Control-FREEC viewer: a tool for the visualization and exploration of copy number variation data.

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.

First-hit SETBP1 mutations cause a myeloproliferative disorder with bone marrow fibrosis.

ABSTRACT: SETBP1 mutations are found in various clonal myeloid disorders. However, it is unclear whether they can initiate leukemia, because SETBP1 mutations typically appear as later events during oncogenesis. To answer this question, we generated a mouse model expressing mutated SETBP1 in hematopoietic tissue: this model showed profound alterations in the differentiation program of hematopoietic progenitors and developed a myeloid neoplasm with megakaryocytic dysplasia, splenomegaly, and bone marrow fibrosis, prompting us to investigate SETBP1 mutations in a cohort of 36 triple-negative primary myelofibrosis (TN-PMF) cases. We identified 2 distinct subgroups, one carrying SETBP1 mutations and the other completely devoid of somatic variants. Clinically, a striking difference in disease aggressiveness was noted, with patients with SETBP1 mutation showing a much worse clinical course. In contrast to myelodysplastic/myeloproliferative neoplasms, in which SETBP1 mutations are mostly found as a late clonal event, single-cell clonal hierarchy reconstruction in 3 patients with TN-PMF from our cohort revealed SETBP1 to be a very early event, suggesting that the phenotype of the different SETBP1+ disorders may be shaped by the opposite hierarchy of the same clonal SETBP1 variants.

Effects of blocking CD24 and CD47 'don't eat me' signals in combination with rituximab in mantle-cell lymphoma and chronic lymphocytic leukaemia.

Mantle-cell lymphoma (MCL) is a B-cell non-Hodgkin Lymphoma (NHL) with a poor prognosis, at high risk of relapse after conventional treatment. MCL-associated tumour microenvironment (TME) is characterized by M2-like tumour-associated macrophages (TAMs), able to interact with cancer cells, providing tumour survival and resistance to immuno-chemotherapy. Likewise, monocyte-derived nurse-like cells (NLCs) present M2-like profile and provide proliferation signals to chronic lymphocytic leukaemia (CLL), a B-cell malignancy sharing with MCL some biological and phenotypic features. Antibodies against TAMs targeted CD47, a 'don't eat me' signal (DEMs) able to quench phagocytosis by TAMs within TME, with clinical effectiveness when combined with Rituximab in pretreated NHL. Recently, CD24 was found as valid DEMs in solid cancer. Since CD24 is expressed during B-cell differentiation, we investigated and identified consistent CD24 in MCL, CLL and primary human samples. Phagocytosis increased when M2-like macrophages were co-cultured with cancer cells, particularly in the case of paired DEMs blockade (i.e. anti-CD24 + anti-CD47) combined with Rituximab. Similarly, unstimulated CLL patients-derived NLCs provided increased phagocytosis when DEMs blockade occurred. Since high levels of CD24 were associated with worse survival in both MCL and CLL, anti-CD24-induced phagocytosis could be considered for future clinical use, particularly in association with other agents such as Rituximab.

Evolutionary signatures of human cancers revealed via genomic analysis of over 35,000 patients.

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 (Agony-baSed Cancer EvoluTion InferenCe) 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.

Characterization of cancer subtypes associated with clinical outcomes by multi-omics integrative clustering.

Cancer patients show heterogeneous phenotypes and very different outcomes and responses even to common treatments, such as standard chemotherapy. This state-of-affairs has motivated the need for the comprehensive characterization of cancer phenotypes and fueled the generation of large omics datasets, comprising multiple omics data reported for the same patients, which might now allow us to start deciphering cancer heterogeneity and implement personalized therapeutic strategies. In this work, we performed the analysis of four cancer types obtained from the latest efforts by The Cancer Genome Atlas, for which seven distinct omics data were available for each patient, in addition to curated clinical outcomes. We performed a uniform pipeline for raw data preprocessing and adopted the Cancer Integration via MultIkernel LeaRning (CIMLR) integrative clustering method to extract cancer subtypes. We then systematically review the discovered clusters for the considered cancer types, highlighting novel associations between the different omics and prognosis.

Structure-activity exploration of a small-molecule allosteric inhibitor of T790M/L858R double mutant EGFR.

EGFR is a protein kinase whose aberrant activity is frequently involved in the development of non-small lung cancer (NSCLC) drug resistant forms. The allosteric inhibition of this enzyme is currently one among the most attractive approaches to design and develop anticancer drugs. In a previous study, we reported the identification of a hit compound acting as type III allosteric inhibitor of the L858R/T790M double mutant EGFR. Herein, we report the design, synthesis and in vitro testing of a series of analogues of the previously identified hit with the aim of exploring the structure-activity relationships (SAR) around this scaffold. The performed analyses allowed us to identify two compounds 15 and 18 showing improved inhibition of double mutant EGFR with respect to the original hit, as well as interesting antiproliferative activity against H1975 NSCLC cancer cells expressing double mutant EGFR. The newly discovered compounds represent promising starting points for further hit-to-lead optimisation.

Characterization of SARS-CoV-2 Mutational Signatures from 1.5+ Million Raw Sequencing Samples.

We present a large-scale analysis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) substitutions, considering 1,585,456 high-quality raw sequencing samples, aimed at investigating the existence and quantifying the effect of mutational processes causing mutations in SARS-CoV-2 genomes when interacting with the human host. As a result, we confirmed the presence of three well-differentiated mutational processes likely ruled by reactive oxygen species (ROS), apolipoprotein B editing complex (APOBEC), and adenosine deaminase acting on RNA (ADAR). We then evaluated the activity of these mutational processes in different continental groups, showing that some samples from Africa present a significantly higher number of substitutions, most likely due to higher APOBEC activity. We finally analyzed the activity of mutational processes across different SARS-CoV-2 variants, and we found a significantly lower number of mutations attributable to APOBEC activity in samples assigned to the Omicron variant.

Intra-abdominal fluid aspirate from a dog.

A 12-year-old, neutered female, Siberian husky, was presented with a 6-months history of progressive abdominal distension, anorexia, and weight loss. The dog appeared normal on physical examination except for marked abdominal distension. A fluid wave was balloted strongly suggesting an abdominal effusion. Ultrasound examination confirmed this clinical finding. Results of the CBC included mild nonregenerative anemia, with an RBC count of 4.9 × 10(6)/µL (reference interval 5.5-8.5 × 10(6)/µL), hemoglobin concentration of 12 g/dL (reference interval 12-18 g/dL), HCT of 36% (reference interval 37-55%), and reticulocytes <60,000/µL. No abnormalities in serum chemistry were detected.

Smoking is associated with impaired glucose regulation and a decrease in insulin sensitivity and the disposition index in first-degree relatives of type 2 diabetes subjects independently of the presence of metabolic syndrome.

The aim of this study was to investigate glucose tolerance, insulin secretion and insulin resistance according to smoking habits in first-degree relatives of type 2 diabetes patients, a population at high risk for developing diabetes. One thousand three hundred (646 females and 654 males) subjects underwent an oral glucose tolerance test (OGTT) to investigate their glucose metabolism and answered questionnaires about their lifestyle habits. Smoker subjects showed significant impairment compared with non-smoker subjects in 2-h post-oral glucose tolerance test (2hOGTT, 129.3 ± 40.2 vs. 117.7 ± 37.6 mg/dl, p < 0.001), the OGTT insulin sensitivity (386.3 ± 54.9 vs. 400.5 ± 53.4 ml min(-1) m(2), p < 0.01) method and the insulin sensitivity and secretion index-2 (ISSI-2, 1.7 ± 0.8 vs. 2.0 ± 1.0, p < 0.005). Metabolic syndrome (MS) was higher in the smoker than in the non-smoker group (46.5 vs. 29.7 %, p < 0001), and smokers were more sedentary than non-smokers (3.94 ± 3.77 vs. 4.86 ± 4.41 h/week, p < 0.001). Smokers showed an increased risk of impaired glucose regulation (IGR: impaired glucose tolerance or diabetes mellitus) with a hazard ratio (HR) adjusted by gender, metabolic syndrome and physical activity of 1.78, 95 % CI 1.27-2.47 (p < 0.001). The association between smoking and MS conferred a risk of IGR that was five times higher (HR 5.495, 95 % CI 4.07-7.41, p < 0.001). Smoking habit was a significant explanatory variable in a multiple forward stepwise regression analysis performed using 2hOGTT and ISSI-2 as dependent variables (p < 0.0001, R = 0.313 and p < 0.0001, R = 0.347, respectively). In conclusions, our results show that tobacco smoking is tightly associated with impairments in glucose metabolism and insulin sensitivity and insulin secretion.