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Yadav, V.

Publications and source records attributed to Yadav, V..

3 recordsLinked to original sources

Bioinformatics workflows for genomic analysis of tumors from Patient Derived Xenografts (PDX): challenges and guidelines

Bioinformatics workflows for analyzing genomic data obtained from xenografted tumor (e.g., human tumors engrafted in a mouse host) must address several challenges, including separating mouse and human sequence reads and accurate identification of somatic mutations and copy number aberrations when paired normal DNA from the patient is not available. We report here data analysis workflows that address these challenges and result in reliable identification of somatic mutations, copy number alterations, and transcriptomic profiles of tumors from patient derived xenograft models. We validated our analytical approaches using simulated data and by assessing concordance of the genomic properties of xenograft tumors with data from primary human tumors in The Cancer Genome Atlas (TCGA). The commands and parameters for the workflows are available at https://github.com/TheJacksonLaboratory/PDX-Analysis-Workflows.

bioinformatics

The Tandem Duplicator Phenotype is a prevalent genome-wide cancer configuration driven by distinct gene mutations

The tandem duplicator phenotype (TDP) is a genome-wide instability configuration primarily observed in breast, ovarian and endometrial carcinomas. Here, we stratify TDP tumors by classifying their tandem duplications (TDs) into three span intervals, with modal values of 11 Kb, 231 Kb, and 1.7 Mb. TDPs with prominent ~11 Kb TDs feature the conjoint loss of TP53 and BRCA1. TDPs with ~231 Kb and ~1.7 Mb TDs associate with CCNE1 pathway activation or CDK12 disruptions, in conjunction with TP53 mutations. We prove the driver role of TP53 and BRCA1 abrogation for TDP induction by generating short-span TDP mammary tumors in genetically modified mouse models harboring deleterious mutations in only these two genes. Lastly, heterogeneous combinations of mutations mediated by TDs are selected for and contribute to the oncogenic burden of TDP tumors.

genomics

Community selection increases biodiversity effects

Species extinctions from local communities can negatively affect ecosystem functioning. Ecological mechanisms underlying these impacts are well studied but the role of evolutionary processes is rarely assessed. Using a long-term field experiment, we tested whether natural selection in plant communities increased the effects of biodiversity on productivity. We re-assembled communities with 8-year co-selection history adjacent to communities with identical species composition but no history of co-selection (\"naive communities\"). Monocultures and in particular mixtures of two to four co-selected species were more productive than their corresponding naive communities over four years in soils with or without co-selected microbial communities. At the highest diversity level of eight plant species, no such differences were observed. Our findings suggest that plant community evolution can lead to rapid increases in ecosystem functioning at low diversity but may take longer at high diversity. This effect was not modified by treatments that simulated additional co-evolutionary processes between plants and soil organisms.

ecology