Biology subjects

Kocher, J.-P.

Publications and source records attributed to Kocher, J.-P..

5 recordsLinked to original sources

bioRxiv2026

A complete human pancreatic cancer genome

Cancer genome sequencing is essential for understanding tumor evolution and advancing precision medicine.1 However, reference gaps and germline variants obscure detection of small and large somatic variants and methylation in repetitive regions.1-3 It is common for tumor cells to gain or lose chromosome arms due to somatic structural changes that occur inside highly repetitive satellite DNA sequences in the centromeres.4 To identify the full spectrum of somatic variants, including complex rearrangements, we construct and curate near-complete, haplotype-resolved assemblies of the most recent common ancestor of an early-passage broadly-consented hypodiploid pancreatic cancer cell line and matched normal tissues. The tumor assembly completely recapitulates all 35 tumor chromosomes observed with karyotyping, with multiple translocation-induced hybrid chromosomes. The hybrid chromosomes contain putative functional dicentric and fused centromeres, nested foldback inversions causing 14 breakpoints with a haplotype switch in a single event, and centromeric satellite tandem duplications up to 136 kbp. Direct comparison of tumor and normal assembly haplotypes uncovers >7,000 variants altering >1 Mbp of sequence in repetitive regions that have been hidden by reference gaps and germline variants. 44 % of somatic small variants change representation because they alter germline variants on GRCh38, impacting mutational signatures and kataegis/omikli clusters. Most somatic LINE insertions originate from two hypomethylated non-reference germline LINE insertions, highlighting their impact on insertion mutation burden. These assemblies demonstrate that centromeric, acrocentric, and telomeric regions conventionally excluded from analysis harbor extensive somatic and epigenetic changes. Resolving complete tumor genomes enables a deeper understanding of cancer structural plasticity and the endpoints of breakage-fusion-bridge cycles. These assembled, curated paired normal-tumor benchmarks will serve as a critical foundation for developing future algorithms to characterize the most intractable regions of cancer genomes.

genomics↗

bioRxiv2024

Immunopipe: A comprehensive and flexible scRNA-seq and scTCR-seq data analysis pipeline

Single-cell sequencing technologies provide us with information at the level of individual cells. The combination of single-cell RNA-seq and single-cell TCR-seq profiling enables the exploration of cell heterogeneity and T-cell receptor repertoires simultaneously. The integration of both types of data can play a crucial role in enhancing our understanding of T-cell-mediated immunity and, in turn, facilitate the advancement of immunotherapy. Here, we present immunopipe, a comprehensive and flexible pipeline to perform integrated analysis of scRNA-seq and scTCR-seq data. In addition to the command line tool, we provide a user-friendly web interface for pipeline configuration and execution monitoring, benefiting researchers without extensive programming experience. With its comprehensive functionality and ease of use, immunopipe empowers researchers to uncover valuable insights from scRNA-seq and scTCR-seq data, ultimately advancing the understanding of immune responses and immunotherapy development.

bioinformatics↗

bioRxiv2021

Exogene: A performant workflow for detecting viral integrations from paired-end next-generation sequencing data

The integration of viruses into the human genome is known to be associated with tumorigenesis in many cancers, but the accurate detection of integration breakpoints from short read sequencing data is made difficult by human-viral homologies, viral genome heterogeneity, coverage limitations, and other factors. To address this, we present Exogene, a sensitive and efficient workflow for detecting viral integrations from paired-end next generation sequencing data. Exogenes read filtering and breakpoint detection strategies yield integration coordinates that are highly concordant with those found in long read validation sets. We demonstrate this concordance across 6 TCGA Hepatocellular carcinoma (HCC) tumor samples, identifying integrations of hepatitis B virus that are validated by long reads. Additionally, we applied Exogene to targeted capture data from 426 previously studied HCC samples, achieving 98.9% concordance with existing methods and identifying 238 high-confidence integrations that were not previously reported. Exogene is applicable to multiple types of paired-end sequence data, including genome, exome, RNA-Seq or targeted capture.

bioinformatics↗

bioRxiv2021

In vivo lentiviral vector gene therapy to cure hereditary tyrosinemia type 1 and prevent development of precancerous and cancerous lesions

Conventional therapy for hereditary tyrosinemia type-1 (HT1) with 2-(2-nitro-4-trifluoromethylbenzoyl)-1,3-cyclohexanedione (NTBC) delays and in some cases fails to prevent disease progression to liver fibrosis, liver failure, and activation of tumorigenic pathways. Here we demonstrate for the first time a cure of HT1 by direct, in vivo administration of a therapeutic lentiviral vector targeting the expression of a human fumarylacetoacetate hydrolase (FAH) transgene in the porcine model of HT1. This therapy was well tolerated and provided stable long-term expression of FAH in pigs with HT1. Genomic integration displayed a benign profile, with subsequent fibrosis and tumorigenicity gene expression patterns similar to wild-type animals as compared to NTBC-treated or diseased untreated animals. Indeed, the phenotypic and genomic data following in vivo lentiviral vector administration demonstrate comparative superiority over other therapies including ex vivo cell therapy and therefore support clinical application of this approach.

cell biology↗

bioRxiv2017

Translational Profiling Of Microglia Reveals Artifacts Of Cell Sorting

Microglia are the resident innate immune population of the central nervous system that constantly survey and influence their local environment. Transcriptomic profiling has led to significant advances in our understanding of microglia in several disease states, but tissue dissociation and purification of microglia is known to lead to cellular activation. Here we use RiboTag translational RNAseq profiling to demonstrate that commonly used cell sorting methods lead to a fundamental alteration of the microglial transcriptome, with several transcripts that can be used to mark artifacts of isolation. Microglial RiboTag RNAseq profiling after peripheral immune challenge with lipopolysaccharide demonstrates unique transcriptional targets that are not evident using cell sorting methodology. Finally, we applied our technique to reveal novel shared and distinct pathways when comparing microglial transcriptomes after peripheral challenge with bacterial or viral mimetics. This study has broad implications for approaches that examine microglial transcriptomes in normal and pathological states.\n\nSummaryKang et al. demonstrate artifactual induction of microglial transcripts associated with cell sorting. Using RiboTag translational profiling methodology, several markers of cell sorting artifact were revealed. Furthermore, RiboTag isolation unveiled changes in microglial transcriptomes following systemic inflammation that would otherwise have been masked by artifacts of cell sorting.

neuroscience↗