Biology subjects

Becht, E.

Publications and source records attributed to Becht, E..

3 recordsLinked to original sources

bioRxiv2018

Cancer stemness, intratumoral heterogeneity, and immune response across cancers

Regulatory programs that control the function of stem cells are active in cancer and confer properties that promote progression and therapy resistance. However, the impact of a stem cell-like tumor phenotype (\"sternness\") on the immunological properties of cancer has not been systematically explored. Using gene expression-based metrics, we evaluate the association of stemness with immune cell infiltration and genomic, transcriptomic, and clinical parameters across 21 solid cancers. We find pervasive negative associations between cancer stemness and anticancer immunity. This occurs despite high stemness cancers exhibiting increased mutation load, cancer-testis antigen expression, and intratumoral heterogeneity. Stemness was also strongly associated with cell-intrinsic suppression of endogenous retroviral expression and type I interferon signaling and increased expression of several therapeutically accessible signaling pathways. Thus, stemness is not only a fundamental process in cancer progression but may represent a unifying mechanism linking antigenicity, intratumoral heterogeneity, and immune suppression across cancers.

cancer biology↗

bioRxiv2018

Evaluation of UMAP as an alternative to t-SNE for single-cell data

Uniform Manifold Approximation and Projection (UMAP) is a recently-published non-linear dimensionality reduction technique. Another such algorithm, t-SNE, has been the default method for such task in the past years. Herein we comment on the usefulness of UMAP high-dimensional cytometry and single-cell RNA sequencing, notably highlighting faster runtime and consistency, meaningful organization of cell clusters and preservation of continuums in UMAP compared to t-SNE.

bioinformatics↗

bioRxiv2018

Reverse-engineering flow-cytometry gating strategies for phenotypic labelling and high-performance cell sorting

MotivationRecent flow and mass cytometers generate 1,000,000 single cell datasets of dimensions 20 to 40. Many tools facilitate the discovery of new cell populations associated with diseases or physiology. These discoveries require the identification of new gating strategies, but gating strategies become exponentially harder to optimize when dimensionality increases. To facilitate this step we developed Hypergate, an algorithm which given a cell population of interest identifies a gating strategy optimized for high yield and purity.\n\nResultsHypergate achieves higher yield and purity than human experts, Support Vector Machines and Random-Forests on public datasets. We use it to revisit some established gating strategies for the identification of Innate lymphoid cells, which identifies concise and efficient strategies that allow gating these cells with fewer parameters but higher yield and purity than the current standards. For phenotypic description, Hypergates outputs are consistent with fields knowledge and sparser than those from a competing method.\n\nAvailability and ImplementationHypergate is implemented in R and available at http://github.com/ebecht/hypergate under an Open Source Initiative-compliant licence.

bioinformatics↗