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Supplemental Table 2 from Fibroblast Growth Factor Receptor Signaling Dramatically Accelerates Tumorigenesis and Enhances Oncoprotein Translation in the Mouse Mammary Tumor Virus–Wnt-1 Mouse Model of Breast Cancer

Supplemental Fig 5 from Fibroblast Growth Factor Receptor Signaling Dramatically Accelerates Tumorigenesis and Enhances Oncoprotein Translation in the Mouse Mammary Tumor Virus–Wnt-1 Mouse Model of Breast Cancer

Supplementary Figure Legends 1-6, Table 1 Legend from Fibroblast Growth Factor Receptor Signaling Dramatically Accelerates Tumorigenesis and Enhances Oncoprotein Translation in the Mouse Mammary Tumor Virus–Wnt-1 Mouse Model of Breast Cancer

Supplemental Fig 2 from Fibroblast Growth Factor Receptor Signaling Dramatically Accelerates Tumorigenesis and Enhances Oncoprotein Translation in the Mouse Mammary Tumor Virus–Wnt-1 Mouse Model of Breast Cancer

Supplemental Fig 3 from Fibroblast Growth Factor Receptor Signaling Dramatically Accelerates Tumorigenesis and Enhances Oncoprotein Translation in the Mouse Mammary Tumor Virus–Wnt-1 Mouse Model of Breast Cancer

Data Supplement from Fibroblast Growth Factor Receptor Signaling Dramatically Accelerates Tumorigenesis and Enhances Oncoprotein Translation in the Mouse Mammary Tumor Virus–Wnt-1 Mouse Model of Breast Cancer

Fibroblast growth factor (FGF) cooperates with the Wnt/β-catenin pathway to promote mammary tumorigenesis. To investigate the mechanisms involved in FGF/Wnt cooperation, we genetically engineered a model of inducible FGF receptor (iFGFR) signaling in the context of the well-established mouse mammary tumor virus–Wnt-1 transgenic mouse. In the bigenic mice, iFGFR1 activation dramatically enhanced mammary tumorigenesis. Expression microarray analysis did not show transcriptional enhancement of Wnt/β-catenin target genes but instead showed a translational gene signature that also correlated with elevated FGFR1 and FGFR2 in human breast cancer data sets. Additionally, iFGFR1 activation enhanced recruitment of RNA to polysomes, resulting in a marked increase in protein expression of several different Wnt/β-catenin target genes. FGF pathway activation stimulated extracellular signal-regulated kinase and the phosphorylation of key translation regulators both in vivo in the mouse model and in vitro in a human breast cancer cell line. Our results suggest that cooperation of the FGF and Wnt pathways in mammary tumorigenesis is based on the activation of protein translational pathways that result in, but are not limited to, increased expression of Wnt/β-catenin target genes (at the level of protein translation). Further, they reveal protein translation initiation factors as potential therapeutic targets for human breast cancers with alterations in FGF signaling. Cancer Res; 70(12); 4868–79. ©2010 AACR.

Supplemental Fig 8 from Fibroblast Growth Factor Receptor Signaling Dramatically Accelerates Tumorigenesis and Enhances Oncoprotein Translation in the Mouse Mammary Tumor Virus–Wnt-1 Mouse Model of Breast Cancer

Supplemental Fig 4 from Fibroblast Growth Factor Receptor Signaling Dramatically Accelerates Tumorigenesis and Enhances Oncoprotein Translation in the Mouse Mammary Tumor Virus–Wnt-1 Mouse Model of Breast Cancer

Supplemental Fig 7 from Fibroblast Growth Factor Receptor Signaling Dramatically Accelerates Tumorigenesis and Enhances Oncoprotein Translation in the Mouse Mammary Tumor Virus–Wnt-1 Mouse Model of Breast Cancer

Supplemental Table 3 from Fibroblast Growth Factor Receptor Signaling Dramatically Accelerates Tumorigenesis and Enhances Oncoprotein Translation in the Mouse Mammary Tumor Virus–Wnt-1 Mouse Model of Breast Cancer

Supplemental Table 1 from Fibroblast Growth Factor Receptor Signaling Dramatically Accelerates Tumorigenesis and Enhances Oncoprotein Translation in the Mouse Mammary Tumor Virus–Wnt-1 Mouse Model of Breast Cancer

Supplemental Fig 6 from Fibroblast Growth Factor Receptor Signaling Dramatically Accelerates Tumorigenesis and Enhances Oncoprotein Translation in the Mouse Mammary Tumor Virus–Wnt-1 Mouse Model of Breast Cancer

We created the PDX Network (PDXNet) Portal to provide an intuitive way for researchers to explore and understand the models, sequencing data, and bioinformatics workflows generated by NCI's PDXNet consortium for research access (https://portal.pdxnetwork.org/). The portal also provides metrics for PDXNet's activities, data processing protocols, and training materials for processing PDX data. The PDXNet Portal highlights model and data resources that include 216 new models across 29 cancer types. The most prevalent cancers represented in the PDX model dataset include invasive breast carcinoma (30.6%), melanoma (18.1%), and adenocarcinoma (14.4%). PDXNet teams have provided 2263 sequencing files from 356 samples across 204 patients, comprising whole exome (82.9%) and RNA seq files (17.1%). The most prevalent cancers represented in the PDXNet sequencing data set include Breast Pleural Effusion (27.2%), Breast Poorly Differentiated (12.5%), and Lung Adenocarcinoma (9.6%). The portal also provides access to 9492 sequencing files across 78 disease types that include 2594 samples across 463 patients uploaded from the NCI Patient-Derived Model Repository. The dataset includes both whole exomes (52.8%) and RNA seq (47.2%) data. The PDMR samples include PDX (82.7%), primary tumor (5.7%), normal germline (5.5), organoid culture (3.2), and Mixed Tumor Culture (2.9). The PDMR dataset also has multiple passages: P0 (21.8%), P1(39.5%), P2 (25.6%), and P3 (8.5%). These models and data resources support ten PDXNet Pilot activities, multiple publications, and international collaborations. PDXNet has also developed a set of 13 robust, validated, and standardized workflows for processing PDXNet whole-exome and RNA seq data. Collectively, these workflows allow for the standardized processing of PDX and complementary human tissues (normal and tumor). Our plan is to continuously update the model and data lists on the PDX portal as resources are generated. We expect that the PDXNet generated models, scheduled to grow to 1000 new models by 2022, will support multi-agent treatment studies, determination of mechanisms of sensitivity and resistance, and pre-clinical trials for example through the COMBO-MATCH program. The robust standard workflows currently processing all PDX sequencing data may also facilitate harmonizing data across studies. Lastly, we expect that the generated sequencing data will support computational approaches for studying cancer evolution and the mechanisms underlying cancer treatments. Citation Format: Soner Koc, Mike Lloyd, Steven Neuhauser, Javad Noodbakhsh, Anuj Srivastava, Xing Yi Woo, Ryan Jeon, Jeffrey Grover, Sara Seepo, Christian Frech, Jack DiGiovanna, PDXNet Consortium, Yvonne A. Evard, Tiffany Wallace, Jeffrey Moscow, James H. Doroshow, Nicholas Mitsuade, Salma Kaochar, Chong-xian Pan, Moon S. Chen, Luis Carvarjal-Carmona, Alana Welm, Bryan Welm, Michael T. Lewis, Govindan Ramaswamy, Li Ding, Shunquang Li, Meenherd Herlyn, Mike Davies, Jack Roth, Funda Meric-Bernstam, Peter Robinson, Carol J. Bult, Brandi Davis-Dusenbery, Dennis A. Dean, Jeffrey H. Chuang. Advancing PDX research through model, data, and bioinformatics with the PDXNet Portal [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 3017.

Significant advances in the stem-cell biology of several tissues, including the mammary gland, have occurred over the past several years. Recent progress on stem-cell fate determination, molecular markers, signalling pathways and niche interactions in haematopoietic, neuronal and muscle tissue may provide parallel insight into the biology of mammary epithelial stem cells. Taking advantage of approaches similar to those employed to isolate and characterize haematopoietic and epidermal stem cells, we have identified a mammary epithelial cell population with several stem/progenitor cell qualities. In this article, we review some recent data on mammary epithelial stem/progenitor cells in genetically engineered mouse models. We also discuss several potential molecular markers, including stem-cell antigen-1 (Sca-1), which may be useful for both the isolation of functional mammary epithelial stem/progenitor cells and the analysis of tumour aetiology and phenotype in genetically engineered mouse models. In different transgenic mammary tumour models, Sca-1 expression levels, as well as several other putative markers of progenitors including keratin-6, possess dramatically altered expression profiles. These data suggest that the heterogeneity of mouse models of breast cancer may partially reflect the selection or expansion of different progenitors.