Validation of "left ventricular early inflow-outflow index": A novel echocardiographic method for quantification of mitral regurgitation in an Indian population with special focus on rheumatic etiology.

BACKGROUND: Quantification 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. METHODS: A 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. RESULTS: LVEIO 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. CONCLUSION: LVEIO is a simple yet specific Doppler echocardiographic parameter for estimation of severity of MR including that of rheumatic etiology.

Comprehensive comparison of molecular portraits between cell lines and tumors in breast cancer.

BACKGROUND: Proper 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. RESULTS: Using 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. CONCLUSIONS: The 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.