Comparative expression analysis of four breast cancer subtypes versus matched normal tissue from the same patients

Gene expression studies have been widely used in an effort to identify signatures that can predict clinical progression of cancer. In this study we focused instead on identifying gene expression differences between breast tumors and adjacent normal tissue, and between different subtypes of tumor classified by clinical marker status. We have collected a set of 20 breast cancer tissues, matched with the adjacent pathologically normal tissue from the same patient. The cancer samples representing each subtype of breast cancer identified by estrogen receptor ER(+/-) and Her2(+/-) status and divided into four subgroups (ER+/Her2+, ER+/Her2-, ER-/Her2+, and ER-/Her2-) were hybridized on Affymetrix HG-133 Plus 2.0 microarrays. By comparing cancer samples with their matched normal controls we have identified 3537 overall differentially expressed genes using data analysis methods from Bioconductor. When we looked at the genes in common of the four subgroups, we found 151 regulated genes, some of them encoding known targets for breast cancer treatment. Unique genes in the four subgroups instead suggested gene regulation dependent on the ER/Her2 markers selection. In conclusion, the results indicate that microarray studies using robust analysis of matched tumor and normal samples from the same patients can be used to identify genes differentially expressed in breast cancer tumor subtypes even when small numbers of samples are considered and can further elucidate molecular features of breast cancer.     

丙戊酸协同顺铂抑制乳腺癌和结直肠癌细胞生长

摘要 目的：探究丙戊酸（Valproic acid, VPA）协同顺铂抑制乳腺癌和结直肠癌细胞增殖。方法：首先使用Western blot 检测 VPA 对Acetyl-Histone H3蛋白水平的影响,使用Cell Counting Kit-8（CCK-8）法检测 VPA 对乳腺癌和结直肠癌细胞的细胞活力的影响。其次单药顺铂、VPA 和联合用药处理乳腺癌细胞 MDA-MB-231 和结直肠癌细胞 HCT-15,使用 IncuCyte 动态检测细胞生长过程和生长终点。结果：发现VPA 可抑制组蛋白去乙酰化酶的功能,升高Acetyl-Histone H3的蛋白水平,VPA 可抑制乳腺癌细胞和结直肠癌细胞增殖,且对 VPA 的药物敏感性相似;顺铂和 VPA 连用后可显著抑制乳腺癌和结直肠癌细胞增殖和活力。结论：本文发现 VPA 抑制组蛋白去乙酰化酶发挥抑制乳腺癌和结直肠癌细胞生长的新机制,并可以与顺铂连用提高抗肿瘤效果和药物敏感性,为同时患有癫痫和肿瘤的人群提供新的治疗思路。     

Systems Biology and Genomics of Breast Cancer

It is now accepted that breast cancer is not a single disease, but instead it is composed of a spectrum of tumor subtypes with distinct cellular origins, somatic changes, and etiologies. Gene expression profiling using DNA microarrays has contributed significantly to our understanding of the molecular heterogeneity of breast tumor formation, progression, and recurrence. For example, at least two clinical diagnostic assays exist (i.e., OncotypeDX RS and Mammaprint®) that are able to predict outcome in patients using patterns of gene expression and predetermined mathematical algorithms. In addition, a new molecular taxonomy based upon the inherent, or “intrinsic,” biology of breast tumors has been developed; this taxonomy is called the “intrinsic subtypes of breast cancer,” which now identifies five distinct tumor types and a normal breast-like group. Importantly, the intrinsic subtypes of breast cancer predict patient relapse, overall survival, and response to endocrine and chemotherapy regimens. Thus, most of the clinical behavior of a breast tumor is already written in its subtype profile. Here, we describe the discovery and basic biology of the intrinsic subtypes of breast cancer, and detail how this interacts with underlying genetic alternations, response to therapy, and the metastatic process.     

Raman spectroscopy can differentiate malignant tumors from normal breast tissue and detect early neoplastic changes in a mouse model

Raman spectroscopy shows potential in differentiating tumors from normal tissue. We used Raman spectroscopy with near-infrared light excitation to study normal breast tissue and tumors from 11 mice injected with a cancer cell line. Spectra were collected from 17 tumors, 18 samples of adjacent breast tissue and lymph nodes, and 17 tissue samples from the contralateral breast and its adjacent lymph nodes. Discriminant function analysis was used for classification with principal component analysis scores as input data. Tissues were examined by light microscopy following formalin fixation and hematoxylin and eosin staining. Discriminant function analysis and histology agreed on the diagnosis of all contralateral normal, tumor, and mastitis samples, except one tumor which was found to be more similar to normal tissue. Normal tissue adjacent to each tumor was examined as a separate data group called tumor bed. Scattered morphologically suspicious atypical cells not definite for tumor were present in the tumor bed samples. Classification of tumor bed tissue showed that some tumor bed tissues are diagnostically different from normal, tumor, and mastitis tissue. This may reflect malignant molecular alterations prior to morphologic changes, as expected in preneoplastic processes. Raman spectroscopy not only distinguishes tumor from normal breast tissue, but also detects early neoplastic changes prior to definite morphologic alteration.     

Allele-specific copy number analysis of tumor samples with aneuploidy and tumor heterogeneity

We describe a bioinformatic tool, Tumor Aberration Prediction Suite (TAPS), for the identification of allele-specific copy numbers in tumor samples using data from Affymetrix SNP arrays. It includes detailed visualization of genomic segment characteristics and iterative pattern recognition for copy number identification, and does not require patient-matched normal samples. TAPS can be used to identify chromosomal aberrations with high sensitivity even when the proportion of tumor cells is as low as 30%. Analysis of cancer samples indicates that TAPS is well suited to investigate samples with aneuploidy and tumor heterogeneity, which is commonly found in many types of solid tumors.     

Allele-specific copy number analysis of tumor samples with aneuploidy and tumor heterogeneity

We describe a bioinformatic tool, Tumor Aberration Prediction Suite (TAPS), for the identification of allele-specific copy numbers in tumor samples using data from Affymetrix SNP arrays. It includes detailed visualization of genomic segment characteristics and iterative pattern recognition for copy number identification, and does not require patient-matched normal samples. TAPS can be used to identify chromosomal aberrations with high sensitivity even when the proportion of tumor cells is as low as 30%. Analysis of cancer samples indicates that TAPS is well suited to investigate samples with aneuploidy and tumor heterogeneity, which is commonly found in many types of solid tumors.     

Human breast cancer associated fibroblasts exhibit subtype specific gene expression profiles

ABSTRACT: BACKGROUND: Breast cancer is a heterogeneous disease for which prognosis and treatment strategies are largely governed by the receptor status (estrogen, progesterone and Her2) 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. METHODS: 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+. RESULTS: 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 likely contribute to the enhanced migration of breast cancer cells in transwell assays and may contribute to the unfavorable prognosis of Her2+ breast cancer. CONCLUSIONS: 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.     

Age-related DNA methylation in normal breast tissue and its relationship with invasive breast tumor methylation

Age is a key risk factor for breast cancer and epigenetic alterations may contribute to age-related increases in breast cancer risk, though the relation of age-related methylation in normal breast tissues with altered methylation in breast tumors is unclear. We investigated the relation of age with DNA methylation in normal breast tissues genome-wide using two data sets from the Gene Expression Omnibus (GEO) database (GSE32393 and GSE31979). We validated our observations in an independent set of normal breast tissues, examined age-related methylation in normal breast for enrichment of genomic features, and compared age-related methylation in normal tissue with methylation alterations in breast tumors. Between the two array-based methylation data sets, there were 204 CpG loci with significant (P < 0.05) and consistent age-related methylation, 97% of which were increases in methylation. Our validation sets confirmed the direction of age-related DNA methylation changes in all measured regions. Among the 204 age-related CpG loci, we observed a significant enrichment for CpG islands (P = 8.7E-6) and polycomb group protein target genes (P = 0.03). In addition, 24 of the 204 CpGs with age-related methylation in normal breast were significantly differentially methylated between normal and breast tumor tissues. We identified consistent age-related methylation changes in normal breast tissue that are further altered in breast tumors and may represent early events contributing to breast carcinogenesis. This work identifies age-related methylation in normal breast tissue and begins to deconstruct the contribution of aging to epigenetic alterations present in breast tumors.     

Expression of Peripheral Benzodiazepine Receptor (PBR) in Human Tumors: Relationship to Breast,Colorectal, and Prostate Tumor Progression

Abstract

High levels of peripheral‐type benzodiazepine receptor (PBR), the alternative‐binding site for diazepam, are part of the aggressive human breast cancer cell phenotype in vitro. We examined PBR levels and distribution in normal tissue and tumors from multiple cancer types by immunohistochemistry. Among normal breast tissues, fibroadenomas, primary and metastatic adenocarcinomas, there is a progressive increase in PBR levels parallel to the invasive and metastatic ability of the tumor (p < 0.0001). In colorectal and prostate carcinomas, PBR levels were also higher in tumor than in the corresponding non‐tumoral tissues and benign lesions (p < 0.0001). In contrast, PBR was highly concentrated in normal adrenal cortical cells and hepatocytes, whereas in adrenocortical tumors and hepatomas PBR levels were decreased. Moreover, malignant skin tumors showed decreased PBR expression compared with normal skin. These results indicate that elevated PBR expression is not a common feature of aggressive tumors, but rather may be limited to certain cancers, such as those of breast, colon‐rectum and prostate tissues, where elevated PBR expression is associated with tumor progression. Thus, we propose that PBR overexpression could serve as a novel prognostic indicator of an aggressive phenotype in breast, colorectal and prostate cancers.     

Tumor‐stroma interactions differentially alter drug sensitivity based on the origin of stromal cells

Due to tumor heterogeneity, most believe that effective treatments should be tailored to the features of an individual tumor or tumor subclass. It is still unclear, however, what information should be considered for optimal disease stratification, and most prior work focuses on tumor genomics. Here, we focus on the tumor microenvironment. Using a large‐scale coculture assay optimized to measure drug‐induced cell death, we identify tumor–stroma interactions that modulate drug sensitivity. Our data show that the chemo‐insensitivity typically associated with aggressive subtypes of breast cancer is not observed if these cells are grown in 2D or 3D monoculture, but is manifested when these cells are cocultured with stromal cells, such as fibroblasts. Furthermore, we find that fibroblasts influence drug responses in two distinct and divergent manners, associated with the tissue from which the fibroblasts were harvested. These divergent phenotypes occur regardless of the drug tested and result from modulation of apoptotic priming within tumor cells. Our study highlights unexpected diversity in tumor–stroma interactions, and we reveal new principles that dictate how fibroblasts alter tumor drug responses.     

Class discovery analysis of the lung cancer gene expression data

Traditional histological classification of lung cancer subtypes is informative, but incomplete. Recent studies of gene expression suggest that molecular classification can be used for effective diagnostic and prediction of the treatment outcome. We attempt to build a molecular classification based on the public data available from a few independent sources. The data is reanalyzed with a new cluster analysis algorithm. This algorithm allows us to preserve the high dimensionality of data and produce the cluster structure without preliminary selection of significant genes or any other presumption about the relation between different cancer and normal tissue samples. The resulting clusters are generally consistent with the histological classification. However, our analysis reveals many additional details and subtypes of previously defined types of lung cancer. Large histological cancer types can be further divided into subclasses with different patterns of gene expression. These subtypes should be taken into account in diagnostics, drug testing, and treatment development for lung cancer patients.     

Feasibility of chemosensitivity testing in soft tissue sarcomas

BACKGROUND: Soft tissue sarcomas comprise less than 1% of all solid malignancies. The presentation and behavior of these tumors differs depending on location and histological characteristics. Standard therapy consists of complete surgical resection in combination with adjuvant radiotherapy. The role of chemotherapy is not clearly defined and is largely restricted to clinical trials. Only a limited number of agents have proved to be effective in soft tissue sarcomas. The use of doxorubicin, epirubicin and ifosfamide allowed response rates of more than 20%. In addition, recent chemotherapy trials did not demonstrate any significant differences in efficacy for various histological subtypes. METHODS: The objective of this study was to gain additional information about the chemosensitivity of soft tissue sarcomas to seven 7 different chemotherapy agents as single drugs and 4 combinations. Therefore we used an established ATP based in-vitro testing system and examined 50 soft tissue sarcomas. Chemosensitivity was assessed using a luciferin-luciferase-based luminescence assay providing individual chemosensitivity indices for each agent tested. RESULTS: The sensitivity varied widely according to the histological subtypes. The tumors state of cellular dedifferentiation played a crucial role for the efficiency of the chemotherapeutic agents. The sensitivity also depended on the presentation of the sarcoma as a primary or recurrent tumor. The highest sensitivity was demonstrated for actinomycin D as a single agent, with 74% of the tumor samples exhibiting a high-grade sensitivity (20% low sensitivity, no resistance). The combination of actinomycin D and ifosfamide yielded a high sensitivity in 76% (2% resistance). Doxorubicin as a mono-therapy or in combination with ifosfamide achieved high sensitivity in 70% and 72%, respectively, and resistance in 6% of the samples. CONCLUSION: Chemosensitivity testing is feasible in soft tissue sarcomas. It can be used to create sensitivity and resistance profiles of established and new cytotoxic agents and their combinations in soft tissue sarcomas. Our data demonstrate measurable discrepancies of the drug efficiency in soft tissue sarcomas, sarcoma subtypes and tumor recurrencies. However, current therapeutic regime does not take this in consideration, yet.