Richard Berendt

Next generation sequencing profiling identifies miR-574-3p and miR-660-5p as potential novel prognostic markers for breast cancer

BackgroundPrognostication of Breast Cancer (BC) relies largely on traditional clinical factors and biomarkers such as hormone or growth factor receptors. Due to their suboptimal specificities, it is challenging to accurately identify the subset of patients who are likely to undergo recurrence and there remains a major need for markers of higher utility to guide therapeutic decisions. MicroRNAs (miRNAs) are small non-coding RNAs that function as post-transcriptional regulators of gene expression and have shown promise as potential prognostic markers in several cancer types including BC.ResultsIn our study, we sequenced miRNAs from 104 BC samples and 11 apparently healthy normal (reduction mammoplasty) breast tissues. We used Case–control (CC) and Case-only (CO) statistical paradigm to identify prognostic markers. Cox-proportional hazards regression model was employed and risk score analysis was performed to identify miRNA signature independent of potential confounders. Representative miRNAs were validated using qRT-PCR. Gene targets for prognostic miRNAs were identified using in silico predictions and in-house BC transcriptome dataset. Gene ontology terms were identified using DAVID bioinformatics v6.7. A total of 1,423 miRNAs were captured. In the CC approach, 126 miRNAs were retained with predetermined criteria for good read counts, from which 80 miRNAs were differentially expressed. Of these, four and two miRNAs were significant for Overall Survival (OS) and Recurrence Free Survival (RFS), respectively. In the CO approach, from 147 miRNAs retained after filtering, 11 and 4 miRNAs were significant for OS and RFS, respectively. In both the approaches, the risk scores were significant after adjusting for potential confounders. The miRNAs associated with OS identified in our cohort were validated using an external dataset from The Cancer Genome Atlas (TCGA) project. Targets for the identified miRNAs were enriched for cell proliferation, invasion and migration.ConclusionsThe study identified twelve non-redundant miRNAs associated with OS and/or RFS. These signatures include those that were reported by others in BC or other cancers. Importantly we report for the first time two new candidate miRNAs (miR-574-3p and miR-660-5p) as promising prognostic markers. Independent validation of signatures (for OS) using an external dataset from TCGA further strengthened the study findings.

Inappropriate calibration and optimisation of pan-keratin (pan-CK) and low molecular weight keratin (LMWCK) immunohistochemistry tests: Canadian Immunohistochemistry Quality Control (CIQC) experience

Aims Pan-cytokeratin (pan-CK) and low molecular weight cytokeratin (LMWCK) tests are the most common immunohistochemistry (IHC) tests used to support evidence of epithelial differentiation. Canadian Immunohistochemistry Quality Control (CIQC), a new provider of proficiency testing for Canadian clinical IHC laboratories, has evaluated the performance of Canadian IHC laboratories in two proficiency testing challenges for both pan-CK and LMWCK. Methods CIQC has designed a 70-sample tissue microarray (TMA) for challenge 1 and a 30-sample TMA for challenge 2. There were 13 participants in challenge 1, and 62 in challenge 2. All results were evaluated and scored by CIQC assessors and compared with reference laboratory results. Results Participating laboratories often produced false-negative results that ranged from 20% to 80%. False-positive results were also detected. About half of participating clinical laboratories have inappropriately calibrated IHC tests for pan-CK and LMWCK, which are the most commonly used markers for demonstration of epithelial differentiation. The great majority of laboratories were not aware of the problem with calibration of pan-CK and LMWCK tests because of inappropriate selection of external positive controls and samples for optimisation of these tests. Benign liver and kidney are the most important tissues to include as positive controls for both pan-CK and LMWCK. Conclusions Participation in external quality assurance is important for peer comparison and proper calibration of IHC tests, which is also helpful for appropriate selection of positive control material and material for optimisation of the tests.

Outcome analysis of breast cancer patients who declined evidence-based treatment

BackgroundTo analyze the characteristics and outcomes of women with breast cancer in the Northern Alberta Health Region (NAHR) who declined recommended primary standard treatments.MethodsA chart review was performed of breast cancer patients who refused recommended treatments during the period 1980 to 2006. A matched pair analysis was performed to compare the survival data between those who refused or received standard treatments.ResultsA total of 185 (1.2%) patients refused standard treatment. Eighty-seven (47%) were below the age of 75 at diagnosis. The majority of those who refused standard treatments were married (50.6%), 50 years or older (60.9%), and from the urban area (65.5%). The 5-year overall survival rates were 43.2% (95% CI: 32.0 to 54.4%) for those who refused standard treatments and 81.9% (95% CI: 76.9 to 86.9%) for those who received them. The corresponding values for the disease-specific survival were 46.2% (95% CI: 34.9 to 57.6%) vs. 84.7% (95% CI: 80.0 to 89.4%).ConclusionsWomen who declined primary standard treatment had significantly worse survival than those who received standard treatments. There is no evidence to support using Complementary and Alternative Medicine (CAM) as primary cancer treatment.

Automatic Nuclei Detection Based on Generalized Laplacian of Gaussian Filters

Efficient and accurate detection of cell nuclei is an important step toward automatic analysis in histopathology. In this work, we present an automatic technique based on generalized Laplacian of Gaussian (gLoG) filter for nuclei detection in digitized histological images. The proposed technique first generates a bank of gLoG kernels with different scales and orientations and then performs convolution between directional gLoG kernels and the candidate image to obtain a set of response maps. The local maxima of response maps are detected and clustered into different groups by mean-shift algorithm based on their geometrical closeness. The point which has the maximum response in each group is finally selected as the nucleus seed. Experimental results on two datasets show that the proposed technique provides a superior performance in nuclei detection compared to existing techniques.

Automatic Nuclear Segmentation Using Multiscale Radial Line Scanning With Dynamic Programming

In the diagnosis of various cancers by analyzing histological images, automatic nuclear segmentation is an important step. However, nuclear segmentation is a difficult problem because of overlapping nuclei, inhomogeneous staining, and presence of noisy pixels and other tissue components. In this paper, we present an automatic technique for nuclear segmentation in skin histological images. The proposed technique first applies a bank of generalized Laplacian of Gaussian kernels to detect nuclear seeds. Based on the detected nuclear seeds, a multiscale radial line scanning method combined with dynamic programming is applied to extract a set of candidate nuclear boundaries. The gradient, intensity, and shape information are then integrated to determine the optimal boundary for each nucleus in the image. Nuclear overlap limitation is finally imposed based on a Dice coefficient measure such that the obtained nuclear contours do not severely intersect with each other. Experiments have been thoroughly performed on two datasets with H&E and Ki-67 stained images, which show that the proposed technique is superior to conventional schemes of nuclear segmentation.

Automated analysis and classification of melanocytic tumor on skin whole slide images

This paper presents a computer-aided technique for automated analysis and classification of melanocytic tumor on skin whole slide biopsy images. The proposed technique consists of four main modules. First, skin epidermis and dermis regions are segmented by a multi-resolution framework. Next, epidermis analysis is performed, where a set of epidermis features reflecting nuclear morphologies and spatial distributions is computed. In parallel with epidermis analysis, dermis analysis is also performed, where dermal cell nuclei are segmented and a set of textural and cytological features are computed. Finally, the skin melanocytic image is classified into different categories such as melanoma, nevus or normal tissue by using a multi-class support vector machine (mSVM) with extracted epidermis and dermis features. Experimental results on 66 skin whole slide images indicate that the proposed technique achieves more than 95% classification accuracy, which suggests that the technique has the potential to be used for assisting pathologists on skin biopsy image analysis and classification.

Computerized measurement of melanoma depth of invasion in skin biopsy images

Melanoma is the deadliest form of skin cancer, and its depth of invasion (DoI) is an important factor used by pathologist for grading the severity of skin disease. In this paper, we propose an automated technique for measuring melanoma DoI in MART1 stained skin histopathological images. The proposed technique first segments skin melanoma areas based on image color features. The skin epidermis is then segmented by a multi-thresholding method. After that, the skin granular layer is identified based on Bayesian classification of segmented epidermis pixels. Finally, the melanoma DoI is computed using a Hausdorff distance measure. Experiments on 28 skin biopsy images show that the proposed technique provides a superior performance in measuring the melanoma DoI than two closely related works.

Automatic measurement of melanoma depth of invasion in skin histopathological images

Measurement of melanoma depth of invasion (DoI) in skin tissues is of great significance in grading the severity of skin disease and planning patients treatment. However, accurate and automatic measurement of melanocytic tumor depth is a challenging problem mainly due to the difficulty of skin granular identification and melanoma detection. In this paper, we propose a technique for measuring melanoma DoI in microscopic images digitized from MART1 (i.e., meleanoma-associated antigen recognized by T cells) stained skin histopathological sections. The technique consists of four modules. First, skin melanoma areas are detected by combining color features with the Mahalanobis distance measure. Next, skin epidermis is segmented by a multi-thresholding method. The skin granular layer is then identified based on Bayesian classification of segmented skin epidermis pixels. Finally, the melanoma DoI is computed using a multi-resolution approach with Hausdorff distance measurement. Experimental results show that the proposed technique provides a superior performance in measuring the melanoma DoI than two closely related techniques.

Automated nuclear segmentation in skin histopathological images using multi-scale radial line scanning

Segmentation of cell nuclei is an important step towards automatic analysis of microscopic images. This paper presents an automated technique for nuclear segmentation in skin histopathological images. The proposed technique first detects nuclear seeds using a bank of generalized Laplacian of Gaussian (gLoG) kernels. Based on the detected nuclear seeds, a multi-scale radial line scanning (mRLS) method combined with dynamic programming (DP) is utilized to delineate a set of candidate nuclear boundaries. The gradient, intensity and shape information are then integrated to determine the optimal boundary for each nucleus in the image. Experimental results on 28 H&E stained skin histopathological images show that the proposed technique is superior to conventional schemes in nuclear segmentation.