Connexin-43 upregulation in micrometastases and tumor vasculature and its role in tumor cell attachment to pulmonary endothelium

<p>Abstract</p> <p>Background</p> <p>The modulation of gap junctional communication between tumor cells and between tumor and vascular endothelial cells during tumorigenesis and metastasis is complex. The notion of a role for loss of gap junctional intercellular communication in tumorigenesis and metastasis has been controversial. While some of the stages of tumorigenesis and metastasis, such as uncontrolled cell division and cellular detachment, would necessitate the loss of intercellular junctions, other stages, such as intravasation, endothelial attachment, and vascularization, likely require increased cell-cell contact. We hypothesized that, in this multi-stage scheme, connexin-43 is centrally involved as a cell adhesion molecule mediating metastatic tumor attachment to the pulmonary endothelium.</p> <p>Methods</p> <p>Tumor cell attachment to pulmonary vasculature, tumor growth, and connexin-43 expression was studied in metastatic lung tumor sections obtained after tail-vein injection into nude mice of syngeneic breast cancer cell lines, overexpressing wild type connexin-43 or dominant-negatively mutated connexin-43 proteins. High-resolution immunofluorescence microscopy and Western blot analysis was performed using a connexin-43 monoclonal antibody. Calcein Orange Red AM dye transfer by fluorescence imaging was used to evaluate the gap junction function.</p> <p>Results</p> <p>Adhesion of breast cancer cells to the pulmonary endothelium increased with cancer cells overexpressing connexin-43 and markedly decreased with cells expressing dominant-negative connexin-43. Upregulation of connexin-43 was observed in tumor cell-endothelial cell contact areas <it>in vitro </it>and <it>in vivo</it>, and in areas of intratumor blood vessels and in micrometastatic foci.</p> <p>Conclusion</p> <p>Connexin-43 facilitates metastatic 'homing' by increasing adhesion of cancer cells to the lung endothelial cells. The marked upregulation of connexin-43 in tumor cell-endothelial cell contact areas, whether in preexisting 'homing' vessels or in newly formed tumor vessels, suggests that connexin-43 can serve as a potential marker of micrometastases and tumor vasculature and that it may play a role in the early incorporation of endothelial cells into small tumors as seeds for vasculogenesis.</p

Trastuzumab impairs systolic function in mice.

<p>Mice received either trastuzumab (10mg/kg/day) (N = 10) or vehicle injections (N = 6). VEF (a),FS (b), heart rate (d), LVPW thickness (c), left ventricular systolic (f) and diastolic (e) diameter were evaluated at base line (day 0), and days 3 and 7 post injection. Data are presented as percentage of control animals (mean ± SEM). *P < 0.05 vs. the vehicle treated animals.</p

Effect of trastuzumab treatment on the levels of cTn-I and cMLC1 in mice sera.

<p>(<b>a</b>) The results of sandwich ELISA detecting the levels of cMLC1 in the serum of trastuzumab-treated mice compared to control mice injected with vehicle only. (<b>b</b>) The results of sandwich ELISA detecting the levels of cTn-I in the serum of trastuzumab-treated mice compared to control mice injected with vehicle only. Standard curve is constructed by plotting optical density (OD) values obtained from each reference point against its concentration in mg/mL. Absorbance values of vehicle and trastuzumab-treated mice samples are determined by corresponding concentrations from standard curve using linear regression analysis. Student’s t-test was used to compare the two groups. Significance is determined as *P < 0.05 vs. the vehicle treated animals. Six animal were used for each group.</p

Electron microscopy imaging of cardiomyocyte ultrastuctures.

<p>Two mice from trastuzumab-treated and two mice from vehicle-treated groups were evaluated by EM. Animals were either treated daily with trastuzumab (10 mg/kg) or with vehicle control. On day 7, mice were euthanized and the hearts were harvested. See materials and methods for details of the fixation, embedding, and staining procedures. (<b>a</b>) A representative section of the left ventricle from a control mouse (<b>a1</b>) showing the typical mitochondrial density and intimate contacts (green arrows), connected myofibers (red arrows) and normal thickness of myofibers (pairs of blue arrows), compared to a section from trastuzumab-treated mice (<b>a2</b>) showing sporadic mitochondrial (green arrows), damaged disconnected myofibers (red arrows) and thinner myofibers (pairs of blue arrows). (<b>b</b>) Bar graph, quantification of damaged myofibers in trastuzumab-treated animals compared to control mice. Data are presented as a percentage of damaged and discontinued myofibers out of the total numbers of myofibers in the segment. (<b>c</b>) Bar graph showing a quantification of the distance between mitochondrial in trastuzumab-treated animals compared to control animals. Data presented in µm representing an average distance between mitochondria in the image. Magnification, 1000X (<b>d</b>) Bar graph showing a measurement of myofibers thickness in trastuzumab-treated animals compared to control mice. Data presented in µm representing the average thickness of myofibers. Magnification, 1000X. (<b>e</b>) Bar graph showing quantification of the number of mitochondria in sections from trastuzumab-treated animals compared to control mice. Data presented as average number of mitochondrial per segment. (f) Bar graph showing the percentage of damaged mitochondria (membrane disintegration, thinning of cristae and significant cavelae formation). In this figure, similar segments from the two animals in each group were used for this quantification. Student’s t-test was used to compare the two groups and significance is determined as *P < 0.05 or ** P < 0.01 vs. the vehicle treated animals.</p

Trastuzumab altered the expression of genes that are essential for cardiac functions.

<p>(a) Heatmap representation of differentially expressed genes in the trastuzumab-treated animals compared to the control vehicle injected animals (blue = downregulation and red = upregulation); n=4 for each group. (b) Bar graph showing microarray results as Log₂ of fold change. (c) Bar graph showing the qPCR validation of randomly selected genes from the microarray data. Results are shown as log₂ of fold change. Analysis of variance was used to determine those probe sets significantly different between the two groups. The gene list was filtered with a fold-change cutoff of 2.</p

Trastuzumab induces oxidative stress and increases Caspase 3/7 activity in cardiomyocytes.

<p>(<b>a</b>) Quantification of ELISA measurement of NT in the hearts of animals treated with trastuzumab compared to the control vehicle injected animals. (<b>b</b>) Quantification of ELISA measurement of 4-HNE adducts in the hearts of animals treated with trastuzumab compared to the control vehicle injected animals. (<b>c</b>) Quantification of ELISA measurement of caspase 3 and 7 in the trastuzumab-treated animals compared to the control vehicle injected animals. For all the graphs in Figure 4, the results are expressed as fold changes relative to that of control vehicle-treated animals. Student’s t-test was used to compare the two groups and significance is determined as *P < 0.05 or **P< 0.01 vs. the vehicle treated animals. At least six animal were used for each group (n=6). </p

Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

A growing number of artificial intelligence (AI)-based clinical decision support systems are showing promising performance in preclinical, in silico evaluation, but few have yet demonstrated real benefit to patient care. Early-stage clinical evaluation is important to assess an AI system's actual clinical performance at small scale, ensure its safety, evaluate the human factors surrounding its use and pave the way to further large-scale trials. However, the reporting of these early studies remains inadequate. The present statement provides a multi-stakeholder, consensus-based reporting guideline for the Developmental and Exploratory Clinical Investigations of DEcision support systems driven by Artificial Intelligence (DECIDE-AI). We conducted a two-round, modified Delphi process to collect and analyze expert opinion on the reporting of early clinical evaluation of AI systems. Experts were recruited from 20 pre-defined stakeholder categories. The final composition and wording of the guideline was determined at a virtual consensus meeting. The checklist and the Explanation & Elaboration (E&E) sections were refined based on feedback from a qualitative evaluation process. In total, 123 experts participated in the first round of Delphi, 138 in the second round, 16 in the consensus meeting and 16 in the qualitative evaluation. The DECIDE-AI reporting guideline comprises 17 AI-specific reporting items (made of 28 subitems) and ten generic reporting items, with an E&E paragraph provided for each. Through consultation and consensus with a range of stakeholders, we developed a guideline comprising key items that should be reported in early-stage clinical studies of AI-based decision support systems in healthcare. By providing an actionable checklist of minimal reporting items, the DECIDE-AI guideline will facilitate the appraisal of these studies and replicability of their findings