Biology subjects

Kamoun, A.

Publications and source records attributed to Kamoun, A..

7 recordsLinked to original sources

bioRxiv2026

Federated cross-biobank conditional analysis identifies LDL-C lowering effects of DNAJC13 haploinsufficiency and LDLR regulation

Whole genome sequencing in diverse population-scale biobanks offers new insights into the genetic architecture of complex traits from rare and non-coding variants. However, rare single variant and aggregate associations are often confounded by linkage disequilibrium and haplotype structure, resulting in large numbers of false-positive associations. Previous methods that rely on reference panels or linkage disequilibrium-matrices to determine conditional independence in meta-analyses do not scale to very rare variants, which may be observed in only one biobank and can exhibit long-range haplotypes. Here, we implement a federated approach to perform iterative conditional meta-analysis on individual-level genotype and phenotype data across biobanks while adhering to data sharing policies. We applied our methodology to a meta-analysis of LDL-C in 614,375 individuals from UK Biobank and All of Us, encompassing six genetic ancestry groups. After conditioning, only 4.3% of significantly associated rare single variants and 6.9% of aggregates remained statistically independent. The proportion of significant aggregates that remained independent after conditioning was higher for coding-based tests than non-coding. We further validate that our approach effectively suppresses false-positive associations using simulations centred on the LDLR locus. We identify allelic series of variants associated with reduced LDL-C, including loss-of-function variants in DNAJC13 and variants in the 3-prime untranslated region of LDLR. Our results highlight that federated conditioning can distinguish independent rare variant signals from linkage and haplotype structure artifacts in multi-ancestry meta-analyses across separate biobanks.

genetics↗

bioRxiv2022

Deep Learning Allows Assessment of Risk of Metastatic Relapse from Invasive Breast Cancer Histological Slides

BackgroundCorrectly classifying early estrogen receptor-positive and HER2-negative (ER+/HER2) breast cancer (EBC) cases allows to propose an adapted adjuvant systemic treatment strategy. We developed a new AI-based tool to assess the risk of distant relapse at 5 years for ER+/HER2-EBC patients from pathological slides. Patients and MethodsThe discovery dataset (GrandTMA) included 1429 ER+/HER2-EBC patients, with long-term follow-up and an available hematoxylin-eosin and saffron (HES) whole slide image (WSI). A Deep Learning (DL) network was trained to predict metastasis free survival (MFS) at five years, based on the HES WSI only (termed RlapsRisk). A combined score was then built using RlapsRisk and well established prognostic factors. A threshold corresponding to a probability of MFS event of 5% at 5 years was applied to dichotomize patients into low or high-risk groups. The external validation, as well as assessment of the additional prognosis value of the DL model beyond standard clinico-pathologic factors were carried out on an independent, prospective cohort (CANTO, NCT01993498) including 889 HES WSI of ER+/HER2-EBC patients. ResultsRlapsRisk was an independent prognostic factor of MFS in multivariable analysis adjusted for established clinico-pathological factors (p<0.005 in GrandTMA and CANTO). Combining RlapsRisk score and the clinico-pathological factors improved the prognostic discrimination as compared to the clinico-pathological factors alone (increment of c-index in the validation set 0.80 versus 0.76, +0.04, p-value < 0.005). After dichotomization, the Combined Model showed a higher cumulative sensitivity on the entire population (0.76 vs 0.61) for an equal dynamic specificity (0.76) in comparison with the clinical score alone. ConclusionsOur deep learning model developed on digitized HES slides provided additional prognostic information as compared to current clinico-pathological factors and has the potential of valuably informing the decision making process in the adjuvant setting when combined with current clinico-pathological factors.

pathology↗

bioRxiv2022

Integrated molecular and pharmacological characterization of patient-derived xenografts from bladder and ureteral cancers identifies new potential therapies.

BackgroundMuscle-invasive bladder cancer (MIBC) and upper urinary tract urothelial carcinoma (UTUC) are molecularly heterogeneous. Despite chemotherapies, immunotherapies or anti-FGFR treatments, these tumors are still of poor outcome. Our objective was to develop a bank of patient-derived xenografts (PDXs) recapitulating molecular heterogeneity of MIBC and UTUC, to facilitate preclinical identification of therapies. MethodsFresh tumors were obtained from patients and subcutaneously engrafted into immune-compromised mice. Patient tumors and matched PDXs were compared regarding histopathology, transcriptomic (microarrays) and genomic profiles (targeted-NGS). Several PDXs were treated with chemotherapy (cisplatin/gemcitabine) or targeted therapies (FGFR and EGFR inhibitors). Results31 PDXs were established from 1 non-MIBC, 25 MIBC, 5 upper urinary tract tumors, including 28 urothelial (UCC) and 3 squamous-cell carcinomas (SCC). Integrated genomic and transcriptomic profiling identified PDXs of 3 different consensus molecular subtypes (Basal/Squamous, Luminal papillary and Luminal unstable), and included FGFR3-mutated PDXs. High histological and genomic concordance was found between matched patient tumor/PDX. Discordance in molecular subtypes, such as a basal/squamous patient tumor giving rise to a luminal papillary PDX, was observed (n=5) at molecular and histological levels. Ten models were treated with cisplatin-based chemotherapy and we did not observe association between subtypes and response. Of the 3 basal/squamous models treated with anti-EGFR therapy, two models were sensitive and one model, of sarcomatoid variant, was resistant. Treatment of 3 FGFR3-mutant PDXs with combined FGFR/EGFR inhibitors was more efficient than anti-FGFR3 treatment alone. ConclusionsWe developed preclinical PDX models that recapitulate the molecular heterogeneity of MIBCs and UTUC, including actionable mutations, which will represent an essential tool in therapy development. Pharmacological characterization of the PDXs suggested that upper urinary tract and MIBCs, UCC but also SCC, with similar molecular characteristics could benefit from the same treatments including anti-FGFR for FGFR3-mutated tumors and anti-EGFR for basal ones and showed a benefit for combined FGFR/EGFR inhibition in FGFR3-mutant PDXs, compared to FGFR inhibition alone.

cancer biology↗

bioRxiv2022

PACpAInt: a deep learning approach to identify molecular subtypes of pancreatic adenocarcinoma on histology slides

Pancreatic ductal adenocarcinoma (PAC) is a highly heterogeneous and plastic tumor with different transcriptomic molecular subtypes that hold great prognostic and theranostic values. We developed PACpAInt, a multistep approach using deep learning models to determine tumor cell type and their molecular phenotype on routine histological preparation at a resolution enabling to decipher complete intratumor heterogeneity on a massive scale never achieved before. PACpAInt effectively identified molecular subtypes at the slide level in three validation cohorts and had an independent prognostic value. It identified an interslide heterogeneity within a case in 39% of tumors that impacted survival. Diving at the cell level, PACpAInt identified "pure" classical and basal-like main subtypes as well as an intermediary phenotype and hybrid tumors that co-carried both classical and basal-like phenotypes. These novel artificial intelligence-based subtypes, together with the proportion of basal-like cells within a tumor had a strong prognostic impact.

cancer biology↗

bioRxiv2021

FGFR3 activating mutations induce luminal-like papillary bladder tumor formation and favor a male gender bias.

BackgroundFGFR3 mutations are among the most frequent genetic alterations in bladder cancer and are enriched in the luminal papillary subtype of muscle-invasive tumors (MIBC) and luminal-like classes 1 and 3 of non-MIBC. To study their oncogenic properties in vivo, we developed here a genetically engineered mouse (GEM) model expressing the most frequent FGFR3 mutation, FGFR3-S249C, in urothelial cells. MethodsBladder tumorigenesis was monitored in FGFR3-S249C mice. FGFR3 expression was assessed by RT-qPCR in the transgenic mice urothelium and in various human epithelia. Transcriptomic data were obtained from mouse bladder tumors and crossspecies comparisons were performed. Sex bias in FGFR3-mutated tumors was evaluated in our GEM model and in the TCGA and UROMOL cohorts of patients including 408 MIBC and 419 NMIBC, respectively. The association of androgen receptor (AR) activity, based on the expression of its target genes, with FGFR3 mutations was examined in these two cohorts. Binding of AR to its response element and AR phosphorylation in FGFR3-dependent cell lines were evaluated. ResultsFGFR3-S249C expression in the urothelium of mice induced spontaneous low-grade papillary bladder tumors resembling the human counterpart at the histological and transcriptomic levels. Mutant-FGFR3 expression levels impacted tumor formation incidence in mice and mutant-FGFR3-driven human tumors were restricted to epithelia presenting high normal expression levels of FGFR3. The known bladder cancer male gender bias, also found in our model, was even higher in human FGFR3-mutated compared to wild-type tumors and associated with a higher AR regulon activity considering gender adjustment. AR phosphorylation and regulon activity were modulated by FGFR3 in FGFR3-dependent models. ConclusionsMutant-FGFR3 is an oncogene per se, inducing bladder tumorigenesis. Patients with early stage bladder lesions could thus potentially benefit from FGFR3 targeting. Our results also reinforce the interest in elucidating the role of AR in bladder carcinogenesis, specifically in FGFR3-mutated driven tumors. Finally, our results suggest FGFR3 expression level in epithelium as a determinant for the FGFR3-driven tumors tissue specificity.

cancer biology↗

bioRxiv2019

PPARG is a tumor suppressor in basal bladder tumors offering new potential therapeutic opportunities.

PPAR{gamma} activation is a critical event in luminal muscle-invasive bladder cancer (MIBC) tumorigenesis, favoring both tumor cell growth and microenvironment modulation toward tumor immune escape. Conversely, the down-regulation of PPAR{gamma} activity in basal MIBC suggests tumor suppressive effects in this subgroup. Here, we report genetic, epigenetic and functional evidence to support the tumor suppressor role for PPAR{gamma} in basal bladder tumors. We identified hemizygous deletions, DNA hyper-methylation and loss-of-function mutations of PPAR{gamma} in basal MIBC, associated with PPAR{gamma} under-expression and its decreased activity. Re-expression of PPAR{gamma} in basal tumor cells resulted in the activation of PPAR{gamma} -dependent transcription program that modulated fatty acid metabolism and cell differentiation and decreased cell growth, which could partly rely on EGFR down-regulation. Structure-function studies of two PPAR{gamma} mutant revealed a destabilization of a region important for coactivator recruitment and should help develop potent molecules to activate PPAR{gamma} as a therapeutic strategy for basal MIBC. The identification of this subtype-dependent dual role of PPAR{gamma} in MIBC strengthens the critical role of PPAR{gamma} in bladder tumorigenesis and reinforces the interest in stratified medicine based on tumor molecular subtyping. One sentence summaryGenetic, epigenetic and functional evidence of a tumor suppressor role for PPAR{gamma} in basal bladder tumors offer new therapeutic opportunities for this subgroup.

cancer biology↗

bioRxiv2018

The consensus molecular classification of muscle-invasive bladder cancer

Muscle-Invasive Bladder Cancer (MIBC) is a molecularly diverse disease with heterogeneous clinical outcomes. Several molecular classifications have been proposed, yielding diverse sets of subtypes, which hampers the clinical implications of such knowledge. Here, we report the results of a large international effort to reach a consensus on MIBC molecular subtypes. Using 1750 MIBC transcriptomes and a network-based analysis of six independent MIBC classification systems, we identified a consensus set of six molecular classes: Luminal Papillary (24%), Luminal Non-Specified (8%), Luminal Unstable (15%), Stroma-rich (15%), Basal/Squamous (35%), and Neuroendocrine-like (3%). These consensus classes differ regarding underlying oncogenic mechanisms, infiltration by immune and stromal cells, and histological and clinical characteristics. This consensus system offers a robust framework that will enable testing and validating predictive biomarkers in future clinical trials.

cancer biology↗