Short Abstract: Monica Marie Arroyo obtuvo un bachillerato en ciencias en bioquímica con concentración menor en química de la Universidad de Maine, Orono, y un doctorado en bioquímica, biología molecular y biofísica de la Universidad de Minnesota, Minneapolis. Luego hizo un postdoctorado en biquímica estructural en St Jude Children’s Research Hospital en Memphis. Tiene un Diplomado en Fundamentos de Bioética (144 horas) del Centro de Investigación Social Avanzada en Querétaro, México, así como varias certificaciones en educación virtual de la Universidad de California, Irvine, y la Universidad Central de Florida. Miembro de las prestigiosas sociedades de Honor Phi Kappa Phi y Sigma Xi, actualmente es catedrática asociada en el departamento de química de la PUCPR, Ponce. Del 2015-2019 hizo estancias de investigación en el laboratorio del Dr. Javier de Las Rivas en el Centro de Investigación del Cáncer en la Universidad de Salamanca, España. Monica Marie Arroyo earned a Bachelor of Science in Biochemistry with a minor in Chemistry from the University of Maine, Orono, and a PhD in Biochemistry, Molecular Biology, and Biophysics from the University of Minnesota, Minneapolis. She did a postdoc in Structural Biology at St Jude Children's Research Hospital in Memphis. She has a Diploma in Fundamentals of Bioethics (144 hours) from the Center for Advanced Social Research in Querétaro, Mexico, as well as several certifications in virtual education from the University of California, Irvine, and the University of Central Florida. Member of the prestigious Honor Societies Phi Kappa Phi and Sigma Xi, she is currently an associate professor in Chemistry at Pontifical Catholic University of Puerto Rico, Ponce. From 2015-2019 she did research stays in the laboratory of Dr. Javier de Las Rivas at the Cancer Research Center at the University of Salamanca, Spain.

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Short Abstract: Claudia Carranza has a PhD in Genetics and Master in Bioethics. She has obtained many awards, as TWAS price for Young Scientists from developing Countries and a Developing Country travel award for presenting a research work at the Annual American Meeting of Human Genetics at Houston 2019. Her research focus in Cancer genetics, hereditary cancer syndromes and genetics of human diseases. She is Pioneer in the Human Genetic Research area in Guatemala and She started the first postgraduate education programs in the Country. She has published twelve international papers and participated with nineteen communications at international meetings.

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Short Abstract: Glioblastoma (GBM) is the most aggressive and common brain cancer in adults with the lowest life expectancy. The current neuro-oncology practice has incorporated as biomarkers to guide diagnosis and design treatment genes involved in key molecular events that drive GBM tumorigenesis. This study summarizes findings describing the significant heterogeneity of GBM at the transcriptional and genomic levels, emphasizing eighteen driver genes with clinical relevance. A pattern fitting the stem cell model for GBM ontogenesis, with an up-regulation profile for MGMT and down-regulation for ATRX, H3F3A, TP53, and EGFR in the mesenchymal subtype, was identified. We also detected overexpression for EGFR, NES, VIM, and TP53 in the classical subtype and for MKi67 and OLIG2 genes in the proneural subtype. A unique distribution of somatic mutations was found for the young and adult population, particularly for genes related to DNA repair and chromatin remodeling, highlighting ATRX, MGMT, and IDH1. Our results revealed that highly lesioned genes undergo differential regulation with particular biological pathways for young patients with dead vital status. This meta-analysis will help delineate future strategies related to the use of those molecular markers for clinical decision-making in the medical routine.

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Short Abstract: Breast cancer is a complex, heterogeneous disease at the phenotypic and molecular level. In particular, the transcriptional regulatory programs are known to be significantly affected and such transcriptional alterations are able to capture some of the heterogeneity of the disease, leading to the emergence of breast cancer molecular subtypes. Recently, it has ben found that network biology approaches to decipher such abnormal gene regulation programs, for instance by means of gene co-expression networks have been able to recapitulate the differences between breast cancer subtypes providing elements to further understand their functional origins and consequences. Network biology approaches may be extended to include other co-expression patterns, like those found between genes and non-coding transcripts such as microRNAs (miRs). As is known, miRs play relevant roles in the establishment of normal and anomalous transcription processes. Commodore miRs (cdre-miRs) have been defined as microRNAs that, based on their connectivity and redundancy in co-expression networks, are potential control elements of biological functions. In this work, we reconstructed miR-gene co-expression networks for each breast cancer molecular subtype from high throughput data in 424 samples from the Cancer Genome Atlas consortium. We identified Commodore miRs in three out of four molecular subtypes. We found that in each subtype, each cdremiR was linked to a different set of associated genes, as well as a different set of associated biological functions. We used a systematic literature validation strategy, and identified that the associated biological functions to these cdremiRs are hallmarks of cancer such as angiogenesis, cell adhesion, cell cycle and regulation of apoptosis. The relevance of such cdre-miRs as actionable molecular targets in breast cancer is still to be determined from functional studies.

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Keywords: pharmacogenomics drug cancer cancer-drug drug-target transcriptomics gene network bipartite network

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Short Abstract: Pharmacogenomics is a growing field that studies the use and effect of drugs together with the genomic information of individuals. The study of the genome-wide activity of the genes (i.e. the expression of the genes or their genetic variation) may be related to differences in the effects of drugs. This information, integrated into clinical trials and drug development, could help to understand the behaviour and/or results of the action of these drugs in complex diseases. Cancer is a complex disease with hundreds of clinically approved drugs. Pharmacogenomics allows better mapping of the targets of cancer drugs and potential interacting secondary agents, but there are many drugs whose mechanisms of action have not been fully deciphered. The study of these drug-targets can lead to possible new treatments or an improvement of existing ones. This study comprised a large-scale screening method to find associations of many chemical substances and human genes using transcriptomic profiling. It’s focused on a compilation of two types of drugs: approved by the Food and Drug Administration (FDA) and not approved by the FDA (No-FDA). The second one includes more than two thousand chemical compounds. These compounds are related to the transcriptomic profiles of 60 human cell lines, for which gene expression profiles are also available. The standard expression of each gene versus the standardized activity of each biological compound was used to calculate pairwise correlations for all available gene-drug combinations. With these data, global bipartite networks were built to further study the interactions between compounds and their targets, to better unravel their mechanisms of action. In addition, all these data were included in GEDA, an online web-tool that allows the user to navigate the networks obtained. Furthermore the user can access the used data and review the results obtained in this work. The results obtained provide information on the complex action of the studied compounds, presenting a relational and integrative view to address the different biomolecular effects that each drug can produce.

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Keywords: cancer mutations breast cancer metastasis exome whole exome sequencing gene mutation polygenic signature machine learning

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Short Abstract: Whole Exomes Sequences (WES) from more than six thousand Breast Cancer Primary Tumors were used to find a polygenic mutation prognosis signature specific of tumors that are likely to develop metastasis. Samples were divided in two groups: primary tumors that did not develop metastasis in the first five years after biopsy (negative samples: 6334), and primary tumors that, while being metastasis-free at the time of biopsy, they developed metastasis in that time frame (positive metastatic samples: 382). A train set was used to find mutations exclusive from positives, and these were ranked based on frequency and pathogenicity-scores. Then, on a balanced validation set, supervised k-means-clustering was used to exclude the 20% samples from this set that were most difficult to separate. Clustering was based on the mutations selected on the train set. On the remaining validation samples, step-wise regression was carried to output a refined list of the mutations selected. The train/validation split was repeated 10,000 times and the 600 most frequent mutations defined the final signature. With this mutation signature, we were able to correctly classify 84% of the samples from a test set consisting of 100 samples from each class.

Keywords: Cancer Lactate Monocarboxylate transporters AR-C155858

Poster:

Short Abstract: Cancer is the second leading cause of death worldwide and corresponds to the uncontrolled development of abnormal cells that infiltrate and destroy normal tissue. To support their proliferation, tumor cells depend on lactate-based metabolism. Lactate is transported over membranes by monocarboxylate transporters (MCTs), which in humans are divided into 14 types. Of these, MCTs 1 and 4 are primarily responsible for the transport of lactate over the plasma membrane rendering them potential targets for the inhibition of tumor development. Despite the interest in generating MCT antagonistic drugs, few selective inhibitors for these transporters have been developed so far. This is due to the transmembrane nature of these proteins, that complicate the determination of their three-dimensional structures and make it difficult to obtain relevant information about the residues that mediate their transport function. As of now, there are no crystal structures available for MCT 1 and 4, a situation that has also made difficult a more detailed study of this type of transporter. Here, we present comparative protein structure models of the three-dimensional structures of MCT1 and MCT4. The models were built with the MODELLER software and validated with the ProSa-web and Saves v5.0 servers. The template used for construction was the first structure of a human MCT transporter published recently thanks to an electronic cryo-microscopy technique, a human MCT2 with high sequence identity to MCT 1 and 4 (59% and 46%, respectively). Next, we performed molecular docking of AR-C155858, a commercial MCT1-selective inhibitor. Our results suggest that AR-C155858 binds to residues present in the 7-10 transmembrane segments of MCT1 and not of MCT4, due to the presence of non-conserved residues in the inhibitor binding site of each transporter. These structural models can provide a starting point for the structural and functional analysis of human MCTs and the design of potential inhibitors of the activity of these proteins.