BackgroundProper cell models for breast cancer primary tumors have long been the focal point in the cancer’s research. The genomic comparison between cell lines and tumors can investigate the similarity and dissimilarity and help to select right cell model to mimic tumor tissues to properly evaluate the drug reaction in vitro. In this paper, a comprehensive comparison in copy number variation (CNV), mutation, mRNA expression and protein expression between 68 breast cancer cell lines and 1375 primary breast tumors is conducted and presented.ResultsUsing whole genome expression arrays, strong correlations were observed between cells and tumors. PAM50 gene expression differentiated them into four major breast cancer subtypes: Luminal A and B, HER2amp, and Basal-like in both cells and tumors partially. Genomic CNVs patterns were observed between tumors and cells across chromosomes in general. High C > T and C > G trans-version rates were observed in both cells and tumors, while the cells had slightly higher somatic mutation rates than tumors. Clustering analysis on protein expression data can reasonably recover the breast cancer subtypes in cell lines and tumors. Although the drug-targeted proteins ER/PR and interesting mTOR/GSK3/TS2/PDK1/ER_P118 cluster had shown the consistent patterns between cells and tumor, low protein-based correlations were observed between cells and tumors. The expression consistency of mRNA verse protein between cell line and tumors reaches 0.7076. These important drug targets in breast cancer, ESR1, PGR, HER2, EGFR and AR have a high similarity in mRNA and protein variation in both tumors and cell lines. GATA3 and RP56KB1 are two promising drug targets for breast cancer. A total score developed from the four correlations among four molecular profiles suggests that cell lines, BT483, T47D and MDAMB453 have the highest similarity with tumors.ConclusionsThe integrated data from across these multiple platforms demonstrates the existence of the similarity and dissimilarity of molecular features between breast cancer tumors and cell lines. The cell lines only mirror some but not all of the molecular properties of primary tumors. The study results add more evidence in selecting cell line models for breast cancer research.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2911-z) contains supplementary material, which is available to authorized users.

BackgroundQuantification of mitral regurgitation (MR) has always required an “integrated approach” as there is no single gold-standard method. We investigated a new Doppler-derived parameter “left ventricular early inflow-outflow index (LVEIO)” for the quantification of MR and its likelihood to predict severe MR in correlation with already established parameters in an Indian population including a large subset of patients with rheumatic etiology.MethodsA prospective study was performed at a major tertiary care center in western India over a 5-month period. Five hundred patients diagnosed with isolated MR including 260 (52%) patients with rheumatic etiology were included in the study after applying exclusion criteria. We analyzed MR using color flow jet, effective regurgitant orifice area (EROA), and vena contracta (VC) width. LVEIO is a simplification of the regurgitant volume (RV) method, which was calculated as “E velocity divided by LV outflow velocity integrated over the systolic ejection period left ventricular outflow tract velocity time integral” and compared with the established parameters.ResultsLVEIO was 4.65 ± 1.45, 6.56 ± 1.52, and 9.91 ± 3.70 among patients diagnosed with mild, moderate, and severe MR, respectively (p < 0.001). Those with LVEIO ≥8 were the most likely to have severe MR (positive likelihood ratio: 10.42). LVEIO had specificity of 93.25% for diagnosis of severe MR with positive predictive value of 86.36%. There was positive correlation observed between LVEIO and VC width (r = 0.591), RV (r = 0.410), and EROA (r = 0.778) (all p < 0.001) in the Pearson correlation test. The specificity of LVEIO remained consistent in diagnosing severe MR in patients with rheumatic etiology.ConclusionLVEIO is a simple yet specific Doppler echocardiographic parameter for estimation of severity of MR including that of rheumatic etiology.

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Introduction and Objective: Diagnosis of prostate cancer (CaP) has relied on prostate specific antigen (PSA) level and digital rectal examination (DRE) followed by prostate biopsies. These modalities have the potential to yield false-positive and false-negative results for CaP. These challenges prompted efforts to develop more specific body fluid based assays including PCA3, TMPRSS2:ERG, K4csore and PHI tests. Further, emerging data on significant racial differences of common CaP driver genes, e.g., PTEN and ERG in CaP can lead to significant limitations in biomarker performance. Thus, the goal of our study was to discover CaP serum markers with equal performance among African American (AA) and Caucasian American (CA) men. We employed proteomics, signal- and structural lipidomics and metabolomics platforms to discover serum biomarkers and evaluate their utility for diagnosis and prognosis of CaP in AA and CA men. Methods: Sera from 700 individuals were analyzed, which included AA and CA CaP patients stratified for ERG oncoprotein expression by immunohistochemistry (N=495). Sera from age-matched healthy control men were also included (N=205) in this study. Quantitative global profiles of lipidome, proteome and metabolome were analyzed by high resolution MS-based technologies. Random forest and Interrogative Biology® analytical platforms were used to identify analytes differentiating healthy from CaP cases including clinical-pathologic data. Results: The unbiased global profiling and integration of the data and clinical-pathologic features have led to the identification of molecular fingerprints differentiating cancer patients from healthy controls. Specifically, three analytes in serum metabolome showed robust separation between both AA and CA CaP and control groups. Elevated levels of nicotinamide and eicosenoic acid and decreased levels of a decanoylcarnitate alone have indicated strong separation between cases and controls. Further inclusion of additional 9 analytes provided an optimal multi-omics panel for distinguishing the combined cohort of AA and CA cases vs. healthy controls. Conclusions: The findings presented here support that an integrated multiomics approach has the potential to define serum marker panels for diagnosis of CaP in the context of racial diversity and molecular annotation (e.g., ERG) of CaP. These promising data are undergoing validation in additional patient cohorts. Citation Format: Michael A. Kiebish, Jennifer Cullen, Albert Dobi, Amina Ali, Leonardo O. Rodrigues, Yezhou Sun, Aniruddha Pawar, Aditee Dalvi, Denise Young, Vivek K. Vishnudas, Jason Sedarsky, Gyorgy Petrovics, Emily Chen, Viatcheslav Akmaev, Inger L. Rosner, David McLeod, Isabell A. Sesterhenn, Rangaprasad Sarangarajan, Alagarsamy Srinivasan, Elder Grainger, Niven R. Narain, Shiv Srivastava. A serum multiomics signature for enhancing prostate cancer diagnosis and prognosis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4645. doi:10.1158/1538-7445.AM2017-4645