Transposon mutagenesis identifies genes that cooperate with...

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Transposon mutagenesis identifies genes that cooperate with mutant Pten in breast cancer progression | PNAS Research Article Transposon mutagenesis identifies genes that cooperate with mutant Pten in breast cancer progression Roberto Rangel, Song-Choon Lee, Kenneth Hon-Kim Ban, Liliana Guzman-Rojas, Michael B. Mann, Justin Y. Newberg,View ORCID ProfileTakahiro Kodama, Leslie A. McNoe, Luxmanan Selvanesan, Jerrold M. Ward, Alistair G. Rust, Kuan-Yew Chin, Michael A. Black, Nancy A. Jenkins, and Neal G. CopelandaCancer Research Program, Houston Methodist Research Institute, Houston, TX 77030;bDivision of Genomics and Genetics, Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, Biopolis, Singapore 138673;cDeparment of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 138673;dDepartment of Biochemistry, University of Otago, Dunedin 9016, New Zealand;eExperimental Cancer Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1HH, United KingdomSee allHide authors and affiliationsPNAS November 29, 2016 113 (48) E7749-E7758; first published November 14, 2016; https://doi.org/10.1073/pnas.1613859113 Roberto Rangel aCancer Research Program, Houston Methodist Research Institute, Houston, TX 77030;Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteSong-Choon Lee bDivision of Genomics and Genetics, Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, Biopolis, Singapore 138673;Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteKenneth Hon-Kim Ban bDivision of Genomics and Genetics, Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, Biopolis, Singapore 138673;cDeparment of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 138673;Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteLiliana Guzman-Rojas aCancer Research Program, Houston Methodist Research Institute, Houston, TX 77030;Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteMichael B. Mann aCancer Research Program, Houston Methodist Research Institute, Houston, TX 77030;Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteJustin Y. Newberg aCancer Research Program, Houston Methodist Research Institute, Houston, TX 77030;Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteTakahiro Kodama aCancer Research Program, Houston Methodist Research Institute, Houston, TX 77030;Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Takahiro Kodama Leslie A. McNoe dDepartment of Biochemistry, University of Otago, Dunedin 9016, New Zealand;Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteLuxmanan Selvanesan dDepartment of Biochemistry, University of Otago, Dunedin 9016, New Zealand;Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteJerrold M. Ward bDivision of Genomics and Genetics, Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, Biopolis, Singapore 138673;Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteAlistair G. Rust eExperimental Cancer Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1HH, United KingdomFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteKuan-Yew Chin bDivision of Genomics and Genetics, Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, Biopolis, Singapore 138673;Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteMichael A. Black dDepartment of Biochemistry, University of Otago, Dunedin 9016, New Zealand;Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteNancy A. Jenkins aCancer Research Program, Houston Methodist Research Institute, Houston, TX 77030;bDivision of Genomics and Genetics, Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, Biopolis, Singapore 138673;Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteNeal G. Copeland aCancer Research Program, Houston Methodist Research Institute, Houston, TX 77030;bDivision of Genomics and Genetics, Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, Biopolis, Singapore 138673;Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteFor correspondence: ncopeland@houstonmethodist.org Contributed by Neal G. Copeland, October 4, 2016 (sent for review July 14, 2016; reviewed by Kent W. Hunter and Branden Moriarty) SignificanceTriple-negative breast cancer (TNBC) is the most aggressive breast cancer subtype. Despite extensive cancer genome-sequencing efforts, there is still an incomplete understanding of the genetic networks driving TNBC. Here, we used Sleeping Beauty transposon mutagenesis to identify genes that cooperate with mutant Pten in the induction of TNBC. We identified 12 candidate TNBC trunk drivers and a larger number of progression genes. Subsequent functional validation studies identified eight human TNBC tumor suppressor genes, including the GATA-like transcriptional repressor TRPS1, which was shown to inhibit lung metastasis by deregulating the expression of multiple serpin and epithelial-to-mesenchymal transition (EMT) pathway genes. Our study provides a better understanding of the genetic forces driving TNBC and discovered genes with clinical importance in TNBC.AbstractTriple-negative breast cancer (TNBC) has the worst prognosis of any breast cancer subtype. To better understand the genetic forces driving TNBC, we performed a transposon mutagenesis screen in a phosphatase and tensin homolog (Pten) mutant mice and identified 12 candidate trunk drivers and a much larger number of progression genes. Validation studies identified eight TNBC tumor suppressor genes, including the GATA-like transcriptional repressor TRPS1. Down-regulation of TRPS1 in TNBC cells promoted epithelial-to-mesenchymal transition (EMT) by deregulating multiple EMT pathway genes, in addition to increasing the expression of SERPINE1 and SERPINB2 and the subsequent migration, invasion, and metastasis of tumor cells. Transposon mutagenesis has thus provided a better understanding of the genetic forces driving TNBC and discovered genes with potential clinical importance in TNBC.Sleeping Beautybreast cancerTRPS1metastasistumor suppressorsBreast cancer is the second leading cause of cancer-related deaths in the United States. The Cancer Genome Atlas (TCGA) network has classified breast cancer into four main subtypes: luminal A, luminal B, HER2+, and basal-like (1⇓⇓⇓–5). Basal-like or triple-negative breast cancer (TNBC) constitutes 10–20% of all breast cancers and has a higher rate of distal recurrence and a poorer prognosis than other breast cancer subtypes. Less than 30% of women with metastatic TNBC survive 5 y and almost all die from their disease despite adjuvant chemotherapy (1, 3⇓–5). Mutations, rearrangements, or deletions in highly penetrant genes such as BRCA1, BRCA2, TP53, CDH1, STK11, and PTEN are important drivers of TNBC (6⇓–8). PTEN is a dual-specificity phosphatase that antagonizes the PI3K/AKT pathway through its lipid phosphatase activity and negatively regulates the MAPK pathway through its protein phosphatase activity (9, 10). Mutations in PTEN drive epithelial–mesenchymal transition (EMT) and promote metastasis in TNBC (11⇓–13). Similarly, in mice, heterozygous deletion of Pten induces mammary tumors with basal-like characteristics (14⇓⇓–17).Despite all of the cancer genome-sequencing efforts, there is still an incomplete understanding of the genes and genetic networks driving TNBC. New technologies that would provide a more complete understanding of the genetics of TNBC are still needed to deconvolute the complexity of this deadly cancer. Our laboratory and others have pioneered the use of transposon mutagenesis in mice as a tool for cancer gene discovery (18⇓⇓⇓⇓⇓⇓⇓–26). Transposons induce cancer by randomly inserting into the mouse genome, mutating, and disrupting potential cancer genes. Transposon insertions in tumors thus serve as molecular tags for the high-throughput cloning and identification of cancer genes. In addition, because transposon insertions are PCR-amplified before they are sequenced, insertional mutations in cancer genes that are present in only a small fraction of tumor cells can be identified. Transposon mutagenesis can thus identify genes that are functioning at the tips of the cancer evolutionary tree and help deconvolute tumor evolution on a scale that is not yet possible through the sequencing of human tumors.To identify genetic drivers of TNBC, we induced Sleeping Beauty (SB) transposition in breast epithelial cells of mice that were heterozygous for a Pten-null allele. Breast tumors were subsequently classified using a PAM50 subtyping approach and found to represent a collection of different breast cancer subtypes including basal-like (45%), luminal A (39%), HER2 (11%), and normal-like (5%). Cloning and sequencing of the transposon insertion sites in tumors identified 12 candidate trunk drivers and a much larger number of progression genes. Subsequent validation studies identified eight TNBC tumor suppressor genes (TSGs), including a tumor and metastasis suppressor with clinical relevance in TNBC.ResultsSB Mutagenesis Promotes the Development of Multiple Breast Cancer Subtypes in Ptenfl/+ Mice.Loss of the TSG PTEN is implicated in breast cancer progression, clonally selected in TNBC, and favors the activation of the EMT pathway to promote metastasis (11⇓–13). To identify genes that cooperate with PTEN in the progression of breast cancer, we crossed Ptenfl/fl mice with K5-Cre transgenic mice to generate K5-CreTg/+;Ptenfl/+ mice. The mice were then crossed to mice carrying one of two conditional SB transposition systems (SB11fl/fl;T2/Onc2Tg/Tg) (18) or (SB11fl/fl;T2/Onc3Tg/Tg) (27) to generate K5-CreTg/+;Ptenfl/+;SB11fl/+;T2/Onc2Tg/+ (SB/Pten–Onc2) or K5-CreTg/+;Ptenfl/+;SB11fl/+;T2/Onc3Tg/+ (SB/Pten–Onc3) mice. SB/Pten–Onc2 mice carry 350 copies of T2/Onc2, all linked together at a single site on chromosome 1, whereas SB/Pten–Onc3 mice carry a 30-copy T2/Onc3 transposon concatamer located on chromosome 9 (20, 27). By using two different transposon concatamers located on different donor chromosomes, we were able to eliminate problems caused by local hopping (28) and achieve genome-wide coverage of SB mutagenesis. K5-CreTg/+ is active in early mammary progenitors (29). Therefore, K5-driven Cre expression should lead to excision of the conditional floxed allele from the entire mammary epithelium, which is consistent with our LacZ reporter assays (Fig. S1 A and B) and those of others (30) who showed that LacZ is expressed in all basal cells and most luminal cells in the mammary epithelium. K5-CreTg/+ should therefore induce mammary tumors with both luminal and basal cell origins.Download figureOpen in new tabDownload powerpointFig. S1. Detection of β-galactosidase activity in the epithelial cells of mouse mammary glands. (A and B) Shown are representative tissue sections of mouse mammary glands from 8-wk-old K5CreTg/+;LacZTg/+ mice stained with X-gal. Arrows indicate β-galactosidase activity in all mammary epithelium. Boxed regions are enlarged images. Left and Right magnification, 100× and 200×, respectively. [Scale bar, 100 μm (Left) and 200 μm (Right).]SB mutagenesis accelerated mammary tumor formation in Ptenfl/+ mice with a median survival of 250 d for SB/Pten–Onc2 mice and 313 d for SB/Pten–Onc3 mice (Fig. 1A). Tumor latency for SB/Pten–Onc2 mice was significantly earlier than SB/Pten–Onc3 mice (P = 0.003), which may reflect the higher number of transposons carried by SB/Pten–Onc2 mice. Fifty-nine percent of the tumors were classified as adenocarcinomas, whereas 29% were classified as adenosquamous carcinomas and 12% as adenomyoepitheliomas (Dataset S1, Table S1).Download figureOpen in new tabDownload powerpointFig. 1. SB mutagenesis promotes the development of multiple mammary tumor subtypes. (A) Kaplan–Meier survival curves of five different genotypic combinations of mice. Pten/SB–Onc2 and Pten/SB–Onc3 mice showed significant tumor acceleration compared with various control mice (Pten/SB–Onc2, P = 0.0003; Pten/SB–Onc3, P = 0.0001). (B and C) H E or immunohistochemical staining of mammary adenocarcinoma (B) or adenosquamous carcinoma (C). The adenocarcinoma shows areas of less differentiation (Upper H E). The adenosquamous carcinoma also has areas of no squamous differentiation invading muscle (Lower H E). Both tumors showed a high degree of heterogeneity, expressing both basal (CK14) and luminal (CK18) cytokeratins. Both tumors have low or focal high-proliferation rate tumors, based upon their Ki67 staining (NOTE-B shows a focally high rate), and express high levels of nuclear SBT protein. (Scale bar, 100 μm.) (D) Mammary tumor subtype classification based upon its PAM50 expression signature (31). The heat map displays gene expression data (log scale, right legend) for the PAM50 breast cancer subtype classifier for each mouse tumor (columns). The left side indicates the centroids for each breast cancer subtype. The rows in the heat map represent genes in the PAM50 panel, and columns represent each mammary tumor. Top panels show proliferation scores (blue, low; red, high) and PAM50 subtypes: basal-like (black), Her2 (purple), luminal A (light blue), and normal-like (light green).Adenocarcinomas were more frequent in SB/Pten–Onc2 mice (74%), whereas adenomyoepitheliomas were only identified in SB/Pten–Onc3 mice. Hematoxylin and eosin (H E) staining of individual tumors revealed a mixed histology, whereas immunohistochemical staining showed that both basal (cytokeratin 14) and luminal (cytokeratin 18) markers were often expressed in the same tumor (Fig. 1 B and C), which is suggestive of extensive intratumor heterogeneity (Dataset S1, Table S1). Immunohistochemical analysis also showed that SB transposase (SBT) was expressed at high levels in tumors, consistent with their SB-induced origins (Fig. 1 B and C).To further define the mammary tumor subtypes derived from SB mutagenesis, gene expression arrays were performed on 21 mammary tumors. For each tumor, an \"intrinsic subtype” was assigned based on the previously described PAM50 subtyping approach (31). Mouse orthologs for the PAM50 genes were identified, and the microarray data were used to determine the closest intrinsic subtype centroid for each sample, based on Spearman correlation using logged mean-centered expression data. A gene proliferation signature was also used to generate a proliferation score for each sample (32). Basal-like (45%) and luminal A (39%) were the most abundant tumor subtypes, although HER2 (11%) and normal-like (5%) were detected at lower frequencies (Fig. 1D). As expected, basal-like tumors were generally more proliferative than the other tumor subtypes. The SB–Pten mouse model thus provides a resource for functional genomic studies across multiple human breast cancer subtypes.Identification of Candidate Cancer Genes.To identify genes mutated by SB that drive tumor development, we PCR-amplified and sequenced the transposon insertions from 18 SB/Pten–Onc2 and 16 SB/Pten–Onc3 tumors using 454 next-generation sequencing. Using the Wellcome Trust Sanger Institute’s transposon common insertion site calling pipeline (33), we identified 105,540 mapped transposon reads corresponding to 23,137 unique transposon insertion sites. Using the locus-centric Gaussian kernel convolution (GKC) (33) and gene-centric common integration site (gCIS) (34) calling methods, we identified 448 statistically significant candidate cancer genes (CCGs) by combining the two lists of CIS genes (Dataset S1, Table S2). CISs are genomic regions that contain more transposon insertions than predicted by chance and are thus likely to mark the location of CCGs. Pten was the most highly mutated CIS gene (Table 1 and Dataset S1, Table S2), which likely reflects the strong selective pressure to inactivate the wild-type Pten allele present in tumor cells.View this table:View inlineView popupTable 1. Most highly mutated CIS genes identified in SB–Pten mammary tumorsT2/Onc2 and T2/Onc3 contain transcriptional stop cassettes in both orientations and can inactivate the expression of a TSG if inserted within one. They also contain promoters and downstream splice donor sites and can deregulate the expression of a proto-oncogene if inserted upstream or in the 5′ end, in the same transcriptional orientation. The pattern of transposon insertions in CCGs can therefore be used to infer whether an insertionally mutated gene is an oncogene or TSG. Visual analysis of insertion patterns of the 448 CCGs identified in mammary tumors suggested that most (97%) are functioning as TSGs. In addition, in many cases, only a single transposon insertion was present at a CCG in tumor cells, suggesting that many CCGs are functioning as haploinsufficient TSGs. This is similar to what has been observed in other SB mutagenesis screens performed in solid tumors (18⇓⇓⇓⇓⇓⇓⇓–26).Comparative Oncogenomic Filtering.To assess the biological relevance of the 446 SB-identified CCGs with human orthologs to human breast cancer, we performed a number of cross-species comparisons. We found that 51 CCGs are listed in the Cancer Gene Census database (35), a catalog of known cancer genes, which is a highly significant overlap (P = 3.61E–18, two-sided Fisher’s exact test; Fig. 2A and Dataset S1, Table S3). We also compared our CCGs to the 127 significantly mutated genes found in 12 major human cancer types (36) and found that 16 are SB-identified CCGs (P = 1.23E–08, two-sided Fisher’s exact test; Fig. 2B and Dataset S1, Table S4), indicating that our screen is selecting for genes that are relevant to many types of cancer. Finally, we used the TCGA breast cancer database (37, 38) to identify genes mutated in human breast cancer (i.e., missense, nonsense, sense, and in-frame mutations). Among the 1,375 mutated genes listed in TCGA, 70 are SB-identified CCGs (P = 9.21E–12, two-sided Fisher’s exact test; Fig. 2C and Dataset S1, Table S5). SB-identified CCGs in mice therefore appear to be highly relevant to human breast cancer.Download figureOpen in new tabDownload powerpointFig. 2. Comparative oncogenomic and pathway analysis of SB-identified CCGs. Cross-species comparison of SB-identified CCGs against genes listed in (A) the Cancer Gene Census, (B) Pan-cancer 12, and (C) TCGA breast cancer databases. Significant enrichment of SB-identified CCGs was obtained using the Fisher exact test. (D) SB-identified trunk drivers along with mean and median sequence read counts for insertions containing ≥9 sequence reads and mutations in ≥3 tumors. (E) List of the top 12 trunk drivers, along with their mean read counts, predicted effect of transposon insertions on gene expression and gene function. (F) Biological pathway (yellow nodes) interaction network of CCG connections between 14 human orthologs that show a clinical association between RNA abundance and breast cancer patient survival (blue nodes) and reported cancer gene drivers (red nodes), including known breast cancer drivers PTEN, MAP2K4, TBL1XR1, AXIN1, and NF1. Chromatin, chromatin remodeling; EGFR, epidermal growth factor receptor signaling; MAPK, mitogen-activated protein kinase signaling; NTR, neurotrophin signaling; TNF/NF–κB, tumor necrosis factor and nuclear factor kappa B signaling; and WNT, Wnt signaling.Identification of Trunk Driver Genes.Most CCGs identified by SB mutagenesis are thought to function during late stages of tumor progression (18⇓⇓⇓⇓⇓⇓⇓–26). To identify the CCGs that function early in mammary tumor development, we selected transposon insertion sites represented by the highest number of sequencing reads (SB insertions in the CCG in ≥3 tumors with ≥9 reads per tumor), arguing that these insertions would be present in the largest number of tumor cells. This analysis identified 12 CCGs that we subsequently refer to as trunk drivers (Fig. 2D). Strikingly, 50% are known TSGs, including Pten, Arhgap35, Nf1, Rasa1, Axin1, and Apc. Transposon insertions in these genes were mostly located throughout the coding regions, consistent with their TSG function (Fig. 2E and Fig. S2). The one exception was Jup (also called gamma catenin), which contains a cluster of insertions on the sense strand near exon 2, suggesting that Jup might function as an oncogene (Fig. S2). Recent reports have shown that Jup overexpression is crucial for maintaining circulating tumor cells as clusters and for facilitating homing to the lungs (39).Download figureOpen in new tabDownload powerpointFig. S2. Location of transposon insertions in SB-identified trunk driver genes. Transposon insertions in the sense (green arrow) and antisense (red arrows) DNA strand are shown. Black arrow shows the transcription initiation site.Altered Signaling Pathways and Cellular Processes.Using Enrichr functional annotation (40), we identified 22 pathways and cellular processes (FDR P 0.05) (Dataset S1, Table S6) that are enriched in SB-identified CCGs. Notably, the top six pathways and cellular processes contained 51% of the SB-identified CCGs with known function. The Wnt pathway contained the most CCGs, with loss-of-function mutations detected in Apc, Axin1, and Gsk3b, which are involved in beta-catenin degradation, in addition to Tcf7l2, Amer1, and Smad4 (41, 42). We also identified numerous insertions in Pten, Rasa1, and Nf1, which are important regulators of EGFR, TNF-alpha, and MAPK signaling and are commonly altered in patients with breast cancer.SB-Identified CCGs Have Clinical Relevance.To determine whether SB-identified CCGs belonging to the top six signaling pathways have clinical relevance, we interrogated a breast cancer expression microarray dataset that also reports recurrence-free patient survival. All CCGs showing a significant association were also analyzed within each breast cancer subtype. This analysis identified 14 CCGs that were significantly associated with patient survival in one or more breast cancer subtypes (Fig. 2F and Dataset S1, Table S7). SB-identified CCGs thus appear to have clinical relevance.SB-Identified CCGs Act as Tumor Suppressors in Human Breast Cancer.To provide additional evidence that SB-identified CCGs are important in human breast cancer, we selected 20 CCGs that are mutated in human breast cancer, possess tumor suppressor activity in loss-of-function studies, and have been identified as CCGs in other SB mutagenesis screens. We then used small hairpin RNAs (shRNAs) delivered by lentivirus to silence the expression of these CCGs in two TNBC cell lines, HCC70 and MDA-MB-468, which were selected because they carry mutations in PTEN (Figs. S3 and S4). The cells were then orthotopically injected into the mammary fat pad of athymic nude mice, and after 45 d, the animals were necropsied and tumor volumes measured. The silencing of eight CCGs, including Man1a1, Pkp4, Rab10, Rasa1, Trps1, Vps26a, Xpnpep3, and Znf326, accelerated tumor growth, whereas the silencing of R3hcc1l reduced tumor growth (Table 2). The silencing of Ppp1r12a and Pum2 induced cell death during puromycin selection, suggesting that these genes are essential for cellular growth. These results show that these CCGs are enriched for mammary cancer TSGs.View this table:View inlineView popupTable 2. Functional validation screening of CCGsDownload figureOpen in new tabDownload powerpointFig. S3. RT-PCR analysis of the CCGs down-regulated by shRNA pools in the HCC70 cell line.Download figureOpen in new tabDownload powerpointFig. S4. RT-PCR analysis of the CCGs down-regulated by shRNA pools in the MDA-MB-231 cell line.TRPS1 Is a Tumor Suppressor in TNBC.Down-regulation of Trps1 resulted in the largest acceleration of tumor growth in our functional validation studies. Trps1 encodes a GATA-like transcription factor that regulates gene expression by acting as a transcriptional repressor (43, 44). Mutations in TRPS1 lead to Tricho-rhino-phalangeal syndrome, an autosomal-dominant disorder characterized by craniofacial and skeletal malformations (45). Mutations in TRPS1 have been reported in several human cancers, including leukemia, prostate, colon, endometrial, and breast cancer (46⇓⇓–49). Quantitative immunohistochemistry has also shown that TRPS1 is a prognostic marker in early-stage breast cancer and I–II ER+ patients receiving antihormone therapy (50). Stage II/III breast cancer patients with high expression levels of TRPS1 also have a better survival outcome compared with low TRPS1 expression patients (51), suggesting that tumors arising from TRPS1-negative/low cells are intrinsically more aggressive. This is in agreement with our TRPS1 expression analysis across different human breast cancer subtypes, which showed that TNBC has the lowest expression levels of TRPS1 (Fig. S5).Download figureOpen in new tabDownload powerpointFig. S5. TRPS1 expression in different human breast cancer subtypes. Shown is a box plot of TRPS1 expression (probe set \"218502_s_at”) versus breast cancer subtype across a collection of 2,116 human breast tumors profiled using Affymetrix microarrays.Down-regulation of TRPS1 in TNBC results from activation of the RAS–RAF–MEK pathway, which is common in basal-like breast cancers (52). This in turn leads to the activation of the Fos family transcription factor FOSL1 and its downstream target miR-221/miR-222, followed by the down-regulation of TRPS1, which is a downstream target of miR-221/miR-222 (52). These results predict a negative correlation between the expression of TRPS1 and FOSL1/miRNA-221/222 in breast cancer, which we confirmed by quantitative real time (qRT)-PCR (Fig. S6). To determine whether down-regulation of TRPS1 expression accelerates tumor growth in vivo, we used two different TRPS1 shRNAs to down-regulate TRPS1 expression in HCC70 and HCC1569 TNBC cells (Fig. S7 A and B). Down-regulation of TRPS1 significantly accelerated tumor growth in nude mice following orthotopic injection (Fig. 3 A and B). Finally, to determine whether TRPS1 overexpression inhibits tumor growth, we overexpressed TRPS1 in MDA-MB-231 and HCC1954 TNBC cells (Fig. S7 C and D), which have reduced TRPS1 expression. We used a lentivirus construct expressing the TRPS1 ORF minus the 3′UTR to avoid miRNA-221/222 targeting degradation. Tumor cells were injected into the fat pad of nude mice, and after several weeks, we observed a statistically significant reduction in tumor growth (Fig. 3 C and D). These results show that TRPS1 functions as a TSG in TNBC.Download figureOpen in new tabDownload powerpointFig. 3. TRPS1 reduces tumor growth and suppresses metastasis in breast cancer. (A and B) Inactivation of TRPS1 in HCC70 and HCC1569 TNBC cell lines accelerates tumor growth in orthotopic xenografts. (C and D) Ectopic overexpression of TRPS1 reduces tumor growth in HCC1954 and MDA-MB-231 orthotopic xenografts. n =10 mice per group. One-sided unpaired t test was run for all experiments. (E) TRPS1 expression in MDA-MB-231 cells reduces metastasis. Tumor lung colonization was measured by bioluminescent imaging. The panels show mouse images at different time points. (F) Line graphs represent three time-point measurements of the radiance average from five injected animals. All data were evaluated using two-sided t test. Error bars represent SEM.Download figureOpen in new tabDownload powerpointFig. S6. TRPS1, FOSL1 (Top), and miRNA221/222 (Bottom) expression was determined by qRT-PCR across different breast cancer cell lines. The human mammary epithelial immortalized cell line (HMEC1) was used on the nontumorigenic cell line. Data represent means of triplicates ± SEM.Download figureOpen in new tabDownload powerpointFig. S7. Stable TRPS1 knockdown in HCC70 (A) and HCC1569 (B) TNBC cell lines using two independent shRNAs was confirmed by qRT-PCR and Western blot. Error bars represent SEM (*P 0.0001). Ectopic expression of TRPS1 in (C) HCC1954 and (D) MDA-MB-231 cells transduced with lentivirus particles expressing TRPS1 cDNA and empty vector (control), respectively. TRPS1 mRNA levels were quantified by qRT-PCR.TRPS1 Is a Breast Cancer Metastasis TSG.miR-221/222 promotes EMT in breast cancer cells in part by down-regulating TRPS1 expression, which is a direct transcriptional repressor of ZEB2 and a driver of EMT (52). miR-221/222 expression also has been shown to enhance the migration and invasion of nontransformed human mammary epithelial MCF10A cells, whereas synthetic oligo inhibitors of miR-221/222 attenuate the migration and invasion of MDA-MD-231 cells through the basement membrane matrix (52). To determine whether TRPS1 is a breast cancer metastasis TSG, we examined the ability of MDA-MB-231 TNBC cells overexpressing the TRPS1 ORF, minus the 3′UTR, to colonize the lung. TRPS1-overexpressing cells were injected into the mammary fat pad of athymic nude mice, and lung metastatic progression was monitored by bioluminescence. Five weeks after injection, we observed a significant decrease in luciferase activity in the lungs of injected mice compared with control mice (Fig. 3 E and F). These results indicate that TRPS1 is a TSG that inhibits lung metastasis.TRPS1 Regulates the Expression of Multiple Genes in the EMT Pathway.To determine whether other EMT pathway genes in addition to ZEB2 are transcriptionally regulated by TRPS1, we knocked down TRPS1 expression in HCC70 TNBC cells using two independent TRPS1 shRNAs and then used an EMT PCR gene expression array to identify genes that might be regulated by TRPS1. The EMT PCR array profiles the expression of 84 key genes that either change their expression during the process of EMT or regulate the expression of these genes. We observed a significant up-regulation of BMP2, MMP2, MMP9, SERPINE1, SNAI2, TFPI2, TGFB2, and ZEB1 and a significant down-regulation of COL5A, FN1, KRT14, SNAI1, SNAI3, and SOX10 using this array (Fig. 4A). These results indicate that TRPS1 regulates multiple genes in the EMT pathway. To further explore the biological effects of these expression changes, we performed migration and invasion assays using TNBC cells that over- or underexpress TRPS1. We observed increased migration and invasion of HCC70 cells that had reduced TRPS1 expression (Fig. 4 B and C) and decreased migration and invasion of MDA-MB-231 and HCC1954 cells that had increased TRPS1 expression (Fig. 4 B and C). Collectively, these results show that TRPS1 regulates multiple genes in the EMT pathway that have effects on cell migration and invasion and are consistent with previous studies that measured the effects of miR-221/222 expression on the migration and invasion of tumor cells (52).Download figureOpen in new tabDownload powerpointFig. 4. TRPS1 regulates the expression of genes in the EMT pathway. (A) EMT gene expression in HCC70 TRPS1 knockdown cells. Two independent TRPS1 shRNA knockdown clones showed consistent mRNA expression levels over control. (B) Migration and (C) invasion assays of TRPS1 shRNA knockdown in HCC70 cells and TRPS1 overexpression in MDA-MB-231 and HCC1954 cells. Data represent means ± SEM of three independent experiments.SERPINE1 and SERPINB2 Expression Is Negatively Regulated by TRPS1.SERPINE1 was the gene whose expression was most increased in TRPS1 knockdown cells (Fig. 4A). SERPINE1 encodes endothelial plasminogen activator inhibitor-1 (PAI1), a member of the serine protease inhibitor family that inhibits tissue-type plasminogen activator (PLAT) and urokinase-type plasminogen activator (PLAU), which breaks down fibrin clots. Interestingly, high SERPINE1 expression has been shown to enhance cell migration and apoptosis resistance in head and neck carcinoma patients (53), whereas its down-regulation in nasopharyngeal carcinoma has been associated with reduced metastasis (54). This led us to explore the effects of TRPS1 knockdown on the expression of other serpin family members, which showed that SERPINB2 is also highly overexpressed in TRPS1 knockdown cells (Fig. 5A). Plasmin from reactive brain stroma provides a defense against metastatic invasion of lung and breast cancer cells (55). Plasmin does this by converting membrane-bound astrocytic FasL into a paracrine death signal for cancer cells and by inactivating the axon guidance molecule L1CAM, which metastatic cells express for spreading along brain capillaries and for metastatic outgrowth. Importantly, brain metastatic lung and breast cancer cells that express high levels of neuroserpin and serpin B2 inhibit plasmin generation and its metastasis-suppressive effects (55).Download figureOpen in new tabDownload powerpointFig. 5. TRPS1 directly regulates the expression of SERPINE1 and SERPINB2. (A) Gene expression analysis of different serpin family members. SERPINE1 and SERPINB2 were up-regulated after TRPS1 depletion in HCC70 cells. qRT-PCR validation was performed in triplicates. *P 0.001. (B) Increased serpin protein levels in the supernatants of TRPS1 knockdown cells were detected by ELISA, *P 0.001. (C and D) Serpin expression levels are regulated by TRPS1 in tumor xerographs. TRPS1 and SERPINE1 and SERPINB2 expression levels are negatively correlated in (C) HCC70 (n = 10 per group) and (D) MDA-MB-231 (n = 7) tumor xenografts. mRNA expression levels were detected by qRT-PCR. (E) ChIP assays confirmed that TRPS1 binds to SERPINB2 and SERPINE1 promoters. (Top) Possible TRPS1 binding sites in SERPINB2 and SERPINE1 promoters and control regions. (Bottom) Fold enrichment of TRPS1 binding to SERPINE1 and SERPINB2 promoters. To validate anti-TRPS1 specificity, we used TRPS1 knockdown (shTRPS1) and nontargeted control (NTC) cells. The ZEB2 promoter was included as a positive control as previously described (52). (F and G) Luciferase reporter expression assays were performed using SERPINE1 and SERPINB2 promoters in (F) HCC70 control and TRSP1 shRNA cells and (G) MDA-MB-231 control and TRPS1-overexpressing cells. ZEB2 and GAPDH promoters were used as positive controls. Random control promoter (RCP) was used as a negative control. All promoters contained 1,000 base pairs of DNA. In all experiments, the results are reported as means ± SEM of three independent experiments. P values were calculated using two-sided t test.To confirm that SERPINE1 and SERPINB2 are secreted from TRPS1 knockdown cells, we grew the cells in serum free-media for 24 h and then collected the supernatants. Sandwich ELISA showed that SERPINE1 and SERPINB2 proteins are highly secreted from TRPS1 knockdown TNBC cells (Fig. 5B). Finally, we used qRT-PCR to confirm that SERPINE1 and SERPINB2 expression is negatively regulated by TRPS1 in orthotopic tumor xenografts (Fig. 5 C and D). These results indicate that the suppressive effects of TRPS1 on tumor metastases are mediated in part through the suppressive effects of TRPS1 on SERPINE1 and SERPINB2 expression.TRPS1 Is a Direct Transcriptional Repressor of SERPINE1 and SERPINB2.To determine whether TRPS1 is a direct transcriptional repressor of SERPINE1 and SERPINB2, we asked whether TRPS1 could bind to the SERPINE1 and SERPINB2 promoters using chromatin immunoprecipitation (ChIP) assays. Because TRPS1 represses transcription from GATA-containing binding sites (43, 56⇓⇓–59), we searched for conserved GATA sites in the promoter regions of SERPINE1 and SERPINB2. We then designed oligonucleotides specific for these binding sites. As a negative control, we targeted a serpin promoter region that lacked a GATA binding site. ChIP assays showed that TRPS1 could bind to the SERPINE1 and SERPINB2 promoters but only in parental HCC70 cells and not in cells expressing TRPS1 shRNA (Fig. 5E). To further examine TRPS1 repressor activity, we performed luciferase reporter assays. SERPINE1 and SERPINB2 promoter-luciferase DNA constructs were transfected into TRPS1 knockdown HCC70 or TRPS1-overexpressing MDA-MB-231 cells. Subsequent quantification of luciferase activity showed that SERPINE1 and SERPINB2 luciferase activity was significantly increased in TRPS1-deficient HCC70 cells compared with control cells. Conversely, SERPINE1 and SERPINB2 luciferase activity was significantly decreased in MDA-MB-231 cells overexpressing TRPS1. These results are in agreement with our in vitro and in vivo studies showing that TRPS1 is a transcriptional repressor of SERPINE1 and SERPINB2 (Fig. 5 F and G).To determine whether SERPINE1 and SERPINB2 are important for the tumor acceleration observed in TRPS1 knockdown cells, we performed a rescue experiment by disrupting SERPINE1 and SERPINB2 expression in TRPS1 knockdown HCC70 cells and then measuring the effect of this disruption on tumor growth in transplanted mice (Fig. 6A). As shown in Fig. 6B, knockdown of SERPINE1 or SERPINB2 in TRPS1 knockdown cells inhibited tumor growth, confirming that SERPINE1 or SERPINB2 is important for the tumor acceleration seen in TRPS1 knockdown cells.Download figureOpen in new tabDownload powerpointFig. 6. Serpins mediate tumor growth in TRPS1-deficient tumor cells. (A) qRT-PCR analysis of TRPS1, SERPINB2, and SERPINE1 expression in stable lentivirus-transfected HCC70 cells. (B) Tumor growth is significantly reduced in serpin-deficient TRPS1 shRNA tumor cells. n = 8 mice per group. Two-sided t test was run for all experiments. Error bars represent SEM.TRPS1 Expression Levels Are Clinically Associated with Patient Survival.Finally, to determine whether TRPS1 expression levels are clinically associated with patient survival, we queried a publically available breast cancer database (60). We found that high TRPS1 expression is associated with increased relapse-free survival but only in luminal A (ER+) breast cancer patients (Fig. S8A). This is in agreement with previous reports showing that TRPS1 is a positive prognostic marker in ER+ breast cancer (51). Luminal B and basal-like breast cancer patients with reduced expression of TRPS1 showed a negative trend in survival, but the results do not reach statistical significance (Fig. S8A). Interestingly, SERPINE1, SERPINB2, and FOSL1 expression was inversely correlated with TRPS1 expression in ER– breast cancer (Fig. S8B) (61), in agreement with our findings. Collectively, these results suggest that high TRPS1 expression in ER+ breast cancer represses EMT and serpin gene expression, leading to reduced tumor growth and metastases. However, in patients with ER– breast cancer, where FOSL1 levels are high and mir221/222 expression is increased, reduced TRPS1 expression leads to an increase in EMT and serpin gene expression, with concomitant increased tumor growth and tumor metastasis (Fig. S8C).Download figureOpen in new tabDownload powerpointFig. S8. TRPS1 expression levels predict patient survival in ER+ breast cancer. (A) Kaplan–Meier plots depicting the recurrence-free survival of patients with different breast cancer subtypes based on TRPS1 expression levels. HR, hazard ratio. Log-rank test was used in the analysis. We used the probe set \"222651_s_at” for all the analysis. (B) Box plots of TRPS1, SERPINE1, SERPINB2, and FOSL1 expression in ER+ and ER– breast cancer subtypes (n = 395 ER– and n =1,225 ER+ breast cancer samples). Gene expression-based outcome for breast cancer online was used to obtain the expression analysis. (C) Schematic model depicting the regulation of serpins by TRPS1 in ER– and ER+ breast cancer. TRPS1 expression in ER+ tumor cells directly regulates the expression of serpins and EMT genes. ER– tumor cells express high levels of FOSL1, which activates the expression of mir221/222, targeting TRPS1 mRNA for degradation and activating the expression of SERPINB2, SERPINE1, and EMT genes to promote tumor growth and metastasis.DiscussionHere we have used SB mutagenesis to identify genes that cooperate with mutant Pten in the induction of breast cancer. Previous studies have shown that Pten mutant mice develop well-differentiated adenocarcinomas with prominent stromal proliferation (16) in addition to well-differentiated fibroadenomas and pleomorphic adenocarcinomas (15). By using gene expression microarrays and molecular signatures that are associated with different breast cancer subtypes, we were able to identify multiple mammary tumor subtypes in our SB–Pten model. The two major subtypes we identified were luminal A and basal-like tumors, suggesting that transposon mutagenesis is occurring in all mammary epithelium cell populations. Consistent with this, studies in K5Cre transgenic mice have identified Cre activity in early mammary cell progenitors (30). K5Cre should therefore inactivate Pten and activate SB transposition in both luminal and basal cell progenitors and might explain the development of different breast cancer subtypes in our SB mouse model.Cloning and sequencing of SB insertion sites in tumors led to the identification of 12 candidate trunk drivers and a much larger number of tumor progression genes. Comparative oncogenomic filtering showed that these genes are enriched for genes causally associated with human cancer, including breast cancer and cancers of many other cell types. Strikingly, 6 of the 12 trunk drivers identified by SB are known TSGs, including Pten, Arhgap35, Nf1, Rasa1, Axin1, and Apc. Pathway analysis showed that these genes function in multiple signaling pathways and cellular processes important in cancer. Notably, the top six pathways and cellular processes contained 51% of the SB-identified cancer genes, with the Wnt pathway showing a very high enrichment for mutations in negative regulators of β-catenin. We also identified a high frequency of mutation in Map2k4 and Mapk8. These genes are important components in the activation of apoptosis in response to stress (62), which might explain why these genes are inactivated in SB–Pten tumors. Finally, we also identified a high frequency of transposon insertions in chromatin-remodeling and -modifying enzymes. Collectively, these data suggest that SB is targeting cooperative signaling networks that promote tumor growth without inducing apoptosis.An interesting finding in our studies was the identification of tumor suppressors that have already been described in human breast cancer. These results indicate that humans and mice have similar selection pressures and provide evidence that these mutations are important driving events in tumorigenesis. Although SB mutagenesis can uncover hundreds of genes driving tumor progression and enable system-level studies, this technology also possesses a challenge for functional validation. Here, we show that we can use high-throughput shRNA screens in human breast cancer cells in conjunction with SB mutagenesis to validate genes important for human breast cancer progression. Among 20 candidate TSGs we assayed using this high-throughput approach, the silencing of eight genes was found to accelerate tumor growth, whereas the silencing of one gene reduced tumor growth. The silencing of two genes induced cell death during puromycin selection, suggesting that they are essential genes. Gene inactivation in orthotopic tumor xenografts therefore appears to be a highly efficient platform for functional validation studies of candidate TSGs.A major finding of our screen was the discovery and functional validation of TRPS1 as a metastasis tumor suppressor in human TNBC. Consistent with these results, in SB–Pten tumors, Trsp1 was insertionally mutated only in basal-like tumors. This is in contrast to what has been reported in ER+ breast cancer, where increased TRPS1 expression correlates with improved survival and a favorable response to antihormone therapy. Remarkably, tumor cells from ER+ breast cancer patients after antihormone therapy have decreased TRPS1 expression and increased expression of mesenchymal markers (63), suggesting that breast tumors with low TRPS1 expression might be more resistant to chemotherapy and have a higher probability to metastasize.TRPS1 is a GATA-like transcription factor, which functions as a transcriptional repressor or activator, depending on cell type, stage of development, or pathological conditions. In early hair follicle progenitors, TRPS1 is a transcriptional activator of genes that inhibit Wnt signaling (56), whereas it is a repressor of RUNX2, a gene important for osteoclast differentiation and maturation (57). Moreover, in odontoblast development, Trsp1 is an inhibitor of Dspp expression (58), whereas in chondrocytes, Trps1 controls proliferation and survival by repressing Stat3 expression (59). In pathological conditions, TRPS1 represses the expression of ZEB2, a component of the EMT pathway (52). Studies by Stinson et al. (52) have demonstrated that mir221/222 targets the 3′UTR of the TRPS1 mRNA transcript. This degradation leads to increased ZEB2 expression as well as increased migration and invasion of tumor cells. In our studies, we show that TRPS1 regulates many other genes in the EMT pathway in addition to ZEB2. We also show that TRPS1 directly represses the expression of SERPINE1 and SERPINB2 in TNBC. Recent studies using a breast metastasis model have shown that SERPINB2 protects metastatic cells from plasminogen activator plasmin in the brain, providing a metastatic advantage (55). Another serpin family member, Serpine2, was initially discovered in a model of breast cancer metastasis to bone and later reported to facilitate tumor cells to form vascular-like networks enabling perfusion and lung metastasis (64, 65). Likewise, SERPINE1 has been used as a prognostic marker of breast cancer and evaluated during chemotherapy administration (66, 67). In rescue experiments, we demonstrated that tumor growth in TRPS1 knockdown cells is mediated by SERPINE1 and SERPINB2. This is in agreement with mouse serpine1 knockout studies, which have also revealed a defect in tumor growth (68). Collectively, our studies suggest that TRPS1 is a master regulator of several gene networks that reduce tumor growth and metastasis.In summary, our studies have provided insights into the genetic and evolutionary forces driving breast cancer and identified eight TNBC TSGs. Our SB–Pten mouse model can be used to study many subtypes of breast cancer, including basal-like, luminal A, HER2, and normal-like. Finally, our studies have helped to elucidate an important signaling pathway in TNBC with potential clinical importance to a disease that currently has limited treatment options.Materials and MethodsMice.SB–Pten mice were generated by crossing the following mice: K5-Cre transgenic mice (K5-CreTg/+) (69), Pten conditional knockout mice (C;129S4-Ptentm1Hwu/J) (15), Rosa26-loxP-STOP-LoxP-SB11 transposase conditional knock-in mice [Gt(ROSA)26Sortm2(sb11)Njen] (70), T2/Onc2 (SB-6113) transposon transgenic mice [TgTn(sb-T2/Onc2)6113Njen] (20), and T2/Onc3 (SB-12740) transposon transgenic mice [TgTn(sb-T2/Onc3)12740Njen] (27). To examine the expression of Cre within the mammary epithelium, we used the following mice: K5-Cre transgenic mice (K5-CreTg/+) (69) and LacZ transgenic reporter mice [B6;129S4-Gt(ROSA)26Sortm1Sor/J; Jackson Laboratory, Stock No. 003309]. Mice were housed in a specific pathogen-free facility with a 12-h light/12-h dark cycle. Mice with the selected genotypes were aged and monitored twice a week for mammary tumor development. Kaplan–Meier analysis was performed, and survival curves were generated using Prism6 software (GraphPad). Tumors 5 mm in diameter were dissected at necropsy; half of the tumor was frozen for DNA sequence analysis, and the other half was fixed with 4% (vol/vol) paraformaldehyde. Fixed tissues were then processed to paraffin blocks. H E staining was used to characterize tumor histopathology. All mouse procedures were approved by the Institutional Animal Care and Use Committee, Institute of Molecular and Cell Biology, Singapore, and the Animal Care and Use Committee, Houston Methodist Research Institute, Houston.Cell Culture, Functional Validation Screening, and Generation of Cell Clones.Human breast cancer cell lines HCC70 (CRL-2315), HCC1569 (CRL-2330), HCC1954 (CRL-2338), MDA-MB-231 (HTB-26), MDA-MB-468 (HTB-132), and BT-549 (HTB-122) were obtained from the American Type Culture Collection. MDA-MB-231 Luciferase (AKR-231) was obtained from Cell Biolabs. All cell lines were cultured according to the vendor’s instructions. All cell lines usage was approved by the IRB at the Houston Methodist Research Institute. Lentiviral shRNA particles were originally obtained from Open Biosystems. For the functional validation screening, we plated MDA-MB-468 and HCC70 cells at a density of 5 × 105 cells per well in a six-well plate the night before lentiviral transduction. The next day, the cells were infected overnight in serum-free medium containing polybrene (8 μg/mL) mixed with lentiviral particles at a multiplicity of infection (MOI) of 6, consisting of individual shRNA NTC or pools of three CCG shRNAs. After overnight incubation, complete medium was added. At 48 h postinfection, puromycin selection medium was added (1.5 μg/mL), and the medium was change every 2 d until 95% of the cells expressed GFP fluorescence. All shRNA lentiviral clones were obtained from Open Biosystems (Dataset S1, Table S8). For the generation of tumor cells expressing single shRNA clones, we used shRNA-NTC (RHS4348), shRNA-TRPS1 (V3LHS_366300, V3LHS_366303), and TRPS1-ORF (OHS5900-224626817). Moreover, we used lentivirus expressing shRNA-SERPINE1, shRNA-SERPINB2, and shRNA-TFPI2 with a blasticidin resistant marker. The virus particles were obtained from GenTarget Inc. For stable shRNA knockdown studies, HCC70 and HCC1569 cells were selected with 1.5 μg/mL of puromycin for 1 wk. For knockdown or overexpression studies, all cell lines were selected with blasticidin at 5 μg/mL, except for MDA-MB-231 (20 μg/mL), for 2 wk.SI Materials and MethodsTumor Xenograft Studies.Female 6- to 7-wk-old Crl:NU(NCr)-Foxn1nu mice were purchased from Charles River Laboratories. Mammary fat pad injections into athymic nude mice were performed using 3 × 106 cells (HCC70, HCC1954, MDA-MB-468, and MDA-MB-231). The human cancer cells were resuspended in 100 μL of a 1:1 mix of PBS and matrigel (TREVIGEN). For HCC1569 human breast cancer cells, we injected 4 × 106 cells in 100 μL of a 1:1 mix of PBS and matrigel. Injections were done into the fourth mammary gland. Tumors were measured using a digital caliper, and the tumor volume was calculated using the following formula: volume (mm3) = width × length/2. At the end of the experiment, tumor tissues were sectioned for fixation (10% formalin or 4% paraformaldehyde) and RNA isolation.Lung Metastasis Imaging.We injected 3 × 106 MDA-MB-231 luciferase cells in 100 μL of a 1:1 mix of PBS and matrigel. Injections were done into the fourth mammary gland. Lung metastases were subsequently analyzed in vivo by bioluminescence imaging. Mice anesthetized with isoflurane were injected intraperitoneally with d-luciferin (150 mg/kg) and imaged using an IVIS spectrum Xenogen machine (Caliper Life Sciences). Bioluminescence analysis was performed using living image software.Detection of β-Galactosidase Activity in Frozen Sections.Mammary glands were formalin-fixed and embedded in an optimal cutting temperature compound. Next, 10-mm frozen sections were fixed with cold formalin for 10 min. Slides were then washed three times with PBS, rinsed with water, and incubated overnight at 37 °C with X-gal working solution. The slides were subsequently removed from the humidified chamber, washed with PBS, and rinsed with water. Finally, the tissue sections were counterstained with fast red and mounted with aqueous medium.Immunostaining of Paraffin Sections.Harvested tumors were fixed in 10% neutral buffered formalin, dehydrated, and embedded in paraffin. Mammary tumor immunostaining was performed on 5-mm sections with antigen retrieval and overnight incubation with antibody for cytokeratin 14 (1:100, Abcam), cytokeratin 18 (1:100, Progen), and anti-Ki67 (1:50 dilution, Abcam). To detect nuclear transposase protein expression, we performed antigen retrieval and incubation with polyclonal goat anti-transposase antibody (1:200 dilution, R D Systems). After primary antibody incubation, chromogen detection (Envision System from Dako) and hematoxylin counterstaining were performed.Microarray Gene Expression Analysis.Gene expression profiling of 21 SB-induced mouse mammary tumors was performed using Affymetrix microarrays. RNA was extracted using a NORGEN Biotek Animal Tissue RNA Purification kit (cat. no. 25700), according to the manufacturer’s instructions. RNA was then labeled using an Affymetrix 3′ IVT Express kit (cat. no. 901229), using 100 ng of total RNA for each sample, as per the manufacturer’s instructions. After labeling, samples were hybridized to Affymetrix GeneChip Mouse Genome 430 2.0 arrays and scanned at the University of Otago Genomics Bioinformatics Facility. Raw data were processed in R (version 2.15) (71) using the \"rma” function of the \"affy” package (72). The \"affyQCReport” package (73) for R was used to perform quality assessment of the microarray data. For each sample, an intrinsic subtype was assigned based on the previously described PAM50 subtyping approach (31). Mouse gene orthologs for the PAM50 genes were identified, and the microarray data were used to determine the closest intrinsic subtype centroid for each sample, based on Spearman correlation using logged mean-centered expression data. To estimate cellular proliferation, a \"gene proliferation signature” (32) was used to generate a proliferation score for each sample. Briefly, using the logged expression data for a subset of proliferation-related genes, singular value decomposition was used to produce a \"proliferation metagene,” which was then scaled to generate a score between 0 and 1, with a higher score denoting an increased level of proliferation relative to samples with lower scores.Human Data.Data from a combined cohort of 2,116 breast tumors were used for gene expression analysis in a human cancer context. These data comprise tumors profiled on the Affymetrix HGU133A, HGU133A2, and HGU133PLUS2 microarray platforms. The normalization and subtyping procedures associated with this dataset have been described previously (74). The Affymetrix probe set \"218502_s_at” was used to define the expression of TRPS1 in this dataset.Cell Migration and Invasion Assays.Transwell migration assays were performed in a 24-multiwell insert system with a porous polycarbonate membrane (8-mm pore size) according to the manufacturer’s instructions (Cell Biolabs). Cells were allowed to grow to subconfluency (∼75–80%) and were then serum-starved for 24 h. After detachment with trypsin, the cells were washed with PBS, resuspended in serum-free medium, and 300 μL of cell suspension (5 × 105 cell/mL−1) was added to the upper chamber. We then added 500 μL of complete medium to the bottom wells of the chamber. Cells that did not migrate were removed from the upper face of the filters using cotton swabs, and cells that had migrated to the lower face of the filters were fixed, stained, and quantified according to the manufacturer’s instructions (Cell Biolabs). Similar inserts coated with matrigel were used in the invasion assay.qRT-PCR.Total RNA was purified and DNase treated using the RNeasy Mini Kit (Qiagen). Synthesis of cDNA was performed using SuperScript VILO Master Mix (Life Technologies). Quantitative PCR analysis was performed on the QuantStudio 12K Flex System (Life Technologies). All signals were normalized to the levels of GAPDH TaqMan probes.TaqMan probes were obtained from Life Technologies: AFTPH (Hs00214281_m1), ANO6 (Hs03805835_m1), ASH1L (Hs00218516_m1), ERBB2IP (Hs01049966_m1), EP400 (Hs01566078_m1), LPP (Hs00944352_m1), LRRC4 (Hs01934623_s1), MAN1A1 (Hs00195458_m1), NIPBL (Hs00209846_m1), PKP4 (Hs00269305_m1), PPP1R12A (Hs01552899_m1), PUM2 (Hs00209677_m1), R3HCC1L (Hs00402062_m1), RAB10 (Hs00211643_m1), RASA1 (Hs00963554_m1), SOS2 (Hs00183311_m1), VPS26A (Hs01013219_g1), XPNPEP3 (Hs00223094_m1), YTHDF3 (Hs00405590_m1), ZNF143 (Hs00366181_m1), ZNF326 (Hs00299025_m1), TRPS1 (Hs00936363_m1), FOSL1 (Hs04187685_m1), SERPINE1 (Hs01126606_m1), SERPINB2 (Hs01010736_m1), TFPI2 (Hs04334126_m1), SERPIND1 (Hs00164821_m1), SERPINE2 (Hs00299953_m1), SERPINI1 (Hs01115397_m1), SERPINC1 (Hs00166654_m1), SERPINF2 (Hs00168686_m1), GAPDH (Hs03929097_g1), miRNA221 (Hs04231481_s1), miRNA222 (Hs04415495_s1), and RNU44 (001094). EMT RT-PCR arrays (PAHS-090Z) were purchased from QIAGEN. The assays were performed according to the manufacturer’s instructions, and the results were evaluated using the QIAGEN data analysis center.ELISA.We collected the supernatants from HCC70 serum-free cell cultures after 24 h of culture. The ELISAs were commercially available from Cloud-Clone Corp: SERPINE1 (SEA532Hu) and SERPINB2 (SEA531Hu). The ELISAs were run according to the manufacturer’s instructions.ChIP Studies.HCC70 cells were fixed in 1% formaldehyde at 37 °C for 10 min. Cells were then washed twice with ice-cold PBS containing protease inhibitors, scraped, and centrifuged at 4 °C. Cell pellets were resuspended in lysis buffer and sonicated (Covaris S220) to shear DNA to a fragment size of 300–400 bp. After sonication, the lysates were centrifuged and the supernatants diluted 10-fold with ChIP dilution buffer (EZ-CHIP kit, EMD-MILLIPORE). Anti-TRPS1 (sc-26974X, Santa Cruz Biotechnology) or normal goat IgG (AB-108-C, R D Systems) were added to the diluted chromatin and incubated overnight at 4 °C with rotation. Antigen–antibody complexes were precipitated with protein A/G agarose and washed sequentially with low-salt buffer, high-salt buffer, and lithium chloride wash buffer and then eluted with elution buffer (1% SDS, 0.1 M NaHCO3, and 200 mM NaCl). Reversal of cross-linking was then performed by heating at 65 °C overnight in the presence of NaCl. DNA was purified using DNA-binding columns provided by the EZ-ChIP kit. The amount of immunoprecipitated DNA was quantified using high-sensitive detection DNA reagent (Q32851, Life Technologies) and measured using a Qubit 3.0 Fluorometer (Life Technologies). qPCRs were run in triplicate using primers for the SERPINE1 promoter target region (forward, 5′-GCTCTTTCCTGGAGGTGGTC-3′; reverse, 5′-CCCTAGTGTTCAGCTTGGAG-3′) and control region (forward, 5′-GCGCTGTCAAGAAGACCCAC-3′; reverse, 5′-ATTGGCGGTTCGTCCTGCTC TG-3′), SERPINB2 promoter target region (forward, 5′-GAATCACTCAAAGGAC ACAGATC-3′; reverse, 5′-CATGAAACCCTATTTCCCATAGAC-3′) and control region (forward, 5′-TTCCCTCCCATGCCCTAAGC-3′; reverse, 5′-TCTTCTAGCTTTG GACAACCATG-3′), and ZEB2 promoter target region (forward, 5′-CCCGAGGTGTAG AGAGATTCAGAG-3′; reverse, 5′-GCTTCTGGAACAAAGTTCTCTGC-3′) and control region (forward, 5′-ATGATGCTCACGCTCAGG-3′; reverse, 5′-AGCATG AAGAAGCCGCGAAG-3′). We used the Quantstudio 12K Real-Time PCR system (Applied Biosystems) with SYBR green. Data were analyzed using the 2–∆Ct method and normalized with the input sample.Luciferase Assays.SERPINE1, SERPINB2, ZEB2, GAPDH, and negative control (RPC) promoter reporter clones were obtained from Active Motif (LightSwitch Promoter Reporter GoClone). For luciferase assays in HCC70 cells, we used nontargeting control and TRPS1 knockdown, and for MDA-MB-231 cells, we used vector control and TRPS1-ORF. Cells were plated in 96-well plates, and transfection was performed with promoter reporter plasmids (50 ng/well) using Lipofectamine 2000 (Life Technologies) according to the manufacturer’s instructions. After 48 h, 100 μL of luciferase assay reagent was added and incubated for 30 min at room temperature (LightSwitch luciferase assay system, Active Motif). Cell lysates were then transferred to a white 96-well plate, and each well was then read for 2 s in a luminometer (Synergy H1 Hybrid Reader, BioTek).Bioinformatic Analysis.Mouse CISs were converted to human genes using two different annotation databases [MGI EntrezGene associations (www.informatics.jax.org/) and HGNC complete annotations (www.genenames.org/)]. Mouse genetic markers were downloaded from MGI (www.informatics.jax.org/tools.shtml). The gene catalog registered in The Cancer Gene Census was downloaded from catalog of somatic mutations in cancer (COSMIC) (cancer.sanger.ac.uk/census/). Enrichr Bioinformatics Resources (amp.pharm.mssm.edu/Enrichr/) was used for pathway analysis. To investigate the association between expression of genes and prognosis in breast cancer patients, we used expression data and clinical information from two websites (www.kmplot.com/analysis and co.bmc.lu.se/gobo/gsa.pl).Statistics.All data are provided as mean ± SEM unless otherwise indicated. Statistical analyses were performed using a paired Student’s t test using GraphPad Prism 6 software (Version 6.0f), unless otherwise indicated.AcknowledgmentsWe thank K. Rogers, S. Rogers and the Institute of Molecular and Cell Biology histopathology core for performing histological analysis, and P. Cheok, N. Lim, D. Chen, C. Wee, E. Freiter, and H. Lee for monitoring mice and animal technical assistance. The Biomedical Research Council, Agency for Science, Technology and Research (A-STAR), Singapore, and The Cancer Research Institute of Texas (CIPRIT), supported this research. A.G.R. was supported by the Cancer Research UK and the Wellcome Trust. Both N.G.C. and N.A.J. are CPRIT Scholars in Cancer Research.Footnotes↵1Present address: Global Vet Pathology, Montgomery Village, MD 20886.↵2Present address: Tumor Profiling Unit, The Institute of Cancer Research, Chester Beatty Laboratories, London SW3 6JB, United Kingdom.↵3N.A.J. and N.G.C. contributed equally to this work.↵4To whom correspondence should be addressed. Email: ncopeland{at}houstonmethodist.org.Author contributions: R.R., S.-C.L., K.H.-K.B., N.A.J., and N.G.C. designed research; R.R., S.-C.L., L.G.-R., T.K., L.A.M., and L.S. performed research; R.R., S.-C.L., K.H.-K.B., M.B.M., J.Y.N., J.M.W., A.G.R., K.-Y.C., M.A.B., N.A.J., and N.G.C. analyzed data; and R.R., N.A.J., and N.G.C. wrote the paper.Reviewers: K.W.H., National Cancer Institute; and B.M., University of Minnesota.The authors declare no conflict of interest.This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1613859113/-/DCSupplemental.Freely available online through the PNAS open access option. References↵Banerji S, et al. (2012) Sequence analysis of mutations and translocations across breast cancer subtypes. Nature 486(7403):405–409..OpenUrlCrossRefPubMed↵Curtis C, et al., METABRIC Group (2012) The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. 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(2011) Combined genomic and phenotype screening reveals secretory factor SPINK1 as an invasion and survival factor associated with patient prognosis in breast cancer. EMBO Mol Med 3(8):451–464..OpenUrlAbstract/FREE Full Text Thank you for your interest in spreading the word on PNAS.NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.CAPTCHAThis question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Forward genetic screen of TNBC tumor suppressors Roberto Rangel, Song-Choon Lee, Kenneth Hon-Kim Ban, Liliana Guzman-Rojas, Michael B. Mann, Justin Y. Newberg, Takahiro Kodama, Leslie A. McNoe, Luxmanan Selvanesan, Jerrold M. Ward, Alistair G. Rust, Kuan-Yew Chin, Michael A. Black, Nancy A. Jenkins, Neal G. Copeland Proceedings of the National Academy of Sciences Nov 2016, 113 (48) E7749-E7758; DOI: 10.1073/pnas.1613859113 Forward genetic screen of TNBC tumor suppressors Roberto Rangel, Song-Choon Lee, Kenneth Hon-Kim Ban, Liliana Guzman-Rojas, Michael B. Mann, Justin Y. Newberg, Takahiro Kodama, Leslie A. McNoe, Luxmanan Selvanesan, Jerrold M. Ward, Alistair G. Rust, Kuan-Yew Chin, Michael A. Black, Nancy A. Jenkins, Neal G. Copeland Proceedings of the National Academy of Sciences Nov 2016, 113 (48) E7749-E7758; DOI: 10.1073/pnas.1613859113 Sign up for the PNAS Highlights newsletter to get in-depth stories of science sent to your inbox twice a month: Relatively clean snow and ice in the Indus River Basin during the COVID-19 pandemic may have reduced meltwater in 2020, compared with the 20-year average. Atmospheric and climate conditions could have created a cloud greenhouse effect to warm Mars and support liquid surface water. 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