific SE signatures at the constituent level. Here, by revisiting pan-cancer SE activities with H3K27Ac ChIP-seq datasets, we report fingerprint SE signatures for 28 cancer types in the NCI-60 cell panel. We implement a mixture model to discriminate active CEs from inactive CEs by taking into consideration ChIP-seq variabilities between cancer samples and across CEs. We demonstrate that the model-based estimation of CE states provides improved functional interpretation of SE-associated regulation. We identify cancer-specific CEs by balancing their active prevalence with their capability of encoding cancer type identities. We further demonstrate that cancer-specific CEs have the strongest per-base enhancer activities in independent enhancer sequencing assays, suggesting their importance in understanding critical SE signatures. We summarize fingerprint SEs based on the cancer-specific statuses of their component CEs and build an easy-to-use R package to facilitate the query, exploration, and visualization of fingerprint SEs across cancers. Author summary: Super enhancers are large genomic elements comprised of multiple enhancers working together to drive gene transcription. They play a crucial role in defining cell identity and act as drivers of oncogenic gene expression in cancer cells. Characterizing cancer-specific super enhancer signatures can reveal transcriptional deregulation associated with cell origin and malignant transformation. Here, we generated a high-resolution fingerprint of super enhancers across 60 cancer cell lines through statistical modeling of both active and inactive components inside super enhancers. Our study revealed that cancer-specific super enhancer components are highly informative in delineating the identity of cancer cells. Our findings further revealed that cancer-specific active components exhibit stronger enhancer activities compared to non-cancer-specific components, suggesting the importance of studying the functional divergence inside super enhancers across different cancer types. Finally, we generated a database of cancer-specific super enhancer signatures for 28 cancer types with a companion computational tool to facilitate the query, exploration, and visualization of these signatures across cancers. [ABSTRACT FROM AUTHOR]

The networks of transcription factors (TFs) that control intestinal-resident memory CD8+ T (TRM) cells, including multipotency and effector programs, are poorly understood. In this work, we investigated the role of the TF Bcl11b in TRM cells during infection with Listeria monocytogenes using mice with post-activation, conditional deletion of Bcl11b in CD8+ T cells. Conditional deletion of Bcl11b resulted in increased numbers of intestinal TRM cells and their precursors as well as decreased splenic effector and circulating memory cells and precursors. Loss of circulating memory cells was in part due to increased intestinal homing of Bcl11b−/− circulating precursors, with no major alterations in their programs. Bcl11b−/− TRM cells had altered transcriptional programs, with diminished expression of multipotent/multifunctional (MP/MF) program genes, including Tcf7, and up-regulation of the effector program genes, including Prdm1. Bcl11b also limits the expression of Ahr, another TF with a role in intestinal CD8+ TRM cell differentiation. Deregulation of TRM programs translated into a poor recall response despite TRM cell accumulation in the intestine. Reduced expression of MP/MF program genes in Bcl11b−/− TRM cells was linked to decreased chromatin accessibility and a reduction in activating histone marks at these loci. In contrast, the effector program genes displayed increased activating epigenetic status. These findings demonstrate that Bcl11b is a frontrunner in the tissue residency program of intestinal memory cells upstream of Tcf1 and Blimp1, promoting multipotency and restricting the effector program. Fate in the hands of Bcl11b: During infection, antigen-specific CD8+ T cells undergo clonal expansion and differentiate into effector or memory cells. Whether memory cells become tissue resident (TRM) or circulatory is a fate decision governed by multiple transcription factors. In this work, Helm et al. investigate how Bcl11b influences TRM differentiation in the context of foodborne pathogen infection. They identify that deletion of Bcl11b in CD8+ T cells after their activation causes accumulation of TRM cells in the gut and a reduction of circulating memory cells. The authors show that Bcl11b regulates multipotency of TRM through recruitment to and activation of essential genes involved in the multipotent/multifunctional program and repression of genes involved in effector function. Bcl11b therefore determines the lineage fate of gut CD8+ resident memory cells. —HMI [ABSTRACT FROM AUTHOR]

The article presents a study that explores the chromatin architecture at susceptible gene loci in cerebellar Purkinje cells characterizes DNA damage–induced neurodegeneration. It mentions about the pathogenic basis of DNA damage in the nervous system and suggest chromatin conformation as a feature in directing genome instability–associated neuropathology.

Aicardi-Goutières syndrome (AGS) is a monogenic type I interferonopathy characterized by neurodevelopmental defects and upregulation of type I interferon signaling and neuroinflammation. Mutations in genes that function in nucleic acid metabolism, including RNASEH2 , are linked to AGS. Ribonuclease H2 (RNASEH2) is a genome surveillance factor critical for DNA integrity by removing ribonucleotides incorporated into replicating DNA. Here we show that RNASEH2 is necessary for neurogenesis and to avoid activation of interferon-responsive genes and neuroinflammation. Cerebellar defects after RNASEH2B inactivation are rescued by p53 but not cGAS deletion, suggesting that DNA damage signaling, not neuroinflammation, accounts for neuropathology. Coincident inactivation of Atm and Rnaseh2 further affected cerebellar development causing ataxia, which was dependent upon aberrant activation of non-homologous end-joining (NHEJ). The loss of ATM also markedly exacerbates cGAS-dependent type I interferon signaling. Thus, DNA damage-dependent signaling rather than type I interferon signaling underlies neurodegeneration in this class of neurodevelopmental/neuroinflammatory disease. • RNASEH2 is necessary for neurogenesis and prevention of neuroinflammation • ATM suppresses the neural impact of Rnaseh2 inactivation • Neuropathology from disabled RNASEH2B is rescued by p53 but not cGAS inactivation Mutations in RNASEH2 are linked to Aicardi-Goutières syndrome. Aditi et al. show that RNASEH2 is necessary for neurogenesis and to avoid activation of interferon-responsive genes. These defects are rescued by p53 but not cGAS inactivation, suggesting that DNA damage signaling, not neuroinflammation, accounts for neuropathology in this class of disease. [ABSTRACT FROM AUTHOR]

Motivation: Integrative analysis of heterogeneous expression data remains challenging due to variations in platform, RNA quality, sample processing, and other unknown technical effects. Selecting the approach for removing unwanted batch effects can be a time-consuming and tedious process, especially for more biologically focused investigators., Results: Here, we present BatchFLEX, a Shiny app that can facilitate visualization and correction of batch effects using several established methods. BatchFLEX can visualize the variance contribution of a factor before and after correction. As an example, we have analyzed ImmGen microarray data and enhanced its expression signals that distinguishes each immune cell type. Moreover, our analysis revealed the impact of the batch correction in altering the gene expression rank and single-sample GSEA pathway scores in immune cell types, highlighting the importance of real-time assessment of the batch correction for optimal downstream analysis., Availability and Implementation: Our tool is available through Github https://github.com/shawlab-moffitt/BATCH-FLEX-ShinyApp with an online example on Shiny.io https://shawlab-moffitt.shinyapps.io/batch_flex/., (© The Author(s) 2024. Published by Oxford University Press.)

Immunotherapy with chimeric antigen receptor T cells for pediatric solid and brain tumors is constrained by available targetable antigens. Cancer-specific exons present a promising reservoir of targets; however, these have not been explored and validated systematically in a pan-cancer fashion. To identify cancer specific exon targets, here we analyze 1532 RNA-seq datasets from 16 types of pediatric solid and brain tumors for comparison with normal tissues using a newly developed workflow. We find 2933 exons in 157 genes encoding proteins of the surfaceome or matrisome with high cancer specificity either at the gene (n = 148) or the alternatively spliced isoform (n = 9) level. Expression of selected alternatively spliced targets, including the EDB domain of fibronectin 1, and gene targets, such as COL11A1, are validated in pediatric patient derived xenograft tumors. We generate T cells expressing chimeric antigen receptors specific for the EDB domain or COL11A1 and demonstrate that these have antitumor activity. The full target list, explorable via an interactive web portal ( https://cseminer.stjude.org/ ), provides a rich resource for developing immunotherapy of pediatric solid and brain tumors using gene or AS targets with high expression specificity in cancer., (© 2024. The Author(s).)