Fusion-negative rhabdomyosarcoma 3D organoids to predict effective drug combinations: A proof-of-concept on cell death inducers.

Rhabdomyosarcoma (RMS) is the main form of pediatric soft-tissue sarcoma. Its cure rate has not notably improved in the last 20 years following relapse, and the lack of reliable preclinical models has hampered the design of new therapies. This is particularly true for highly heterogeneous fusion-negative RMS (FNRMS). Although methods have been proposed to establish FNRMS organoids, their efficiency remains limited to date, both in terms of derivation rate and ability to accurately mimic the original tumor. Here, we present the development of a next-generation 3D organoid model derived from relapsed adult and pediatric FNRMS. This model preserves the molecular features of the patients' tumors and is expandable for several months in 3D, reinforcing its interest to drug combination screening with longitudinal efficacy monitoring. As a proof-of-concept, we demonstrate its preclinical relevance by reevaluating the therapeutic opportunities of targeting apoptosis in FNRMS from a streamlined approach based on transcriptomic data exploitation.

G2GSnake: a Snakemake workflow for host-pathogen genomic association studies.

Summary: Joint analyses of paired host and pathogen genome sequences have the potential to enhance our understanding of host-pathogen interactions. A systematic approach to conduct such a joint analysis is through a "genome-to-genome" (G2G) association study, which involves testing for associations between all host and pathogen genetic variants. Significant associations reveal host genetic factors that might drive pathogen variation, highlighting biological mechanisms likely to be involved in host control and pathogen escape. Here, we present a Snakemake workflow that allows researchers to conduct G2G studies in a reproducible and scalable manner. In addition, we have developed an intuitive R Shiny application that generates custom summaries of the results, enabling users to derive relevant insights. Availability and implementation: G2GSnake is freely available at: https://github.com/zmx21/G2GSnake under the MIT license.

The combined impact of persistent infections and human genetic variation on C-reactive protein levels.

Multiple human pathogens establish chronic, sometimes life-long infections. Even if they are often latent, these infections can trigger some degree of local or systemic immune response, resulting in chronic low-grade inflammation. There remains an incomplete understanding of the potential contribution of both persistent infections and human genetic variation on chronic low-grade inflammation. We searched for potential associations between seropositivity for 13 persistent pathogens and the plasma levels of the inflammatory biomarker C-reactive protein (CRP), using data collected in the context of the UK Biobank and the CoLaus|PsyCoLaus Study, two large population-based cohorts. We performed backward stepwise regression starting with the following potential predictors: serostatus for each pathogen, polygenic risk score for CRP, and demographic and clinical factors known to be associated with CRP. We found evidence for an association between Chlamydia trachomatis (P-value = 5.04e - 3) and Helicobacter pylori (P-value = 8.63e - 4) seropositivity and higher plasma levels of CRP. We also found an association between pathogen burden and CRP levels (P-value = 4.12e - 4). These results improve our understanding of the relationship between persistent infections and chronic inflammation, an important determinant of long-term morbidity in humans.

Improving polygenic prediction with genetically inferred ancestry.

Genome-wide association studies (GWASs) have demonstrated that most common diseases have a strong genetic component from many genetic variants each with a small effect size. GWAS summary statistics have allowed the construction of polygenic scores (PGSs) estimating part of the individual risk for common diseases. Here, we propose to improve PGS-based risk estimation by incorporating genetic ancestry derived from genome-wide genotyping data. Our method involves three cohorts: a base (or discovery) for association studies, a target for phenotype/risk prediction, and a map for ancestry mapping; successively, (1) it generates for each individual in the base and target cohorts a set of principal components based on the map cohort-called mapped PCs, (2) it associates in the base cohort the phenotype with the mapped-PCs, and (3) it uses the mapped PCs in the target cohort to generate a phenotypic predictor called the ancestry score. We evaluated the ancestry score by comparing a predictive model using a PGS with one combining a PGS and an ancestry score. First, we performed simulations and found that the ancestry score has a greater impact on traits that correlate with ancestry-specific variants. Second, we showed, using UK Biobank data, that the ancestry score improves genetic prediction for our nine phenotypes to very different degrees. Third, we performed simulations and found that the more heterogeneous the base and target cohorts, the more beneficial the ancestry score is. Finally, we validated our approach under realistic conditions with UK Biobank as the base cohort and Swiss individuals from the CoLaus|PsyCoLaus study as the target cohort.

The influence of human genetic variation on Epstein-Barr virus sequence diversity.

Epstein-Barr virus (EBV) is one of the most common viruses latently infecting humans. Little is known about the impact of human genetic variation on the large inter-individual differences observed in response to EBV infection. To search for a potential imprint of host genomic variation on the EBV sequence, we jointly analyzed paired viral and human genomic data from 268 HIV-coinfected individuals with CD4 + T cell count < 200/mm3 and elevated EBV viremia. We hypothesized that the reactivated virus circulating in these patients could carry sequence variants acquired during primary EBV infection, thereby providing a snapshot of early adaptation to the pressure exerted on EBV by the individual immune response. We searched for associations between host and pathogen genetic variants, taking into account human and EBV population structure. Our analyses revealed significant associations between human and EBV sequence variation. Three polymorphic regions in the human genome were found to be associated with EBV variation: one at the amino acid level (BRLF1:p.Lys316Glu); and two at the gene level (burden testing of rare variants in BALF5 and BBRF1). Our findings confirm that jointly analyzing host and pathogen genomes can identify sites of genomic interactions, which could help dissect pathogenic mechanisms and suggest new therapeutic avenues.

Correcting for Population Stratification Reduces False Positive and False Negative Results in Joint Analyses of Host and Pathogen Genomes.

Studies of host genetic determinants of pathogen sequence variations can identify sites of genomic conflicts, by highlighting variants that are implicated in immune response on the host side and adaptive escape on the pathogen side. However, systematic genetic differences in host and pathogen populations can lead to inflated type I (false positive) and type II (false negative) error rates in genome-wide association analyses. Here, we demonstrate through a simulation that correcting for both host and pathogen stratification reduces spurious signals and increases power to detect real associations in a variety of tested scenarios. We confirm the validity of the simulations by showing comparable results in an analysis of paired human and HIV genomes.