Analysis of genomic and proteomic data using advanced literature mining

High-throughput technologies, such as proteomic screening and DNA micro-arrays, produce vast amounts of data requiring comprehensive analytical methods to decipher the biologically relevant results. One approach would be to manually search the biomedical literature; however, this would be an arduous task. We developed an automated literature-mining tool, termed MedGene, which comprehensively summarizes and estimates the relative strengths of all human gene-disease relationships in Medline. Using MedGene, we analyzed a novel micro-array expression dataset comparing breast cancer and normal breast tissue in the context of existing knowledge. We found no correlation between the strength of the literature association and the magnitude of the difference in expression level when considering changes as high as 5-fold; however, a significant correlation was observed (r = 0.41; p = 0.05) among genes showing an expression difference of 10-fold or more. Interestingly, this only held true for estrogen receptor (ER) positive tumors, not ER negative. MedGene identified a set of relatively understudied, yet highly expressed genes in ER negative tumors worthy of further examination.     

ER/PR阳性和阴性乳腺癌的定量蛋白质组学和生物信息学比较研究

目的:建立雌/孕激素受体(ER/PR)阴性和阳性乳腺癌的蛋白质表达谱,寻找ER/PR阴性和阳性乳腺癌中差异表达蛋白,为乳腺癌患者提供新的预后预测指标和治疗新靶点。方法:应用蛋白质组学i TRAQ技术建立ER/PR阳性和阴性乳腺癌的蛋白质差异表达谱,鉴定两组乳腺癌的差异表达蛋白,对部分差异表达蛋白进行生物信息学分析,包括蛋白功能注释和分类GO分析和KEGG通路分析。结果:应用i TRAQ蛋白质组学技术对乳腺癌组织进行了蛋白组学分析,鉴定出ER/PR阳性和阴性组间有差异表达的蛋白4999种,以ER/PR阳性:ER/PR阴性≥3为上调标准,确定ER/PR阳性组上调蛋白101种。以ER/PR阳性:ER/PR阴性≤0.5为下调标准,ER/PR阳性组下调蛋白122种。GO分析结果显示ER/PR受体阴性和阳性乳腺癌的差异表达蛋白的分子功能、生物过程、细胞定位较为复杂,并且在上调蛋白和下调蛋白上存在分布差异。KEGG通路分析发现部分差异表达蛋白涉及201条信号通路。结论:ER/PR阳性和阴性乳腺癌间存在差异表达蛋白,这些蛋白涉及复杂的分子功能、生物过程和信号通路。     

Proteomic characterization of Her2/neu-overexpressing breast cancer cells

The receptor tyrosine kinase HER2 is an oncogene amplified in invasive breast cancer and its overexpression in mammary epithelial cell lines is a strong determinant of a tumorigenic phenotype. Accordingly, HER2-overexpressing mammary tumors are commonly indicative of a poor prognosis in patients. Several quantitative proteomic studies have employed two-dimensional gel electrophoresis in combination with MS/MS, which provides only limited information about the molecular mechanisms underlying HER2/neu signaling. In the present study, we used a SILAC-based approach to compare the proteomic profile of normal breast epithelial cells with that of Her2/neu-overexpressing mammary epithelial cells, isolated from primary mammary tumors arising in mouse mammary tumor virus-Her2/neu transgenic mice. We identified 23 proteins with relevant annotated functions in breast cancer, showing a substantial differential expression. This included overexpression of creatine kinase, retinol-binding protein 1, thymosin 4 and tumor protein D52, which correlated with the tumorigenic phenotype of Her2-overexpressing cells. The differential expression pattern of two genes, gelsolin and retinol binding protein 1, was further validated in normal and tumor tissues. Finally, an in silico analysis of published cancer microarray data sets revealed a 23-gene signature, which can be used to predict the probability of metastasis-free survival in breast cancer patients.     

Proteins and an inflammatory network expressed in colon tumors

The adenomatous polyposis coli (APC) protein is crucial to homeostasis of normal intestinal epithelia because it suppresses the β-catenin/TCF pathway. Consequently, loss or mutation of the APC gene causes colorectal tumors in humans and mice. Here, we describe our use of multidimensional protein identification technology (MudPIT) to compare protein expression in colon tumors to that of adjacent healthy colon tissue from Apc(Min/+) mice. Twenty-seven proteins were found to be up-regulated in colon tumors and 25 were down-regulated. As an extension of the proteomic analysis, the differentially expressed proteins were used as "seeds" to search for coexpressed genes. This approach revealed a coexpression network of 45 genes that is up-regulated in colon tumors. Members of the network include the antibacterial peptide cathelicidin (CAMP), Toll-like receptors (TLRs), IL-8, and triggering receptor expressed on myeloid cells 1 (TREM1). The coexpression network is associated with innate immunity and inflammation, and there is significant concordance between its connectivity in humans versus mice (Friedman: p value = 0.0056). This study provides new insights into the proteins and networks that are likely to drive the onset and progression of colon cancer.     

Proliferation: the Most Prominent Predictor of Clinical Outcome in Breast Cancer

We recently identified a gene expression cassette of 97 unique genes that were consistently differentially expressed between low and high grade breast carcinomas. The majority of these genes were overexpressed in high grade tumors and, as expected, they were associated with cell cycle progression and proliferation. Interestingly, by applying this gene expression cassette to several datasets, we demonstrated that intermediate grade tumors were composed of a mixture of well- and poorly- differentiated tumors with statistically distinct clinical outcome similar to those of low and high grade carcinomas. Furthermore, these proliferation-related genes appear to be a common denominator of several existing prognostic gene expression signatures. This recapitulates their prognostic power far beyond the estrogen receptor (ER) status and highlights the importance of proliferation genes in breast cancer biology. Importantly, their weight seems to be far more important in ER-positive than in ER-negative disease.     

Folate Receptor-α (FOLR1) Expression and Function in Triple Negative Tumors

Folate receptor alpha (FOLR1) has been identified as a potential prognostic and therapeutic target in a number of cancers. A correlation has been shown between intense overexpression of FOLR1 in breast tumors and poor prognosis, yet there is limited examination of the distribution of FOLR1 across clinically relevant breast cancer subtypes. To explore this further, we used RNA-seq data from multiple patient cohorts to analyze the distribution of FOLR1 mRNA across breast cancer subtypes comprised of estrogen receptor positive (ER+), human epidermal growth factor receptor positive (HER2+), and triple negative (TNBC) tumors. FOLR1 expression varied within breast tumor subtypes; triple negative/basal tumors were significantly associated with increased expression of FOLR1 mRNA, compared to ER+ and HER2+ tumors. However, subsets of high level FOLR1 expressing tumors were observed in all clinical subtypes. These observations were supported by immunohistochemical analysis of tissue microarrays, with the largest number of 3+ positive tumors and highest H-scores of any subtype represented by triple negatives, and lowest by ER+ tumors. FOLR1 expression did not correlate to common clinicopathological parameters such as tumor stage and nodal status. To delineate the importance of FOLR1 overexpression in triple negative cancers, RNA-interference was used to deplete FOLR1 in overexpressing triple negative cell breast lines. Loss of FOLR1 resulted in growth inhibition, whereas FOLR1 overexpression promoted folate uptake and growth advantage in low folate conditions. Taken together, our data suggests patients with triple negative cancers expressing high FOLR1 expression represent an important population of patients that may benefit from targeted anti-FOLR1 therapy. This may prove particularly helpful for a large number of patients who would typically be classified as triple negative and who to this point have been left without any targeted treatment options.     

Proteomic analysis in human breast cancer: identification of a characteristic protein expression profile of malignant breast epithelium

Gene expression analysis has become a promising tool in predicting the clinical course of malignant disease and the response to antineoplastic therapy. Surprisingly, only little is known about the protein expression pattern of human tumors. Recent advances in proteomic analysis allow proteins of interest to be identified by their expression and/or modification pattern in 2-DE rather than using the traditional approach of translating gene expression data. To identify a proteomic pattern that is characteristic for malignant breast epithelium, we performed differential 2-DE analysis in sets of microdissected malignant breast epithelia and corresponding adjacent normal breast epithelia from five patients with invasive breast carcinoma. Thirty-two protein spots were found to be selectively regulated in malignant epithelium, and were subjected to MALDI-TOF and/or immunoblotting for protein identification. Thirteen of the identified proteins had previously not been associated with breast cancer. The validity of these findings was confirmed by literature review and immunohistochemistry for identified proteins in an independent cohort of 50 breast cancer specimens. We here describe, for the first time, a proteomic analysis of matched normal and malignant epithelia from invasive breast carcinomas. This strategy leads to a better understanding of oncogenesis at an operational level and helps to characterize the malignant phenotype of individual tumors, and thereby to identify novel targets for antineoplastic therapy.     

Biomarker discovery using a comparative omics approach in a mouse model developing heterogeneous mammary cancer subtypes

Identification of biomarkers for early breast cancer detection in blood is a challenging task, since breast cancer is a heterogeneous disease with a wide range of tumor subtypes. This is envisioned to result in differences in serum protein levels. The p53(R270H/+) WAPCre mouse model is unique in that these mice spontaneously develop both ER- and ER+ tumors, in proportions comparable to humans. Therefore, these mice provide a well-suited model system to identify human relevant biomarkers for early breast cancer detection that are additionally specific for different tumor subtypes. Mammary gland tumors were obtained from p53(R270H/+) WAPCre mice and cellular origin, ER, and HER2 status were characterized. We compared gene expression profiles for tumors with different characteristics versus control tissue, and determined genes differentially expressed across tumor subtypes. By using literature data (Gene Ontology, UniProt, and Human Plasma Proteome), we further identified protein candidate biomarkers for blood-based detection of breast cancer. Functional overrepresentation analysis (using Gene Ontology, MSigDB, BioGPS, Cancer GeneSigDB, and proteomics literature data) showed enrichment for several processes relevant for human breast cancer. Finally, Human Protein Atlas data were used to obtain a prioritized list of 16 potential biomarkers that should facilitate further studies on blood-based breast cancer detection in humans.     

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.