Weakly-supervised deep learning models enable HER2-low prediction from H &E stained slides.

BACKGROUND: Human epidermal growth factor receptor 2 (HER2)-low breast cancer has emerged as a new subtype of tumor, for which novel antibody-drug conjugates have shown beneficial effects. Assessment of HER2 requires several immunohistochemistry tests with an additional in situ hybridization test if a case is classified as HER2 2+. Therefore, novel cost-effective methods to speed up the HER2 assessment are highly desirable. METHODS: We used a self-supervised attention-based weakly supervised method to predict HER2-low directly from 1437 histopathological images from 1351 breast cancer patients. We built six distinct models to explore the ability of classifiers to distinguish between the HER2-negative, HER2-low, and HER2-high classes in different scenarios. The attention-based model was used to comprehend the decision-making process aimed at relevant tissue regions. RESULTS: Our results indicate that the effectiveness of classification models hinges on the consistency and dependability of assay-based tests for HER2, as the outcomes from these tests are utilized as the baseline truth for training our models. Through the use of explainable AI, we reveal histologic patterns associated with the HER2 subtypes. CONCLUSION: Our findings offer a demonstration of how deep learning technologies can be applied to identify HER2 subgroup statuses, potentially enriching the toolkit available for clinical decision-making in oncology.

Tracking SARS-CoV-2 variants of concern in wastewater: an assessment of nine computational tools using simulated genomic data.

Wastewater-based surveillance (WBS) is an important epidemiological and public health tool for tracking pathogens across the scale of a building, neighbourhood, city, or region. WBS gained widespread adoption globally during the SARS-CoV-2 pandemic for estimating community infection levels by qPCR. Sequencing pathogen genes or genomes from wastewater adds information about pathogen genetic diversity, which can be used to identify viral lineages (including variants of concern) that are circulating in a local population. Capturing the genetic diversity by WBS sequencing is not trivial, as wastewater samples often contain a diverse mixture of viral lineages with real mutations and sequencing errors, which must be deconvoluted computationally from short sequencing reads. In this study we assess nine different computational tools that have recently been developed to address this challenge. We simulated 100 wastewater sequence samples consisting of SARS-CoV-2 BA.1, BA.2, and Delta lineages, in various mixtures, as well as a Delta-Omicron recombinant and a synthetic 'novel' lineage. Most tools performed well in identifying the true lineages present and estimating their relative abundances and were generally robust to variation in sequencing depth and read length. While many tools identified lineages present down to 1 % frequency, results were more reliable above a 5 % threshold. The presence of an unknown synthetic lineage, which represents an unclassified SARS-CoV-2 lineage, increases the error in relative abundance estimates of other lineages, but the magnitude of this effect was small for most tools. The tools also varied in how they labelled novel synthetic lineages and recombinants. While our simulated dataset represents just one of many possible use cases for these methods, we hope it helps users understand potential sources of error or bias in wastewater sequencing analysis and to appreciate the commonalities and differences across methods.

First Transcriptome Analysis of Hepatoblastoma in Brazil: Unraveling the Pivotal Role of Noncoding RNAs and Metabolic Pathways.

Hepatoblastoma stands as the most prevalent liver cancer in the pediatric population. Characterized by a low mutational burden, chromosomal and epigenetic alterations are key drivers of its tumorigenesis. Transcriptome analysis is a powerful tool for unraveling the molecular intricacies of hepatoblastoma, shedding light on the effects of genetic and epigenetic changes on gene expression. In this study conducted in Brazilian patients, an in-depth whole transcriptome analysis was performed on 14 primary hepatoblastomas, compared to control liver tissues. The analysis unveiled 1,492 differentially expressed genes (1,031 upregulated and 461 downregulated), including 920 protein-coding genes (62%). Upregulated biological processes were linked to cell differentiation, signaling, morphogenesis, and development, involving known hepatoblastoma-associated genes (DLK1, MEG3, HDAC2, TET1, HMGA2, DKK1, DKK4), alongside with novel findings (GYNG4, CDH3, and TNFRSF19). Downregulated processes predominantly centered around oxidation and metabolism, affecting amines, nicotinamides, and lipids, featuring novel discoveries like the repression of SYT7, TTC36, THRSP, CCND1, GCK and CAMK2B. Two genes, which displayed a concordant pattern of DNA methylation alteration in their promoter regions and dysregulation in the transcriptome, were further validated by RT-qPCR: the upregulated TNFRSF19, a key gene in the embryonic development, and the repressed THRSP, connected to lipid metabolism. Furthermore, based on protein-protein interaction analysis, we identified genes holding central positions in the network, such as HDAC2, CCND1, GCK, and CAMK2B, among others, that emerged as prime candidates warranting functional validation in future studies. Notably, a significant dysregulation of non-coding RNAs (ncRNAs), predominantly upregulated transcripts, was observed, with 42% of the top 50 highly expressed genes being ncRNAs. An integrative miRNA-mRNA analysis revealed crucial biological processes associated with metabolism, oxidation reactions of lipids and carbohydrates, and methylation-dependent chromatin silencing. In particular, four upregulated miRNAs (miR-186, miR-214, miR-377, and miR-494) played a pivotal role in the network, potentially targeting multiple protein-coding transcripts, including CCND1 and CAMK2B. In summary, our transcriptome analysis highlighted disrupted embryonic development as well as metabolic pathways, particularly those involving lipids, emphasizing the emerging role of ncRNAs as epigenetic regulators in hepatoblastomas. These findings provide insights into the complexity of the hepatoblastoma transcriptome and identify potential targets for future therapeutic interventions.

Germline variants in early and late-onset Brazilian prostate cancer patients.

BACKGROUND: The median age for Prostate Cancer (PCa) diagnosis is 66 years, but 10% are diagnosed before 55 years. Studies on early-onset PCa remain both limited and controversial. This investigation sought to identify and characterize germline variants within Brazilian PCa patients classified as either early or later onset disease. METHODS: Peripheral blood DNA from 71 PCa patients: 18 younger (≤ 55 years) and 53 older (≥ 60 years) was used for Targeted DNA sequencing of 20 genes linked to DNA damage response, transcriptional regulation, cell cycle, and epigenetic control. Subsequent genetic variant identification was performed and variant functional impacts were analyzed with in silico prediction. RESULTS: A higher frequency of variants in the BRCA2 and KMT2C genes across both age groups. KMT2C has been linked to the epigenetic dysregulation observed during disease progression in PCa. We present the first instance of KMT2C mutation within the blood of Brazilian PCa patients. Furthermore, out of the recognized variants within the KMT2C gene, 7 were designated as deleterious. Thirteen deleterious variants were exclusively detected in the younger group, while the older group exhibited 37 variants. Within these findings, 4 novel variants emerged, including 1 designated as pathogenic. CONCLUSIONS: Our findings contribute to a deeper understanding of the genetic factors associated with PCa susceptibility in different age groups, especially among the Brazilian population. This is the first investigation to explore germline variants specifically in younger Brazilian PCa patients, with high relevance given the genetic diversity of the population in Brazil. Additionally, our work presents evidence of functionally deleterious germline variants within the KMT2C gene among Brazilian PCa patients. The identification of novel and functionally significant variants in the KMT2C gene emphasizes its potential role in PCa development and warrants further investigation.

Relating mutational signature exposures to clinical data in cancers via signeR 2.0.

BACKGROUND: Cancer is a collection of diseases caused by the deregulation of cell processes, which is triggered by somatic mutations. The search for patterns in somatic mutations, known as mutational signatures, is a growing field of study that has already become a useful tool in oncology. Several algorithms have been proposed to perform one or both the following two tasks: (1) de novo estimation of signatures and their exposures, (2) estimation of the exposures of each one of a set of pre-defined signatures. RESULTS: Our group developed signeR, a Bayesian approach to both of these tasks. Here we present a new version of the software, signeR 2.0, which extends the possibilities of previous analyses to explore the relation of signature exposures to other data of clinical relevance. signeR 2.0 includes a user-friendly interface developed using the R-Shiny framework and improvements in performance. This version allows the analysis of submitted data or public TCGA data, which is embedded in the package for easy access. CONCLUSION: signeR 2.0 is a valuable tool to generate and explore exposure data, both from de novo or fitting analyses and is an open-source R package available through the Bioconductor project at ( https://doi.org/10.18129/B9.bioc.signeR ).

Mature tertiary lymphoid structures are key niches of tumour-specific immune responses in pancreatic ductal adenocarcinomas.

OBJECTIVE: To better understand the immune microenvironment of pancreatic ductal adenocarcinomas (PDACs), here we explored the relevance of T and B cell compartmentalisation into tertiary lymphoid structures (TLSs) for the generation of local antitumour immunity. DESIGN: We characterised the functional states and spatial organisation of PDAC-infiltrating T and B cells using single-cell RNA sequencing (scRNA-seq), flow cytometry, multicolour immunofluorescence, gene expression profiling of microdissected TLSs, as well as in vitro assays. In addition, we performed a pan-cancer analysis of tumour-infiltrating T cells using scRNA-seq and sc T cell receptor sequencing datasets from eight cancer types. To evaluate the clinical relevance of our findings, we used PDAC bulk RNA-seq data from The Cancer Genome Atlas and the PRINCE chemoimmunotherapy trial. RESULTS: We found that a subset of PDACs harbours fully developed TLSs where B cells proliferate and differentiate into plasma cells. These mature TLSs also support T cell activity and are enriched with tumour-reactive T cells. Importantly, we showed that chronically activated, tumour-reactive T cells exposed to fibroblast-derived TGF-ß may act as TLS organisers by producing the B cell chemoattractant CXCL13. Identification of highly similar subsets of clonally expanded CXCL13 + tumour-infiltrating T cells across multiple cancer types further indicated a conserved link between tumour-antigen recognition and the allocation of B cells within sheltered hubs in the tumour microenvironment. Finally, we showed that the expression of a gene signature reflecting mature TLSs was enriched in pretreatment biopsies from PDAC patients with longer survival after receiving different chemoimmunotherapy regimens. CONCLUSION: We provided a framework for understanding the biological role of PDAC-associated TLSs and revealed their potential to guide the selection of patients for future immunotherapy trials.

Premature ovarian insufficiency is associated with global alterations in the regulatory landscape and gene expression in balanced X-autosome translocations.

BACKGROUND: Patients with balanced X-autosome translocations and premature ovarian insufficiency (POI) constitute an interesting paradigm to study the effect of chromosome repositioning. Their breakpoints are clustered within cytobands Xq13-Xq21, 80% of them in Xq21, and usually, no gene disruption can be associated with POI phenotype. As deletions within Xq21 do not cause POI, and since different breakpoints and translocations with different autosomes lead to this same gonadal phenotype, a "position effect" is hypothesized as a possible mechanism underlying POI pathogenesis. OBJECTIVE AND METHODS: To study the effect of the balanced X-autosome translocations that result in POI, we fine-mapped the breakpoints in six patients with POI and balanced X-autosome translocations and addressed gene expression and chromatin accessibility changes in four of them. RESULTS: We observed differential expression in 85 coding genes, associated with protein regulation, multicellular regulation, integrin signaling, and immune response pathways, and 120 differential peaks for the three interrogated histone marks, most of which were mapped in high-activity chromatin state regions. The integrative analysis between transcriptome and chromatin data pointed to 12 peaks mapped less than 2 Mb from 11 differentially expressed genes in genomic regions not related to the patients' chromosomal rearrangement, suggesting that translocations have broad effects on the chromatin structure. CONCLUSION: Since a wide impact on gene regulation was observed in patients, our results observed in this study support the hypothesis of position effect as a pathogenic mechanism for premature ovarian insufficiency associated with X-autosome translocations. This work emphasizes the relevance of chromatin changes in structural variation, since it advances our knowledge of the impact of perturbations in the regulatory landscape within interphase nuclei, resulting in the position effect pathogenicity.

Exome and Tissue-Associated Microbiota as Predictive Markers of Response to Neoadjuvant Treatment in Locally Advanced Rectal Cancer.

The clinical and pathological responses to multimodal neoadjuvant therapy in locally advanced rectal cancers (LARCs) remain unpredictable, and robust biomarkers are still lacking. Recent studies have shown that tumors present somatic molecular alterations related to better treatment response, and it is also clear that tumor-associated bacteria are modulators of chemotherapy and immunotherapy efficacy, therefore having implications for long-term survivorship and a good potential as the biomarkers of outcome. Here, we performed whole exome sequencing and 16S ribosomal RNA (rRNA) amplicon sequencing from 44 pre-treatment LARC biopsies from Argentinian and Brazilian patients, treated with neoadjuvant chemoradiotherapy or total neoadjuvant treatment, searching for predictive biomarkers of response (responders, n = 17; non-responders, n = 27). In general, the somatic landscape of LARC was not capable to predict a response; however, a significant enrichment in mutational signature SBS5 was observed in non-responders (p = 0.0021), as well as the co-occurrence of APC and FAT4 mutations (p < 0.05). Microbiota studies revealed a similar alpha and beta diversity of bacteria between response groups. Yet, the linear discriminant analysis (LDA) of effect size indicated an enrichment of Hungatella, Flavonifractor, and Methanosphaera (LDA score ≥3) in the pre-treatment biopsies of responders, while non-responders had a higher abundance of Enhydrobacter, Paraprevotella (LDA score ≥3) and Finegoldia (LDA score ≥4). Altogether, the evaluation of these biomarkers in pre-treatment biopsies could eventually predict a neoadjuvant treatment response, while in post-treatment samples, it could help in guiding non-operative treatment strategies.

A mixture model for determining SARS-Cov-2 variant composition in pooled samples.

MOTIVATION: Despite of the fast development of highly effective vaccines to control the current COVID-19 pandemics, the unequal distribution and availability of these vaccines worldwide and the number of people infected in the world lead to the continuous emergence of Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) variants of concern. Therefore, it is likely that real-time genomic surveillance will be continuously needed as an unceasing monitoring tool, necessary to follow the spread of the disease and the evolution of the virus. In this context, new genomic variants of SARS-CoV-2, including variants refractory to current vaccines, makes genomic surveillance programs tools of utmost importance. Nevertheless, the lack of appropriate analytical tools to quickly and effectively access the viral composition in meta-transcriptomic sequencing data, including environmental surveillance, represent possible challenges that may impact the fast adoption of this approach to mitigate the spread and transmission of viruses. RESULTS: We propose a statistical model for the estimation of the relative frequencies of SARS-CoV-2 variants in pooled samples. This model is built by considering a previously defined selection of genomic polymorphisms that characterize SARS-CoV-2 variants. The methods described here support both raw sequencing reads for polymorphisms-based markers calling and predefined markers in the variant call format. Results obtained using simulated data show that our method is quite effective in recovering the correct variant proportions. Further, results obtained by considering longitudinal data from wastewater samples of two locations in Switzerland agree well with those describing the epidemiological evolution of COVID-19 variants in clinical samples of these locations. Our results show that the described method can be a valuable tool for tracking the proportions of SARS-CoV-2 variants in complex mixtures such as waste water and environmental samples. AVAILABILITY AND IMPLEMENTATION: http://github.com/rvalieris/LCS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Evaluation of Bacteria and Fungi DNA Abundance in Human Tissues.

Whereas targeted and shotgun sequencing approaches are both powerful in allowing the study of tissue-associated microbiota, the human: microorganism abundance ratios in tissues of interest will ultimately determine the most suitable sequencing approach. In addition, it is possible that the knowledge of the relative abundance of bacteria and fungi during a treatment course or in pathological conditions can be relevant in many medical conditions. Here, we present a qPCR-targeted approach to determine the absolute and relative amounts of bacteria and fungi and demonstrate their relative DNA abundance in nine different human tissue types for a total of 87 samples. In these tissues, fungi genomes are more abundant in stool and skin samples but have much lower levels in other tissues. Bacteria genomes prevail in stool, skin, oral swabs, saliva, and gastric fluids. These findings were confirmed by shotgun sequencing for stool and gastric fluids. This approach may contribute to a more comprehensive view of the human microbiota in targeted studies for assessing the abundance levels of microorganisms during disease treatment/progression and to indicate the most informative methods for studying microbial composition (shotgun versus targeted sequencing) for various samples types.

Latency-associated DNA methylation patterns among HIV-1 infected individuals with distinct disease progression courses or antiretroviral virologic response.

DNA methylation is one of the epigenetic modifications that configures gene transcription programs. This study describes the DNA methylation profile of HIV-infected individuals with distinct characteristics related to natural and artificial viremia control. Sheared DNA from circulating mononuclear cells was subjected to target enrichment bisulfite sequencing designed to cover CpG-rich genomic regions. Gene expression was assessed through RNA-seq. Hypermethylation in virologic responders was highly distributed closer to Transcription Start Sites (p-value = 0.03). Hyper and hypomethylation levels within TSS adjacencies varied according to disease progression status (Kruskal-Wallis, p < 0.001), and specific differentially methylated regions associated genes were identified for each group. The lower the promoter methylation, the higher the gene expression in subjects undergoing virologic failure (R = - 0.82, p = 0.00068). Among the inversely correlated genes, those supporting glycolysis and its related pathways were hypomethylated and up-regulated in virologic failures. Disease progression heterogeneity was associated with distinct DNA methylation patterns in terms of rates and distribution. Methylation was associated with the expression of genes sustaining intracellular glucose metabolism in subjects undergoing antiretroviral virologic failure. Our findings highlight that DNA methylation is associated with latency, disease progression, and fundamental cellular processes.

Mutational Signatures Driven by Epigenetic Determinants Enable the Stratification of Patients with Gastric Cancer for Therapeutic Intervention.

DNA mismatch repair deficiency (dMMR) is associated with the microsatellite instability (MSI) phenotype and leads to increased mutation load, which in turn may impact anti-tumor immune responses and treatment effectiveness. Various mutational signatures directly linked to dMMR have been described for primary cancers. To investigate which mutational signatures are associated with prognosis in gastric cancer, we performed a de novo extraction of mutational signatures in a cohort of 787 patients. We detected three dMMR-related signatures, one of which clearly discriminates tumors with MLH1 gene silencing caused by promoter hypermethylation (area under the curve = 98%). We then demonstrated that samples with the highest exposure of this signature share features related to better prognosis, encompassing clinical and molecular aspects and altered immune infiltrate composition. Overall, the assessment of the prognostic value and of the impact of modifications in MMR-related genes on shaping specific dMMR mutational signatures provides evidence that classification based on mutational signature exposure enables prognosis stratification.

Caracterização computacional de neoantígenos e sua implicação prognóstica / Computational characterization of neoantigens and their prognostic implications

Mutações somáticas não sinônimas podem iniciar a tumorigênese e, também, uma resposta citotóxica antitumoral. Com o desenvolvimento das tecnologias de sequenciamento, tornou-se possível identificar as mutações em todos os genes humanos e, consequentemente, as variantes que induzem uma resposta imune (neoantígenos), representando uma oportunidade para pacientes que possam se beneficiar de imunoterapias, mas também um desafio com a necessidade de várias camadas de informações e a integração computacional de vários tipos de dados. Neste trabalho, foi desenvolvido o pipeline de identificação de neoantígeno neo2P, o qual realiza a integração completa de todos os passos necessários para a detecção e neoantígenos e apresentou uma eficiência computacional superior de até seis vezes em comparação com outro método. Além disso, foi proposto um score para priorizaração das mutações somáticas a partir da distribuição dos níveis da expressão gênica de 9.679 pacientes de 32 projetos do TCGA, o qual apresentou um poder de discriminação (AUC) próximo ou superior a 0.7 na maioria das coortes avaliadas. O neo2P foi aplicado em um conjunto de dados de pacientes com melanoma e foram identificados aspectos adicionais da relação de neoantígenos e aspectos imunes, como a expressão de alguns genes marcadores que podem estar relacionados com a resposta ao tratamento. Adicionalmente, a carga de neoantígenos detectados pelo neo2P estratificou, de maneira significativa, pacientes respondedores (R) e não respondedores (NR) quando comparado com o marcador TMB

Somatic non-synonymous mutations can initiate tumorigenesis and, conversely, anti-tumor cytotoxic T cell (CTL) responses. With the development of next-generation sequencing, it has become feasible to detect mutation-derived neoantigens within exome and thereby predict potential neoantigens, which represents an opportunity to patients that may be treated with immunotherapies, but also a challenge due to multiple layers of information and a computational integration of several types of data. In this work, it was developed a neoantigen identification pipeline called neo2P, which integrates all the necessary steps involved for neoantigen detection and presented a six-times superior computational efficiency compared to another method. In addition, a score was proposed to prioritize somatic mutations based on the distribution of gene expression levels in 9,679 patients from 32 TCGA projects, which showed a stratification ability (AUC) close to or greater than 0.7 in most evaluated cohorts.neo2P was applied to a dataset of patients with melanoma and additional aspects of the relationship between neoantigens and immune aspects were identified, such as the expression of some marker genes that may be related to the treatment response. Additionally, the neoantigen load detected by neo2P significantly stratified responders (R) and non-responders (NR) patients when compared to the TMB marker

Genomics and epidemiology for gastric adenocarcinomas (GE4GAC): a Brazilian initiative to study gastric cancer

Gastric cancer (GC) is the fifth most common type of cancer worldwide with high incidences in Asia, Central, and South American countries. This patchy distribution means that GC studies are neglected by large research centers from developed countries. The need for further understanding of this complex disease, including the local importance of epidemiological factors and the rich ancestral admixture found in Brazil, stimulated the implementation of the GE4GAC project. GE4GAC aims to embrace epidemiological, clinical, molecular and microbiological data from Brazilian controls and patients with malignant and pre-malignant gastric disease. In this letter, we summarize the main goals of the project, including subject and sample accrual and current findings