Gene Expression Analysis for Uterine Cervix and Corpus Cancer Characterization.

The analysis of gene expression quantification data is a powerful and widely used approach in cancer research. This work provides new insights into the transcriptomic changes that occur in healthy uterine tissue compared to those in cancerous tissues and explores the differences associated with uterine cancer localizations and histological subtypes. To achieve this, RNA-Seq data from the TCGA database were preprocessed and analyzed using the KnowSeq package. Firstly, a kNN model was applied to classify uterine cervix cancer, uterine corpus cancer, and healthy uterine samples. Through variable selection, a three-gene signature was identified (VWCE, CLDN15, ADCYAP1R1), achieving consistent 100% test accuracy across 20 repetitions of a 5-fold cross-validation. A supplementary similar analysis using miRNA-Seq data from the same samples identified an optimal two-gene miRNA-coding signature potentially regulating the three-gene signature previously mentioned, which attained optimal classification performance with an 82% F1-macro score. Subsequently, a kNN model was implemented for the classification of cervical cancer samples into their two main histological subtypes (adenocarcinoma and squamous cell carcinoma). A uni-gene signature (ICA1L) was identified, achieving 100% test accuracy through 20 repetitions of a 5-fold cross-validation and externally validated through the CGCI program. Finally, an examination of six cervical adenosquamous carcinoma (mixed) samples revealed a pattern where the gene expression value in the mixed class aligned closer to the histological subtype with lower expression, prompting a reconsideration of the diagnosis for these mixed samples. In summary, this study provides valuable insights into the molecular mechanisms of uterine cervix and corpus cancers. The newly identified gene signatures demonstrate robust predictive capabilities, guiding future research in cancer diagnosis and treatment methodologies.

Special Issue: New Advances in Bioinformatics and Biomedical Engineering Using Machine Learning Techniques, IWBBIO-2022.

Bioinformatics is revolutionizing Biomedicine in the way we treat and diagnose pathologies related to biological manifestations resulting from variations or mutations of our DNA [...].

Performance comparison between multi-center histopathology datasets of a weakly-supervised deep learning model for pancreatic ductal adenocarcinoma detection.

BACKGROUND: Pancreatic ductal carcinoma patients have a really poor prognosis given its difficult early detection and the lack of early symptoms. Digital pathology is routinely used by pathologists to diagnose the disease. However, visually inspecting the tissue is a time-consuming task, which slows down the diagnostic procedure. With the advances occurred in the area of artificial intelligence, specifically with deep learning models, and the growing availability of public histology data, clinical decision support systems are being created. However, the generalization capabilities of these systems are not always tested, nor the integration of publicly available datasets for pancreatic ductal carcinoma detection (PDAC). METHODS: In this work, we explored the performace of two weakly-supervised deep learning models using the two more widely available datasets with pancreatic ductal carcinoma histology images, The Cancer Genome Atlas Project (TCGA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC). In order to have sufficient training data, the TCGA dataset was integrated with the Genotype-Tissue Expression (GTEx) project dataset, which contains healthy pancreatic samples. RESULTS: We showed how the model trained on CPTAC generalizes better than the one trained on the integrated dataset, obtaining an inter-dataset accuracy of 90.62% ± 2.32 and an outer-dataset accuracy of 92.17% when evaluated on TCGA + GTEx. Furthermore, we tested the performance on another dataset formed by tissue micro-arrays, obtaining an accuracy of 98.59%. We showed how the features learned in an integrated dataset do not differentiate between the classes, but between the datasets, noticing that a stronger normalization might be needed when creating clinical decision support systems with datasets obtained from different sources. To mitigate this effect, we proposed to train on the three available datasets, improving the detection performance and generalization capabilities of a model trained only on TCGA + GTEx and achieving a similar performance to the model trained only on CPTAC. CONCLUSIONS: The integration of datasets where both classes are present can mitigate the batch effect present when integrating datasets, improving the classification performance, and accurately detecting PDAC across different datasets.

A crowdsourcing database for the copy-number variation of the Spanish population.

BACKGROUND: Despite being a very common type of genetic variation, the distribution of copy-number variations (CNVs) in the population is still poorly understood. The knowledge of the genetic variability, especially at the level of the local population, is a critical factor for distinguishing pathogenic from non-pathogenic variation in the discovery of new disease variants. RESULTS: Here, we present the SPAnish Copy Number Alterations Collaborative Server (SPACNACS), which currently contains copy number variation profiles obtained from more than 400 genomes and exomes of unrelated Spanish individuals. By means of a collaborative crowdsourcing effort whole genome and whole exome sequencing data, produced by local genomic projects and for other purposes, is continuously collected. Once checked both, the Spanish ancestry and the lack of kinship with other individuals in the SPACNACS, the CNVs are inferred for these sequences and they are used to populate the database. A web interface allows querying the database with different filters that include ICD10 upper categories. This allows discarding samples from the disease under study and obtaining pseudo-control CNV profiles from the local population. We also show here additional studies on the local impact of CNVs in some phenotypes and on pharmacogenomic variants. SPACNACS can be accessed at: http://csvs.clinbioinfosspa.es/spacnacs/ . CONCLUSION: SPACNACS facilitates disease gene discovery by providing detailed information of the local variability of the population and exemplifies how to reuse genomic data produced for other purposes to build a local reference database.

Detection of High Level of Co-Infection and the Emergence of Novel SARS CoV-2 Delta-Omicron and Omicron-Omicron Recombinants in the Epidemiological Surveillance of Andalusia.

Recombination is an evolutionary strategy to quickly acquire new viral properties inherited from the parental lineages. The systematic survey of the SARS-CoV-2 genome sequences of the Andalusian genomic surveillance strategy has allowed the detection of an unexpectedly high number of co-infections, which constitute the ideal scenario for the emergence of new recombinants. Whole genome sequence of SARS-CoV-2 has been carried out as part of the genomic surveillance programme. Sample sources included the main hospitals in the Andalusia region. In addition to the increase of co-infections and known recombinants, three novel SARS-CoV-2 delta-omicron and omicron-omicron recombinant variants with two break points have been detected. Our observations document an epidemiological scenario in which co-infection and recombination are detected more frequently. Finally, we describe a family case in which co-infection is followed by the detection of a recombinant made from the two co-infecting variants. This increased number of recombinants raises the risk of emergence of recombinant variants with increased transmissibility and pathogenicity.

NOTCH1 mutation in chronic lymphocytic leukaemia is associated with an enhanced cell cycle G1/S transition and specific cyclin overexpression: Preclinical ground for targeted inhibition.

Studies prior to next-generation sequencing (NGS) showed that the frequent indolent course of chronic lymphocytic leukaemia (CLL) is related to most cells remaining quiescent in the G0 -G1 cell cycle phase, due to the expression of dysregulated cyclin genes. Of note, the activating nature of the NOTCH1 mutation in T lymphoblastic leukaemia also drives the dysregulation of cell cycle genes. Our goal was to comprehensively revisit the cell cycle in NOTCH1-mutated CLL (NOTCH1MUT ) to test for potential therapeutic targets. Among 378 NGS-annotated CLL cases, NOTCH1MUT cells displayed a unique transcriptome profile of G0 -G1 cell cycle components, with an overexpression of early-phase effectors, reaching a 38-, 27- and ninefold change increase for the complex elements CCND3, CDK4 and CDK6, respectively. This NOTCH1MUT cells' profile was related to more cells traversing through the cell cycle. In-vitro targeted inhibition of NOTCH1 gamma-secretase and CDK4/6 reversed the distribution of cells through the cycle phases and enhanced the killing of NOTCH1MUT CLL cells, suggesting new therapeutic approaches.

Accuracy and prognostic impact of FDG PET/CT and biopsy in bone marrow assessment of follicular lymphoma at diagnosis: A Nation-Wide cohort study.

BACKGOUND: In the workup of follicular lymphoma (FL), bone marrow biopsy (BMB) assessment is a key component of FLIPI and FLIPI2, the most widely used outcome scores. During the previous decade, several studies explored the role of FDG-PET/CT for detecting nodal and extranodal disease, with only one large study comparing both techniques. METHODS: The aim of our study was to evaluate the diagnostic accuracy and the prognostic impact of both procedures in a retrospective cohort of 299 FL patients with both tests performed at diagnosis. In order to avoid a collinearity bias, FLIPI2 was deconstructed in its founding parameters, and the bone marrow involvement (BMI) parameter separately included as: a positive BMB, a positive PET/CT, the combined "PET/CT and BMB positive" or "PET/CT or BMB positive". These variables were also confronted independently with the POD24 in 233 patients treated with intensive regimens. RESULTS: In the total cohort, bone marrow was involved in 124 and 60 patients by BMB and PET/CT, respectively. In terms of overall survival, age > 60 y.o. and the combined "PET/CT or BMB positive" achieved statistical independence as a prognostic factor. In patients treated with an intensive regimen, only the combined "PET/CT or BMB positive" added prognostic value for a shorter overall survival, when confronted with the POD24. CONCLUSION: Our results show that in FL both BMB and PET/CT should be considered at diagnosis, as their combined assessment provides independent prognostic value in the context of the most widely use clinical scores.

Assessing the Impact of SARS-CoV-2 Lineages and Mutations on Patient Survival.

OBJECTIVES: More than two years into the COVID-19 pandemic, SARS-CoV-2 still remains a global public health problem. Successive waves of infection have produced new SARS-CoV-2 variants with new mutations for which the impact on COVID-19 severity and patient survival is uncertain. METHODS: A total of 764 SARS-CoV-2 genomes, sequenced from COVID-19 patients, hospitalized from 19th February 2020 to 30 April 2021, along with their clinical data, were used for survival analysis. RESULTS: A significant association of B.1.1.7, the alpha lineage, with patient mortality (log hazard ratio (LHR) = 0.51, C.I. = [0.14,0.88]) was found upon adjustment by all the covariates known to affect COVID-19 prognosis. Moreover, survival analysis of mutations in the SARS-CoV-2 genome revealed 27 of them were significantly associated with higher mortality of patients. Most of these mutations were located in the genes coding for the S, ORF8, and N proteins. CONCLUSIONS: This study illustrates how a combination of genomic and clinical data can provide solid evidence for the impact of viral lineage on patient survival.

Polypill Strategy in Secondary Cardiovascular Prevention.

BACKGROUND: A polypill that includes key medications associated with improved outcomes (aspirin, angiotensin-converting-enzyme [ACE] inhibitor, and statin) has been proposed as a simple approach to the secondary prevention of cardiovascular death and complications after myocardial infarction. METHODS: In this phase 3, randomized, controlled clinical trial, we assigned patients with myocardial infarction within the previous 6 months to a polypill-based strategy or usual care. The polypill treatment consisted of aspirin (100 mg), ramipril (2.5, 5, or 10 mg), and atorvastatin (20 or 40 mg). The primary composite outcome was cardiovascular death, nonfatal type 1 myocardial infarction, nonfatal ischemic stroke, or urgent revascularization. The key secondary end point was a composite of cardiovascular death, nonfatal type 1 myocardial infarction, or nonfatal ischemic stroke. RESULTS: A total of 2499 patients underwent randomization and were followed for a median of 36 months. A primary-outcome event occurred in 118 of 1237 patients (9.5%) in the polypill group and in 156 of 1229 (12.7%) in the usual-care group (hazard ratio, 0.76; 95% confidence interval [CI], 0.60 to 0.96; P = 0.02). A key secondary-outcome event occurred in 101 patients (8.2%) in the polypill group and in 144 (11.7%) in the usual-care group (hazard ratio, 0.70; 95% CI, 0.54 to 0.90; P = 0.005). The results were consistent across prespecified subgroups. Medication adherence as reported by the patients was higher in the polypill group than in the usual-care group. Adverse events were similar between groups. CONCLUSIONS: Treatment with a polypill containing aspirin, ramipril, and atorvastatin within 6 months after myocardial infarction resulted in a significantly lower risk of major adverse cardiovascular events than usual care. (Funded by the European Union Horizon 2020; SECURE ClinicalTrials.gov number, NCT02596126; EudraCT number, 2015-002868-17.).

Low-dose Radiation Therapy in the Management of COVID-19 Pneumonia (LOWRAD-Cov19). Final results of a prospective phase I-II trial.

BACKGROUND AND PURPOSE: To evaluate the results of low-dose radiation therapy (LD-RT) to lungs in the management of patients with COVID-19 pneumonia. MATERIAL AND METHODS: We conducted a prospective phase I-II trial enrolling COVID-19 patients ≥50 years-old, with bilateral lung involvement at imaging study and oxygen requirement (oxygen saturation ≤93% on room air). Patients received 1 Gy to whole lungs in a single fraction. Primary outcome was a radiological response assessed as severity and extension scores at days +3 and +7. Secondary outcomes were toxicity (CTCAE v5.0), days of hospitalization, changes in inflammatory blood parameters (ferritin, lymphocytes, C-reactive protein, d-dimer and LDH) and SatO2/FiO2 index (SAFI), at day +3 and +7. Descriptive analyses were summarized as means with standard deviation (SD) and/or medians with interquartile ranges (IQR). A Wilcoxon sign rank test for paired data was used to assess the CT scores and Chi Square was used to assess for comparison of categorical variables. RESULTS: Forty-one patients were included. Median age was 71 (IQR 60-84). Eighteen patients (44%) previously received an anti-COVID treatment (tocilizumab, lopinavir/ritonavir, remdesivir) and thirty-two patients (84%) received steroids during LD-RT. The extension score improved significantly (p = 0.02) on day +7. Mean baseline extension score was 13.7 (SD ± 4.9) with a score of 12.2 (±5.2) at day 3, and 12.4 ± 4.7 at day 7. No differences were found in the severity score. SAFI improved significantly on day +3 and +7 (p < 0.01). Median SAFI on day 0 was 147 (IQR 118-264), 230 (IQR 120-343) on day +3 and 293 (IQR 121-353) on day +7. Significant decrease was found in C-reactive protein on day +7 (p = 0.02) and in lymphocytes counts on day +3 and +7 (p = 0.02). The median number of days in hospital after RT was 11 (range 4-78). With a median follow-up of 60 days after LD-RT, 26 (63%) patients were discharged, 11 (27%) died because of COVID respiratory failure and 4 (10%) died of other causes. CONCLUSIONS: LD-RT is a feasible and well-tolerated treatment that could lead to rapid clinical improvement. Large randomized trials would be required to establish the efficacy of LD-RT to treat COVID-19 pneumonia.

Highly accurate whole-genome imputation of SARS-CoV-2 from partial or low-quality sequences.

BACKGROUND: The current SARS-CoV-2 pandemic has emphasized the utility of viral whole-genome sequencing in the surveillance and control of the pathogen. An unprecedented ongoing global initiative is producing hundreds of thousands of sequences worldwide. However, the complex circumstances in which viruses are sequenced, along with the demand of urgent results, causes a high rate of incomplete and, therefore, useless sequences. Viral sequences evolve in the context of a complex phylogeny and different positions along the genome are in linkage disequilibrium. Therefore, an imputation method would be able to predict missing positions from the available sequencing data. RESULTS: We have developed the impuSARS application, which takes advantage of the enormous number of SARS-CoV-2 genomes available, using a reference panel containing 239,301 sequences, to produce missing data imputation in viral genomes. ImpuSARS was tested in a wide range of conditions (continuous fragments, amplicons or sparse individual positions missing), showing great fidelity when reconstructing the original sequences, recovering the lineage with a 100% precision for almost all the lineages, even in very poorly covered genomes (<20%). CONCLUSIONS: Imputation can improve the pace of SARS-CoV-2 sequencing production by recovering many incomplete or low-quality sequences that would be otherwise discarded. ImpuSARS can be incorporated in any primary data processing pipeline for SARS-CoV-2 whole-genome sequencing.

Minimal Residual Disease in Multiple Myeloma: Something Old, Something New.

The game-changing outcome effect, due to the generalized use of novel agents in MM, has cre-ated a paradigm shift. Achieving frequent deep responses has placed MM among those neoplasms where the rationale for assessing MRD is fulfilled. However, its implementation in MM has raised specific questions: how might we weight standard measures against deep MRD in the emerging CAR-T setting? Which high sensitivity method to choose? Are current response criteria still useful? In this work, we address lessons learned from the use of MRD in other neoplasms, the steps followed for the harmonization of current methods for comprehensively measuring MRD, and the challenges that new therapies and concepts pose in the MM clinical field.

Thirteen-year trends in hospitalization and outcomes of patients with heart failure in Spain.

BACKGROUND: Heart failure is one of the most pressing current public health concerns. However, in Spain there is a lack of population data. We aimed to examine thirteen-year nationwide trends in heart failure hospitalization, in-hospital mortality and 30-day readmission rates in Spain. METHODS: We conducted a retrospective observational study of patients discharged with the principal diagnosis of heart failure from The National Health System' acute hospitals during 2003-2015. The source of the data was the Minimum Basic Data Set. Temporal trends were modelled using Poisson regression analysis. The risk-standardized in-hospital mortality ratio was calculated using a multilevel risk adjustment logistic regression model. RESULTS: A total of 1 254 830 episodes of heart failure were selected. Throughout 2003-2015, the number of hospital discharges with principal diagnosis of heart failure increased by 61%. Discharge rates weighted by age and sex increased during the period [incidence rate ratio (IRR): 1.03; 95% confidence interval (95% CI): 1.03-1.03; P < .001)], although this increase was motivated by the increase in older age groups (≥75 years old). The crude mortality rate diminished (IRR: 0.99; 95% CI: 0.98-1, P < .001), but 30-day readmission rate increased (IRR: 1.05; 95% CI: 1.04-1.06; P < .001). The risk-standardized in-hospital mortality ratio did not change throughout the study period (IRR: 0.997; 95% CI: 0.992-1; P = .32). CONCLUSIONS: From 2003 to 2015, heart failure admission rates increased significantly in Spain as a consequence of the sustained increase of hospitalization in the population ≥75 years. 30-day readmission rates increased, but the risk-standardized in-hospital mortality ratio did not significantly change for the same period.

KnowSeq R-Bioc package: The automatic smart gene expression tool for retrieving relevant biological knowledge.

KnowSeq R/Bioc package is designed as a powerful, scalable and modular software focused on automatizing and assembling renowned bioinformatic tools with new features and functionalities. It comprises a unified environment to perform complex gene expression analyses, covering all the needed processing steps to identify a gene signature for a specific disease to gather understandable knowledge. This process may be initiated from raw files either available at well-known platforms or provided by the users themselves, and in either case coming from different information sources and different Transcriptomic technologies. The pipeline makes use of a set of advanced algorithms, including the adaptation of a novel procedure for the selection of the most representative genes in a given multiclass problem. Similarly, an intelligent system able to classify new patients, providing the user the opportunity to choose one among a number of well-known and widespread classification and feature selection methods in Bioinformatics, is embedded. Furthermore, KnowSeq is engineered to automatically develop a complete and detailed HTML report of the whole process which is also modular and scalable. Biclass breast cancer and multiclass lung cancer study cases were addressed to rigorously assess the usability and efficiency of KnowSeq. The models built by using the Differential Expressed Genes achieved from both experiments reach high classification rates. Furthermore, biological knowledge was extracted in terms of Gene Ontologies, Pathways and related diseases with the aim of helping the expert in the decision-making process. KnowSeq is available at Bioconductor (https://bioconductor.org/packages/KnowSeq), GitHub (https://github.com/CasedUgr/KnowSeq) and Docker (https://hub.docker.com/r/casedugr/knowseq).