Integrating proteomic and phosphoproteomic data for pathway analysis in breast cancer.
ABSTRACT: BACKGROUND:As protein is the basic unit of cell function and biological pathway, shotgun proteomics, the large-scale analysis of proteins, is contributing greatly to our understanding of disease mechanisms. Proteomics study could detect the changes of both protein expression and modification. With the releases of large-scale cancer proteome studies, how to integrate acquired proteomic and phosphoproteomic data in more comprehensive pathway analysis becomes implemented, but remains challenging. Integrative pathway analysis at proteome level provides a systematic insight into the signaling network adaptations in the development of cancer. RESULTS:Here we integrated proteomic and phosphoproteomic data to perform pathway prioritization in breast cancer. We manually collected and curated breast cancer well-known related pathways from the literature as target pathways (TPs) or positive control in method evaluation. Three different strategies including Hypergeometric test based over-representation analysis, Kolmogorov-Smirnov (K-S) test based gene set analysis and topology-based pathway analysis, were applied and evaluated in integrating protein expression and phosphorylation. In comparison, we also assessed the ranking performance of the strategy using information of protein expression or protein phosphorylation individually. Target pathways were ranked more top with the data integration than using the information from proteomic or phosphoproteomic data individually. In the comparisons of pathway analysis strategies, topology-based method outperformed than the others. The subtypes of breast cancer, which consist of Luminal A, Luminal B, Basal and HER2-enriched, vary greatly in prognosis and require distinct treatment. Therefore we applied topology-based pathway analysis with integrating protein expression and phosphorylation profiles on four subtypes of breast cancer. The results showed that TPs were enriched in all subtypes but their ranks were significantly different among the subtypes. For instance, p53 pathway ranked top in the Basal-like breast cancer subtype, but not in HER2-enriched type. The rank of Focal adhesion pathway was more top in HER2- subtypes than in HER2+ subtypes. The results were consistent with some previous researches. CONCLUSIONS:The results demonstrate that the network topology-based method is more powerful by integrating proteomic and phosphoproteomic in pathway analysis of proteomics study. This integrative strategy can also be used to rank the specific pathways for the disease subtypes.
Project description:Early detection of breast cancer in blood is both appealing clinically and challenging technically due to the disease's illusive nature and heterogeneity. Today, even though major breast cancer subtypes have been characterized, i.e., luminal A, luminal B, HER2+, and basal-like, little is known about the heterogeneity of breast cancer in blood, which could help to discover minimally invasive protein biomarkers with which clinical researchers can detect, classify, and monitor different breast cancer subtypes.In this study, we performed an integrative pathway-assisted clustering analysis of breast cancer subtypes from plasma proteome samples collected from 80 patients diagnosed with breast cancer and 80 healthy women. First, four breast cancer subtypes and additionally unknown subtype (according to existing annotation) were determined based on pathology lab test results in primary tumors of enrolled patients. Next, we developed and applied four distance metrics, i.e., Protein Intensity, Q-Value, Pathway Profile, and Distance Score Function, to measure and characterize these cancer subtypes. Then, we developed a permutation test to evaluate the significant protein level changes in each biological pathway for each breast cancer subtype, using q-value. Lastly, we developed a pathway-protein matrix for each of the four distance methods to estimate the distance between breast cancer subtypes, for which further Pathway Association Network analysis were performed.We found that 1) the luminal group (luminal A and luminal B) are clustered together, as well as the basal group (basal-like and HER2+) and 2) luminal A and luminal B are more close to each other than basal-like and HER2+ to each other. Our results were consistent with a recent independent breast cancer research from the Cancer Genome Atlas Network using genomic DNA copy number arrays, DNA methylation, exome sequencing, messenger RNA arrays, microRNA sequencing and reverse-phase protein arrays. Our results showed that changes of different breast cancer subtypes at the pathway level are more profound and less variable than those at the molecular level. Similar subtypes share distinct yet similar pathway activation networks, while dissimilar subtypes are different also at the level of pathway activation networks. The results also showed that distance or similarity of cancer subtypes based on pathway analysis might be able to provide further insight into the intrinsic relationship of breast cancer subtypes. We believe integrative pathway-assisted proteomics analysis described here can become a model for reliable clustering or classification of other cancer subtypes.
Project description:Breast tumors are characterized into different subtypes based on their surface marker expression, which affects their prognosis and treatment. For example, triple negative breast cancer cells (ER-/PR-/Her2-) show reduced susceptibility towards radiotherapy and chemotherapeutic agents. Poly (ADP-ribose) polymerase (PARP) inhibitors have shown promising results in clinical trials, both as single agents and in combination with other chemotherapeutics, in several subtypes of breast cancer patients. PARP1 is involved in DNA repair, apoptosis, and transcriptional regulation and an understanding of the effects of PARP inhibitors, specifically on metabolism, is currently lacking. Here, we have used NMR-based metabolomics to probe the cell line-specific effects of PARP inhibitor and radiation on metabolism in three distinct breast cancer cell lines. Our data reveal several cell line independent metabolic changes upon PARP inhibition, including an increase in taurine. Pathway enrichment and topology analysis identified that nitrogen metabolism, glycine, serine and threonine metabolism, aminoacyl-tRNA biosynthesis and taurine and hypotaurine metabolism were enriched after PARP inhibition in the three breast cancer cell lines. We observed that the majority of metabolic changes due to radiation as well as PARP inhibition were cell line dependent, highlighting the need to understand how these treatments affect cancer cell response via changes in metabolism. Finally, we observed that both PARP inhibition and radiation induced a similar metabolic response in the HCC1937 (BRCA mutant cell line), but not in MCF-7 and MDAMB231 cells, suggesting that radiation and PARP inhibition share similar interactions with metabolic pathways in BRCA mutant cells. Our study emphasizes the importance of differences in metabolic responses to cancer treatments in different subtypes of cancers.
Project description:Background Metastatic progression of breast cancer is still a challenge in clinical oncology. Therefore, an elucidation how carcinoma cells belonging to different breast cancer subtypes realize their metastatic capacities is needed. The aim of this study was to elucidate a similarity of activated molecular pathways underlying an enhancement of invasiveness of carcinoma cells belonging to different breast carcinoma subtypes. Materials and methods In order to reach this aim, parental and invasive (INV) MDA-MB-231 (triple-negative), T47D (hormone receptor-positive), and Au565 (Her2-positive) breast carcinoma cells were used and their molecular phenotypes were compared using a proteomic approach. Results Independently from breast cancer subtypes, INV cells have demonstrated fibroblast-like morphology accompanied by enhancement of invasive and migratory capacities, increased expression of cancer stem cell markers, and delayed tumor growth in in vivo animal models. However, the global proteomic analysis has highlighted that INV cells were different in protein expressions from the parental cells, and Her2-positive Au565-INV cells showed the most pronounced molecular differences compared to the triple-negative MDA-MB-231-INV and hormone receptor-positive T47D-INV cells. Although Au565-INV breast carcinoma cells possessed the highest number of deregulated proteins, they had the lowest overlapping in proteins commonly expressed in MDA-MB-231-INV and T47D-INV cells. Conclusions We can conclude that hormone receptor-positive cells with increased invasiveness acquire the molecular characteristics of triple-negative breast cancer cells, whereas Her2-positive INV cells specifically changed their own molecular phenotype with very limited partaking in the involved pathways found in the MDA-MB-231-INV and T47D-INV cells. Since hormone receptor-positive invasive cells share their molecular properties with triple-negative breast cancer cells, we assume that these types of metastatic disease can be treated rather equally with an option to add anti-hormonal agents. In contrast, Her2-positive metastasis should be carefully evaluated for more effective therapeutic approaches which are distinct from the triple-negative and hormone-positive metastatic breast cancers.
Project description:Systems-wide profiling of breast cancer has almost always entailed RNA and DNA analysis by microarray and sequencing techniques. Marked developments in proteomic technologies now enable very deep profiling of clinical samples, with high identification and quantification accuracy. We analysed 40 oestrogen receptor positive (luminal), Her2 positive and triple negative breast tumours and reached a quantitative depth of >10,000 proteins. These proteomic profiles identified functional differences between breast cancer subtypes, related to energy metabolism, cell growth, mRNA translation and cell-cell communication. Furthermore, we derived a signature of 19 proteins, which differ between the breast cancer subtypes, through support vector machine (SVM)-based classification and feature selection. Remarkably, only three proteins of the signature were associated with gene copy number variations and eleven were also reflected on the mRNA level. These breast cancer features revealed by our work provide novel insights that may ultimately translate to development of subtype-specific therapeutics.
Project description:There is an unmet clinical need for biomarkers to identify breast cancer patients at an increased risk of developing brain metastases. The objective is to identify gene signatures and biological pathways associated with human epidermal growth factor receptor 2-positive (HER2+) brain metastasis.We combined laser capture microdissection and gene expression microarrays to analyze malignant epithelium from HER2+ breast cancer brain metastases with that from HER2+ nonmetastatic primary tumors. Differential gene expression was performed including gene set enrichment analysis (GSEA) using publicly available breast cancer gene expression data sets.In a cohort of HER2+ breast cancer brain metastases, we identified a gene expression signature that anti-correlates with overexpression of BRCA1. Sequence analysis of the HER2+ brain metastases revealed no pathogenic mutations of BRCA1, and therefore the aforementioned signature was designated BRCA1 Deficient-Like (BD-L). Evaluation of an independent cohort of breast cancer metastases demonstrated that BD-L values are significantly higher in brain metastases as compared to other metastatic sites. Although the BD-L signature is present in all subtypes of breast cancer, it is significantly higher in BRCA1 mutant primary tumors as compared with sporadic breast tumors. Additionally, BD-L signature values are significantly higher in HER2-/ER- primary tumors as compared with HER2+/ER?+?and HER2-/ER?+?tumors. The BD-L signature correlates with breast cancer cell line pharmacologic response to a combination of poly (ADP-ribose) polymerase (PARP) inhibitor and temozolomide, and the signature outperformed four published gene signatures of BRCA1/2 deficiency.A BD-L signature is enriched in HER2+ breast cancer brain metastases without pathogenic BRCA1 mutations. Unexpectedly, elevated BD-L values are found in a subset of primary tumors across all breast cancer subtypes. Evaluation of pharmacological sensitivity in breast cancer cell lines representing all breast cancer subtypes suggests the BD-L signature may serve as a biomarker to identify sporadic breast cancer patients who might benefit from a therapeutic combination of PARP inhibitor and temozolomide and may be indicative of a dysfunctional BRCA1-associated pathway.
Project description:Tumor heterogeneity attributes substantial challenges in determining the treatment regimen. Along with the conventional treatment, such as chemotherapy and radiotherapy, targeted therapy has greater impact in cancer management. Owing to the recent advancements in proteomics, we aimed to mine and re-interrogate the Clinical Proteomic Tumor Analysis Consortium (CPTAC) data sets which contain deep scale, mass spectrometry (MS)-based proteomic and phosphoproteomic data sets conducted on human tumor samples. Quantitative proteomic and phosphoproteomic data sets of tumor samples were explored and downloaded from the CPTAC database for six different cancers types (breast cancer, clear cell renal cell carcinoma (CCRCC), colon cancer, lung adenocarcinoma (LUAD), ovarian cancer, and uterine corpus endometrial carcinoma (UCEC)). We identified 880 phosphopeptide signatures for differentially regulated phosphorylation sites across five cancer types (breast cancer, colon cancer, LUAD, ovarian cancer, and UCEC). We identified the cell cycle to be aberrantly activated across these cancers. The correlation of proteomic and phosphoproteomic data sets identified changes in the phosphorylation of 12 kinases with unchanged expression levels. We further investigated phosphopeptide signature across five cancer types which led to the prediction of aurora kinase A (AURKA) and kinases-serine/threonine-protein kinase Nek2 (NEK2) as the most activated kinases targets. The drug designed for these kinases could be repurposed for treatment across cancer types.
Project description:BACKGROUND:It remains unclear whether breast cancer subtypes are associated with clinical outcome in patients without any treatment including systemic and radiation therapy as an independent entity. Understanding the survival profiles among subtypes by treatment status could impact optimal selection of treatments. METHODS:Patients were diagnosed with invasive breast cancer from the community hospitals across four geographical regions of the United States. Expression of hormone receptor (HR) and HER2 in tumor specimens from 1169 patients was centrally determined by immunohistochemistry and fluorescence in situ; breast cancer was classified into HR+/HER2-, HR+/HER2+, triple-negative, and HER2+ subtypes. Overall survival (OS) at a median follow-up of about 15 years among subtypes in untreated patients and those with systemic treatments and radiotherapy was analyzed by Kaplan-Meier method and multivariable analysis adjusting for age, tumor size and grade, number of positive nodes, stage and breast cancer subtypes. RESULTS:Without treatment, breast cancer subtypes were not associated with OS (P = 0.983) and remained insignificant for prognosis by multivariable analysis after adjusting for confounders. This contrasted with a significant survival difference across the subtypes in patients with conventional therapies (P < 0.0001). Compared with HR+/HER2- subtype, triple-negative subtype (HR 1.5, 95% CI 1.11-2.04; P = 0.009) and HER2+ subtype (HR 2.18, 95% CI 1.48-3.28; P = 0.0001) were significantly associated with worse survival by multivariable analyses. CONCLUSION:Breast cancer subtypes are not associated with survival in untreated patient population and, in contrast, significantly associated with prognosis in patients with conventional therapy. The data provide evidence of treatment-associated differential outcomes among breast cancer subtypes.
Project description:Protein glycosylation plays fundamental roles in many cellular processes, and previous reports have shown dysregulation to be associated with several human diseases, including diabetes, cancer, and neurodegenerative disorders. Despite the vital role of glycosylation for proper protein function, the analysis of glycoproteins has been lagged behind to other protein modifications. In this study, we describe the reanalysis of global proteomic data from breast cancer xenograft tissues using recently developed software package GPQuest 2.0, revealing a large number of previously unidentified N-linked glycopeptides. More importantly, we found that using immobilized metal affinity chromatography (IMAC) technology for the enrichment of phosphopeptides had coenriched a substantial number of sialoglycopeptides, allowing for a large-scale analysis of sialoglycopeptides in conjunction with the analysis of phosphopeptides. Collectively, combined tandem mass spectrometry (MS/MS) analyses of global proteomic and phosphoproteomic data sets resulted in the identification of 6?724 N-linked glycopeptides from 617 glycoproteins derived from two breast cancer xenograft tissues. Next, we utilized GPQuest 2.0 for the reanalysis of global and phosphoproteomic data generated from 108 human breast cancer tissues that were previously analyzed by Clinical Proteomic Analysis Consortium (CPTAC). Reanalysis of the CPTAC data set resulted in the identification of 2?683 glycopeptides from the global proteomic data set and 4?554 glycopeptides from phosphoproteomic data set, respectively. Together, 11?292 N-linked glycopeptides corresponding to 1?731 N-linked glycosites from 883 human glycoproteins were identified from the two data sets. This analysis revealed an extensive number of glycopeptides hidden in the global and enriched in IMAC-based phosphopeptide-enriched proteomic data, information which would have remained unknown from the original study otherwise. The reanalysis described herein can be readily applied to identify glycopeptides from already existing data sets, providing insight into many important facets of protein glycosylation in different biological, physiological, and pathological processes.
Project description:Breast cancer is a highly heterogeneous disease at the molecular level and >90% of mortalities are due to metastasis and its associated complications. The present study determined the impact of molecular subtypes on metastatic behavior and overall survival (OS) of patients with metastatic breast cancer. The influence of molecular subtypes on the sites and number of metastases in 166 patients with metastatic breast cancer from a single center were assessed; and the influence of molecular subtypes on the sites and number of metastases and OS in 15,322 metastatic cases among 329,770 patients with primary breast cancer from the Surveillance, Epidemiology and End Results database were assessed. Analysis of both datasets revealed that different molecular subtypes exhibited differences in the prevalence of different metastatic sites and number of metastases. A larger proportion of bone metastasis was observed in the hormone receptor (HR)+/human epidermal growth factor receptor 2 (HER2)+ subtype than in other subtypes, more lung metastasis was observed in the HR-/HER2+ subtype and more liver metastasis occurred in the HR+/HER2+ and HR-/HER2+ subtypes. Single-site metastasis was more common for the HR+/HER2- subtype than in other subtypes, while 2-3 sites of metastases were more common for the HR+/HER2+ subtype and ?4 sites of metastases were more frequent in the HR-/HER2+ and HR-/HER2- subtypes. The mean OS of patients with primary breast cancer in the HR+/HER2- subtype group was the longest (78.5 months), while the HR-/HER2- group had the shortest mean OS (69.1 months). The mean OS of the metastatic HR+/HER2+ group was the longest (46.0 months), while the mean OS of the metastatic HR-/HER2- group was the shortest (18.5 months). In conclusion, the results of the present study suggested that different molecular subtypes of breast cancer have different metastatic behavior, as well as mean OS.
Project description:The AP-2? transcription factor, encoded by the TFAP2C gene, regulates the expression of estrogen receptor-alpha (ER?) and other genes associated with hormone response in luminal breast cancer. Little is known about the role of AP-2? in other breast cancer subtypes. A subset of HER2+ breast cancers with amplification of the TFAP2C gene locus becomes addicted to AP-2?. Herein, we sought to define AP-2? gene targets in HER2+ breast cancer and identify genes accounting for physiologic effects of growth and invasiveness regulated by AP-2?. Comparing HER2+ cell lines that demonstrated differential response to growth and invasiveness with knockdown of TFAP2C, we identified a set of 68 differentially expressed target genes. CDH5 and CDKN1A were among the genes differentially regulated by AP-2? and that contributed to growth and invasiveness. Pathway analysis implicated the MAPK13/p38? and retinoic acid regulatory nodes, which were confirmed to display divergent responses in different HER2+ cancer lines. To confirm the clinical relevance of the genes identified, the AP-2? gene signature was found to be highly predictive of outcome in patients with HER2+ breast cancer. We conclude that AP-2? regulates a set of genes in HER2+ breast cancer that drive cancer growth and invasiveness. The AP-2? gene signature predicts outcome of patients with HER2+ breast cancer and pathway analysis predicts that subsets of patients will respond to drugs that target the MAPK or retinoic acid pathways. IMPLICATIONS: A set of genes regulated by AP-2? in HER2+ breast cancer that drive proliferation and invasion were identified and provided a gene signature that is predictive of outcome in HER2+ breast cancer.