Comparative transcriptional analyses of preclinical models and patient samples reveal MYC and RELA driven expression patterns that define the molecular landscape of IBC.
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2022-01
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Abstract
Inflammatory breast cancer (IBC) is an aggressive disease for which the spectrum of preclinical models was rather limited in the past. More recently, novel cell lines and xenografts have been developed. This study evaluates the transcriptome of an extended series of IBC preclinical models and performed a comparative analysis with patient samples to determine the extent to which the current models recapitulate the molecular characteristics of IBC observed clinically. We demonstrate that the IBC preclinical models are exclusively estrogen receptor (ER)-negative and of the basal-like subtype, which reflects to some extent the predominance of these subtypes in patient samples. The IBC-specific 79-signature we previously reported was retrained and discriminated between IBC and non-IBC preclinical models, but with a relatively high rate of false positive predictions. Further analyses of gene expression profiles revealed important roles for cell proliferation, MYC transcriptional activity, and TNFɑ/NFκB in the biology of IBC. Patterns of MYC expression and transcriptional activity were further explored in patient samples, which revealed interactions with ESR1 expression that are contrasting in IBC and nIBC and notable given the comparatively poor outcomes of ER+ IBC. Our analyses also suggest important roles for NMYC, MXD3, MAX, and MLX in shaping MYC signaling in IBC. Overall, we demonstrate that the IBC preclinical models can be used to unravel cancer cell intrinsic molecular features, and thus constitute valuable research tools. Nevertheless, the current lack of ER-positive IBC models remains a major hurdle, particularly since interactions with the ER pathway appear to be relevant for IBC.
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Rypens, Charlotte, François Bertucci, Pascal Finetti, Fredika Robertson, Sandra V Fernandez, Naoto Ueno, Wendy A Woodward, Kenneth Van Golen, et al. (2022). Comparative transcriptional analyses of preclinical models and patient samples reveal MYC and RELA driven expression patterns that define the molecular landscape of IBC. NPJ breast cancer, 8(1). p. 12. 10.1038/s41523-021-00379-6 Retrieved from https://hdl.handle.net/10161/29294.
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Gayathri R. Devi
Dr. Devi’s research interests include functional genomics, anti-cancer drug discovery and development, mechanisms of cancer cell signaling, tumor immunity and applications thereof for overcoming therapeutic resistance in cancer.
The lab has established prostate, inflammatory breast cancer and ovarian cellular and tumor models.
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