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Clinical Proteomics of Breast Cancer Reveals a Novel Layer of Breast Cancer Classification.

ABSTRACT: Breast cancer classification has been the focus of numerous worldwide efforts, analyzing the molecular basis of breast cancer subtypes and aiming to associate them with clinical outcome and to improve the current diagnostic routine. Genomic and transcriptomic profiles of breast cancer have been well established, however the proteomic contribution to these profiles has yet to be elucidated. In this work, we utilized mass spectrometry-based proteomic analysis on more than 130 clinical breast samples to demonstrate intertumor heterogeneity across three breast cancer subtypes and healthy tissue. Unsupervised analysis identified four proteomic clusters, among them, one that represents a novel luminal subtype characterized by increased PI3K signaling. This subtype was further validated using an independent protein-based dataset, but not in two independent transcriptome cohorts. These results demonstrate the importance of deep proteomic analysis, which may affect cancer treatment decision making.Significance: These findings utilize extensive proteomics to identify a novel luminal breast cancer subtype, highlighting the added value of clinical proteomics in breast cancer to identify unique features not observable by genomic approaches. Cancer Res; 78(20); 6001-10. ©2018 AACR.

SUBMITTER: Yanovich G

PROVIDER: S-EPMC6193543 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Clinical Proteomics of Breast Cancer Reveals a Novel Layer of Breast Cancer Classification.

Yanovich Gali G Agmon Hadar H Harel Michal M Sonnenblick Amir A Peretz Tamar T Geiger Tamar T

Cancer research 20180828 20

Breast cancer classification has been the focus of numerous worldwide efforts, analyzing the molecular basis of breast cancer subtypes and aiming to associate them with clinical outcome and to improve the current diagnostic routine. Genomic and transcriptomic profiles of breast cancer have been well established, however the proteomic contribution to these profiles has yet to be elucidated. In this work, we utilized mass spectrometry-based proteomic analysis on more than 130 clinical breast sampl ...[more]

PMID: 30154156

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Project description:Breast cancer is the second most frequent cancer worldwide, following lung cancer and the fifth leading cause of cancer death and a major cause of cancer death among women. In recent years, convolutional neural networks (CNNs) have been successfully applied for the diagnosis of breast cancer using different imaging modalities. Pooling is a main data processing step in CNN that decreases the feature maps' dimensionality without losing major patterns. However, the effect of pooling layer was not studied efficiently in literature. In this paper, we propose a novel design for the pooling layer called vector pooling block (VPB) for the CCN algorithm. The proposed VPB consists of two data pathways, which focus on extracting features along horizontal and vertical orientations. The VPB makes the CNNs able to collect both global and local features by including long and narrow pooling kernels, which is different from the traditional pooling layer, that gathers features from a fixed square kernel. Based on the novel VPB, we proposed a new pooling module called AVG-MAX VPB. It can collect informative features by using two types of pooling techniques, maximum and average pooling. The VPB and the AVG-MAX VPB are plugged into the backbone CNNs networks, such as U-Net, AlexNet, ResNet18 and GoogleNet, to show the advantages in segmentation and classification tasks associated with breast cancer diagnosis from thermograms. The proposed pooling layer was evaluated using a benchmark thermogram database (DMR-IR) and its results compared with U-Net results which was used as base results. The U-Net results were as follows: global accuracy = 96.6%, mean accuracy = 96.5%, mean IoU = 92.07%, and mean BF score = 78.34%. The VBP-based results were as follows: global accuracy = 98.3%, mean accuracy = 97.9%, mean IoU = 95.87%, and mean BF score = 88.68% while the AVG-MAX VPB-based results were as follows: global accuracy = 99.2%, mean accuracy = 98.97%, mean IoU = 98.03%, and mean BF score = 94.29%. Other network architectures also demonstrate superior improvement considering the use of VPB and AVG-MAX VPB.

Project description:Triple-negative breast cancer is a heterogeneous disease characterized by a lack of hormonal receptors and HER2 overexpression. It is the only breast cancer subgroup that does not benefit from targeted therapies, and its prognosis is poor. Several studies have developed specific molecular classifications for triple-negative breast cancer. However, these molecular subtypes have had little impact in the clinical setting. Gene expression data and clinical information from 494 triple-negative breast tumors were obtained from public databases. First, a probabilistic graphical model approach to associate gene expression profiles was performed. Then, sparse k-means was used to establish a new molecular classification. Results were then verified in a second database including 153 triple-negative breast tumors treated with neoadjuvant chemotherapy. Clinical and gene expression data from 494 triple-negative breast tumors were analyzed. Tumors in the dataset were divided into four subgroups (luminal-androgen receptor expressing, basal, claudin-low and claudin-high), using the cancer stem cell hypothesis as reference. These four subgroups were defined and characterized through hierarchical clustering and probabilistic graphical models and compared with previously defined classifications. In addition, two subgroups related to immune activity were defined. This immune activity showed prognostic value in the whole cohort and in the luminal subgroup. The claudin-high subgroup showed poor response to neoadjuvant chemotherapy. Through a novel analytical approach we proved that there are at least two independent sources of biological information: cellular and immune. Thus, we developed two different and overlapping triple-negative breast cancer classifications and showed that the luminal immune-positive subgroup had better prognoses than the luminal immune-negative. Finally, this work paves the way for using the defined classifications as predictive features in the neoadjuvant scenario.

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Project description:BackgroundThe high heterogeneity of triple negative breast cancer (TNBC) is the main clinical challenge for individualized therapy. Considering that fatty acid metabolism (FAM) plays an indispensable role in tumorigenesis and development of TNBC, we proposed a novel FAM-based classification to characterize the tumor microenvironment immune profiles and heterogeneous for TNBC.MethodsWeighted gene correlation network analysis (WGCNA) was performed to identify FAM-related genes from 221 TNBC samples in Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset. Then, non-negative matrix factorization (NMF) clustering analysis was applied to determine FAM clusters based on the prognostic FAM-related genes, which chosen from the univariate/multivariate Cox regression model and the least absolute shrinkage and selection operator (LASSO) regression algorithm. Then, a FAM scoring scheme was constructed to further quantify FAM features of individual TNBC patient based on the prognostic differentially expressed genes (DEGs) between different FAM clusters. Systematically analyses were performed to evaluate the correlation between the FAM scoring system (FS) with survival outcomes, genomic characteristics, tumor microenvironment (TME) features and immunotherapeutic response for TNBC, which were further validated in the Cancer Genome Atlas (TCGA) and GSE58812 datasets. Moreover, the expression level and clinical significancy of the selected FS gene signatures were further validated in our cohort.Results1860 FAM-genes were screened out using WGCNA. Three distinct FAM clusters were determined by NMF clustering analysis, which allowed to distinguish different groups of patients with distinct clinical outcomes and tumor microenvironment (TME) features. Then, prognostic gene signatures based on the DEGs between different FAM clusters were identified using univariate Cox regression analysis and Lasso regression algorithm. A FAM scoring scheme was constructed, which could divide TNBC patients into high and low-FS subgroups. Low FS subgroup, characterized by better prognosis and abundance with effective immune infiltration. While patients with higher FS were featured with poorer survival and lack of effective immune infiltration. In addition, two independent immunotherapy cohorts (Imvigor210 and GSE78220) confirmed that patients with lower FS demonstrated significant therapeutic advantages from anti-PD-1/PD-L1 immunotherapy and durable clinical benefits. Further analyses in our cohort found that the differential expression of CXCL13, FBP1 and PLCL2 were significantly associated with clinical outcomes of TNBC samples.ConclusionsThis study revealed FAM plays an indispensable role in formation of TNBC heterogeneity and TME diversity. The novel FAM-based classification could provide a promising prognostic predictor and guide more effective immunotherapy strategies for TNBC.

Project description:More than 380,000 new cases of bladder cancer are diagnosed worldwide, accounting for ∼150,200 deaths each year. To discover potential biomarkers of bladder cancer, we employed a strategy combining laser microdissection, isobaric tags for relative and absolute quantitation labeling, and liquid chromatography-tandem MS (LC-MS/MS) analysis to profile proteomic changes in fresh-frozen bladder tumor specimens. Cellular proteins from four pairs of surgically resected primary bladder cancer tumor and adjacent nontumorous tissue were extracted for use in two batches of isobaric tags for relative and absolute quantitation experiments, which identified a total of 3220 proteins. A DAVID (database for annotation, visualization and integrated discovery) analysis of dysregulated proteins revealed that the three top-ranking biological processes were extracellular matrix organization, extracellular structure organization, and oxidation-reduction. Biological processes including response to organic substances, response to metal ions, and response to inorganic substances were highlighted by up-expressed proteins in bladder cancer. Seven differentially expressed proteins were selected as potential bladder cancer biomarkers for further verification. Immunohistochemical analyses showed significantly elevated levels of three proteins-SLC3A2, STMN1, and TAGLN2-in tumor cells compared with noncancerous bladder epithelial cells, and suggested that TAGLN2 could be a useful tumor tissue marker for diagnosis (AUC = 0.999) and evaluating lymph node metastasis in bladder cancer patients. ELISA results revealed significantly increased urinary levels of both STMN1 and TAGLN2 in bladder cancer subgroups compared with control groups. In comparisons with age-matched hernia urine specimens, urinary TAGLN2 in bladder cancer samples showed the largest fold change (7.13-fold), with an area-under-the-curve value of 0.70 (p < 0.001, n = 205). Overall, TAGLN2 showed the most significant overexpression in individual bladder cancer tissues and urine specimens, and thus represents a potential biomarker for noninvasive screening for bladder cancer. Our findings highlight the value of bladder tissue proteome in providing valuable information for future validation studies of potential biomarkers in urothelial carcinoma.

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Clinical Proteomics of Breast Cancer Reveals a Novel Layer of Breast Cancer Classification.

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Clinical Proteomics of Breast Cancer Reveals a Novel Layer of Breast Cancer Classification.

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