Leandro C. Hermida
National Institutes of Health
Network
Latest external collaboration on country level. Dive into details by clicking on the dots.
Publication
Featured researches published by Leandro C. Hermida.
Cell | 2007
Pablo Landgraf; Mirabela Rusu; Robert L. Sheridan; Alain Sewer; Nicola Iovino; Alexei A. Aravin; Sébastien Pfeffer; Amanda Rice; Alice O. Kamphorst; Markus Landthaler; Carolina Lin; Nicholas D. Socci; Leandro C. Hermida; Valerio Fulci; Sabina Chiaretti; Robin Foà; Julia Schliwka; Uta Fuchs; Astrid Novosel; Roman Ulrich Müller; Bernhard Schermer; Ute Bissels; Jason M. Inman; Quang Phan; Minchen Chien; David B. Weir; Ruchi Choksi; Gabriella De Vita; Daniela Frezzetti; Hans Ingo Trompeter
MicroRNAs (miRNAs) are small noncoding regulatory RNAs that reduce stability and/or translation of fully or partially sequence-complementary target mRNAs. In order to identify miRNAs and to assess their expression patterns, we sequenced over 250 small RNA libraries from 26 different organ systems and cell types of human and rodents that were enriched in neuronal as well as normal and malignant hematopoietic cells and tissues. We present expression profiles derived from clone count data and provide computational tools for their analysis. Unexpectedly, a relatively small set of miRNAs, many of which are ubiquitously expressed, account for most of the differences in miRNA profiles between cell lineages and tissues. This broad survey also provides detailed and accurate information about mature sequences, precursors, genome locations, maturation processes, inferred transcriptional units, and conservation patterns. We also propose a subclassification scheme for miRNAs for assisting future experimental and computational functional analyses.
Cancer Research | 2016
Jason E. Farrar; Heather L. Schuback; Rhonda E. Ries; Daniel Wai; Oliver A. Hampton; Lisa R. Trevino; Todd A. Alonzo; Jaime M. Guidry Auvil; Tanja M. Davidsen; Patee Gesuwan; Leandro C. Hermida; Donna M. Muzny; Ninad Dewal; Navin Rustagi; Lora Lewis; Alan S. Gamis; David A. Wheeler; Malcolm A. Smith; Daniela S. Gerhard; Soheil Meshinchi
The genomic and clinical information used to develop and implement therapeutic approaches for acute myelogenous leukemia (AML) originated primarily from adult patients and has been generalized to patients with pediatric AML. However, age-specific molecular alterations are becoming more evident and may signify the need to age-stratify treatment regimens. The NCI/COG TARGET-AML initiative used whole exome capture sequencing (WXS) to interrogate the genomic landscape of matched trios representing specimens collected upon diagnosis, remission, and relapse from 20 cases of de novo childhood AML. One hundred forty-five somatic variants at diagnosis (median 6 mutations/patient) and 149 variants at relapse (median 6.5 mutations) were identified and verified by orthogonal methodologies. Recurrent somatic variants [in (greater than or equal to) 2 patients] were identified for 10 genes (FLT3, NRAS, PTPN11, WT1, TET2, DHX15, DHX30, KIT, ETV6, KRAS), with variable persistence at relapse. The variant allele fraction (VAF), used to measure the prevalence of somatic mutations, varied widely at diagnosis. Mutations that persisted from diagnosis to relapse had a significantly higher diagnostic VAF compared with those that resolved at relapse (median VAF 0.43 vs. 0.24, P < 0.001). Further analysis revealed that 90% of the diagnostic variants with VAF >0.4 persisted to relapse compared with 28% with VAF <0.2 (P < 0.001). This study demonstrates significant variability in the mutational profile and clonal evolution of pediatric AML from diagnosis to relapse. Furthermore, mutations with high VAF at diagnosis, representing variants shared across a leukemic clonal structure, may constrain the genomic landscape at relapse and help to define key pathways for therapeutic targeting. Cancer Res; 76(8); 2197-205. ©2016 AACR.
BMC Genomics | 2004
Thomas Loop; Ronny Leemans; Urs Stiefel; Leandro C. Hermida; Boris Egger; Fukang Xie; Michael Primig; Ulrich Certa; Karl-Friedrich Fischbach; Heinrich Reichert; Frank Hirth
BackgroundMutations and gene expression alterations in brain tumors have been extensively investigated, however the causes of brain tumorigenesis are largely unknown. Animal models are necessary to correlate altered transcriptional activity and tumor phenotype and to better understand how these alterations cause malignant growth. In order to gain insights into the in vivo transcriptional activity associated with a brain tumor, we carried out genome-wide microarray expression analyses of an adult brain tumor in Drosophila caused by homozygous mutation in the tumor suppressor gene brain tumor (brat).ResultsTwo independent genome-wide gene expression studies using two different oligonucleotide microarray platforms were used to compare the transcriptome of adult wildtype flies with mutants displaying the adult bratk06028mutant brain tumor. Cross-validation and stringent statistical criteria identified a core transcriptional signature of bratk06028neoplastic tissue. We find significant expression level changes for 321 annotated genes associated with the adult neoplastic bratk06028tissue indicating elevated and aberrant metabolic and cell cycle activity, upregulation of the basal transcriptional machinery, as well as elevated and aberrant activity of ribosome synthesis and translation control. One fifth of these genes show homology to known mammalian genes involved in cancer formation.ConclusionOur results identify for the first time the genome-wide transcriptional alterations associated with an adult brain tumor in Drosophila and reveal insights into the possible mechanisms of tumor formation caused by homozygous mutation of the translational repressor brat.
Nature Genetics | 2017
Samantha Gadd; Vicki Huff; Amy L. Walz; Ariadne H. A. G. Ooms; Amy E. Armstrong; Daniela S. Gerhard; Malcolm A. Smith; Jaime M. Guidry Auvil; Daoud Meerzaman; Qing Rong Chen; Chih Hao Hsu; Chunhua Yan; Cu Nguyen; Ying Hu; Leandro C. Hermida; Tanja M. Davidsen; Patee Gesuwan; Yussanne Ma; Zusheng Zong; Andrew J. Mungall; Richard A. Moore; Marco A. Marra; Jeffrey S. Dome; Charles G. Mullighan; Jing Ma; David A. Wheeler; Oliver A. Hampton; Nicole Ross; Julie M. Gastier-Foster; Stefan T. Arold
We performed genome-wide sequencing and analyzed mRNA and miRNA expression, DNA copy number, and DNA methylation in 117 Wilms tumors, followed by targeted sequencing of 651 Wilms tumors. In addition to genes previously implicated in Wilms tumors (WT1, CTNNB1, AMER1, DROSHA, DGCR8, XPO5, DICER1, SIX1, SIX2, MLLT1, MYCN, and TP53), we identified mutations in genes not previously recognized as recurrently involved in Wilms tumors, the most frequent being BCOR, BCORL1, NONO, MAX, COL6A3, ASXL1, MAP3K4, and ARID1A. DNA copy number changes resulted in recurrent 1q gain, MYCN amplification, LIN28B gain, and MIRLET7A loss. Unexpected germline variants involved PALB2 and CHEK2. Integrated analyses support two major classes of genetic changes that preserve the progenitor state and/or interrupt normal development.
Nucleic Acids Research | 2004
Leandro C. Hermida; Sophie Brachat; Sylvia Voegeli; Peter Philippsen; Michael Primig
The Ashbya Genome Database (AGD) is a comprehensive online source of information covering genes from the filamentous fungus Ashbya gossypii. The database content is based upon comparative genome annotation between A.gossypii and the closely related budding yeast Saccharomyces cerevisiae taking both sequence similarity and synteny (conserved order and orientation) into account. Release 2 of AGD contains 4718 protein-encoding loci located across seven chromosomes. Information can be retrieved using systematic or standard locus names from A.gossypii as well as budding and fission yeast. Approximately 90% of the genes in the genome of A.gossypii are homologous and syntenic to loci of budding yeast. Therefore, AGD is a useful tool not only for the various yeast communities in general but also for biologists who are interested in evolutionary aspects of genome research and comparative genome annotation. The database provides scientists with a convenient graphical user interface that includes various locus search and genome browsing options, data download and export functionalities and numerous reciprocal links to external databases including SGD, MIPS, GeneDB, KEGG, GermOnline and Swiss-Prot/TrEMBL. AGD is accessible at http://agd.unibas.ch/.
Nature Medicine | 2017
Hamid Bolouri; Jason E. Farrar; Timothy J. Triche; Rhonda E. Ries; Emilia L. Lim; Todd A. Alonzo; Yussanne Ma; Richard G. Moore; Andrew J. Mungall; Marco A. Marra; Jinghui Zhang; Xiaotu Ma; Yu Liu; Yanling Liu; Jaime M. Guidry Auvil; Tanja M. Davidsen; Patee Gesuwan; Leandro C. Hermida; Bodour Salhia; Stephen Capone; Giridharan Ramsingh; Christian M. Zwaan; Sanne Noort; Stephen R. Piccolo; E. Anders Kolb; Alan S. Gamis; Malcolm A. Smith; Daniela S. Gerhard; Soheil Meshinchi
We present the molecular landscape of pediatric acute myeloid leukemia (AML) and characterize nearly 1,000 participants in Childrens Oncology Group (COG) AML trials. The COG–National Cancer Institute (NCI) TARGET AML initiative assessed cases by whole-genome, targeted DNA, mRNA and microRNA sequencing and CpG methylation profiling. Validated DNA variants corresponded to diverse, infrequent mutations, with fewer than 40 genes mutated in >2% of cases. In contrast, somatic structural variants, including new gene fusions and focal deletions of MBNL1, ZEB2 and ELF1, were disproportionately prevalent in young individuals as compared to adults. Conversely, mutations in DNMT3A and TP53, which were common in adults, were conspicuously absent from virtually all pediatric cases. New mutations in GATA2, FLT3 and CBL and recurrent mutations in MYC-ITD, NRAS, KRAS and WT1 were frequent in pediatric AML. Deletions, mutations and promoter DNA hypermethylation convergently impacted Wnt signaling, Polycomb repression, innate immune cell interactions and a cluster of zinc finger–encoding genes associated with KMT2A rearrangements. These results highlight the need for and facilitate the development of age-tailored targeted therapies for the treatment of pediatric AML.
BMC Genomics | 2013
Leandro C. Hermida; Carine Poussin; Michael B Stadler; Sylvain Gubian; Alain Sewer; Dimos Gaidatzis; Hans-Rudolf Hotz; Florian Martin; Vincenzo Belcastro; Stéphane Cano; Manuel C. Peitsch; Julia Hoeng
BackgroundHigh-throughput omics technologies such as microarrays and next-generation sequencing (NGS) have become indispensable tools in biological research. Computational analysis and biological interpretation of omics data can pose significant challenges due to a number of factors, in particular the systems integration required to fully exploit and compare data from different studies and/or technology platforms. In transcriptomics, the identification of differentially expressed genes when studying effect(s) or contrast(s) of interest constitutes the starting point for further downstream computational analysis (e.g. gene over-representation/enrichment analysis, reverse engineering) leading to mechanistic insights. Therefore, it is important to systematically store the full list of genes with their associated statistical analysis results (differential expression, t-statistics, p-value) corresponding to one or more effect(s) or contrast(s) of interest (shortly termed as ” contrast data”) in a comparable manner and extract gene sets in order to efficiently support downstream analyses and further leverage data on a long-term basis. Filling this gap would open new research perspectives for biologists to discover disease-related biomarkers and to support the understanding of molecular mechanisms underlying specific biological perturbation effects (e.g. disease, genetic, environmental, etc.).ResultsTo address these challenges, we developed Confero, a contrast data and gene set platform for downstream analysis and biological interpretation of omics data. The Confero software platform provides storage of contrast data in a simple and standard format, data transformation to enable cross-study and platform data comparison, and automatic extraction and storage of gene sets to build new a priori knowledge which is leveraged by integrated and extensible downstream computational analysis tools. Gene Set Enrichment Analysis (GSEA) and Over-Representation Analysis (ORA) are currently integrated as an analysis module as well as additional tools to support biological interpretation. Confero is a standalone system that also integrates with Galaxy, an open-source workflow management and data integration system. To illustrate Confero platform functionality we walk through major aspects of the Confero workflow and results using the Bioconductor estrogen package dataset.ConclusionConfero provides a unique and flexible platform to support downstream computational analysis facilitating biological interpretation. The system has been designed in order to provide the researcher with a simple, innovative, and extensible solution to store and exploit analyzed data in a sustainable and reproducible manner thereby accelerating knowledge-driven research. Confero source code is freely available from http://sourceforge.net/projects/confero/.
Nature | 2018
Xiaotu Ma; Yu Liu; Yanling Liu; Ludmil B. Alexandrov; Michael Edmonson; Charles Gawad; Xin Zhou; Yongjin Li; Michael Rusch; John Easton; Robert Huether; Veronica Gonzalez-Pena; Mark R. Wilkinson; Leandro C. Hermida; Sean Davis; Edgar Sioson; Stanley Pounds; Xueyuan Cao; Rhonda E. Ries; Zhaoming Wang; Xiang Chen; Li Dong; Sharon J. Diskin; Malcolm A. Smith; Jaime M. Guidry Auvil; Paul S. Meltzer; Ching C. Lau; Elizabeth J. Perlman; John M. Maris; Soheil Meshinchi
Analysis of molecular aberrations across multiple cancer types, known as pan-cancer analysis, identifies commonalities and differences in key biological processes that are dysregulated in cancer cells from diverse lineages. Pan-cancer analyses have been performed for adult but not paediatric cancers, which commonly occur in developing mesodermic rather than adult epithelial tissues. Here we present a pan-cancer study of somatic alterations, including single nucleotide variants, small insertions or deletions, structural variations, copy number alterations, gene fusions and internal tandem duplications in 1,699 paediatric leukaemias and solid tumours across six histotypes, with whole-genome, whole-exome and transcriptome sequencing data processed under a uniform analytical framework. We report 142 driver genes in paediatric cancers, of which only 45% match those found in adult pan-cancer studies; copy number alterations and structural variants constituted the majority (62%) of events. Eleven genome-wide mutational signatures were identified, including one attributed to ultraviolet-light exposure in eight aneuploid leukaemias. Transcription of the mutant allele was detectable for 34% of protein-coding mutations, and 20% exhibited allele-specific expression. These data provide a comprehensive genomic architecture for paediatric cancers and emphasize the need for paediatric cancer-specific development of precision therapies.
Blood | 2016
Julia E. Maxson; Rhonda E. Ries; Yi Cheng Wang; Robert B. Gerbing; E. Anders Kolb; Sarah L Thompson; Jaime M. Guidry Auvil; Marco A. Marra; Yussanne Ma; Zusheng Zong; Andrew J. Mungall; Richard G. Moore; William Long; Patee Gesuwan; Tanja M. Davidsen; Leandro C. Hermida; Seamus B Hughes; Jason E. Farrar; Jerald P. Radich; Malcolm A. Smith; Daniela S. Gerhard; Alan S. Gamis; Todd A. Alonzo; Soheil Meshinchi
Publishers Note: There is an [Inside Blood Commentary][1] on this article in this issue. To the editor: Childhood cancers represent distinct clinical entities, often with unique genomic alterations and therapeutic responses that differ from cancers arising in adults. Pediatric acute myeloid
Nature | 2018
Thomas B. Alexander; Zhaohui Gu; Ilaria Iacobucci; Kirsten Dickerson; John K. Choi; Beisi Xu; Debbie Payne-Turner; Hiroki Yoshihara; Mignon L. Loh; John Horan; Barbara Buldini; Giuseppe Basso; Sarah Elitzur; Valerie de Haas; C. Michel Zwaan; Allen Eng Juh Yeoh; Dirk Reinhardt; Daisuke Tomizawa; Nobutaka Kiyokawa; Tim Lammens; Barbara De Moerloose; Daniel Catchpoole; Hiroki Hori; Anthony V. Moorman; Andrew S. Moore; Ondrej Hrusak; Soheil Meshinchi; Etan Orgel; Meenakshi Devidas; Michael J. Borowitz
Mixed phenotype acute leukaemia (MPAL) is a high-risk subtype of leukaemia with myeloid and lymphoid features, limited genetic characterization, and a lack of consensus regarding appropriate therapy. Here we show that the two principal subtypes of MPAL, T/myeloid (T/M) and B/myeloid (B/M), are genetically distinct. Rearrangement of ZNF384 is common in B/M MPAL, and biallelic WT1 alterations are common in T/M MPAL, which shares genomic features with early T-cell precursor acute lymphoblastic leukaemia. We show that the intratumoral immunophenotypic heterogeneity characteristic of MPAL is independent of somatic genetic variation, that founding lesions arise in primitive haematopoietic progenitors, and that individual phenotypic subpopulations can reconstitute the immunophenotypic diversity in vivo. These findings indicate that the cell of origin and founding lesions, rather than an accumulation of distinct genomic alterations, prime tumour cells for lineage promiscuity. Moreover, these findings position MPAL in the spectrum of immature leukaemias and provide a genetically informed framework for future clinical trials of potential treatments for MPAL.A large-scale genomics study shows that the cell of origin and founding mutations determine disease subtype and lead to the expression of multiple haematopoietic lineage-defining antigens in mixed phenotype acute leukaemia.