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Pinson, X.

Publications and source records attributed to Pinson, X..

2 recordsLinked to original sources

Primary cilia promote EMT-induced triple-negative breast tumor heterogeneity and resistance to therapy

Tumor heterogeneity and plasticity, driven by Epithelial-Mesenchymal Transition (EMT), enable cancer therapeutic resistance. We previously showed that EMT promotes primary cilia formation, which enables stemness and tumorigenesis in triple-negative breast cancer (TNBC). Here, we establish a role for primary cilia in human TNBC chemotherapeutic resistance. We developed patient-derived organoids, and showed that these recapitulated the cellular heterogeneity of TNBC biopsies. Notably, one of the identified cell states bore a quasi-mesenchymal phenotype, primary cilia, and stemness signatures. We treated our TNBC organoids with chemotherapeutics and observed partial killing. The surviving cells with organoid-reconstituting capacity showed selective enrichment for the quasi-mesenchymal ciliated cell subpopulation. Genomic analyses argue that this enrichment reflects a combination of pre-existing cells and ones that arose through drug-induced cellular plasticity. We developed a family of small-molecule inhibitors of ciliogenesis and show that these, or genetic ablation of primary cilia, suppress chemoresistance. We conclude that primary cilia help TNBC to evade chemotherapy. SignificanceCancer cells that activate EMT to acquire a quasi-mesenchymal state form primary cilia to evade chemotherapy in human triple-negative breast cancer. Pharmacological inhibition of primary ciliogenesis counteracts EMT-induced chemoresistance.

cancer biology↗

Only three principal components account for inter-embryo variability of the spindle length over time.

How to quantify inter-individual variability? When measuring many features per experiment/individual, this question becomes non-trivial. One challenge lies in choosing features to recapitulate high-dimensional data. This paper focuses on spindle elongation phenotypes to highlight how a data-driven approach can help tackle this challenge. We showed that only three typical elongation patterns describe spindle elongation in C. elegans one-cell embryo. We called them archetypes. These archetypes were automatically extracted from the experimental data using principal component analysis (PCA) rather than defined a priori. They accounted for more than 95% of inter-individual variability in a dataset of more than 1600 experiments across more than 100 different experimental conditions (RNAi, mutants, changes in temperature, etc.). The two first archetypes were consistent with standard measures in the field, namely the average spindle length and the spindle elongation rate in late metaphase and anaphase. However, our archetypes were not strictly corresponding to these classic, manually-set, features. The third archetype, accounting for 6% of the variability, was novel and corresponded to a transient spindle shortening in late metaphase. We revealed that it is part of spindle elongation dynamics in all conditions. It is reminiscent of the spindle elongation pattern observed upon kinetochore function defects. Interestingly, because these archetypes were all three present from metaphase on, it implied that spindle elongation around the anaphase onset is sufficient to predict its late anaphase length. We validated this idea using a machine-learning approach. The inter-individual differences between embryos depleted from cell division-related proteins have the same underlying nature as inter-individual differences naturally arising between wild-type embryos. The same conclusion holds also when analysing embryos dividing at various temperatures. We thus propose that beyond the apparent complexity of the spindle and variability in the phenotypes of various gene depletions, only three independent mechanisms account for spindle elongation, weighted differently in the various conditions; meanwhile, no mechanism is specific to any condition. As such, given amounts of these three archetypes could represent a quantitative phenotype.

bioinformatics↗