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It is not clear how spontaneous DNA double-strand breaks (DSBs) form and are processed in normal cells, and whether they predispose to cancer-associated translocations. We show that DSBs in normal mammary cells form upon release of paused RNA polymerase II (Pol II) at promoters, 5′ splice sites and active enhancers, and are processed by end-joining in the absence of a canonical DNA-damage response. Logistic and causal-association models showed that Pol II pausing at long genes is the main predictor and determinant of DSBs. Damaged introns with paused Pol II-pS5, TOP2B and XRCC4 are enriched in translocation breakpoints, and map at topologically associating domain boundary-flanking regions showing high interaction frequencies with distal loci. Thus, in unperturbed growth conditions, release of paused Pol II at specific loci and chromatin territories favors DSB formation, leading to chromosomal translocations. Release of paused Pol II at specific intronic loci or chromatin domains favors the formation of abnormal DNA recombination, leading to cancer-associated chromosomal translocations.

Background Protein-DNA binding sites of ChIP-seq experiments are identified where the binding affinity is significant based on a given threshold. The choice of the threshold is a trade-off between conservative region identification and discarding weak, but true binding sites. Results We rescue weak binding sites using MSPC, which efficiently exploits replicates to lower the threshold required to identify a site while keeping a low false-positive rate, and we compare it to IDR, a widely used post-processing method for identifying highly reproducible peaks across replicates. We observe several master transcription regulators (e.g., SP1 and GATA3) and HDAC2-GATA1 regulatory networks on rescued regions in K562 cell line. Conclusions We argue the biological relevance of weak binding sites and the information they add when rescued by MSPC. An implementation of the proposed extended MSPC methodology and the scripts to reproduce the performed analysis are freely available at https://genometric.github.io/MSPC/ ; MSPC is distributed as a command-line application and an R package available from Bioconductor ( https://doi.org/doi:10.18129/B9.bioc.rmspc ).

Background Extremely low gestational age neonates (ELGANs, i.e., neonates born before 28 weeks of gestation) are at high risk of developing retinopathy of prematurity (ROP), with potential long-life visual impairment. Due to concomitant anemia, ELGANs need repeated red blood cell (RBC) transfusions. These produce a progressive replacement of fetal hemoglobin (HbF) by adult hemoglobin (HbA). Furthermore, a close association exists between low levels of HbF and severe ROP, suggesting that a perturbation of the HbF-mediated oxygen release may derange retinal angiogenesis and promote ROP. Methods/design BORN (umBilical blOod to tRansfuse preterm Neonates) is a multicenter double-blinded randomized controlled trial in ELGANs, to assess the effect of allogeneic cord blood RBC transfusions (CB-RBCs) on severe ROP development. Recruitment, consent, and randomization take place at 10 neonatology intensive care units (NICUs) of 8 Italian tertiary hospitals. ELGANs with gestational age at birth comprised between 24+0 and 27+6 weeks are randomly allocated into two groups: (1) standard RBC transfusions (adult-RBCs) (control arm) and (2) CB-RBCs (intervention arm). In case of transfusion need, enrolled patients receive transfusions according to the allocation arm, unless an ABO/RhD CB-RBC is unavailable. Nine Italian public CB banks cooperate to make available a suitable amount of CB-RBC units for all participating NICUs. The primary outcome is the incidence of severe ROP (stage 3 or higher) at discharge or 40 weeks of postmenstrual age, which occurs first. Discussion BORN is a groundbreaking trial, pioneering a new transfusion approach dedicated to ELGANs at high risk for severe ROP. In previous non-randomized trials, this transfusion approach was proven feasible and able to prevent the HbF decrease in patients requiring multiple transfusions. Should the BORN trial confirm the efficacy of CB-RBCs in reducing ROP severity, this transfusion strategy would become the preferential blood product to be used in severely preterm neonates. Trial registration ClinicalTrials.gov NCT05100212. Registered on October 29, 2021

Background High-grade serous ovarian cancer (HGSOC) has poor survival rates due to a combination of diagnosis at advanced stage and disease recurrence as a result of chemotherapy resistance. In BRCA1 (Breast Cancer gene 1) - or BRCA2-wild type (BRCAwt) HGSOC patients, resistance and progressive disease occur earlier and more often than in mutated BRCA. Identification of biomarkers helpful in predicting response to first-line chemotherapy is a challenge to improve BRCAwt HGSOC management. Methods To identify a gene signature that can predict response to first-line chemotherapy, pre-treatment tumor biopsies from a restricted cohort of BRCAwt HGSOC patients were profiled by RNA sequencing (RNA-Seq) technology. Patients were sub-grouped according to platinum-free interval (PFI), into sensitive (PFI > 12 months) and resistant (PFI < 6 months). The gene panel identified by RNA-seq analysis was then tested by high-throughput quantitative real-time PCR (HT RT-qPCR) in a validation cohort, and statistical/bioinformatic methods were used to identify eligible markers and to explore the relevant pathway/gene network enrichments of the identified gene set. Finally, a panel of primary HGSOC cell lines was exploited to uncover cell-autonomous mechanisms of resistance. Results RNA-seq identified a 42-gene panel discriminating sensitive and resistant BRCAwt HGSOC patients and pathway analysis pointed to the immune system as a possible driver of chemotherapy response. From the extended cohort analysis of the 42 DEGs (differentially expressed genes), a statistical approach combined with the random forest classifier model generated a ten-gene signature predictive of response to first-line chemotherapy. The ten-gene signature included: CKB (Creatine kinase B), CTNNBL1 (Catenin, beta like 1), GNG11 (G protein subunit gamma 11), IGFBP7 (Insulin-like growth factor-binding protein 7), PLCG2 (Phospholipase C, gamma 2), RNF24 (Ring finger protein 24), SLC15A3 (Solute carrier family 15 member 3), TSPAN31 (Tetraspanin 31), TTI1 (TELO2 interacting protein 1) and UQCC1 (Ubiquinol-cytochrome c reductase complex assembly factor). Cytotoxicity assays, combined with gene-expression analysis in primary HGSOC cell lines, allowed to define CTNNBL1, RNF24, and TTI1 as cell-autonomous contributors to tumor resistance. Conclusions Using machine-learning techniques we have identified a gene signature that could predict response to first-line chemotherapy in BRCAwt HGSOC patients, providing a useful tool towards personalized treatment modalities.

Background Medulloblastoma (MB) is the most common cerebellar malignancy during childhood. Among MB, MYC-amplified Group 3 tumors display the worst prognosis. MYC is an oncogenic transcription factor currently thought to be undruggable. Nevertheless, targeting MYC-dependent processes (i.e. transcription and RNA processing regulation) represents a promising approach. Methods We have tested the sensitivity of MYC-driven Group 3 MB cells to a pool of transcription and splicing inhibitors that display a wide spectrum of targets. Among them, we focus on THZ531, an inhibitor of the transcriptional cyclin-dependent kinases (CDK) 12 and 13. High-throughput RNA-sequencing analyses followed by bioinformatics and functional analyses were carried out to elucidate the molecular mechanism(s) underlying the susceptibility of Group 3 MB to CDK12/13 chemical inhibition. Data from International Cancer Genome Consortium (ICGC) and other public databases were mined to evaluate the functional relevance of the cellular pathway/s affected by the treatment with THZ531 in Group 3 MB patients. Results We found that pharmacological inhibition of CDK12/13 is highly selective for MYC-high Group 3 MB cells with respect to MYC-low MB cells. We identified a subset of genes enriched in functional terms related to the DNA damage response (DDR) that are up-regulated in Group 3 MB and repressed by CDK12/13 inhibition. Accordingly, MYC- and CDK12/13-dependent higher expression of DDR genes in Group 3 MB cells limits the toxic effects of endogenous DNA lesions in these cells. More importantly, chemical inhibition of CDK12/13 impaired the DDR and induced irreparable DNA damage exclusively in MYC-high Group 3 MB cells. The augmented sensitivity of MYC-high MB cells to CDK12/13 inhibition relies on the higher elongation rate of the RNA polymerase II in DDR genes. Lastly, combined treatments with THZ531 and DNA damage-inducing agents synergically suppressed viability of MYC-high Group 3 MB cells. Conclusions Our study demonstrates that CDK12/13 activity represents an exploitable vulnerability in MYC-high Group 3 MB and may pave the ground for new therapeutic approaches for this high-risk brain tumor.

Purpose To investigate whether the COVID-19 pandemic and national lockdown had an impact on the extent of cancer disease at FDG PET/CT staging as surrogate marker. Methods Retrospective observational study including cancer patients submitted to FDG PET/CT staging from June 1 to October 31, 2020, and June 1 to October 31, 2019, respectively. Data regarding primary tumour, nodal (N) status and number of involved nodal stations, and presence and number of distant metastases (M) were collected. Each scan was classified in limited vs advanced status. Data were aggregated across the study population and tumour type. Bi-weekly frequencies of the observed events were analysed. Results Six hundred eleven patients were included (240 in 2019 vs 371 in 2020, respectively). A significant increase of advanced disease patients (rate 1.56, P  < 0.001), N + or M + patients (rate 1.84 and 2.09, respectively, P  < 0.001), and patients with a greater number of involved N stations or M (rate 2.01 and 2.06, respectively, P  < 0.001) were found in 2020 compared with data of 2019. Analysis by tumour type showed a significant increase of advanced disease in lymphoma and lung cancer in 2020 compared with 2019 ( P  < 0.001). In addition, a significant increase of nodal involvement was found in lung, gastro-intestinal, and breast cancers, as well as in lymphoma patients ( P  < 0.02). A significant increase of distant metastases was found in lung cancers ( P  = 0.002). Conclusion Cancer patients with advanced disease at FDG PET/CT staging increased in 2020 compared with 2019, following the national lockdown due to the COVID-19 pandemic, 1.5-fold with a significant increase of patients with N or M involvement. Targeted health interventions are needed to mitigate the effects of the pandemic on patient outcome.

Brain functional disruption and cognitive shortfalls as consequences of neurodegeneration are among the most investigated aspects in current clinical research. Traditionally, specific anatomical and behavioral traits have been associated with neurodegeneration, thus directly translatable in clinical terms. However, these qualitative traits, do not account for the extensive information flow breakdown within the functional brain network that deeply affect cognitive skills. Behavioural variant Frontotemporal Dementia (bvFTD) is a neurodegenerative disorder characterized by behavioral and executive functions disturbances. Deviations from the physiological cognitive functioning can be accurately inferred and modeled from functional connectivity alterations. Although the need for unbiased metrics is still an open issue in imaging studies, the graph-theory approach applied to neuroimaging techniques is becoming popular in the study of brain dysfunction. In this work, we assessed the global connectivity and topological alterations among brain regions in bvFTD patients using a minimum spanning tree (MST) based analysis of resting state functional MRI (rs-fMRI) data. Whilst several graph theoretical methods require arbitrary criteria (including the choice of network construction thresholds and weight normalization methods), MST is an unambiguous modeling solution, ensuring accuracy, robustness, and reproducibility. MST networks of 116 regions of interest (ROIs) were built on wavelet correlation matrices, extracted from 41 bvFTD patients and 39 healthy controls (HC). We observed a global fragmentation of the functional network backbone with severe disruption of information-flow highways. Frontotemporal areas were less compact, more isolated, and concentrated in less integrated structures, respect to healthy subjects. Our results reflected such complex breakdown of the frontal and temporal areas at both intra-regional and long-range connections. Our findings highlighted that MST, in conjunction with rs-fMRI data, was an effective method for quantifying and detecting functional brain network impairments, leading to characteristic bvFTD cognitive, social, and executive functions disorders.

Frontotemporal Dementia (FTD) is the form of neurodegenerative dementia with the highest prevalence after Alzheimer's disease, equally distributed in men and women. It includes several variants, generally characterized by behavioural instability and language impairments. Although few mendelian genes (MAPT, GRN, and C9orf72) have been associated to the FTD phenotype, in most cases there is only evidence of multiple risk loci with relatively small effect size. To date, there are no comprehensive studies describing FTD at molecular level, highlighting possible genetic interactions and signalling pathways at the origin FTD-associated neurodegeneration. In this study, we designed a broad FTD genetic interaction map of the Italian population, through a novel network-based approach modelled on the concepts of disease-relevance and interaction perturbation, combining Steiner tree search and Structural Equation Model (SEM) analysis. Our results show a strong connection between Calcium/cAMP metabolism, oxidative stress-induced Serine/Threonine kinases activation, and postsynaptic membrane potentiation, suggesting a possible combination of neuronal damage and loss of neuroprotection, leading to cell death. In our model, Calcium/cAMP homeostasis and energetic metabolism impairments are primary causes of loss of neuroprotection and neural cell damage, respectively. Secondly, the altered postsynaptic membrane potentiation, due to the activation of stress-induced Serine/Threonine kinases, leads to neurodegeneration. Our study investigates the molecular underpinnings of these processes, evidencing key genes and gene interactions that may account for a significant fraction of unexplained FTD aetiology. We emphasized the key molecular actors in these processes, proposing them as novel FTD biomarkers that could be crucial for further epidemiological and molecular studies.

The use of next-generation sequencing (NGS) techniques for variant detection has become increasingly important in clinical research and in clinical practice in oncology. Many cancer patients are currently being treated in clinical practice or in clinical trials with drugs directed against specific genomic alterations. In this scenario, the development of reliable and reproducible bioinformatics tools is essential to derive information on the molecular characteristics of each patient’s tumor from the NGS data. The development of bioinformatics pipelines based on the use of machine learning and statistical methods is even more relevant for the determination of complex biomarkers. In this review, we describe some important technologies, computational algorithms and models that can be applied to NGS data from Whole Genome to Targeted Sequencing, to address the problem of finding complex cancer-associated biomarkers. In addition, we explore the future perspectives and challenges faced by bioinformatics for precision medicine both at a molecular and clinical level, with a focus on an emerging complex biomarker such as homologous recombination deficiency (HRD).

Ultrasound examination is an accurate method in the preoperative evaluation of the inguinofemoral lymph nodes when performed by experienced operators. The purpose of the study was to build a robust, multi-modular model based on machine learning to discriminate between metastatic and non-metastatic inguinal lymph nodes in patients with vulvar cancer. One hundred and twenty-seven women were selected at our center from March 2017 to April 2020, and 237 inguinal regions were analyzed (75 were metastatic and 162 were non-metastatic at histology). Ultrasound was performed before surgery by experienced examiners. Ultrasound features were defined according to previous studies and collected prospectively. Fourteen informative features were used to train and test the machine to obtain a diagnostic model (Morphonode Predictive Model). The following data classifiers were integrated: (I) random forest classifiers (RCF), (II) regression binomial model (RBM), (III) decisional tree (DT), and (IV) similarity profiling (SP). RFC predicted metastatic/non-metastatic lymph nodes with an accuracy of 93.3% and a negative predictive value of 97.1%. DT identified four specific signatures correlated with the risk of metastases and the point risk of each signature was 100%, 81%, 16% and 4%, respectively. The Morphonode Predictive Model could be easily integrated into the clinical routine for preoperative stratification of vulvar cancer patients.