Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis

Breast cancer is one of the main causes of cancer death worldwide. Early diagnostics significantly increases the chances of correct treatment and survival, but this process is tedious and often leads to a disagreement between pathologists. Computer-aided diagnosis systems showed potential for improving the diagnostic accuracy. In this work, we develop the computational approach based on deep convolution neural networks for breast cancer histology image classification. Hematoxylin and eosin stained breast histology microscopy image dataset is provided as a part of the ICIAR 2018 Grand Challenge on Breast Cancer Histology Images. Our approach utilizes several deep neural network architectures and gradient boosted trees classifier. For 4-class classification task, we report 87.2% accuracy. For 2-class classification task to detect carcinomas we report 93.8% accuracy, AUC 97.3%, and sensitivity/specificity 96.5/88.0% at the high-sensitivity operating point. To our knowledge, this approach outperforms other common methods in automated histopathological image classification. The source code for our approach is made publicly available at https://github.com/alexander-rakhlin/ICIAR2018Comment: 8 pages, 4 figure

Overexpression of P70 S6 kinase protein is associated with increased risk of locoregional recurrence in node-negative premenopausal early breast cancer patients

The RPS6KB1 gene is amplified and overexpressed in approximately 10% of breast carcinomas and has been found associated with poor prognosis. We studied the prognostic significance of P70 S6 kinase protein (PS6K) overexpression in a series of 452 node-negative premenopausal early-stage breast cancer patients (median follow-up: 10.8 years). Immunohistochemistry was used to assess PS6K expression in the primary tumour, which had previously been analysed for a panel of established prognostic factors in breast cancer. In a univariate analysis, PS6K overexpression was associated with worse distant disease-free survival as well as impaired locoregional control (HR 1.80, P 0.025 and HR 2.50, P 0.006, respectively). In a multivariate analysis including other prognostic factors, PS6K overexpression remained an independent predictor for poor locoregional control (RR 2.67, P 0.003). To our knowledge, P70 S6 kinase protein is the first oncogenic marker that has prognostic impact on locoregional control and therefore may have clinical implications in determining the local treatment strategy in early-stage breast cancer patients

HAGE (DDX43) is a biomarker for poor prognosis and a predictor of chemotherapy response in breast cancer

Background: HAGE protein is a known immunogenic cancer-specific antigen. Methods: The biological, prognostic and predictive values of HAGE expression was studied using immunohistochemistry in three cohorts of patients with BC (n=2147): early primary (EP-BC; n=1676); primary oestrogen receptor-negative (PER-BC; n=275) treated with adjuvant anthracycline-combination therapies (Adjuvant-ACT); and primary locally advanced disease (PLA-BC) who received neo-adjuvant anthracycline-combination therapies (Neo-adjuvant-ACT; n=196). The relationship between HAGE expression and the tumour-infiltrating lymphocytes (TILs) in matched prechemotherapy and postchemotherapy samples were investigated. Results: Eight percent of patients with EP-BC exhibited high HAGE expression (HAGEþ) and was associated with aggressive clinico-pathological features (Ps<0.01). Furthermore, HAGEþexpression was associated with poor prognosis in both univariate and multivariate analysis (Ps<0.001). Patients with HAGE+ did not benefit from hormonal therapy in high-risk ER-positive disease. HAGE+ and TILs were found to be independent predictors for pathological complete response to neoadjuvant-ACT; P<0.001. A statistically significant loss of HAGE expression following neoadjuvant-ACT was found (P=0.000001), and progression-free survival was worse in those patients who had HAGE+ residual disease (P=0.0003). Conclusions: This is the first report to show HAGE to be a potential prognostic marker and a predictor of response to ACT in patients with BC

A comprehensive model for familial breast cancer incorporating BRCA1, BRCA2 and other genes

In computing the probability that a woman is a BRCA1 or BRCA2 carrier for genetic counselling purposes, it is important to allow for the fact that other breast cancer susceptibility genes may exist. We used data from both a population based series of breast cancer cases and high risk families in the UK, with information on BRCA1 and BRCA2 mutation status, to investigate the genetic models that can best explain familial breast cancer outside BRCA1 and BRCA2 families. We also evaluated the evidence for risk modifiers in BRCA1 and BRCA2 carriers. We estimated the simultaneous effects of BRCA1, BRCA2, a third hypothetical gene ‘BRCA3’, and a polygenic effect using segregation analysis. The hypergeometric polygenic model was used to approximate polygenic inheritance and the effect of risk modifiers. BRCA1 and BRCA2 could not explain all the observed familial clustering. The best fitting model for the residual familial breast cancer was the polygenic, although a model with a single recessive allele produced a similar fit. There was also significant evidence for a modifying effect of other genes on the risks of breast cancer in BRCA1 and BRCA2 mutation carriers. Under this model, the frequency of BRCA1 was estimated to be 0.051% (95% CI: 0.021–0.125%) and of BRCA2 0.068% (95% CI: 0.033–0.141%). The breast cancer risk by age 70 years, based on the average incidence over all modifiers was estimated to be 35.3% for BRCA1 and 50.3% for BRCA2. The corresponding ovarian cancer risks were 25.9% for BRCA1 and 9.1% for BRCA2. The findings suggest that several common, low penetrance genes with multiplicative effects on risk may account for the residual non-BRCA1/2 familial aggregation of breast cancer. The modifying effect may explain the previously reported differences between population based estimates for BRCA1/2 penetrance and estimates based on high-risk families

Exposure to depleted uranium does not alter the co-expression of HER-2/neu and p53 in breast cancer patients

<p>Abstract</p> <p>Background</p> <p>Amongst the extensive literature on immunohistochemical profile of breast cancer, very little is found on populations exposed to a potential risk factor such as depleted uranium. This study looked at the immunohistochemical expression of HER-2/neu (c-erbB2) and p53 in different histological types of breast cancer found in the middle Euphrates region of Iraq, where the population has been exposed to high levels of depleted uranium.</p> <p>Findings</p> <p>The present investigation was performed over a period starting from September 2008 to April 2009. Formalin-fixed, paraffin-embedded blocks from 70 patients with breast cancer (62 ductal and 8 lobular carcinoma) were included in this study. A group of 25 patients with fibroadenoma was included as a comparative group, and 20 samples of normal breast tissue sections were used as controls. Labeled streptavidin-biotin (LSAB+) complex method was employed for immunohistochemical detection of HER-2/neu and p53.</p> <p>The detection rate of HER-2/neu and p53 immunohistochemical expression were 47.14% and 35.71% respectively in malignant tumors; expression was negative in the comparative and control groups (p < 0.05).</p> <p>HER-2/neu immunostaining was significantly associated with histological type, tumor size, nodal involvement, and recurrence of breast carcinoma (<it>p </it>< 0.05), p53 immunostaining was significantly associated with tumor size, nodal involvement and recurrence of breast cancer (<it>p </it>< 0.05). There was greater immunoexpression of HER-2/neu in breast cancer in this population, compared with findings in other populations.</p> <p>Both biomarkers were positively correlated with each other. Furthermore, all the cases that co-expressed both HER-2/neu and p53 showed the most unfavorable biopathological profile.</p> <p>Conclusion</p> <p>P53 and HER-2/neu over-expression play an important role in pathogenesis of breast carcinoma. The findings indicate that in regions exposed to high levels of depleted uranium, although p53 and HER-2/neu overexpression are both high, correlation of their expression with age, grade, tumor size, recurrence and lymph node involvement is similar to studies that have been conducted on populations not exposed to depleted uranium. HER-2/neu expression in breast cancer was higher in this population, compared with results on non-exposed populations.</p

Accurate Detection of Recombinant Breakpoints in Whole-Genome Alignments

We propose a novel method for detecting sites of molecular recombination in multiple alignments. Our approach is a compromise between previous extremes of computationally prohibitive but mathematically rigorous methods and imprecise heuristic methods. Using a combined algorithm for estimating tree structure and hidden Markov model parameters, our program detects changes in phylogenetic tree topology over a multiple sequence alignment. We evaluate our method on benchmark datasets from previous studies on two recombinant pathogens, Neisseria and HIV-1, as well as simulated data. We show that we are not only able to detect recombinant regions of vastly different sizes but also the location of breakpoints with great accuracy. We show that our method does well inferring recombination breakpoints while at the same time maintaining practicality for larger datasets. In all cases, we confirm the breakpoint predictions of previous studies, and in many cases we offer novel predictions

Not just a matter of size:a hospital-level risk factor analysis of MRSA bacteraemia in Scotland

Background: Worldwide, there is a wealth of literature examining patient-level risk 6 factors for methicillin-resistant Staphylococcus aureus (MRSA) bacteraemia. At the hospital-level it is generally accepted that MRSA bacteraemia is more common in larger hospitals. In Scotland, size does not fully explain all the observed variation among hospitals. The aim of this study was to identify risk factors for the presence and rate of MRSA bacteraemia cases in Scottish mainland hospitals. Specific hypotheses regarding hospital size, type and connectivity were examined. Methods: Data from 198 mainland Scottish hospitals (defined as having at least one inpatient per year) were analysed for financial year 2007-08 using logistic regression (Model 1: presence/absence of MRSA bacteraemia) and Poisson regression (Model 2: rate of MRSA bacteraemia). The significance of risk factors representing various measures of hospital size, type and connectivity were investigated. Results: In Scotland, size was not the only significant risk factor identified for the presence and rate of MRSA bacteraemia. The probability of a hospital having at least one case of MRSA bacteraemia increased with hospital size only if the hospital exceeded a certain level of connectivity. Higher levels of MRSA bacteraemia were associated with the large, highly connected teaching hospitals with high ratios of patients to domestic staff. Conclusions: A hospital’s level of connectedness within a network may be a better measure of a hospital’s risk of MRSA bacteraemia than size. This result could be used to identify high risk hospitals which would benefit from intensified infection control measures

Morphological predictors of BRCA1 germline mutations in young women with breast cancer

BACKGROUND: Knowing a young woman with newly diagnosed breast cancer has a germline BRCA1 mutation informs her clinical management and that of her relatives. We sought an optimal strategy for identifying carriers using family history, breast cancer morphology and hormone receptor status data.METHODS: We studied a population-based sample of 452 Australian women with invasive breast cancer diagnosed before age 40 years for whom we conducted extensive germline mutation testing (29 carried a BRCA1 mutation) and a systematic pathology review, and collected three-generational family history and tumour ER and PR status. Predictors of mutation status were identified using multiple logistic regression. Areas under receiver operator characteristic (ROC) curves were estimated using five-fold stratified cross-validation.RESULTS: The probability of being a BRCA1 mutation carrier increased with number of selected histology features even after adjusting for family history and ER and PR status (Po0.0001). From the most parsimonious multivariate model, the odds ratio for being a carrier were: 9.7 (95% confidence interval: 2.6-47.0) for trabecular growth pattern (P=0.001); 7.8 (2.7-25.7) for mitotic index over 50 mitoses per 10 high-powered field (P 0.0003); and 2.7 (1.3-5.9) for each first-degree relative with breast cancer diagnosed before age 60 years (P 0.01). The area under the ROC curve was 0.87 (0.83-0.90).CONCLUSION: Pathology review, with attention to a few specific morphological features of invasive breast cancers, can identify almost all BRCA1 germline mutation carriers among women with early-onset breast cancer without taking into account family history. British Journal of Cancer (2011) 104, 903-909. doi: 10.1038/ bjc. 2011.41 www. bjcancer. co

Amplification of HER2 is a marker for global genomic instability

<p>Abstract</p> <p>Background</p> <p>Genomic alterations of the proto-oncogene c-erbB-2 (HER-2/neu) are associated with aggressive behavior and poor prognosis in patients with breast cancer. The variable clinical outcomes seen in patients with similar HER2 status, given similar treatments, suggests that the effects of amplification of HER2 can be influenced by other genetic changes. To assess the broader genomic implications of structural changes at the HER2 locus, we investigated relationships between genomic instability and HER2 status in patients with invasive breast cancer.</p> <p>Methods</p> <p>HER2 status was determined using the PathVysion^®assay. DNA was extracted after laser microdissection from the 181 paraffin-embedded HER2 amplified (n = 39) or HER2 negative (n = 142) tumor specimens with sufficient tumor available to perform molecular analysis. Allelic imbalance (AI) was assessed using a panel of microsatellite markers representing 26 chromosomal regions commonly altered in breast cancer. Student t-tests and partial correlations were used to investigate relationships between genomic instability and HER2 status.</p> <p>Results</p> <p>The frequency of AI was significantly higher (<it>P </it>< 0.005) in HER2 amplified (27%) compared to HER2 negative tumors (19%). Samples with HER2 amplification showed significantly higher levels of AI (<it>P </it>< 0.05) at chromosomes 11q23, 16q22-q24 and 18q21. Partial correlations including ER status and tumor grade supported associations between HER2 status and alterations at 11q13.1, 16q22-q24 and 18q21.</p> <p>Conclusion</p> <p>The poor prognosis associated with HER2 amplification may be attributed to global genomic instability as cells with high frequencies of chromosomal alterations have been associated with increased cellular proliferation and aggressive behavior. In addition, high levels of DNA damage may render tumor cells refractory to treatment. In addition, specific alterations at chromosomes 11q13, 16q22-q24, and 18q21, all of which have been associated with aggressive tumor behavior, may serve as genetic modifiers to HER2 amplification. These data not only improve our understanding of HER in breast pathogenesis but may allow more accurate risk profiles and better treatment options to be developed.</p