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. 2011 Jul;121(7):2723-35.
doi: 10.1172/JCI44745.

The JAK2/STAT3 signaling pathway is required for growth of CD44⁺CD24⁻ stem cell-like breast cancer cells in human tumors

Lauren L C Marotta  1 , Vanessa Almendro, Andriy Marusyk, Michail Shipitsin, Janina Schemme, Sarah R Walker, Noga Bloushtain-Qimron, Jessica J Kim, Sibgat A Choudhury, Reo Maruyama, Zhenhua Wu, Mithat Gönen, Laura A Mulvey, Marina O Bessarabova, Sung Jin Huh, Serena J Silver, So Young Kim, So Yeon Park, Hee Eun Lee, Karen S Anderson, Andrea L Richardson, Tatiana Nikolskaya, Yuri Nikolsky, X Shirley Liu, David E Root, William C Hahn, David A Frank, Kornelia Polyak
Affiliations

The JAK2/STAT3 signaling pathway is required for growth of CD44⁺CD24⁻ stem cell-like breast cancer cells in human tumors

Lauren L C Marotta et al. J Clin Invest. 2011 Jul.

Abstract

Intratumor heterogeneity is a major clinical problem because tumor cell subtypes display variable sensitivity to therapeutics and may play different roles in progression. We previously characterized 2 cell populations in human breast tumors with distinct properties: CD44+CD24- cells that have stem cell-like characteristics, and CD44-CD24+ cells that resemble more differentiated breast cancer cells. Here we identified 15 genes required for cell growth or proliferation in CD44+CD24- human breast cancer cells in a large-scale loss-of-function screen and found that inhibition of several of these (IL6, PTGIS, HAS1, CXCL3, and PFKFB3) reduced Stat3 activation. We found that the IL-6/JAK2/Stat3 pathway was preferentially active in CD44+CD24- breast cancer cells compared with other tumor cell types, and inhibition of JAK2 decreased their number and blocked growth of xenografts. Our results highlight the differences between distinct breast cancer cell types and identify targets such as JAK2 and Stat3 that may lead to more specific and effective breast cancer therapies.

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Figures

Figure 1
Figure 1. Characteristics of genes and cell lines chosen for shRNA screen.
(A) Clustering of SAGE data for the genes in the screen. Genes were selected based on their differential expression between the groups of samples of CD44+CD24– and CD44–CD24+ (primary tumor) cells shown. Red and green (in A and B) indicate high and low gene expression, respectively. PE, pleural effusion; ASC, ascites; IDC, invasive ductal carcinoma. (B) Clustering of previously published microarray data (63) for 54 breast cell lines using the genes in the screen. Clusters of basal-like and luminal cell lines were formed. The 14 cell lines highlighted in bold were chosen for the screen. (C) Expression of CD44 and CD24 in the cell lines used in the screen. Results of flow cytometry analysis of CD44 and CD24 levels are shown. Flow cytometry profiles and gating are shown in Supplemental Figure 1. (D) qMSP data for the cell lines included in the screen for genes differentially methylated between CD44+CD24– and CD44–CD24+ breast cancer cells (as shown at right). Each bar represents the natural log of the ratio of the qMSP reading of either FOXC1, HOXA10, or PACAP (official gene symbol, MGC29506) to the qMSP reading of SLC9A3R1. Error bars show SD of triplicates (for cell lines) or 3 ER+PR+HER2– tumor samples (for primary tumor cells). Asterisks indicate the cell lines that were chosen for phase 1 of the screen based on these results and the ease of their growing in culture.
Figure 2
Figure 2. Identification and validation of genes required in basal-like breast cancer cells.
(A) Robust z test scores ([average viability – plate median average viability]/[plate median absolute deviation of average viability]) from shRNA screen, phase 1, for shRNAs with infection efficiencies greater than 0.25 and robust z test score SD below the 99th percentile for both basal-like and luminal cell lines with and without puromycin (puro). Data representing basal-like and luminal hits, selected based on their 4 basal-like or luminal robust z test scores as described in the text, are red and blue, respectively. (B) Scoring of shRNAs with infection efficiencies greater than 0.25 for all cell lines and classification of hits in shRNA screen, phase 2. Shading indicates shRNAs scored based on percentage of control values ([100 × average viability]/[median average viability of plate controls]). Genes corresponding to hits are listed. (C) Box plots showing viability of breast cell lines treated with siRNAs in triplicate for 3 days. Triangles mark averages. Circles mark outliers (which were included in P value calculations). *P < 0.05, t test; **P < 0.01, t test. (D) GI50 values for inhibitors in breast cell lines. The maximum percentage of growth inhibition observed is shown when cells were not affected enough for GI50 calculation. Error bars show SD of triplicates. All percentage growth inhibition data used to prepare these graphs are depicted in Supplemental Figure 2C.
Figure 3
Figure 3. Preferential activation of the IL-6/JAK2/Stat3 pathway in basal-like breast cancer cells.
(A) ELISA results. Error bars show SD of triplicates. (B) Immunoblots. Positive and negative controls were T-47D cells treated or not treated, respectively, with prolactin and oncostatin M. Stat3, pStat1, pStat5, and tubulin were used as controls. (C) Immunoblots. Two-hour treatment with 2 μM JAK inhibitor reduces pStat3 in basal-like breast cells containing it. pserStat3 and Stat3 were used as controls.
Figure 4
Figure 4. Importance of the IL-6/JAK2/Stat3 pathway in tumor growth.
(A) Representative immunofluorescence staining patterns for CD44, CD24, and pStat3 in ER–PR–HER2+ inflammatory breast carcinoma (IDC31). Scale bar: 10 microns. (B) Representative immunofluorescence staining patterns for CD44, CD24, and pStat3 in SUM159PT and IDC31 xenografts. Scale bars: 10 microns. (C) Box plots showing percentage of pStat3+ cells in SUM159PT and IDC31 mouse xenograft-derived (IDC31-X) xenografts by immunofluorescence and immunohistochemistry, respectively (counting 2–6 fields per sample). (D) Box plots showing xenograft tumor weights 34, 28, 28, 40, or 70 days after injecting SUM159PT, MDA-MB-231, MDA-MB-468, Hs 578T, or IDC31-X cells, respectively, into 2 fat pads of n mice. Mice were administered daily NVP-BSK805 (2 mg/mouse) or vehicle only (control) for 14, 16, 16, 24, or 24 days, respectively (after tumors reached palpable size), beginning 21, 13, 13, 17, or 47 days after injection, respectively. (E) H&E-stained Hs 578T and IDC31-X xenografts. (F) Box plots showing the percentage of area with cells in Hs 578T and IDC31-X xenografts calculated from whole tumor sections with H&E staining. (G) Kaplan-Meier curves of SUM159PT xenografts expressing STAT3 shRNAs (shSTAT3 #1 and #2) in n mice. (H) Immunoblots with cells used for xenografts in G. Tubulin was used as a loading control. (I) pStat3 immunohistochemistry staining for xenografts in G. Scale bars: 50 microns. Triangles in C, D, and F mark averages. *P < 0.05; **P < 0.01; ***P < 0.001, t test (C, D, and F). ***P < 0.001, log-rank test (comparing each STAT3 shRNA group to the shGFP control group) (G).
Figure 5
Figure 5. Network of genes targeted by the basal-like–specific screening hits.
The MetaCore software platform was used to construct the diagram shown. The 15 genes targeted by the basal-like–specific hits from the shRNA screen and links among them based on published findings are included in the network. Genes targeted by screening hits are marked with red concentric circles. Red and green arrows indicate inhibitory and activating interactions, respectively. Gray arrows indicate interactions between substrate and reaction or reaction and product or indicate unspecified effects.
Figure 6
Figure 6. Regulation of the JAK2/Stat3 pathway in basal-like breast cancer cells and its clinical significance in primary human breast tumors.
(A) Immunoblots of pStat3 in basal-like breast cancer cell lines (and MCF7, in which pStat3 is undetectable by immunoblot) after 6-hour inhibitor treatments. Concentrations used were 2 μM JAK, 1 mM PTGIS, 1.5 μM CXCR2, 10 μM PFKFB3, 1 mM HAS1, and 50 μM NQO1 inhibitor. Stat3 was used as a loading control. (B) Quantitation of basal-like immunoblots in A. pStat3/Stat3 values represent ratios to control (no drug). (C) Luciferase assay results using Hs 578T and MCF7 cells treated with inhibitors for 2 days. Error bars show SD of triplicates. **P < 0.01; ***P < 0.001, t test. (D and F) Fold changes in tag counts for genes in our Hs 578T and MCF7 Stat3 signatures in SAGE-Seq libraries prepared from Hs 578T or MCF7 cells treated for 2 days with STAT3 siRNAs (versus nontargeting siRNAs) or inhibitors (versus no drug). Red and green indicate high and low fold changes, respectively. Each gene in the signatures had |fold change| > 2 with STAT3 siRNAs and in the same direction with at least 4 inhibitors (not NQO1). (E and G) Significant association of the presence of the Hs 578T Stat3 signature with shorter distant metastasis-free survival in 2 cohorts of breast cancer patients and lack of such an association for the MCF7 Stat3 signature. Kaplan-Meier curves (for n patients with and without each signature) and their corresponding log-rank test P values are shown.
Figure 7
Figure 7. Specific activation of Stat3 in CD44+CD24– cells in primary human breast tumors.
(A) Representative immunofluorescence staining patterns for CD44, CD24, and pStat3 in primary human breast tumor samples in each of the 4 indicated cell types. pStat3 is primarily in CD44+CD24– cells. Scale bars: 10 microns. (B) Frequency of pStat3 positivity in the indicated breast cancer cell types in 170 samples analyzed by triple immunofluorescence. (C) Box plots showing cells of 4 types positive for pStat3 based on triple immunofluorescence in 4 breast tumor subtypes. Triangles mark averages. *P < 0.001. (D) Model of Stat3 activation in breast cancer. CD44+CD24– stem cell–like cancer cells have constitutive pStat3 due to expression of genes such as IL6, PTGIS, and HAS1. Other cancer cells are sometimes pStat3+ due to uptake of IL-6 secreted by CD44+CD24– cancer cells, fibroblasts, and inflammatory cells.
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