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Supplementary Table S2 from Integration of DNA Copy Number Alterations and Prognostic Gene Expression Signatures in Breast Cancer Patients

Supplementary Table S3 from Integration of DNA Copy Number Alterations and Prognostic Gene Expression Signatures in Breast Cancer Patients

Supplementary Data from Local Recurrence after Breast-Conserving Therapy in Relation to Gene Expression Patterns in a Large Series of Patients

Purpose: Several prognostic gene expression profiles have been identified in breast cancer. In spite of this progress in prognostic classification, the underlying mechanisms that drive these gene expression patterns remain unknown. Specific genomic alterations, such as copy number alterations, are an important factor in tumor development and progression and are also associated with changes in gene expression.Experimental Design: We carried out array comparative genomic hybridization in 68 human breast carcinomas for which gene expression and clinical data were available. We used a two-class supervised algorithm, Supervised Identification of Regions of Aberration in aCGH data sets, for the identification of regions of chromosomal alterations that are associated with specific sample labeling. Using gene expression data from the same tumors, we identified genes in the altered regions for which the expression level is significantly correlated with the copy number and validated our results in public available data sets.Results: Specific chromosomal aberrations are related to clinicopathologic characteristics and prognostic gene expression signatures. The previously identified poor prognosis, 70-gene expression signature is associated with the gain of 3q26.33-27.1, 8q22.1-24.21, and 17q24.3-25.1; the 70-gene good prognosis profile is associated with the loss at 16q12.1-13 and 16q22.1-24.1; basal-like tumors are associated with the gain of 6p12.3-23, 8q24.21-22, and 10p12.33-14 and losses at 4p15.31, 5q12.3-13.1, 5q33.1, 10q23.33, 12q13.13-3, 15q15.1, and 15q21.1; HER2+ breast show amplification at 17q11.1-12 and 17q21.31-23.2 (including HER2 gene).Conclusions: There is a strong correlation between the different gene expression signatures and underlying genomic changes. These findings help to establish a link between genomic changes and gene expression signatures, enabling a better understanding of the tumor biology that causes poor prognosis. Clin Cancer Res; 16(2); 651–63

Purpose: The majority of patients with early-stage breast cancer are treated with breast-conserving therapy (BCT). Several clinical risk factors are associated with local recurrence (LR) after BCT but are unable to explain all instances of LR after BCT. Here, gene expression microarrays are used to identify novel risk factors for LR after BCT.Experimental Design: Gene expression profiles of 56 primary invasive breast carcinomas from patients who developed a LR after BCT were compared with profiles of 109 tumors from patients who did not develop a LR after BCT. Both unsupervised and supervised methods of classification were used to separate patients into groups corresponding to disease outcome. In addition, for 15 patients, the gene expression profile in the recurrence was compared with that of the primary tumor.Results: The two main clusters found by hierarchical cluster analysis of all 165 primary invasive breast carcinomas revealed no association with LR. Predefined gene sets (molecular subtypes and “chromosomal instability” signature) are associated with LR (P = 0.0002 and 0.003, respectively). Significant analysis of microarrays revealed an association between LR and cell proliferation, not captured by histologic grading. Class prediction analysis constructed a gene classifier, which was successfully validated, cross-platform, on an independent data set of 161 patients (log-rank P = 0.041). In multivariate analysis, young age was the only independent predictor of LR.Conclusions: We have constructed and cross-platform validated a gene expression profile predictive for LR after BCT, which is characterized by genes involved in cell proliferation but not a surrogate for high histologic grade.

Supplementary Table S4 from Integration of DNA Copy Number Alterations and Prognostic Gene Expression Signatures in Breast Cancer Patients

Supplementary Table S1 from Integration of DNA Copy Number Alterations and Prognostic Gene Expression Signatures in Breast Cancer Patients

Supplementary Figure Legends 1-2 from Revealing Targeted Therapy for Human Cancer by Gene Module Maps

Supplementary Tables 1-4 from Revealing Targeted Therapy for Human Cancer by Gene Module Maps

Supplementary Figure 1 from Revealing Targeted Therapy for Human Cancer by Gene Module Maps

A major goal of cancer research is to match specific therapies to molecular targets in cancer. Genome-scale expression profiling has identified new subtypes of cancer based on consistent patterns of variation in gene expression, leading to improved prognostic predictions. However, how these new genetic subtypes of cancers should be treated is unknown. Here, we show that a gene module map can guide the prospective identification of targeted therapies for genetic subtypes of cancer. By visualizing genome-scale gene expression in cancer as combinations of activated and deactivated functional modules, gene module maps can reveal specific functional pathways associated with each subtype that might be susceptible to targeted therapies. We show that in human breast cancers, activation of a poor-prognosis “wound signature” is strongly associated with induction of both a mitochondria gene module and a proteasome gene module. We found that 3-bromopyruvic acid, which inhibits glycolysis, selectively killed breast cells expressing the mitochondria and wound signatures. In addition, inhibition of proteasome activity by bortezomib, a drug approved for human use in multiple myeloma, abrogated wound signature expression and selectively killed breast cells expressing the wound signature. Thus, gene module maps may enable rapid translation of complex genomic signatures in human disease to targeted therapeutic strategies. [Cancer Res 2008;68(2):369–78]

Supplementary Figure 2 from Revealing Targeted Therapy for Human Cancer by Gene Module Maps

Purpose: Several prognostic gene expression profiles have been identified in breast cancer. In spite of this progress in prognostic classification, the underlying mechanisms that drive these gene expression patterns remain unknown. Specific genomic alterations, such as copy number alterations, are an important factor in tumor development and progression and are also associated with changes in gene expression.Experimental Design: We carried out array comparative genomic hybridization in 68 human breast carcinomas for which gene expression and clinical data were available. We used a two-class supervised algorithm, Supervised Identification of Regions of Aberration in aCGH data sets, for the identification of regions of chromosomal alterations that are associated with specific sample labeling. Using gene expression data from the same tumors, we identified genes in the altered regions for which the expression level is significantly correlated with the copy number and validated our results in public available data sets.Results: Specific chromosomal aberrations are related to clinicopathologic characteristics and prognostic gene expression signatures. The previously identified poor prognosis, 70-gene expression signature is associated with the gain of 3q26.33-27.1, 8q22.1-24.21, and 17q24.3-25.1; the 70-gene good prognosis profile is associated with the loss at 16q12.1-13 and 16q22.1-24.1; basal-like tumors are associated with the gain of 6p12.3-23, 8q24.21-22, and 10p12.33-14 and losses at 4p15.31, 5q12.3-13.1, 5q33.1, 10q23.33, 12q13.13-3, 15q15.1, and 15q21.1; HER2+ breast show amplification at 17q11.1-12 and 17q21.31-23.2 (including HER2 gene).Conclusions: There is a strong correlation between the different gene expression signatures and underlying genomic changes. These findings help to establish a link between genomic changes and gene expression signatures, enabling a better understanding of the tumor biology that causes poor prognosis. Clin Cancer Res; 16(2); 651–63

Background Patient prognosis and response to endocrine therapy in breast cancer correlate with protein expression of both estrogen receptor (ER) and progesterone receptor (PR), with poorer outcome in patients with ER+/PR− compared to ER+/PR+ tumors. Methods To better understand the underlying biology of ER+/PR− tumors, we examined RNA expression (n > 1000 tumors) and DNA copy number profiles from five previously published studies of human breast cancers with clinically assigned hormone receptor status (ER+/PR+, ER+/PR−, and ER−/PR−). Results We identified an expression “signature” of genes with either elevated or diminished RNA levels specifically in ER+/PR+ compared to ER−/PR− and ER+/PR− tumors. We similarly identified a gene signature specific to ER−/PR− tumors. ER+/PR− tumors, on the other hand, were a mixture of three different subtypes: tumors manifesting the ER+/PR+ signature, tumors manifesting the ER−/PR− signature, and tumors not associating with ER+/PR+ or ER−/PR− tumors (which we considered “true” ER+/PR−). In analyses of both tamoxifen-treated and untreated patients, ER+/PR− breast cancers defined by RNA profiling were associated with poor patient outcome, worse than those with pure ER+/PR+ patterns; these differences were not observed when using clinical assays to assign ER and PR status. ER+/PR− tumors also showed twice as many DNA copy number gains or losses compared to ER+/PR+ and ER−PR− tumors. Targets of transcriptional up-regulation by specific oncogenic pathways, including PI3 K/Akt/mTOR, were enriched in both ER+/PR− and ER−/PR− compared to ER+/PR+ tumors. Conclusion ER+/PR− tumors as defined by RNA profiling represent a distinct subset of breast cancer with aggressive features and poor outcome, despite being clinically ER+. Multigene assays derived from our gene signatures could conceivably provide an improved clinical assay for inferring PR status for prognostic and therapeutic purposes.

BACKGROUND Gene-expression-profiling studies of primary breast tumors performed by different laboratories have resulted in the identification of a number of distinct prognostic profiles, or gene sets, with little overlap in terms of gene identity. METHODS To compare the predictions derived from these gene sets for individual samples, we obtained a single data set of 295 samples and applied five gene-expression-based models: intrinsic subtypes, 70-gene profile, wound response, recurrence score, and the two-gene ratio (for patients who had been treated with tamoxifen). RESULTS We found that most models had high rates of concordance in their outcome predictions for the individual samples. In particular, almost all tumors identified as having an intrinsic subtype of basal-like, HER2-positive and estrogen-receptor-negative, or luminal B (associated with a poor prognosis) were also classified as having a poor 70-gene profile, activated wound response, and high recurrence score. The 70-gene and recurrence-score models, which are beginning to be used in the clinical setting, showed 77 to 81 percent agreement in outcome classification. CONCLUSIONS Even though different gene sets were used for prognostication in patients with breast cancer, four of the five tested showed significant agreement in the outcome predictions for individual patients and are probably tracking a common set of biologic phenotypes.

Background: Histologic grade in breast cancer provides clinically important prognostic information. However, 30% – 60% of tumors are classifi ed as histologic grade 2. This grade is associated with an intermediate risk of recurrence and is thus not informative for clinical decision making. We examined whether histologic grade was associated with gene expression profi les of breast cancers and whether such profi les could be used to improve histologic grading. Methods: We analyzed microarray data from 189 invasive breast carcinomas and from three published gene expression datasets from breast carcinomas. We identifi ed differentially expressed genes in a training set of 64 estrogen receptor (ER) – positive tumor samples by comparing expression profi les between histologic grade 3 tumors and histologic grade 1 tumors and used the expression of these genes to defi ne the gene expression grade index. Data from 597 independent tumors were used to evaluate the association between relapse-free survival and the gene expression grade index in a Kaplan – Meier analysis. All statistical tests were two-sided. Results: We identifi ed 97 genes in our training set that were associated with histologic grade; most of these genes were involved in cell cycle regulation and proliferation. In validation datasets, the gene expression grade index was strongly associated with histologic grade 1 and 3 status; however, among histologic grade 2 tumors, the index spanned the values for histologic grade 1 – 3 tumors. Among patients with histologic grade 2 tumors, a high gene expression grade index was associated with a higher risk of recurrence than a low gene expression grade index (hazard ratio = 3.61, 95% confi dence interval = 2.25 to 5.78; P

Gene expression signatures encompassing dozens to hundreds of genes have been associated with many important parameters of cancer, but mechanisms of their control are largely unknown. Here we present a method based on genetic linkage that can prospectively identify functional regulators driving large-scale transcriptional signatures in cancer. Using this method we show that the wound response signature, a poor-prognosis expression pattern of 512 genes in breast cancer, is induced by coordinate amplifications of MYC and CSN5 (also known as JAB1 or COPS5). This information enabled experimental recapitulation, functional assessment and mechanistic elucidation of the wound signature in breast epithelial cells.

Based on the hypothesis that features of the molecular program of normal wound healing might play an important role in cancer metastasis, we previously identified consistent features in the transcriptional response of normal fibroblasts to serum, and used this “wound-response signature” to reveal links between wound healing and cancer progression in a variety of common epithelial tumors. Here, in a consecutive series of 295 early breast cancer patients, we show that both overall survival and distant metastasis-free survival are markedly diminished in patients whose tumors expressed this wound-response signature compared to tumors that did not express this signature. A gene expression centroid of the wound-response signature provides a basis for prospectively assigning a prognostic score that can be scaled to suit different clinical purposes. The wound-response signature improves risk stratification independently of known clinico-pathologic risk factors and previously established prognostic signatures based on unsupervised hierarchical clustering (“molecular subtypes”) or supervised predictors of metastasis (“70-gene prognosis signature”).

Mouse mammary gland involution resembles a wound healing response with suppressed inflammation. Wound healing and inflammation are also associated with tumour development, and a 'wound-healing' gene expression signature can predict metastasis formation and survival. Recent studies have shown that an involuting mammary gland stroma can promote metastasis. It could therefore be hypothesised that gene expression signatures from an involuting mouse mammary gland may provide new insights into the physiological pathways that promote breast cancer progression. Indeed, using the HOPACH clustering method, the human orthologues of genes that were differentially regulated at day 3 of mammary gland involution and showed prolonged expression throughout the first 4 days of involution distinguished breast cancers in the NKI 295 breast cancer dataset with low and high metastatic activity. Most strikingly, genes associated with copper ion homeostasis and with HIF-1 promoter binding sites were the most over-represented, linking this signature to hypoxia. Further, six out of the ten mRNAs with strongest up-regulation in cancers with poor survival code for secreted factors, identifying potential candidates that may be involved in stromal/matrix-enhanced metastasis formation/breast cancer development. This method therefore identified biological processes that occur during mammary gland involution, which may be critical in promoting breast cancer metastasis that could form a basis for future investigation, and supports a role for copper in breast cancer development.

INTRODUCTION: Gene expression profiling has been extensively used to predict outcome in breast cancer patients. We have previously reported on biological hypothesis-driven analysis of gene expression profiling data and we wished to extend this approach through the combinations of various gene signatures to improve the prediction of outcome in breast cancer. METHODS: We have used gene expression data (25.000 gene probes) from a previously published study of tumours from 295 early stage breast cancer patients from the Netherlands Cancer Institute using updated follow-up. Tumours were assigned to three prognostic groups using the previously reported Wound-response and hypoxia-response signatures, and the outcome in each of these subgroups was evaluated. RESULTS: We have assigned invasive breast carcinomas from 295 stages I and II breast cancer patients to three groups based on gene expression profiles subdivided by the wound-response signature (WS) and hypoxia-response signature (HS). These three groups are (1) quiescent WS/non-hypoxic HS; (2) activated WS/non-hypoxic HS or quiescent WS/hypoxic tumours and (3) activated WS/hypoxic HS. The overall survival at 15 years for patients with tumours in groups 1, 2 and 3 are 79%, 59% and 27%, respectively. In multivariate analysis, this signature is not only independent of clinical and pathological risk factors; it is also the strongest predictor of outcome. Compared to a previously identified 70-gene prognosis profile, obtained with supervised classification, the combination of signatures performs roughly equally well and might have additional value in the ER-negative subgroup. In the subgroup of lymph node positive patients, the combination signature outperforms the 70-gene signature in multivariate analysis. In addition, in multivariate analysis, the WS/HS combination is a stronger predictor of outcome compared to the recently reported invasiveness gene signature combined with the WS. CONCLUSION: A combination of biological gene expression signatures can be used to identify a powerful and independent predictor for outcome in breast cancer patients

The chemokine receptor CXCR6 and its ligand CXCL16 are involved in inflammation. Thus far, they were known to be expressed mainly by T cells and macrophages, respectively. However, we detected both in all of 170 human primary mammary carcinomas and at similar levels in all 8 human mammary carcinoma cell lines tested by microarray analysis. Expression was confirmed by reverse transcription-PCR and for the cell lines also by fluorescence-activated cell sorting analysis. CXCR6 and CXCL16 were also detected in several mouse and human mammary, colon, and pancreatic carcinoma cell lines. CXCL16 is a transmembrane protein from which the soluble chemokine can be cleaved off. The transmembrane form is present on the surface of the carcinoma cells. Surprisingly, suppression of either CXCR6 or CXCL16 led to greatly enhanced proliferation in vitro as well as in vivo, indicating that their interaction inhibits proliferation. This notion was verified using inhibitory antibodies and by introduction of CXCL16 into a rare CXCL16-negative cell line. The effect was mediated by the G protein–coupled receptor CXCR6 because it was blocked by the Gi protein inhibitor pertussis toxin. In contrast, the soluble CXCL16 chemokine enhanced proliferation, and this was also mediated by CXCR6 but not via Gi protein. It is remarkable that both CXCR6 and CXCL16 are expressed by all mammary carcinomas because cells that lose either acquire a growth advantage and should be selected during tumor progression. This suggests an unknown important role in tumor formation. Proteases, possibly macrophage derived, might convert inhibitory transmembrane CXCL16 into the stimulatory chemokine. [Cancer Res 2008;68(12):4701–8]

A major goal of cancer research is to match specific therapies to molecular targets in cancer. Genome-scale expression profiling has identified new subtypes of cancer based on consistent patterns of variation in gene expression, leading to improved prognostic predictions. However, how these new genetic subtypes of cancers should be treated is unknown. Here, we show that a gene module map can guide the prospective identification of targeted therapies for genetic subtypes of cancer. By visualizing genome-scale gene expression in cancer as combinations of activated and deactivated functional modules, gene module maps can reveal specific functional pathways associated with each subtype that might be susceptible to targeted therapies. We show that in human breast cancers, activation of a poor-prognosis “wound signature” is strongly associated with induction of both a mitochondria gene module and a proteasome gene module. We found that 3-bromopyruvic acid, which inhibits glycolysis, selectively killed breast cells expressing the mitochondria and wound signatures. In addition, inhibition of proteasome activity by bortezomib, a drug approved for human use in multiple myeloma, abrogated wound signature expression and selectively killed breast cells expressing the wound signature. Thus, gene module maps may enable rapid translation of complex genomic signatures in human disease to targeted therapeutic strategies. [Cancer Res 2008;68(2):369–78]

Individualization of cancer management requires prognostic markers and therapy-predictive markers. Prognostic markers assess risk of disease progression independent of therapy, whereas therapy-predictive markers identify patients whose disease is sensitive or resistant to treatment. We show that an experimentally derived IFN-related DNA damage resistance signature (IRDS) is associated with resistance to chemotherapy and/or radiation across different cancer cell lines. The IRDS genes STAT1, ISG15, and IFIT1 all mediate experimental resistance. Clinical analyses reveal that IRDS(+) and IRDS(−) states exist among common human cancers. In breast cancer, a seven–gene-pair classifier predicts for efficacy of adjuvant chemotherapy and for local-regional control after radiation. By providing information on treatment sensitivity or resistance, the IRDS improves outcome prediction when combined with standard markers, risk groups, or other genomic classifiers.

Microarray analysis makes it possible to study the expression levels of tens of thousands of genes in one single experiment and is widely available for research purposes. Gene expression profiling is currently being used in many research projects aimed at identifying gene expression signatures in malignant tumors associated with prognosis and response to therapy. An important goal of such research is to develop gene expression-based diagnostic tests that can be used to guide therapy in cancer patients. Here we provide examples of studies using microarrays, especially focusing on breast cancer, in a wide range of fields including prediction of prognosis, distant metastasis and local recurrence, therapy response to radio- and chemotherapy, and normal tissue response.

Contains fulltext : 53443.pdf (Publisher’s version ) (Closed access) PURPOSE: The goal of the present study was to improve prediction of outcome after chemoradiation in advanced head and neck cancer using gene expression analysis. MATERIALS AND METHODS: We collected 92 biopsies from untreated head and neck cancer patients subsequently given cisplatin-based chemoradiation (RADPLAT) for advanced squamous cell carcinomas (HNSCC). After RNA extraction and labeling, we performed dye swap experiments using 35k oligo-microarrays. Supervised analyses were performed to create classifiers to predict locoregional control and disease recurrence. Published gene sets with prognostic value in other studies were also tested. RESULTS: Using supervised classification on the whole series, gene sets separating good and poor outcome could be found for all end points. However, when splitting tumors into training and validation groups, no robust classifiers could be found. Using Gene Set Enrichment analysis, several gene sets were found to be enriched in locoregional recurrences, although with high false-discovery rates. Previously published signatures for radiosensitivity, hypoxia, proliferation, "wound," stem cells, and chromosomal instability were not significantly correlated with outcome. However, a recently published signature for HNSCC defining a "high-risk" group was shown to be predictive for locoregional control in our dataset. CONCLUSION: Gene sets can be found with predictive potential for locoregional control after combined radiation and chemotherapy in HNSCC. How treatment-specific these gene sets are needs further study.

The association between large tumor size and metastatic risk in a majority of clinical cancers has led to questions as to whether these observations are causally related or whether one is simply a marker for the other. This is partly due to an uncertainty about how metastasis-promoting gene expression changes can arise in primary tumors. We investigated this question through the analysis of a previously defined “lung metastasis gene-expression signature” (LMS) that mediates experimental breast cancer metastasis selectively to the lung and is expressed by primary human breast cancer with a high risk for developing lung metastasis. Experimentally, we demonstrate that the LMS promotes primary tumor growth that enriches for LMS + cells, and it allows for intravasation after reaching a critical tumor size. Clinically, this corresponds to LMS + tumors being larger at diagnosis compared with LMS − tumors and to a marked rise in the incidence of metastasis after LMS + tumors reach 2 cm. Patients with LMS-expressing primary tumors selectively fail in the lung compared with the bone or other visceral sites and have a worse overall survival. The mechanistic linkage between metastasis gene expression, accelerated tumor growth, and likelihood of metastatic recurrence provided by the LMS may help to explain observations of prognostic gene signatures in primary cancer and how tumor growth can both lead to metastasis and be a marker for cells destined to metastasize.

Contains fulltext : 51604.pdf (Publisher’s version ) (Closed access) BACKGROUND AND PURPOSE: Hypoxia is a common feature of solid tumors associated with therapy resistance, increased malignancy and poor prognosis. Several approaches have been developed with the hope of identifying patients harboring hypoxic tumors including the use of microarray based gene signatures. However, studies to date have largely ignored the strong time dependency of hypoxia-regulated gene expression. We hypothesized that use of time-dependent patterns of gene expression during hypoxia would enable development of superior prognostic expression signatures. MATERIALS AND METHODS: Using published data from the microarray study of Chi et al., we extracted gene signatures correlating with induction during either early or late hypoxic exposure. Gene signatures were derived from in vitro exposed human mammary epithelial cell line (HMEC) under 0% or 2% oxygen. Gene signatures correlating with early and late up-regulation were tested by means of Kaplan-Meier survival, univariate, and multivariate analysis on a patient data set with primary breast cancer treated conventionally (surgery plus on indication radiotherapy and systemic therapy). RESULTS: We found that the two early hypoxia gene signatures extracted from 0% and 2% hypoxia showed significant prognostic power (log-rank test: p=0.004 at 0%, p=0.034 at 2%) in contrast to the late hypoxia signatures. Both early gene signatures were linked to the insulin pathway. From the multivariate Cox-regression analysis, the early hypoxia signature (p=0.254) was found to be the 4th best prognostic factor after lymph node status (p=0.002), tumor size (p=0.016) and Elston grade (p=0.111). On this data set it indeed provided more information than ER status or p53 status. CONCLUSIONS: The hypoxic stress elicits a wide panel of temporal responses corresponding to different biological pathways. Early hypoxia signatures were shown to have a significant prognostic power. These data suggest that gene signatures identified from in vitro experiments could contribute to individualized medicine.

A better understanding of tumor metastasis requires development of animal models that authentically reproduce the metastatic process. By modifying an existing mouse model of breast cancer, we discovered that macrophage-stimulating protein promoted breast tumor growth and metastasis to several organs. A special feature of our findings was the occurrence of osteolytic bone metastases, which are prominent in human breast cancer. To explore the clinical relevance of our model, we examined expression levels of three genes involved in activation of the MSP signaling pathway ( MSP , MT-SP1 , and MST1R ) in human breast tumors. We found that overexpression of MSP , MT-SP1 , and MST1R was a strong independent indicator of both metastasis and death in human breast cancer patients and significantly increased the accuracy of an existing gene expression signature for poor prognosis. These data suggest that signaling initiated by MSP is an important contributor to metastasis of breast cancer and introduce an independent biomarker for assessing the prognosis of humans with breast cancer.

Smooth muscle is present in a wide variety of anatomical locations, such as blood vessels, various visceral organs, and hair follicles. Contraction of smooth muscle is central to functions as diverse as peristalsis, urination, respiration, and the maintenance of vascular tone. Despite the varied physiological roles of smooth muscle cells (SMCs), we possess only a limited knowledge of the heterogeneity underlying their functional and anatomic specializations. As a step toward understanding the intrinsic differences between SMCs from different anatomical locations, we used DNA microarrays to profile global gene expression patterns in 36 SMC samples from various tissues after propagation under defined conditions in cell culture. Significant variations were found between the cells isolated from blood vessels, bronchi, and visceral organs. Furthermore, pervasive differences were noted within the visceral organ subgroups that appear to reflect the distinct molecular pathways essential for organogenesis as well as those involved in organ-specific contractile and physiological properties. Finally, we sought to understand how this diversity may contribute to SMC-involving pathology. We found that a gene expression signature of the responses of vascular SMCs to serum exposure is associated with a significantly poorer prognosis in human cancers, potentially linking vascular injury response to tumor progression., Author Summary It has been estimated that the human body contains approximately 200–400 distinct cell types. These estimates are largely based on the morphological characteristics of cells and have yielded, among many others, the category of smooth muscle cells, which have a distinct appearance and are present in a wide variety of tissues. By using DNA microarrays to interrogate the gene expression of anatomically varying smooth muscle cells, we were able to accurately tease apart many of the distinct cell subtypes that are classically categorized as smooth muscle cells. Remarkably, genes expressed by these newly identified, distinct subtypes corroborate many of their known biological properties and give clues about their susceptibility to specific disease states, retained developmental programs, and potential drugable targets. Additionally, from a smooth muscle cell model of vascular injury, we were able to extract a gene expression signature that provides prognostic information for human breast cancers. Of particular interest for modeling tumor progression was the finding that this gene expression signature was associated with tumor hypoxia. This study adds much to our ever-growing depth of understanding of cellular diversity and the contributions of this diversity to normal physiology and disease.

INTRODUCTION: To tailor local treatment in breast cancer patients there is a need for predicting ipsilateral recurrences after breast-conserving therapy. After adequate treatment (excision with free margins and radiotherapy), young age and incompletely excised extensive intraductal component are predictors for local recurrence, but many local recurrences can still not be predicted. Here we have used gene expression profiling by microarray analysis to identify gene expression profiles that can help to predict local recurrence in individual patients. METHODS: By using previously established gene expression profiles with proven value in predicting metastasis-free and overall survival (wound-response signature, 70-gene prognosis profile and hypoxia-induced profile) and training towards an optimal prediction of local recurrences in a training series, we establish a classifier for local recurrence after breast-conserving therapy. RESULTS: Validation of the different gene lists shows that the wound-response signature is able to separate patients with a high (29%) or low (5%) risk of a local recurrence at 10 years (sensitivity 87.5%, specificity 75%). In multivariable analysis the classifier is an independent predictor for local recurrence. CONCLUSION: Our findings indicate that gene expression profiling can identify subgroups of patients at increased risk of developing a local recurrence after breast-conserving therapy

Understanding and preventing the development of distant metastases is the most important aim in research and treatment of malignant tumors, including breast cancer. In patients with primary breast cancer without lymph node metastases who are under 50 years of age, approximately 25% will develop distant metastases after 5 years. When treated with adjuvant chemotherapy, this can be reduced to approximately 18%. When lymph node metastases are present at primary treatment, approximately 50% of the patients will develop distant metastases and this figure can be reduced to less than 40% by adjuvant chemotherapy treatment. In elderly women (50-69 years) the benefit of chemotherapy decreases from approximately 10% absolute benefit to 5% absolute benefit [1]. These numbers illustrate on the one hand the benefit for adjuvant chemotherapy, on the other hand that a large number of patients will also remain free of recurrence without adjuvant chemotherapy and suffer from the site effects without any benefit from the toxic treatment. It will be of great clinical benefit to be able to better predict which tumors will develop distant metastases, as adjuvant systemic treatment can than be better tailored to individual patients. In addition, identification of such predictive factors for distant metastases will lead to more insight in the biological processes leading to the development of distant metastases.

Many soft tissue tumors recapitulate features of normal connective tissue. We hypothesize that different types of fibroblastic tumors are representative of different populations of fibroblastic cells or different activation states of these cells. We examined two tumors with fibroblastic features, solitary fibrous tumor (SFT) and desmoid-type fibromatosis (DTF), by DNA microarray analysis and found that they have very different expression profiles, including significant differences in their patterns of expression of extracellular matrix genes and growth factors. Using immunohistochemistry and in situ hybridization on a tissue microarray, we found that genes specific for these two tumors have mutually specific expression in the stroma of nonneoplastic tissues. We defined a set of 786 gene spots whose pattern of expression distinguishes SFT from DTF. In an analysis of DNA microarray gene expression data from 295 previously published breast carcinomas, we found that expression of this gene set defined two groups of breast carcinomas with significant differences in overall survival. One of the groups had a favorable outcome and was defined by the expression of DTF genes. The other group of tumors had a poor prognosis and showed variable expression of genes enriched for SFT type. Our findings suggest that the host stromal response varies significantly among carcinomas and that gene expression patterns characteristic of soft tissue tumors can be used to discover new markers for normal connective tissue cells., The authors used two different fibroblastic tumors to identify markers expressed by normal stroma cells (the supporting framework of body tissues and/or tumors), and show that different stromal backgrounds exist in different breast carcinomas.

In an effort to deconvolute global gene-expression profiles, an interaction between some breast cancer cells and stromal fibroblasts was found to induce an interferon response, which may be associated with a greater propensity for tumor progression., Background Perturbations in cell-cell interactions are a key feature of cancer. However, little is known about the systematic effects of cell-cell interaction on global gene expression in cancer. Results We used an ex vivo model to simulate tumor-stroma interaction by systematically co-cultivating breast cancer cells with stromal fibroblasts and determined associated gene expression changes with cDNA microarrays. In the complex picture of epithelial-mesenchymal interaction effects, a prominent characteristic was an induction of interferon-response genes (IRGs) in a subset of cancer cells. In close proximity to these cancer cells, the fibroblasts secreted type I interferons, which, in turn, induced expression of the IRGs in the tumor cells. Paralleling this model, immunohistochemical analysis of human breast cancer tissues showed that STAT1, the key transcriptional activator of the IRGs, and itself an IRG, was expressed in a subset of the cancers, with a striking pattern of elevated expression in the cancer cells in close proximity to the stroma. In vivo, expression of the IRGs was remarkably coherent, providing a basis for segregation of 295 early-stage breast cancers into two groups. Tumors with high compared to low expression levels of IRGs were associated with significantly shorter overall survival; 59% versus 80% at 10 years (log-rank p = 0.001). Conclusion In an effort to deconvolute global gene expression profiles of breast cancer by systematic characterization of heterotypic interaction effects in vitro, we found that an interaction between some breast cancer cells and stromal fibroblasts can induce an interferon-response, and that this response may be associated with a greater propensity for tumor progression.

BACKGROUND: Inadequate oxygen (hypoxia) triggers a multifaceted cellular response that has important roles in normal physiology and in many human diseases. A transcription factor, hypoxia-inducible factor (HIF), plays a central role in the hypoxia response; its activity is regulated by the oxygen-dependent degradation of the HIF-1alpha protein. Despite the ubiquity and importance of hypoxia responses, little is known about the variation in the global transcriptional response to hypoxia among different cell types or how this variation might relate to tissue- and cell-specific diseases. METHODS AND FINDINGS: We analyzed the temporal changes in global transcript levels in response to hypoxia in primary renal proximal tubule epithelial cells, breast epithelial cells, smooth muscle cells, and endothelial cells with DNA microarrays. The extent of the transcriptional response to hypoxia was greatest in the renal tubule cells. This heightened response was associated with a uniquely high level of HIF-1alpha RNA in renal cells, and it could be diminished by reducing HIF-1alpha expression via RNA interference. A gene-expression signature of the hypoxia response, derived from our studies of cultured mammary and renal tubular epithelial cells, showed coordinated variation in several human cancers, and was a strong predictor of clinical outcomes in breast and ovarian cancers. In an analysis of a large, published gene-expression dataset from breast cancers, we found that the prognostic information in the hypoxia signature was virtually independent of that provided by the previously reported wound signature and more predictive of outcomes than any of the clinical parameters in current use. CONCLUSIONS: The transcriptional response to hypoxia varies among human cells. Some of this variation is traceable to variation in expression of the HIF1A gene. A gene-expression signature of the cellular response to hypoxia is associated with a significantly poorer prognosis in breast and ovarian cancer