Columnar cell lesions of the breast: clinical significance and molecular background

Columnar cell lesions (CCLs) of the breast have since long been regarded as possible precursor lesions of breast cancer. CCLs are cystically dilated ducts lined by columnar cell epithelium, with or without atypia. Intraluminal secretions and microcalcifications are frequently seen and the microcalcifications characterize the CCLs at mammography. On the 31st of January 2012, Anoek H.J. Verschuur-Maes defends her PhD-thesis ‘Columnar cell lesions of the breast: clinical significance and molecular background’ at University Utrecht under supervision of promotor P.J. van Diest, MD, PhD, and co-promotor dr. P.C. de Bruin, MD, PhD. Data has shown that CCLs have been diagnosed more frequently since the introduction of digital mammography compared to screen-filmed mammography. This seems to be related to a higher number of core needle biopsies taken for microcalcifications. However, in terms of percentage, the same percentage of columnar cell lesions with atypia were found during the full-field digital mammography period as in the screen-filmed mammography period (1.8%) and significantly more columnar cell lesions without atypia (8.2% and 2.8%, respectively). Since we questioned whether it is relevant to find CCLs in core needle biopsies, we investigated the progression risk of CCLs. In addition a systematic review was performed to provide more evidence based advice for the best treatment. Overall, a relatively high pooled underestimation risk was found for (in situ) carcinomas for all patients initially diagnosed with CCL with atypia (9%). Therefore, this seems to support the indication for surgical (‘mini’) excision. For CCLs without atypia, the underestimation and progression rates seem to be limited and only follow-up is advised. Different studies applying molecular techniques have demonstrated that CCLs with atypia exhibit increasing genetic alterations from normal breast epithelium to CCL with atypia to DCIS and invasive cancer. During tumour development, DNA promoter hypermethylation (further denoted “methylation”) is also considered to be an early event. We found that the cumulative methylation increased significantly from normal breast tissue through CCL, towards ductal carcinoma in situ and invasive carcinoma. For some genes, the epigenetic abnormalities that were seen in invasive cancer were already present in CCLs, suggesting that methylation increases stepwise in the progression to invasive cancer via CCL. In another study, copy number changes of 17 genes, located on chromosome 8 and 17 in particular, were studied. It seemed that these alterations mostly occur at a stage later than CCL in the progression to invasive cancer. Then, we studied the mucinous variant of CCLs in 4164 breast core needle biopsies and we found an incidence of 0.5% of the mucinous CCL. Also, significantly more mucinous CCLs were present in mucinous carcinoma than in ductal carcinoma cases. This supports a possible role of mucinous CCLs in the developmental pathway towards mucinous carcinoma. Then, by studying 89 male breast cancer specimens, it seems that CCLs do not exist in the male breast and that there is no role for CCLs in male breast carcinogenesis. In conclusion, more evidence is provided for CCLs, with atypia being a possible precursor lesion of female breast cancer and thus needing treatment

Prognostic value of automatically extracted nuclear morphometric features in whole slide images of male breast cancer

Numerous studies have shown the prognostic significance of nuclear morphometry in breast cancer patients. Wide acceptance of morphometric methods has, however, been hampered by the tedious and time consuming nature of the manual segmentation of nuclei and the lack of equipment for high throughput digitization of slides. Recently, whole slide imaging became more affordable and widely available, making fully digital pathology archives feasible. In this study, we employ an automatic nuclei segmentation algorithm to extract nuclear morphometry features related to size and we analyze their prognostic value in male breast cancer. The study population comprised 101 male breast cancer patients for whom survival data was available (median follow-up of 5.7 years). Automatic segmentation was performed on digitized tissue microarray slides, and for each patient, the mean nuclear area and the standard deviation of the nuclear area were calculated. In univariate survival analysis, a significant difference was found between patients with low and high mean nuclear area (P0.022), while nuclear atypia score did not provide prognostic value. In Cox regression, mean nuclear area had independent additional prognostic value (P0.032) to tumor size and tubule formation. In conclusion, we present an automatic method for nuclear morphometry and its application in male breast cancer prognosis. The automatically extracted mean nuclear area proved to be a significant prognostic indicator. With the increasing availability of slide scanning equipment in pathology labs, these kinds of quantitative approaches can be easily integrated in the workflow of routine pathology practice. © 2012 USCAP, Inc

Development of prediction models for upper and lower respiratory and gastrointestinal tract infections using social network parameters in middle-aged and older persons : The Maastricht Study

The ability to predict upper respiratory infections (URI), lower respiratory infections (LRI), and gastrointestinal tract infections (GI) in independently living older persons would greatly benefit population and individual health. Social network parameters have so far not been included in prediction models. Data were obtained from The Maastricht Study, a population-based cohort study (N = 3074, mean age (±s.d.) 59·8 ± 8·3, 48·8% women). We used multivariable logistic regression analysis to develop prediction models for self-reported symptomatic URI, LRI, and GI (past 2 months). We determined performance of the models by quantifying measures of discriminative ability and calibration. Overall, 953 individuals (31·0%) reported URI, 349 (11·4%) LRI, and 380 (12·4%) GI. The area under the curve was 64·7% (95% confidence interval (CI) 62·6–66·8%) for URI, 71·1% (95% CI 68·4–73·8) for LRI, and 64·2% (95% CI 61·3–67·1%) for GI. All models had good calibration (based on visual inspection of calibration plot, and Hosmer–Lemeshow goodness-of-fit test). Social network parameters were strong predictors for URI, LRI, and GI. Using social network parameters in prediction models for URI, LRI, and GI seems highly promising. Such parameters may be used as potential determinants that can be addressed in a practical intervention in older persons, or in a predictive tool to compute an individual's probability of infections