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Background Copy number alterations (CNAs) are genetic changes commonly found in cancer that involve different regions of the genome and impact cancer progression by affecting gene expression and genomic stability. Computational techniques can analyze copy number data obtained from high-throughput sequencing platforms, and various tools visualize and analyze CNAs in cancer genomes, providing insights into genetic mechanisms driving cancer development and progression. However, tools for visualizing copy number data in cancer research have some limitations. In fact, they can be complex to use and require expertise in bioinformatics or computational biology. While copy number data analysis and visualization provide insights into cancer biology, interpreting results can be challenging, and there may be multiple explanations for observed patterns of copy number alterations. Results We created Control-FREEC Viewer, a tool that facilitates effective visualization and exploration of copy number data. With Control-FREEC Viewer, experimental data can be easily loaded by the user. After choosing the reference genome, copy number data are displayed in whole genome or single chromosome view. Gain or loss on a specific gene can be found and visualized on each chromosome. Analysis parameters for subsequent sessions can be stored and images can be exported in raster and vector formats. Conclusions Control-FREEC Viewer enables users to import and visualize data analyzed by the Control-FREEC tool, as well as by other tools sharing a similar tabular output, providing a comprehensive and intuitive graphical user interface for data visualization.

Colorectal malignancies are a leading cause of cancer-related death 1 and have undergone extensive genomic study 2 , 3 . However, DNA mutations alone do not fully explain malignant transformation 4 – 7 . Here we investigate the co-evolution of the genome and epigenome of colorectal tumours at single-clone resolution using spatial multi-omic profiling of individual glands. We collected 1,370 samples from 30 primary cancers and 8 concomitant adenomas and generated 1,207 chromatin accessibility profiles, 527 whole genomes and 297 whole transcriptomes. We found positive selection for DNA mutations in chromatin modifier genes and recurrent somatic chromatin accessibility alterations, including in regulatory regions of cancer driver genes that were otherwise devoid of genetic mutations. Genome-wide alterations in accessibility for transcription factor binding involved CTCF, downregulation of interferon and increased accessibility for SOX and HOX transcription factor families, suggesting the involvement of developmental genes during tumourigenesis. Somatic chromatin accessibility alterations were heritable and distinguished adenomas from cancers. Mutational signature analysis showed that the epigenome in turn influences the accumulation of DNA mutations. This study provides a map of genetic and epigenetic tumour heterogeneity, with fundamental implications for understanding colorectal cancer biology. A study maps genetic and epigenetic heterogeneity of primary colorectal adenomas and cancers at single-clone resolution through spatial multi-omic profiling of individual glands and adjacent normal tissue.

SETBP1 variants occur as somatic mutations in several hematological malignancies such as atypical chronic myeloid leukemia and as de novo germline mutations in the Schinzel–Giedion syndrome. Here we show that SETBP1 binds to gDNA in AT-rich promoter regions, causing activation of gene expression through recruitment of a HCF1/KMT2A/PHF8 epigenetic complex. Deletion of two AT-hooks abrogates the binding of SETBP1 to gDNA and impairs target gene upregulation. Genes controlled by SETBP1 such as MECOM are significantly upregulated in leukemias containing SETBP1 mutations. Gene ontology analysis of deregulated SETBP1 target genes indicates that they are also key controllers of visceral organ development and brain morphogenesis. In line with these findings, in utero brain electroporation of mutated SETBP1 causes impairment of mouse neurogenesis with a profound delay in neuronal migration. In summary, this work unveils a SETBP1 function that directly affects gene transcription and clarifies the mechanism operating in myeloid malignancies and in the Schinzel–Giedion syndrome caused by SETBP1 mutations. SETBP1 variants occur as somatic mutations in several malignancies and as de novo germline mutations in developmental disorders. Here the authors provide evidence that SETBP1 binds to gDNA in AT-rich promoter regions to promote target gene upregulation, indicating SETBP1 functions directly to regulate transcription.

Carlo Gambacorti-Passerini and colleagues identify recurrent SETBP1 mutations in atypical chronic myeloid leukemia. The mutations, which cluster in a small region of SETBP1 , are associated with high white blood cell counts and poor prognosis. Atypical chronic myeloid leukemia (aCML) shares clinical and laboratory features with CML, but it lacks the BCR - ABL1 fusion. We performed exome sequencing of eight aCMLs and identified somatic alterations of SETBP1 (encoding a p.Gly870Ser alteration) in two cases. Targeted resequencing of 70 aCMLs, 574 diverse hematological malignancies and 344 cancer cell lines identified SETBP1 mutations in 24 cases, including 17 of 70 aCMLs (24.3%; 95% confidence interval (CI) = 16–35%). Most mutations (92%) were located between codons 858 and 871 and were identical to changes seen in individuals with Schinzel-Giedion syndrome. Individuals with mutations had higher white blood cell counts ( P = 0.008) and worse prognosis ( P = 0.01). The p.Gly870Ser alteration abrogated a site for ubiquitination, and cells exogenously expressing this mutant exhibited higher amounts of SETBP1 and SET protein, lower PP2A activity and higher proliferation rates relative to those expressing the wild-type protein. In summary, mutated SETBP1 represents a newly discovered oncogene present in aCML and closely related diseases.

The investigation of genetic forms of juvenile neurodegeneration could shed light on the causative mechanisms of neuronal loss. Schinzel-Giedion syndrome (SGS) is a fatal developmental syndrome caused by mutations in the SETBP1 gene, inducing the accumulation of its protein product. SGS features multi-organ involvement with severe intellectual and physical deficits due, at least in part, to early neurodegeneration. Here we introduce a human SGS model that displays disease-relevant phenotypes. We show that SGS neural progenitors exhibit aberrant proliferation, deregulation of oncogenes and suppressors, unresolved DNA damage, and resistance to apoptosis. Mechanistically, we demonstrate that high SETBP1 levels inhibit P53 function through the stabilization of SET, which in turn hinders P53 acetylation. We find that the inheritance of unresolved DNA damage in SGS neurons triggers the neurodegenerative process that can be alleviated either by PARP-1 inhibition or by NAD + supplementation. These results implicate that neuronal death in SGS originates from developmental alterations mainly in safeguarding cell identity and homeostasis. Schinzel-Giedion syndrome (SGS) is a fatal developmental syndrome characterized by severe intellectual and physical deficits due, at least in part, to early neurodegeneration. Here the authors introduce a human SGS model that displays disease-relevant phenotypes to demonstrate that neuronal death in SGS originates from developmental alterations mainly in safeguarding cell identity and homeostasis.

Recurring sequences of genomic alterations occurring across patients can highlight repeated evolutionary processes with significant implications for predicting cancer progression. Leveraging the ever-increasing availability of cancer omics data, here we unveil cancer’s evolutionary signatures tied to distinct disease outcomes, representing “favored trajectories” of acquisition of driver mutations detected in patients with similar prognosis. We present a framework named ASCETIC ( A gony-ba S ed C ancer E volu T ion I nferen C e) to extract such signatures from sequencing experiments generated by different technologies such as bulk and single-cell sequencing data. We apply ASCETIC to (i) single-cell data from 146 myeloid malignancy patients and bulk sequencing from 366 acute myeloid leukemia patients, (ii) multi-region sequencing from 100 early-stage lung cancer patients, (iii) exome/genome data from 10,000+ Pan-Cancer Atlas samples, and (iv) targeted sequencing from 25,000+ MSK-MET metastatic patients, revealing subtype-specific single-nucleotide variant signatures associated with distinct prognostic clusters. Validations on several datasets underscore the robustness and generalizability of the extracted signatures. The identification of cancer type-specific evolutionary signatures could be used for patient stratification. Here, the authors develop a method, ASCETIC, that utilises bulk and single-cell sequencing data and identifies evolutionary signatures associated with different prognostic outcomes across different cancer types.

Extracellular vesicles (EVs) are a new mechanism of cellular communication, by delivering their cargo into target cells to modulate molecular pathways. EV-mediated crosstalk contributes to tumor survival and resistance to cellular stress. However, the role of EVs in B-cell Acute Lymphoblastic Leukaemia (B-ALL) awaits to be thoroughly investigated. We recently published that ActivinA increases intracellular calcium levels and promotes actin polymerization in B-ALL cells. These biological processes guide cytoskeleton reorganization, which is a crucial event for EV secretion and internalization. Hence, we investigated the role of EVs in the context of B-ALL and the impact of ActivinA on this phenomenon. We demonstrated that leukemic cells release a higher number of EVs in response to ActivinA treatment, and they can actively uptake EVs released by other B-ALL cells. Under culture-induced stress conditions, EVs coculture promoted cell survival in B-ALL cells in a dose-dependent manner. Direct stimulation of B-ALL cells with ActivinA or with EVs isolated from ActivinA-stimulated cells was even more effective in preventing cell death. This effect can be possibly ascribed to the increase of vesiculation and modifications of EV-associated microRNAs induced by ActivinA. These data demonstrate that ActivinA boosts EV-mediated B-ALL crosstalk, improving leukemia survival in stress conditions.

Recurrent somatic mutations in ETNK1 (Ethanolamine-Kinase-1) were identified in several myeloid malignancies and are responsible for a reduced enzymatic activity. Here, we demonstrate in primary leukemic cells and in cell lines that mutated ETNK1 causes a significant increase in mitochondrial activity, ROS production, and Histone H2AX phosphorylation, ultimately driving the increased accumulation of new mutations. We also show that phosphoethanolamine, the metabolic product of ETNK1, negatively controls mitochondrial activity through a direct competition with succinate at mitochondrial complex II. Hence, reduced intracellular phosphoethanolamine causes mitochondria hyperactivation, ROS production, and DNA damage. Treatment with phosphoethanolamine is able to counteract complex II hyperactivation and to restore a normal phenotype. ETNK1 mutations are recurrent in leukemia but how they contribute to oncogenesis is still unclear. Here, the authors show that ETNK1 mutations increase mitochondrial activity, ROS and H2AX levels and that these effects can be rescued upon phosphoethanolamine supplementation.

Background Longitudinal single-cell sequencing experiments of patient-derived models are increasingly employed to investigate cancer evolution. In this context, robust computational methods are needed to properly exploit the mutational profiles of single cells generated via variant calling, in order to reconstruct the evolutionary history of a tumor and characterize the impact of therapeutic strategies, such as the administration of drugs. To this end, we have recently developed the LACE framework for the Longitudinal Analysis of Cancer Evolution. Results The LACE 2.0 release aimed at inferring longitudinal clonal trees enhances the original framework with new key functionalities: an improved data management for preprocessing of standard variant calling data, a reworked inference engine, and direct connection to public databases. Conclusions All of this is accessible through a new and interactive Shiny R graphical interface offering the possibility to apply filters helpful in discriminating relevant or potential driver mutations, set up inferential parameters, and visualize the results. The software is available at: github.com/BIMIB-DISCo/LACE .