Epithelial-Stromal Network Deconvolution Analysis Reveals New Targetable Epithelial-Stromal Network Dependencies In Breast Cancer

Proceedings: AACR 106th Annual Meeting 2015; April 18-22, 2015; Philadelphia, PABACKGROUND:Epithelial-stromal crosstalk plays a critical role in breast carcinogenesis. However, the crosstalk networks involved in breast carcinogenesis are not yet known. Uncovering and understanding these networks may enable the development of diagnostics and therapeutics to target epithelial-stromal crosstalk networks for the prevention and treatment of breast cancer. Thus, we performed a genomewide epithelial-stromal co-expression and network deconvolution analysis to identify novel epithelial-stromal crosstalk partners in breast cancer.METHODS:We acquired five breast cancer laser capture microdissection data sets from the Gene Expression Omnibus. Paired epithelial-stromal tumor samples were obtained from 82 patients with invasive breast cancer (IBC) (164 samples) and paired epithelial-stromal normal samples were obtained from 41 patients (82 samples). IBC cases were divided into ER+ and ER- groups. To build epithelial-stromal co-expression networks, we computed the mutual information (MI) between all possible epithelial (E) and stromal (S) pairwise relationships (E-E, E-S, S-S). We then performed network deconvolution to prioritize direct dependencies. A subset of top-ranking dependencies was further evaluated by western blot in breast cancer cell lines and by immunohistochemistry on breast cancer tissue microarrays.RESULTS AND CONCLUSION:A massive number of total interactions were evaluated in our analyses (u003e400,000,000). The top interactions in ER+ IBC, ER- IBC and normal breast groups are known to functionally interact, validating our approach: E-E lipophilin B-mammaglobin interaction (ER+ IBC), S-S metallothionein 2A-metallothionein interaction (ER- IBC; MI = 0.95) and E-E RPS16-RPS3 interaction (normal breast; MI = 1). The functional gene set enrichment analysis using the DAVID bioinformatics tool revealed that the most enriched functional terms in ER+ and ER- IBC groups include: signal peptides, extracellular matrix proteins, and extracellular matrix:receptor interaction (all FDR u003c 0.00001). Using the ClusterONE algorithm, we identified a network cluster driven by Placental Growth Factor (PGF) in both ER+ and ER- IBC. PGF promotes angiogenesis, tumor growth, cell motility, and is expressed by 30-50% of primary breast cancers. We experimentally validated the positive association of PGF and GTF2H3 expression on tissue microarrays and in cell lines. PGF induces the GTF2H3 transcription factor expression in both MCF7 (ER+) and MDA231 (ER-) breast cancer cell lines. In conclusion, epithelial-stromal network deconvolution analysis represents a new approach for the prioritization of interacting epithelial and stromal crosstalk partners in breast cancer, providing new insights into network dependencies in breast carcinogenesis and potentially enabling the identification of new diagnostic and therapeutic targets.Citation Format: Octavian Bucur, Laleh Montaser-Kouhsari, Eun-Yeong Oh, Andrew H. Beck. Epithelial-stromal network deconvolution analysis reveals new targetable epithelial-stromal network dependencies in breast cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2381. doi:10.1158/1538-7445.AM2015-2381