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Paisie, C.

Publications and source records attributed to Paisie, C..

2 recordsLinked to original sources

Large complex structural rearrangements in human genomes harbor cryptic structures

Structural variation is a major contributor to human diversity, adaptation, and disease. Simple structural variant (SV) types include deletions, insertions, duplications, inversions, and translocations, and SVs account for most of the variable bases between genomes. Complex structural variants (CSVs) that consist of one or more simple events in cis appear more frequently in diseases and cancers where DNA repair, apoptosis, and cell cycle checkpoints are compromised, although CSVs can also appear in germline genome sequences of healthy individuals. CSVs are often characterized by short tracts of homology or no homology, and while CSVs are more prevalent in complex regions that contain large repeats, smaller stretches of homology can also enable their formation across more unique loci. Long-read assemblies have increased the size of detectable SVs and expanded variant detection into more complex regions of the genome, and while they reconstruct CSVs, methods for identifying CSVs from assemblies is limited. Here, we have developed a new assembly-based approach to trace through complex loci rather than relying upon reference representations of alignments. We can now access CSVs in large complex segmental duplications, reveal structures that were previously unknown, and identify SV breakpoints with greater accuracy. We find 72 large CSVs per genome and 128 unique complex structures and CSVs in highly repetitive regions can now be detected including several distinct complex events in repetitive NBPF genes that was not previously callable with short-read or long-read CSV methods. This approach is implemented within a key assembly-based variant calling tool, PAV, and represents a substantial improvement identifying complex variants now ascertainable from contiguous genome assemblies.

genomics↗

A Genomically and Clinically Annotated Patient Derived Xenograft (PDX) Resource for Preclinical Research in Non-Small Cell Lung Cancer

Patient-derived xenograft models (PDXs) are an effective preclinical in vivo platform for testing the efficacy of novel drug and drug combinations for cancer therapeutics. Here we describe a repository of 79 genomically and clinically annotated lung cancer PDXs available from The Jackson Laboratory that have been extensively characterized for histopathological features, mutational profiles, gene expression, and copy number aberrations. Most of the PDXs are models of non-small cell lung cancer (NSCLC), including 37 lung adenocarcinoma (LUAD) and 33 lung squamous cell carcinoma (LUSC) models. Other lung cancer models in the repository include four small cell carcinomas, two large cell neuroendocrine carcinomas, two adenosquamous carcinomas, and one pleomorphic carcinoma. Models with both de novo and acquired resistance to targeted therapies with tyrosine kinase inhibitors are available in the collection. The genomic profiles of the LUAD and LUSC PDX models are consistent with those observed in patient tumors of the same tumor type from The Cancer Genome Atlas (TCGA) and to previously characterized gene expression-based molecular subtypes. Clinically relevant mutations identified in the original patient tumors were confirmed in engrafted tumors. Treatment studies performed for a subset of the models recapitulated the responses expected based on the observed genomic profiles. SignificanceThe collection of lung cancer Patient Derived Xenograft (PDX) models maintained at The Jackson Laboratory retain both the histologic features and treatment-relevant genomic alterations observed in the originating patient tumors and show expected responses to treatment with standard-of-care agents. The models serve as a valuable preclinical platform for translational cancer research. Information and data for the models are freely available from the Mouse Models of Human Cancer database (MMHCdb, http://tumor.informatics.jax.org/mtbwi/pdxSearch.do).

cancer biology↗