Biology subjects

Reyal, F.

Publications and source records attributed to Reyal, F..

8 recordsLinked to original sources

bioRxiv2023

Plakins are involved in the maintenance of epithelial polarity

As epithelia are the interface between the organism and the external environment, they are often subject to damage and must be frequently renewed. However, maintaining epithelial integrity during this renewal is challenging, and loss of cell polarity is a potent inducer of tumorigenesis. In this study, we used transcriptomic data from breast cancer cells at different stages of tumor development to identify molecular changes associated with the early stages of tumor transformation. We correlated these protein expression profiles with either cell polarity defects or cell progression along the epithelial-to-mesenchymal transition (EMT). We identified plakins, namely epiplakin (EPPK1), desmoplakin (DSP) and periplakin (PPL), that were downregulated in cells that had lost their epithelial polarity and also downregulated in cells that had progressed through EMT. We further tested them experimentally by knocking down their expression in a non-tumorigenic epithelial breast cell line (MCF10A). We demonstrated their causal role in the loss of polarity, as revealed by the misorientation of the nuclear centrosome vector. We also found that vimentin, a marker of EMT, was overexpressed in plakin knocked-down cells, suggesting that plakins may have both a structural and a regulatory role in maintaining the epithelial state.

cell biology↗

bioRxiv2022

Optical Genome Mapping for detecting Homologous Recombination Deficiency (HRD) in human breast cancers

Homologous recombination deficiency (HRD) leads to genomic instability that marks HRD tumor genome with a specific genomic scar. Present in many cancers, HRD is important to be detected as it is associated with a hyper-sensitivity to some classes of drugs, in particular the PARP inhibitors. Here, we investigated the use of structural variants (SVs) detected by the Optical Genome Mapping (OGM) technology as biomarkers to identify HRD tumors. We analyzed SVs data obtained by OGM from 37 samples of triple-negative breast cancer or high grade ovarian cancer with the known HRD status. We found that HRD cases were enriched with duplications and reciprocal translocations, while nonHRD cases were enriched with inversions. The number of translocations, defined as inter-chromosomal or intra-chromosomal rearrangements of more 5Mb were similar in HRD and nonHRD cases. We defined isolated translocations as the subset of translocations having no other translocation within 2 megabase zone around both junctions, and demonstrated that the number of isolated translocations perfectly discriminated HRD and nonHRD cases in the training series. Validation series consisting from 26 cases showed 20% false positive and zero false negative error rate, which proved isolated translocations to be 100% sensitive and 80% specific SV marker of HRD. Our results demonstrate that the OGM technology is an affordable way of getting an insight of the structural variants present in solid tumors, even with low tumoral cellularity. It represents a promising technology for HRD diagnosis, where a single marker already gives 80% correct recognition.

cancer biology↗

bioRxiv2022

Neural network for the prediction of treatment response in triple negative breast cancer

AO_SCPLOWBSTRACTC_SCPLOWThe automatic analysis of stained histological sections is becoming increasingly popular. Deep Learning is today the method of choice for the computational analysis of such data, and has shown spectacular results for large datasets for a large variety of cancer types and prediction tasks. On the other hand, many scientific questions relate to small, highly specific cohorts. Such cohorts pose serious challenges for Deep Learning, typically trained on large datasets. In this article, we propose a modification of the standard nested cross-validation procedure for hyper-parameter tuning and model selection, dedicated to the analysis of small cohorts. We also propose a new architecture for the particularly challenging question of treatment prediction, and apply this workflow to the prediction of response to neoadjuvant chemotherapy for Triple Negative Breast Cancer.

bioinformatics↗

bioRxiv2021

Tissue-resident FOLR2+ macrophages associate with tumor-infiltrating CD8+ T cells and with increased survival of breast cancer patients.

Macrophage infiltration is a hallmark of solid cancers and overall macrophage infiltration is correlated with lower patient survival and resistance to therapy. However, tumor-associated macrophages are phenotypically and functionally heterogeneous. Specific tumor-associated macrophage subsets might be endowed with antagonistic role on cancer progression and on the development of anti-tumor immunity. For instance, monocyte-derived TREM2+ tumor-associated macrophages have pro-tumorigenic and immunosuppressive functions. Here, we identify a discrete population of FOLR2+ tumor-associated macrophages positively correlating with patient survival in breast cancer. FOLR2+ macrophages are evolutionarily conserved across species and populate human and murine healthy mammary gland. Moreover, FOLR2+ macrophages co-localize with lymphoid aggregates containing CD8+ T cells in breast cancer and across ten other types of cancers. This study highlights antagonistic roles for tumor-associated macrophage subsets and paves the way for subset-specific therapeutic interventions in macrophages-based cancer therapies.

immunology↗

bioRxiv2019

CloneSig: Joint inference of intra-tumor heterogeneity and signature deconvolution in tumor bulk sequencing data

Systematic DNA sequencing of cancer samples has highlighted the importance of two aspects of cancer genomics: intra-tumor heterogeneity (ITH) and mutational processes. These two aspects may not always be independent, as different mutational processes could be involved in different stages or regions of the tumor, but existing computational approaches to study them largely ignore this potential dependency. Here, we present CloneSig, a computational method to jointly infer ITH and mutational processes in a tumor from bulk-sequencing data. Extensive simulations show that CloneSig outperforms current methods for ITH inference and detection of mutational processes when the distribution of mutational signatures changes between clones. Applied to a large cohort of 8,951 tumors with whole-exome sequencing data from The Cancer Genome Atlas, and on a pan-cancer dataset of 2,632 whole-genome sequencing tumor samples from the Pan-Cancer Analysis of Whole Genomes initiative, CloneSig obtains results overall coherent with previous studies.

bioinformatics↗

bioRxiv2019

Biophysical properties of intermediate states of EMT outperform both epithelial and mesenchymal states.

Potential metastatic cells can dissociate from a primary breast tumor by undergoing an epithelial-to-mesenchymal transmission (EMT). Recent work has revealed that cells in intermediate states of EMT acquire an augmented capacity for tumor-cell dissemination. These states have been characterized by molecular markers, but the structural features and the cellular mechanisms that underlie the acquisition of their invasive properties are still unknown. Using human mammary epithelial cells, we generated cells in intermediate states of EMT through the induction of a single EMT-inducing transcription factor, ZEB1, and cells in a mesenchymal state by stimulation with TGF{beta}. In stereotypic and spatially-defined culture conditions, the architecture, internal organization and mechanical properties of cells in the epithelial, intermediate and mesenchymal state were measured and compared. We found that the lack of intercellular cohesiveness in epithelial and mesenchymal cells can be detected early by microtubule destabilization and the repositioning of the centrosome from the cell-cell junction to the cell center. Consistent with their high migration velocities, cells in intermediate states produced low contractile forces compared with epithelial and mesenchymal cells. The high contractile forces in mesenchymal cells powered a retrograde flow pushing the nucleus away from cell adhesion to the extracellular matrix. Therefore, cells in intermediate state had structural and mechanical properties that were distinct but not necessarily intermediate between epithelial and mesenchymal cells. Based on these observations, we found that a panel of triple-negative breast cancer lines had intermediate rather than mesenchymal characteristics suggesting that the structural and mechanical properties of the intermediate state are important for understanding tumor-cell dissemination.

cell biology↗

bioRxiv2019

Domain-invariant features for mechanism of action prediction in a multi-cell-line drug screen

High Content Screening is an important tool in drug discovery and characterisation. Often, high content drug screens are performed on one single cell line. Yet, a single cell line cannot be thought of as a perfect disease model. Many diseases feature an important molecular heterogeneity. Consequently, a drug may be effective against one molecular subtype of a disease, but less so against another. To characterise drugs with respect to their effect not only on one cell line but on a panel of cell lines is therefore a promising strategy to streamline the drug discovery process. The contribution of this paper is twofold. First, we investigate whether we can predict drug mechanism of action (MOA) at the molecular level without optimisation of the MOA classes to the screen specificities. To this end, we benchmark a set of algorithms within a conventional pipeline, and evaluate their MOA prediction performance according to a statistically rigorous framework. Second, we extend this conventional pipeline to the simultaneous analysis of multiple cell lines, each manifesting potentially different morphological baselines. For this, we propose multitask autoencoders, including a domain-adaptive model used to construct domain-invariant feature representations across cell lines. We apply these methods to a pilot screen of two triple negative breast cancer cell lines as models for two different molecular subtypes of the disease.

bioinformatics↗

bioRxiv2018

Assessing reliability of intra-tumor heterogeneity estimates from single sample whole exome sequencing data

Tumors are made of evolving and heterogeneous populations of cells which arise from successive appearance and expansion of subclonal populations, following acquisition of mutations conferring them a selective advantage. Those subclonal populations can be sensitive or resistant to different treatments, and provide information about tumor aetiology and future evolution. Hence, it is important to be able to assess the level of heterogeneity of tumors with high reliability for clinical applications.\n\nIn the past few years, a large number of methods have been proposed to estimate intra-tumor heterogeneity from whole exome sequencing (WES) data, but the accuracy and robustness of these methods on real data remains elusive. Here we systematically apply and compare 6 computational methods to estimate tumor heterogeneity on 1,697 WES samples from the cancer genome atlas (TCGA) covering 3 cancer types (breast invasive carcinoma, bladder urothelial carcinoma, and head and neck squamous cell carcinoma), and two distinct input mutation sets. We observe significant differences between the estimates produced by different methods, and identify several likely confounding factors in heterogeneity assessment for the different methods. We further show that the prognostic value of tumor heterogeneity for survival prediction is limited in those datasets, and find no evidence that it improves over prognosis based on other clinical variables.\n\nIn conclusion, heterogeneity inference from WES data on a single sample, and its use in cancer prognosis, should be considered with caution. Other approaches to assess intra-tumoral heterogeneity such as those based on multiple samples may be preferable for clinical applications.

bioinformatics↗