An international multicenter study to evaluate reproducibility of automated scoring for assessment of Ki67 in breast cancer.

The nuclear proliferation biomarker Ki67 has potential prognostic, predictive, and monitoring roles in breast cancer. Unacceptable between-laboratory variability has limited its clinical value. The International Ki67 in Breast Cancer Working Group investigated whether Ki67 immunohistochemistry can be analytically validated and standardized across laboratories using automated machine-based scoring. Sets of pre-stained core-cut biopsy sections of 30 breast tumors were circulated to 14 laboratories for scanning and automated assessment of the average and maximum percentage of tumor cells positive for Ki67. Seven unique scanners and 10 software platforms were involved in this study. Pre-specified analyses included evaluation of reproducibility between all laboratories (primary) as well as among those using scanners from a single vendor (secondary). The primary reproducibility metric was intraclass correlation coefficient between laboratories, with success considered to be intraclass correlation coefficient >0.80. Intraclass correlation coefficient for automated average scores across 16 operators was 0.83 (95% credible interval: 0.73-0.91) and intraclass correlation coefficient for maximum scores across 10 operators was 0.63 (95% credible interval: 0.44-0.80). For the laboratories using scanners from a single vendor (8 score sets), intraclass correlation coefficient for average automated scores was 0.89 (95% credible interval: 0.81-0.96), which was similar to the intraclass correlation coefficient of 0.87 (95% credible interval: 0.81-0.93) achieved using these same slides in a prior visual-reading reproducibility study. Automated machine assessment of average Ki67 has the potential to achieve between-laboratory reproducibility similar to that for a rigorously standardized pathologist-based visual assessment of Ki67. The observed intraclass correlation coefficient was worse for maximum compared to average scoring methods, suggesting that maximum score methods may be suboptimal for consistent measurement of proliferation. Automated average scoring methods show promise for assessment of Ki67 scoring, but requires further standardization and subsequent clinical validation.

Localization of human TACC3 to mitotic spindles is mediated by phosphorylation on Ser558 by Aurora A: a novel pharmacodynamic method for measuring Aurora A activity.

Aurora A is a serine/threonine protein kinase essential for normal mitotic progression. Aberrant increased expression of Aurora A, which occurs frequently in human cancers, results in abnormal mitoses leading to chromosome instability and possibly tumorigenesis. Consequently, Aurora A has received considerable attention as a potential target for anticancer therapeutic intervention. Aurora A coordinates several essential mitotic activities through phosphorylation of a variety of proteins, including TACC3, which modulates microtubule stabilization of the mitotic spindle. Recent studies identified a conserved serine in Xenopus (Ser(626)) and Drosophila (Ser(863)) TACC3 orthologues that is phosphorylated by Aurora A. We show that this conserved serine on human TACC3 (Ser(558)) is also phosphorylated by Aurora A. Moreover, phosphorylation of TACC3 by Aurora A in human cells is essential for its proper localization to centrosomes and proximal mitotic spindles. Inhibition of Aurora A with the selective small molecule inhibitor MLN8054 in cultured human tumor cells resulted in mislocalization of TACC3 away from mitotic spindles in a concentration-dependent manner. Furthermore, oral administration of MLN8054 to nude mice bearing HCT-116 human tumor xenografts caused a dose-dependent mislocalization of TACC3 away from spindle poles that correlated with tumor growth inhibition. As TACC3 localization to mitotic spindles depends on Aurora A-mediated phosphorylation, quantifying TACC3 mislocalization represents a novel pharmacodynamic approach for measuring Aurora A activity in cancer patients treated with inhibitors of Aurora A kinase.

Application of surface roughness analysis on micro-computed tomographic images of bone erosion: examples using a rodent model of rheumatoid arthritis.

Quantifying the bone erosion in preclinical models of rheumatoid arthritis is valuable for the evaluation of drug treatments. This study introduces a three-dimensional method for bone surface roughness measurement from micro-computed tomographic data obtained from rats subjected to collagen-induced arthritis (CIA), in which the degree of bone erosion is related to the severity and the duration of the disease. In two studies of rat CIA, the surface roughness of the talus bone following 21 days of disease increased 559% and 486% from the control group. At 41 days following disease induction, the roughness of the bone surface increased 857% above baseline. The roughness of the control samples was similar from each study (less than 4% different), demonstrating the robustness of the algorithm. Treatment with methotrexate at 0.1 mg/kg daily demonstrated significant protection from bone erosion, whereas the 0.05 mg/kg daily dose was not efficacious (98% versus 22% inhibition of roughness-measured bone erosion). The main advantage of such an algorithm is demonstrated in the preclinical drug study of rat CIA with methotrexate treatment, indicating the immediate utility of this approach in drug development studies.

A novel approach to spot detection for two-dimensional gel electrophoresis images using pixel value collection.

Separation of complex mixtures of proteins by two-dimensional gel electrophoresis (2-DE) is a fundamental component of current proteomic technology. Quantitative analysis of the images generated by digitization of such gels is critical for the identification of alterations in protein expression within a given biological system. Despite the availability of several commercially available software packages designed for this purpose, image analysis is extremely resource intensive, subjective and remains a major bottleneck. In addition to reducing throughput, the requirement for manual intervention results in the introduction of operator subjectivity, which can limit the statistical significance of the numerical data generated. A key requirement of image analysis is the accurate definition of protein spot boundaries using a suitable method of image segmentation. We describe a method of spot detection applicable to 2-DE image files using a segmentation method involving pixel value collection via serial analysis of the image through its range of density levels. This algorithm is reproducible, sensitive, accurate and primarily designed to be automatic, removing operator subjectivity. Furthermore, it is believed that this method may offer the potential for improved spot detection over currently available software.

Quantitative analysis of micro-CT imaging and histopathological signatures of experimental arthritis in rats.

Micro-computed tomographic (micro-CT) imaging provides a unique opportunity to capture 3-D architectural information in bone samples. In this study of pathological joint changes in a rat model of adjuvant-induced arthritis (AA), quantitative analysis of bone volume and roughness were performed by micro-CT imaging and compared with histopathology methods and paw swelling measurement. Micro-CT imaging of excised rat hind paws (n = 10) stored in formalin consisted of approximately 600 30-mum slices acquired on a 512 x 512 image matrix with isotropic resolution. Following imaging, the joints were scored from H&E stained sections for cartilage/bone erosion, pannus development, inflammation, and synovial hyperplasia. From micro-CT images, quantitative analysis of absolute bone volumes and bone roughness was performed. Bone erosion in the rat AA model is substantial, leading to a significant decline in tarsal volume (27%). The result of the custom bone roughness measurement indicated a 55% increase in surface roughness. Histological and paw volume analyses also demonstrated severe arthritic disease as compared to controls. Statistical analyses indicate correlations among bone volume, roughness, histology, and paw volume. These data demonstrate that the destructive progression of disease in a rat AA model can be quantified using 3-D micro-CT image analysis, which allows assessment of arthritic disease status and efficacy of experimental therapeutic agents.