Biology subjects

Fiaux, P. C.

Publications and source records attributed to Fiaux, P. C..

3 recordsLinked to original sources

bioRxiv2023

Next-generation sequencing-based liquid biopsy can be used for detection of residual disease and cancer recurrence monitoring in dogs

ObjectiveThe purpose of this study was to evaluate the performance of a next-generation sequencing-based liquid biopsy test for cancer monitoring in dogs. SamplesPre- and post-operative blood samples were collected prospectively from dogs with confirmed cancer diagnoses originally enrolled in the CANcer Detection in Dogs (CANDiD) study. A subset of these dogs also had longitudinal blood samples collected for recurrence monitoring. MethodsAll patients had a pre-operative blood sample collected (after diagnosis but prior to surgical intervention) in which a cancer signal was detected, and had at least one post-operative sample collected. Clinical data were collected for all patients and used to assign a clinical disease status for each follow-up visit. ResultsFollowing excisional surgery, in the absence of clinical residual disease at the post-operative visit, patients with Cancer Signal Detected results at that visit were 1.95-times as likely to have clinical recurrence within 6 months compared to patients with Cancer Signal Not Detected results. In the subset of patients with longitudinal liquid biopsy samples that had clinical recurrence documented during the study period, 73% (8/11; 95% CI: 39 - 93%) of patients had Cancer Signal Detected in blood prior to or concomitant with clinical recurrence; in the 6 patients where molecular recurrence was detected prior to clinical recurrence, the median lead time was 168 days (range: 47 - 238). Clinical RelevanceNext-generation sequencing-based liquid biopsy is a non-invasive tool for cancer monitoring in dogs that can be used as an adjunct to current standard-of-care clinical assessment methods.

genomics↗

bioRxiv2022

Deletion mapping of regulatory elements for GATA3 reveals a distal T helper 2 cell enhancer involved in allergic diseases

The GATA3 gene is essential for T cell differentiation and is surrounded by risk variants for immune traits. Interpretation of these variants is challenging because the regulatory landscape of GATA3 is complex with dozens of potential enhancers spread across a large topological associating domain (TAD) and gene expression quantitative trait locus (eQTL) studies provide limited evidence for variant function. Here, we perform a tiling deletion screen in Jurkat T cells to identify 23 candidate regulatory elements. Using small deletions in primary T helper 2 (Th2) cells, we validate the function of five of these elements, two of which contain risk variants for asthma and allergic diseases. We fine-map genome-wide association study (GWAS) signals in a distal regulatory element, 1 Mb downstream, to identify 14 candidate causal variants. Small deletions spanning candidate rs725861 decrease GATA3 expression in Th2 cells suggesting a causal mechanism for this variant in allergic diseases. Our study demonstrates the power of integrating GWAS signals with deletion mapping and identifies critical regulatory sequences for GATA3.

genomics↗

bioRxiv2019

Discovering functional sequences with RELICS, an analysis method for CRISPR regulatory screens

CRISPR screens are a powerful new technology for the identification of genome sequences that affect cellular phenotypes such as gene expression, survival, and proliferation. By tiling single-guide RNA (sgRNA) target sites across large genomic regions, CRISPR screens have the potential to systematically discovery novel functional sequences, however, a lack of purpose-built analysis tools limits the effectiveness of this approach. Here we describe RELICS, a Bayesian hierarchical model for the discovery of functional sequences from tiling CRISPR screens. RELICS considers the overlapping effects of multiple nearby functional sequences, accounts for the area of effect surrounding sgRNA target sites, models overdispersion in sgRNA counts, combines information across multiple pools, and estimates the number of functional sequences supported by the data. In simulations, RELICS outperforms existing methods and provides higher resolution predictions. We apply RELICS to published CRISPR interference and CRISPR activation screens and predict novel regulatory sequences, several of which we experimentally validate. In summary, RELICS is a powerful new analysis method for tiling CRISPR screens that enables the discovery of functional sequences with unprecedented resolution and accuracy.

bioinformatics↗