Fibroblast deletion of ROCK2 attenuates cardiac hypertrophy, fibrosis, and diastolic dysfunction.

Although left ventricular (LV) diastolic dysfunction is often associated with hypertension, little is known regarding its underlying pathophysiological mechanism. Here, we show that the actin cytoskeletal regulator, Rho-associated coiled-coil containing kinase-2 (ROCK2), is a critical mediator of LV diastolic dysfunction. In response to angiotensin II (Ang II), mutant mice with fibroblast-specific deletion of ROCK2 (ROCK2Postn-/-) developed less LV wall thickness and fibrosis, along with improved isovolumetric relaxation. This corresponded with decreased connective tissue growth factor (CTGF) and fibroblast growth factor-2 (FGF2) expression in the hearts of ROCK2Postn-/- mice. Indeed, knockdown of ROCK2 in cardiac fibroblasts leads to decreased expression of CTGF and secretion of FGF2, and cardiomyocytes incubated with conditioned media from ROCK2-knockdown cardiac fibroblasts exhibited less hypertrophic response. In contrast, mutant mice with elevated fibroblast ROCK activity exhibited enhanced Ang II-stimulated cardiac hypertrophy and fibrosis. Clinically, higher leukocyte ROCK2 activity was observed in patients with diastolic dysfunction compared with age- and sex-matched controls, and correlated with higher grades of diastolic dysfunction by echocardiography. These findings indicate that fibroblast ROCK2 is necessary to cause cardiac hypertrophy and fibrosis through the induction CTGF and FGF2, and they suggest that targeting ROCK2 may have therapeutic benefits in patients with LV diastolic dysfunction.

Comparative analysis of image-based phenotypes of mammographic density and parenchymal patterns in distinguishing between BRCA1/2 cases, unilateral cancer cases, and controls.

We statistically compare the contributions of parenchymal phenotypes to mammographic density in distinguishing between high-risk cases and low-risk controls. The age-matched evaluation included computerized mammographic assessment of breast percent density (PD) and parenchymal patterns (phenotypes of coarseness and contrast) from radiographic texture analysis (RTA) of the full-field digital mammograms from 456 cases: 53 women with BRCA1/2 gene mutations, 75 with unilateral cancer, and 328 at low risk of developing breast cancer. Image-based phenotypes of parenchymal pattern coarseness and contrast were each found to significantly discriminate between the groups; however, PD did not. From ROC analysis, PD alone yielded area under the fitted ROC curve (AUC) values of 0.53 ([Formula: see text]) and 0.57 ([Formula: see text]) in the classification task between BRCA1/2 gene-mutation carriers and low-risk women, and between unilateral cancer and low-risk women, respectively. In a round-robin evaluation with Bayesian artificial neural network (BANN) analysis, RTA yielded AUC values of 0.81 (95% confidence interval [0.71, 0.89]) and 0.70 (95% confidence interval [0.63, 0.77]) between the BRCA1/2 gene-mutation carriers and low-risk women, and between unilateral cancer and low-risk women, respectively. These results show that high-risk and low-risk women have different mammographic parenchymal patterns with significantly higher discrimination resulting from characteristics of the parenchymal patterns than just the breast PD.

Quantitative ultrasound image analysis of axillary lymph node status in breast cancer patients.

PURPOSE: Ultrasonography has the potential to accurately stage breast cancer with automated analysis to detect axillary lymph node metastasis. The aim of this study was to develop and test automated quantitative ultrasound image analysis of axillary lymph nodes for breast cancer staging. METHODS: Following an IRB-approved HIPAA compliant protocol, ultrasound images of 90 breast cancer patients presenting for lymph node assessment were retrospectively collected. There were 51 node-positive and 39 node-negative patients, yielding images of 223 lymph nodes (109 positive for metastasis and 114 negative for metastasis). The analysis was completely automated apart from the manual indication of the approximate center of each lymph node. Mathematical descriptors of the nodes, which served as image-based biomarkers, were computer-extracted and input to a classifier for the task of distinguishing between positive (i.e., metastatic) and negative lymph nodes. The performance of this task was assessed using receiver operating characteristic (ROC) analysis with evaluation by-node and by-patient using the area under the ROC curve (AUC) as the performance metric. RESULTS: The AUC was 0.85 (standard error 0.03) for by-node evaluation when distinguishing between positive and negative lymph nodes. The AUC was 0.87 (0.04) for patient-based prognosis, i.e., assessing whether patients were lymph node-positive or lymph node-negative. CONCLUSION: Based on these classification results, we conclude that mathematical descriptors of sonographically imaged lymph nodes may be useful as prognostic biomarkers in breast cancer staging and demonstrate potential for predicting patient lymph node status.