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.Fil: Takenaka, Isabella Kuniko T. M.. No especifíca;Fil: Bartelli, Thais F.. No especifíca;Fil: Defelicibus, Alexandre. No especifíca;Fil: Sendoya, Juan Martín. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Golubicki, Mariano. Gobierno de la Ciudad de Buenos Aires. Hospital de Gastroenterología "Dr. Carlos B. Udaondo"; ArgentinaFil: Robbio, Juan. No especifíca;Fil: Serpa, Marianna S.. No especifíca;Fil: Branco, Gabriela P.. No especifíca;Fil: Santos, Luana B. C.. No especifíca;Fil: Claro, Laura C. L.. No especifíca;Fil: Oliveira dos Santos, Gabriel. No especifíca;Fil: Kupper, Bruna E. C.. No especifíca;Fil: da Silva, Israel T.. No especifíca;Fil: Llera, Andrea Sabina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: de Mello, Celso A. L.. No especifíca;Fil: Riechelmann, Rachel P.. No especifíca;Fil: Dias Neto, Emmanuel. Universidade de Sao Paulo; BrasilFil: Iseas, Soledad. Gobierno de la Ciudad de Buenos Aires. Hospital de Gastroenterología "Dr. Carlos B. Udaondo"; ArgentinaFil: Aguiar, Samuel. No especifíca;Fil: Nunes, Diana Noronha. No especifíca

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

An analysis and evaluation of the WeFold collaborative for protein structure prediction and its pipelines in CASP11 and CASP12

Every two years groups worldwide participate in the Critical Assessment of Protein Structure Prediction (CASP) experiment to blindly test the strengths and weaknesses of their computational methods. CASP has significantly advanced the field but many hurdles still remain, which may require new ideas and collaborations. In 2012 a web-based effort called WeFold, was initiated to promote collaboration within the CASP community and attract researchers from other fields to contribute new ideas to CASP. Members of the WeFold coopetition (cooperation and competition) participated in CASP as individual teams, but also shared components of their methods to create hybrid pipelines and actively contributed to this effort. We assert that the scale and diversity of integrative prediction pipelines could not have been achieved by any individual lab or even by any collaboration among a few partners. The models contributed by the participating groups and generated by the pipelines are publicly available at the WeFold website providing a wealth of data that remains to be tapped. Here, we analyze the results of the 2014 and 2016 pipelines showing improvements according to the CASP assessment as well as areas that require further adjustments and research

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

Abstract 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

Koala: system for integration of methods for protein structures prediction and analysis

A Biologia Computacional tem desenvolvido algoritmos aplicados a problemas relevantes da Biologia. Um desses problemas é a Protein Structure Prediction (PSP). Vários métodos têm sido desenvolvidos na literatura para lidar com esse problema. Porém a reprodução de resultados e a comparação dos mesmos não têm sido uma tarefa fácil. Nesse sentido, o Critical Assessment of protein Structure Prediction (CASP), busca entre seus objetivos, realizar tais comparações. Além disso, os sistemas desenvolvidos para esse problema em geral não possuem interface amigável, não favorecendo o uso por não especialistas da computação. Buscando reduzir essas dificuldades, este trabalho propões o Koala, um sistema baseado em uma plataforma web, que integra vários métodos de predição e análises de estruturas de proteínas, possibilitando a execução de experimentos complexos com o uso de fluxos de trabalhos. Os métodos de predição disponíveis podem ser integrados para a realização de análises dos resultados, usando as métricas RMSD, GDT-TS ou TM-Score. Além disso, o método Sort by front dominance (baseado no critério de optimalidade de Pareto), proposto nesse trabalho, consegue avaliar predições sem uma estrutura de referência. Os resultados obtidos, usando proteínas alvo de artigos recentes e do CASP11, indicam que o Koala tem capacidade de realizar um conjunto relativamente grande de experimentos estruturados, beneficiando a determinação de melhores estruturas de proteínas, bem como o desenvolvimento de novas abordagens para predição e análise por meio de fluxos de trabalho.Computational Biology has developed algorithms applied to relevant problems from Biology. One of these probems is Protein Structure Prediction (PSP). Several methods have been developed on the liteture to deal with this problem. However, the reproduction of results and the comparison of the methods have not been an easy task. Accordingly, the Critical Assessment of protein Structure Prediction (CASP), has among his objectives, perform these comparisons. Besides, the developed systems for this problem have low usability, not benefiting the investigation of various methods by non experts. In order to minimize those difficulties, this project proposes Koala, a web-based system that integrates several algorithms applied to PSP and analysis, allowing the execution of complex experiments by using workflows. The prediction methods can be integrated to perform some analysis of the results, by using the RMSD, GDT-TS and TM-Score metrics. Moreover, the Sort by front dominance method (based on the criterion of Pareto optimalidad), proposed on this work, can evaluate predictions with no reference structure. The results obtained, using target proteins from recent articles and CASP11, indicate that Koala has the capability to execute a relatively large set of organized experiments, benefiting determining of better protein structures, as well as the development of new approaches for prediction and analysis through workflows

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

Abstract 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 )

Optimizing computational resource management for the scientific gateways ecosystems based on the service‐oriented paradigm

Science Gateways provide portals for experiments execution, regardless of the users' computational background. Nowadays its construction and performance need enhancement in terms of resource provision and task scheduling. We present the Modular Distributed Architecture to support the Protein Structure Prediction (MDAPSP), a Service‐Oriented Architecture for management and construction of Science Gateways, with resource provisioning on a heterogeneous environment. The Decision Maker, central module of MDAPSP, defines the best computational environment according to experiment parameters. The proof of concept for MDAPSP is presented in WorkflowSim, with two novel schedulers. Our results demonstrate good Quality of Service (QoS), capable of correctly distributing the workload, fair response times, providing load balance, and overall system improvement. The study case relies on PSP algorithms and the Galaxy framework, with monitoring experiments to show the bottlenecks and critical aspects.Conselho Nacional de Desenvolvimento Científico e Tecnológico. Grant Number: 165009/2015-2 Fundação de Amparo à Pesquisa do Estado de São Paulo (at CEMEAI). Grant Number: 2013/07375-

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

Abstract 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

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

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