Care, M. A. | bioRxiv Science

bioRxiv2018

Growth-factor like gene regulation is separable from survival and maturation in antibody secreting cells

Recurrent mutational activation of the MAP kinase pathway in plasma cell myeloma implicates growth factor-like signaling responses in the biology of antibody secreting cells (ASCs). Physiological ASCs survive in niche microenvironments, but how niche signals are propagated and integrated is poorly understood. Here we dissect such a response in human ASCs using an in vitro model. Applying time course expression data and parsimonious gene correlation networking analysis (PGCNA), we map expression changes that occur during the maturation of proliferating plasmablast to quiescent plasma cell under survival conditions including the potential niche signal TGFB3. This analysis demonstrates a convergent pattern of differentiation, linking UPR/ER stress to secretory optimization, co-ordinated with cell cycle exit. TGFB3 supports ASC survival while having a limited effect on gene expression including up-regulation of CXCR4. This is associated with a significant shift in response to SDF1 in ASCs with amplified ERK1/2 activation, growth factor-like immediate early gene regulation and EGR1 protein expression. Similarly, ASCs responding to survival conditions initially induce partially overlapping sets of immediate early genes, without sustaining the response. Thus, in human ASCs growth factor-like gene regulation is transiently imposed by niche signals but is not sustained during subsequent survival and maturation.

immunology↗

bioRxiv2018

Defining common principles of gene co-expression refines molecular stratification in cancer

Cancers converge onto shared patterns that arise from constraints placed by the biology of the originating cell lineage and microenvironment on recurrent programs driven by oncogenic events. This structure should be transferable to molecular stratification. We exploit expression data resources and a parsimonious and computationally efficient network analysis method to define consistent expression modules in colon and breast cancer. Comparison between cancer types identifies principles of gene co-expression: cancer hallmarks, functional and structural gene batteries, copy number variation and biology of originating lineage. Mapping outcome data at gene and module level onto these networks generates a detailed interactive resource. Testing the utility of the resulting modules in TCGA data defines specific associations of module expression with mutation state, identifying striking associations such as mast cell gene expression and mutation pattern in breast cancer. These analyses provide evidence for a generalizable framework to enhance molecular stratification in cancer.

bioinformatics↗