Project description:Primary tumor growth induces host tissue responses that are believed to support and promote tumor progression. Identification of the molecular characteristics of the tumor microenvironment and elucidation of its crosstalk with tumor cells may therefore be crucial for improving our understanding of the processes implicated in cancer progression, identifying potential therapeutic targets, and uncovering stromal gene expression signatures that may predict clinical outcome. A key issue to resolve, therefore, is whether the stromal response to tumor growth is largely a generic phenomenon, irrespective of the tumor type, or whether the response reflects tumor-specific properties. To address similarity or distinction of stromal gene expression changes during cancer progression, oligonucleotide-based Affymetrix microarray technology was used to compare the transcriptomes of laser-microdissected stromal cells derived from invasive human breast and prostate carcinoma. Invasive breast and prostate cancer-associated stroma was observed to display distinct transcriptomes, with a limited number of shared genes. Interestingly, both breast and prostate tumor-specific dysregulated stromal genes were observed to cluster breast and prostate cancer patients, respectively, into two distinct groups with statistically different clinical outcomes. By contrast, a gene signature that was common to the reactive stroma of both tumor types did not have survival predictive value. Univariate Cox analysis identified genes whose expression level was most strongly associated with patient survival. Taken together, these observations suggest that the tumor microenvironment displays distinct features according to the tumor type that provides survival-predictive value. 6 samples of stroma surrounding invasive breast primary tumors; 6 matched samples of normal stroma. 6 samples of stroma surrounding invasive prostate primary tumors; 6 matched samples of normal stroma.
Project description:Tumor-associated breast stroma was laser-capture microdissected from IDC breast cancer cases. The goal of the study was to characterize the heterogeneity of breast tumor-assocaited stroma and identify gene expression signatures predictive of clinical outcome. Experiment Overall Design: Common reference design, 53 samples, with dye-swap replicates. Some samples replicated three or four times, a total of 111 arrays.
Project description:Breast cancer is an extremely heterogeneous disease. This heterogeneity can be observed at multiple levels, including gene expression, chromosomal aberrations, and disease pathology. A clear understanding of these differences is important since they impact upon treatment efficacy and clinical outcome. Many studies have shown that the tumor microenvironment also plays a critical role in cancer initiation and progression. Although genomic technologies have been used to gain a better understanding of the impact of gene expression heterogeneity on breast cancer outcome by identifying gene expression signatures associated with clinical outcome, histopathological breast cancer subtypes, and a variety of cancer--related pathways and processes, relatively little is known about the influence of of heterogeneity in the tumor microenvironment on these factors. We show that gene expression in the breast tumor microenvironment is highly heterogeneous, identifying at least six different classes of tumor stroma with distinct expression patterns and distinct biological processes. Two of these classes recapitulate the processes identified in the stroma--derived prognostic predictor, while the others are new classes of stroma associated with distinct clinical outcomes. One of these is associated with matrix remodeling and is strongly associated with the basal molecular subtype of breast cancer. The remainder are independent of the previously published molecular subtypes of breast cancer. Additionally, based on independent data from over 800 tumors, the combinations of stroma classes and breast cancer subtypes identify new subgroups of breast tumors that show better discrimination between good and poor outcome individuals than the molecular breast cancer subtypes or the stroma classes alone, suggesting a novel classification scheme for breast cancer . This further demonstrates an important role for the tumor microenvironment in defining breast cancer heterogeneity, with a consequent impact upon clinical outcome.
Project description:Tumor-associated breast stroma was laser-capture microdissected from IDC breast cancer cases. The goal of the study was to characterize the heterogeneity of breast tumor-assocaited stroma and identify gene expression signatures predictive of clinical outcome. Keywords: disease state analysis
Project description:To better understand the role of tumor microenvironment in breast cancer progression, we combined laser capture microdissection and microarray analysis to provide a comprehensive catalog of gene expression changes in both tumor and tumor-associated stroma. Experiment Overall Design: We used LCM to isolate the epithelial and stroma compartments separately from each of 14 fresh frozen primary breast cancer biopsies. In the epithelial compartment, we captured normal (N) and malignant (DCIS or IDC or both where available) epithelium from each tissue slide. In the stroma compartment, we captured both normal stroma away from the malignant lesion (NSS) and the DCIS-associated stroma (ISS) and/or IDC-associated stroma (INVS) whenever possible.
Project description:Primary tumor growth induces host tissue responses that are believed to support and promote tumor progression. Identification of the molecular characteristics of the tumor microenvironment and elucidation of its crosstalk with tumor cells may therefore be crucial for improving our understanding of the processes implicated in cancer progression, identifying potential therapeutic targets, and uncovering stromal gene expression signatures that may predict clinical outcome. A key issue to resolve, therefore, is whether the stromal response to tumor growth is largely a generic phenomenon, irrespective of the tumor type, or whether the response reflects tumor-specific properties. To address similarity or distinction of stromal gene expression changes during cancer progression, oligonucleotide-based Affymetrix microarray technology was used to compare the transcriptomes of laser-microdissected stromal cells derived from invasive human breast and prostate carcinoma. Invasive breast and prostate cancer-associated stroma was observed to display distinct transcriptomes, with a limited number of shared genes. Interestingly, both breast and prostate tumor-specific dysregulated stromal genes were observed to cluster breast and prostate cancer patients, respectively, into two distinct groups with statistically different clinical outcomes. By contrast, a gene signature that was common to the reactive stroma of both tumor types did not have survival predictive value. Univariate Cox analysis identified genes whose expression level was most strongly associated with patient survival. Taken together, these observations suggest that the tumor microenvironment displays distinct features according to the tumor type that provides survival-predictive value.
Project description:Triple-negative primary breast cancer tumors, rich in inflammatory stroma, were laser microdissected and global mRNA expression was analyzed in stroma compartment, cancer cell compartment, and in total tumor tissue. Three FFPE breast cancer tumors wer laser microdissected and the cancer cell and stroma compartments were collected. A piece of t
Project description:Availability of patient-derived sarcoma models that closely mimic human tumors remains a significant gap in cancer research as these models may not recapitulate the spectrum of sarcoma heterogeneity seen in patients. To characterize patient-derived models for functional studies, we made proteomic comparisons with originating sarcomas representative of the three intrinsic subtypes by mass spectrometry. Human protein profiling was found to be retained with high fidelity in patient-derived models. Patient derived xenografts locally invade and colonize stroma in mice which enables unambiguous molecular discrimination of human proteins in the tumor from mouse proteins in the microenvironment. We characterized protein profiling of patient sarcoma tumors and mouse stroma by species-specific quantitative proteomics. We found that protein expression in mouse stroma was affected by the primary human tumor. Our results showed that levels of stromal proteins derived from the tumor were lowered in PDXs and cell lines and part of human stromal proteins were replaced by corresponding mouse proteins in PDXs. This suggests that the effects of the microenvironment on drug response may not reflect those in the primary tumor. This cross-species proteomic analysis in PDXs could potentially improve preclinical evaluation of treatment modalities and enhance the ability to predict clinical trial responses.
Project description:Breast cancer is a heterogeneous disease for which prognosis and treatment strategies are largely governed by the receptor status (estrogen, progesterone and Her2-neu) of the tumor cells. Gene expression profiling of whole breast tumors further stratifies breast cancer into several molecular subtypes which also co-segregate with the receptor status of the tumor cells. We postulated that cancer associated fibroblasts (CAFs) within the tumor stroma may exhibit subtype specific gene expression profiles and thus contribute to the biology of the disease in a subtype specific manner. Several studies have reported gene expression profile differences between CAFs and normal breast fibroblasts but in none of these studies were the results stratified based on tumor subtypes. To address whether gene expression in breast cancer associated fibroblasts varies between breast cancer subtypes, we compared the gene expression profiles of early passage primary CAFs isolated from twenty human breast cancer samples representing three main subtypes; seven ER+, seven triple negative (TNBC) and six Her2+. We observed significant expression differences between CAFs derived from Her2+ breast cancer and CAFs from TNBC and ER+ cancers, particularly in pathways associated with cytoskeleton and integrin signaling. In the case of Her2+ breast cancer, the signaling pathways found to be selectively up regulated in CAFs may contribute to the more invasive properties and unfavorable prognosis of Her2+ breast cancer. These data demonstrate that in addition to the distinct molecular profiles that characterize the neoplastic cells, CAF gene expression is also differentially regulated in distinct subtypes of breast cancer. We isolated CAFs from twenty primary breast cancer samples representing three main subtypes (ER+ (n=7), TNBC (n=7), Her2+ (n=6)) and performed gene expression profile analyses on RNA isolated from these early passage CAFs. Those samples were done in two batches with 4 samples repeated in both batches. One TNBC sample was found to be an outlier and not used in the analysis.