PIK3Cδ expression by fibroblasts promotes triple-negative breast cancer progression
Teresa Gagliano, … , Justin Stebbing, Georgios Giamas
Teresa Gagliano, … , Justin Stebbing, Georgios Giamas
Published March 10, 2020
Citation Information: J Clin Invest. 2020;130(6):3188-3204. https://doi.org/10.1172/JCI128313.
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Research Article Cell biology Oncology

PIK3Cδ expression by fibroblasts promotes triple-negative breast cancer progression

Abstract

As there is growing evidence for the tumor microenvironment’s role in tumorigenesis, we investigated the role of fibroblast-expressed kinases in triple-negative breast cancer (TNBC). Using a high-throughput kinome screen combined with 3D invasion assays, we identified fibroblast-expressed PIK3Cδ (f-PIK3Cδ) as a key regulator of cancer progression. Although PIK3Cδ was expressed in primary fibroblasts derived from TNBC patients, it was barely detectable in breast cancer (BC) cell lines. Genetic and pharmacological gain- and loss-of-function experiments verified the contribution of f-PIK3Cδ in TNBC cell invasion. Integrated secretomics and transcriptomics analyses revealed a paracrine mechanism via which f-PIK3Cδ confers its protumorigenic effects. Inhibition of f-PIK3Cδ promoted the secretion of factors, including PLGF and BDNF, that led to upregulation of NR4A1 in TNBC cells, where it acts as a tumor suppressor. Inhibition of PIK3Cδ in an orthotopic BC mouse model reduced tumor growth only after inoculation with fibroblasts, indicating a role of f-PIK3Cδ in cancer progression. Similar results were observed in the MMTV-PyMT transgenic BC mouse model, along with a decrease in tumor metastasis, emphasizing the potential immune-independent effects of PIK3Cδ inhibition. Finally, analysis of BC patient cohorts and TCGA data sets identified f-PIK3Cδ (protein and mRNA levels) as an independent prognostic factor for overall and disease-free survival, highlighting it as a therapeutic target for TNBC.

Authors

Teresa Gagliano, Kalpit Shah, Sofia Gargani, Liyan Lao, Mansour Alsaleem, Jianing Chen, Vasileios Ntafis, Penghan Huang, Angeliki Ditsiou, Viviana Vella, Kritika Yadav, Kamila Bienkowska, Giulia Bresciani, Kai Kang, Leping Li, Philip Carter, Graeme Benstead-Hume, Timothy O’Hanlon, Michael Dean, Frances M.G. Pearl, Soo-Chin Lee, Emad A. Rakha, Andrew R. Green, Dimitris L. Kontoyiannis, Erwei Song, Justin Stebbing, Georgios Giamas

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Figure 4

Global secretome analysis of CAL-101–treated fibroblasts and transcriptomics analysis of MDA-MB-231 cells.

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Global secretome analysis of CAL-101–treated fibroblasts and transcripto...
(A) To obtain CM from HMF and MRC5, cells were treated with vehicle or 10 μM CAL-101 in serum-free medium for 24 hours. CM was used to perform secretome analysis using the Human L1000 Array. Venn diagram showing differences in the secreted proteins significantly regulated by CAL-101 in HMF and MRC5 cells (Padj < 0.05 and log2 fold difference of ≥|0.5|). (B) UpSet plot showing common and unique CAL-101–regulated proteins significantly upregulated (up) or downregulated (down) in each data set (MRC5 and HMF). (C) Heatmap comparing log2 fold change of secreted proteins between CAL-101–treated HMF and MRC5 cells. (D) MRC5 cells were treated with either DMSO or 10 μM CAL-101 for 24 hours. Then cells were washed with PBS to remove the treatment, and complete fresh medium was added to each well. Five-micrometer inserts containing MDA-MB-231 cells were then added in the well containing previously treated MRC5. Twenty-four hours after coculture, cancer cells were collected for RNA extraction and subsequent RNA sequencing. Volcano plot showing the log2 fold change of genes in MDA-MB-231 cells that responded differently to CAL-101 treatment of MRC5 cells (DMSO:CAL-101). The log10 of P value, for significance in fold change, is plotted on the y axis. (E) Heatmap showing amounts by which the read counts of the top 24 regulated genes (ordered based on log2 fold change ≥|0.5| and Padj ≤0.05) deviate from the genes’ average across all the samples. (F) qRT-PCR validation of genes identified via the RNA-seq and DESeq2 analysis. Significance was calculated using unpaired t tests. Results are expressed as mean ± SEM; *P < 0.05, **P < 0.01 vs. vector. (G) Western blotting of NR4A1 in MDA-MB-231 cells following coculture with CAL-101–treated MRC5 cells. α-Tubulin was used as loading control. Densitometry analysis of the blot is displayed as a ratio between CAL-101–treated and DMSO-treated cells.