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      Comprehensive omic characterization of breast cancer in Mexican-Hispanic women

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          Abstract

          Breast cancer is a heterogeneous pathology, but the genomic basis of its variability remains poorly understood in populations other than Caucasians. Here, through DNA and RNA portraits we explored the molecular features of breast cancers in a set of Hispanic-Mexican (HM) women and compared them to public multi-ancestry datasets. HM patients present an earlier onset of the disease, particularly in aggressive clinical subtypes, compared to non-Hispanic women. The age-related COSMIC signature 1 was more frequent in HM women than in those from other ancestries. We found the AKT1 E17K hotspot mutation in 8% of the HM women and identify the AKT1/PIK3CA axis as a potentially druggable target. Also, HM luminal breast tumors present an enhanced immunogenic phenotype compared to Asiatic and Caucasian tumors. This study is an initial effort to include patients from Hispanic populations in the research of breast cancer etiology and biology to further understand breast cancer disparities.

          Abstract

          Cancers in different populations have been shown to be genetically distinct. Here, the authors sequence breast cancers from Mexican-Hispanic patients and find that these patients have a higher percentage of Akt1 mutations compared to Caucasian and Asian populations, suggesting these are clinically actionable.

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          Most cited references 68

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          Fast and accurate short read alignment with Burrows–Wheeler transform

          Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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            limma powers differential expression analyses for RNA-sequencing and microarray studies

            limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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              The Protein Data Bank.

              The Protein Data Bank (PDB; http://www.rcsb.org/pdb/ ) is the single worldwide archive of structural data of biological macromolecules. This paper describes the goals of the PDB, the systems in place for data deposition and access, how to obtain further information, and near-term plans for the future development of the resource.
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                Author and article information

                Contributors
                sromero@iibiomedicas.unam.mx
                ahidalgo@inmegen.gob.mx
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                14 April 2021
                14 April 2021
                2021
                : 12
                Affiliations
                [1 ]GRID grid.9486.3, ISNI 0000 0001 2159 0001, Departamento de Medicina Genómica y Toxicología Ambiental, Instituto de Investigaciones Biomédicas, , Universidad Nacional Autónoma de México, ; Mexico City, Mexico
                [2 ]GRID grid.452651.1, ISNI 0000 0004 0627 7633, Cancer Genomics Laboratory, , National Institute of Genomic Medicine, ; México City, Mexico
                [3 ]GRID grid.416850.e, ISNI 0000 0001 0698 4037, Biochemistry Department, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, ; Mexico City, Mexico
                [4 ]GRID grid.419179.3, ISNI 0000 0000 8515 3604, Laboratorio de Biología Computacional, Instituto Nacional de Enfermedades Respiratorias Ismael Cosío Villegas, ; Mexico City, Mexico
                [5 ]GRID grid.416850.e, ISNI 0000 0001 0698 4037, Genomics Laboratory, Red de Apoyo a la Investigación, Universidad Nacional Autónoma de México-Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, ; México City, México
                [6 ]GRID grid.9486.3, ISNI 0000 0001 2159 0001, Programa de Doctorado en Ciencias Biomédicas, , Universidad Nacional Autónoma de México (UNAM), ; México City, México
                [7 ]Instituto de Enfermedades de la Mama FUCAM, Mexico City, México
                [8 ]GRID grid.452651.1, ISNI 0000 0004 0627 7633, Computational Genomics Laboratory, , National Institute of Genomic Medicine, ; Mexico City, Mexico
                Article
                22478
                10.1038/s41467-021-22478-5
                8046804
                33854067
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                Funding
                Funded by: FundRef https://doi.org/10.13039/501100003141, Consejo Nacional de Ciencia y Tecnología (National Council of Science and Technology, Mexico);
                Award ID: 1285
                Award Recipient :
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                © The Author(s) 2021

                Uncategorized

                molecular biology, computational biology and bioinformatics, cancer

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