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Some leiomyomas have chromosomal rearrangements implicating chromothripsis, a process involving the formation of complex chromosomal rearrangements. In three instances, tumors obtained from the same woman were documented to be clonally related. Uterine leiomyomas are benign smooth-muscle tumors with an estimated prevalence of 77% among women of reproductive age in the United States 1 and can cause a range of health problems. 2 According to a nationwide analysis of 518,828 hysterectomies performed in 2005 in the United States, 282,291 of the patients who underwent the procedure (54%) had leiomyomas. 3 Hormonal factors, family history, African ancestry, and obesity increase the risk of leiomyomas. 4 Presentation with multiple tumors is typical (an estimated average is six to seven 1 ). Whether leiomyosarcomas develop from leiomyomas or arise independently is not known. Uterine leiomyosarcoma is very rare, 5 and it . . .

Background Typical human genome differs from the reference genome at 4-5 million sites. This diversity is increasingly catalogued in repositories such as ExAC/gnomAD, consisting of >15,000 whole-genomes and >126,000 exome sequences from different individuals. Despite this enormous diversity, resequencing data workflows are still based on a single human reference genome. Identification and genotyping of genetic variants is typically carried out on short-read data aligned to a single reference, disregarding the underlying variation. Results We propose a new unified framework for variant calling with short-read data utilizing a representation of human genetic variation – a pan-genomic reference. We provide a modular pipeline that can be seamlessly incorporated into existing sequencing data analysis workflows. Our tool is open source and available online: https://gitlab.com/dvalenzu/PanVC . Conclusions Our experiments show that by replacing a standard human reference with a pan-genomic one we achieve an improvement in single-nucleotide variant calling accuracy and in short indel calling accuracy over the widely adopted Genome Analysis Toolkit (GATK) in difficult genomic regions.

Intratumoral cellular heterogeneity necessitates multi-targeting therapies for improved clinical benefits in advanced malignancies. However, systematic identification of patient-specific treatments that selectively co-inhibit cancerous cell populations poses a combinatorial challenge, since the number of possible drug-dose combinations vastly exceeds what could be tested in patient cells. Here, we describe a machine learning approach, scTherapy, which leverages single-cell transcriptomic profiles to prioritize multi-targeting treatment options for individual patients with hematological cancers or solid tumors. Patient-specific treatments reveal a wide spectrum of co-inhibitors of multiple biological pathways predicted for primary cells from heterogenous cohorts of patients with acute myeloid leukemia and high-grade serous ovarian carcinoma, each with unique resistance patterns and synergy mechanisms. Experimental validations confirm that 96% of the multi-targeting treatments exhibit selective efficacy or synergy, and 83% demonstrate low toxicity to normal cells, highlighting their potential for therapeutic efficacy and safety. In a pan-cancer analysis across five cancer types, 25% of the predicted treatments are shared among the patients of the same tumor type, while 19% of the treatments are patient-specific. Our approach provides a widely-applicable strategy to identify personalized treatment regimens that selectively co-inhibit malignant cells and avoid inhibition of non-cancerous cells, thereby increasing their likelihood for clinical success. The identification of treatments that selectively co-inhibit cancerous cell populations remains a challenge. Here, a machine learning approach, scTherapy, leverages single-cell transcriptomic profiles to prioritize multi-targeting treatment options for individual patients with hematological cancers or solid tumors.

Background A deep understanding of carcinogenesis at the DNA level underpins many advances in cancer prevention and treatment. Mutational signatures provide a breakthrough conceptualisation, as well as an analysis framework, that can be used to build such understanding. They capture somatic mutation patterns and at best identify their causes. Most studies in this context have focused on an inherently additive analysis, e.g. by non-negative matrix factorization, where the mutations within a cancer sample are explained by a linear combination of independent mutational signatures. However, other recent studies show that the mutational signatures exhibit non-additive interactions. Results We carefully analysed such additive model fits from the PCAWG study cataloguing mutational signatures as well as their activities across thousands of cancers. Our analysis identified systematic and non-random structure of residuals that is left unexplained by the additive model. We used hierarchical clustering to identify cancer subsets with similar residual profiles to show that both systematic mutation count overestimation and underestimation take place. We propose an extension to the additive mutational signature model—multiplicatively acting modulatory processes—and develop a maximum-likelihood framework to identify such modulatory mutational signatures. The augmented model is expressive enough to almost fully remove the observed systematic residual patterns. Conclusion We suggest the modulatory processes biologically relate to sample specific DNA repair propensities with cancer or tissue type specific profiles. Overall, our results identify an interesting direction where to expand signature analysis.

Genomic instability pathways in colorectal cancer (CRC) have been extensively studied, but the role of retrotransposition in colorectal carcinogenesis remains poorly understood. Although retrotransposons are usually repressed, they become active in several human cancers, in particular those of the gastrointestinal tract. Here we characterize retrotransposon insertions in 202 colorectal tumor whole genomes and investigate their associations with molecular and clinical characteristics. We find highly variable retrotransposon activity among tumors and identify recurrent insertions in 15 known cancer genes. In approximately 1% of the cases we identify insertions in APC , likely to be tumor-initiating events. Insertions are positively associated with the CpG island methylator phenotype and the genomic fraction of allelic imbalance. Clinically, high number of insertions is independently associated with poor disease-specific survival. Retrotransposons are usually dormant in healthy tissue, but become activated during malignancy. Here, in colorectal cancer, Cajuso et al. show that retrotransposon activity associates with clinical features of the disease.

Small bowel adenocarcinoma (SBA) is an aggressive disease with limited treatment options. Despite previous studies, its molecular genetic background has remained somewhat elusive. To comprehensively characterize the mutational landscape of this tumor type, and to identify possible targets of treatment, we conducted the first large exome sequencing study on a population-based set of SBA samples from all three small bowel segments. Archival tissue from 106 primary tumors with appropriate clinical information were available for exome sequencing from a patient series consisting of a majority of confirmed SBA cases diagnosed in Finland between the years 2003-2011. Paired-end exome sequencing was performed using Illumina HiSeq 4000, and OncodriveFML was used to identify driver genes from the exome data. We also defined frequently affected cancer signalling pathways and performed the first extensive allelic imbalance (AI) analysis in SBA. Exome data analysis revealed significantly mutated genes previously linked to SBA (TP53, KRAS, APC, SMAD4, and BRAF), recently reported potential driver genes (SOX9, ATM, and ARID2), as well as novel candidate driver genes, such as ACVR2A, ACVR1B, BRCA2, and SMARCA4. We also identified clear mutation hotspot patterns in ERBB2 and BRAF. No BRAF V600E mutations were observed. Additionally, we present a comprehensive mutation signature analysis of SBA, highlighting established signatures 1A, 6, and 17, as well as U2 which is a previously unvalidated signature. Finally, comparison of the three small bowel segments revealed differences in tumor characteristics. This comprehensive work unveils the mutational landscape and most frequently affected genes and pathways in SBA, providing potential therapeutic targets, and novel and more thorough insights into the genetic background of this tumor type.

Meta-analysis has been established as an effective approach to combining summary statistics of several genome-wide association studies (GWAS). However, the accuracy of meta-analysis can be attenuated in the presence of cross-study heterogeneity. We present sPLINK , a hybrid federated and user-friendly tool, which performs privacy-aware GWAS on distributed datasets while preserving the accuracy of the results. sPLINK is robust against heterogeneous distributions of data across cohorts while meta-analysis considerably loses accuracy in such scenarios. sPLINK achieves practical runtime and acceptable network usage for chi-square and linear/logistic regression tests. sPLINK is available at https://exbio.wzw.tum.de/splink .

Up to 86% of uterine leiomyomas harbour somatic mutations in mediator complex subunit 12 (MED12) . These mutations have been associated with conventional histology, smaller tumour size, and larger number of tumours within the uterus. Prior studies, with limited sample sizes, have failed to detect associations between other clinical features and MED12 mutations. Here, we prospectively collected 763 uterine leiomyomas and the corresponding normal myometrial tissue from 244 hysterectomy patients, recorded tumour characteristics, collected clinical data from medical records, and screened the tissue samples for MED12 mutations to assess potential associations between clinical variables and mutation status. Out of 763 leiomyomas, 599 (79%) harboured a MED12 mutation. In the analysis of tumour characteristics, positive MED12- mutation status was significantly associated with smaller tumour size, conventional histology, and subserous location, relative to intramural. In the analysis of clinical variables, the number of MED12- mutation-positive tumours showed an inverse association with parity, and the number of mutation-negative tumours showed a positive association with a history of pelvic inflammatory disease. This study confirmed the previously reported differences and discovered novel differentiating features for MED12- mutation-positive and -negative leiomyomas. These findings emphasise the relevance of specific driver mutations in genesis and presentation of uterine leiomyomas.

Uterine leiomyomas are common benign smooth muscle tumors that impose a major burden on women’s health. Recent sequencing studies have revealed recurrent and mutually exclusive mutations in leiomyomas, suggesting the involvement of molecularly distinct pathways. In this study, we explored transcriptional differences among leiomyomas harboring different genetic drivers, including high mobility group AT-hook 2 (HMGA2) rearrangements, mediator complex subunit 12 (MED12) mutations, biallelic inactivation of fumarate hydratase (FH), and collagen, type IV, alpha 5 and collagen, type IV, alpha 6 (COL4A5-COL4A6) deletions. We also explored the transcriptional consequences of 7q22, 22q, and 1p deletions, aiming to identify possible target genes. We investigated 94 leiomyomas and 60 corresponding myometrial tissues using exon arrays, whole genome sequencing, and SNP arrays. This integrative approach revealed subtype-specific expression changes in key driver pathways, including Wnt/β-catenin, Prolactin, and insulin-like growth factor (IGF)1 signaling. Leiomyomas with HMGA2 aberrations displayed highly significant up-regulation of the proto-oncogene pleomorphic adenoma gene 1 (PLAG1), suggesting that HMGA2 promotes tumorigenesis through PLAG1 activation. This was supported by the identification of genetic PLAG1 alterations resulting in expression signatures as seen in leiomyomas with HMGA2 aberrations. RAD51 paralog B (RAD51B), the preferential translocation partner of HMGA2, was up-regulated in MED12 mutant lesions, suggesting a role for this gene in the genesis of leiomyomas. FH-deficient leiomyomas were uniquely characterized by activation of nuclear factor erythroid 2-related factor 2 (NRF2) target genes, supporting the hypothesis that accumulation of fumarate leads to activation of the oncogenic transcription factor NRF2. This study emphasizes the need for molecular stratification in leiomyoma research and possibly in clinical practice as well. Further research is needed to determine whether the candidate biomarkers presented herein can provide guidance for managing the millions of patients affected by these lesions.

Background Endometrial polyps are common, localized overgrowths of endometrial glands and stroma that protrude into the uterine cavity. These tumor-like lesions can cause symptoms like abnormal uterine bleeding and infertility, and they may undergo malignant transformation. The etiology of endometrial polyps remains largely unknown. Methods Here, we conducted whole-genome sequencing and global gene expression profiling on 23 polyps. Major findings were validated with targeted DNA (Sanger sequencing) and protein (immunohistochemistry) level analyses. Sanger sequencing was also utilized to validate the observed novel alterations in an additional set of 54 polyp samples. Results The most common alterations were chromosomal rearrangements affecting HMGA1 and HMGA2 , identified in 74% (17/23) of the polyps. These rearrangements involved LRMDA , RAD51B , TRAF3IP2 , and 7p15.2 as recurrent rearrangement partners. 3′RNA sequencing indicated corresponding overexpression of HMGA1 and HMGA2 as well as a downstream target PLAG1 . Elevated protein level expression of HMGA1 and HMGA2 was further shown using immunohistochemistry. In addition to frequent HMGA1 and HMGA2 alterations, we found UBE2A as a novel candidate driver gene with highly specific recurrent mutations. We also identified recurrent low-allelic fraction mutations in well-established cancer genes KRAS , PIK3CA , PIK3R1 , and PTEN . Conclusions Here, we have characterized the genomic landscape of endometrial polyps. We show that chromosomal alterations affecting HMGA1 and HMGA2 are a major underlying cause for polyp development. In addition, we present UBE2A as a novel candidate gene for human tumorigenesis. Our results contribute to a better understanding of endometrial polyp development and pave the way towards the development of targeted, non-invasive treatment options.