Citations

Abstract Background: High percent mammographic density (MD), which reflects the relative fibroglandular tissue content of the breast, is one of the strongest breast cancer risk factors; however, the pathologic mediators of this risk are unknown. We hypothesize that analysis of breast tissue sections using deep learning approaches may characterize histologic features that underpin risk associated with high MD. Methods: Non-targeted H&E stained breast tissue sections of diagnostic image-guided breast biopsies were evaluated among 588 women enrolled following an abnormal mammogram in the Breast Radiology Evaluation and Study of Tissues (BREAST) Stamp Project (2007-2010). Overall volumetric percent MD for the biopsied breast and localized volumetric percent MD surrounding the biopsy site were determined for each participant. A deep convolutional neural network (CNN) model was trained to identify and quantitatively assess breast epithelial, stroma and fat tissue and their organizational and spatial arrangements. Least absolute shrinkage and selection operator (Lasso) regression was used to determine relationships between MD measures and pathological features. To ensure reliability of the model, a cross-validation strategy was employed to build and assess the performance of the fitted model. Finally, Spearman correlation coefficients were estimated to test the association between the predicted density values by each model (predicting overall or localized MD) and the actual MD measurements. We report the average and standard deviation (SD) of the correlation coefficients. Results: In an independent validation set, the CNN model was 95.5% accurate in classifying epithelial, stromal and fat tissue. The mean (SD) correlations between the predicted model and the actual measurements for overall and localized MD were 0.70 (0.06) and 0.65 (0.06) respectively. The amount of stroma identified (normalized to tissue area) had the highest selection probability (P-value) by the Lasso model and thus the strongest positive relationship with MD (P-value >0.9 for each MD measurement). In contrast, the amount of normalized epithelial tissue was not related to MD (P-value=0.01 for each MD measurement). No association was observed for the total normalized fat area with MD (P-value <0.31 for each MD measurement). In addition, the number of distributed epithelial regions was positively associated, whereas the distance between epithelial regions was inversely associated with overall MD (P-value <0.87 and 0.62, respectively). Conclusions: These results show that greater stromal tissue amount and spatial distribution patterns of epithelial regions, rather than total epithelial amounts, had the strongest relationships with elevated MD. Future work will determine the relationship of these MD features with biopsy diagnosis. Citation Format: Maeve Mullooly, Babak Ehteshami Bejnordi, Maya Palakal, Pamela M. Vacek, Donald L. Weaver, John A. Shepherd, Bo Fan, Amir Pasha Mahmoudzadeh, Jeff Wang, Jason M. Johnson, Sally D. Herschorn, Brian L. Sprague, Ruth M. Pfeiffer, Louise A. Brinton, Mark E. Sherman, Andrew Beck, Gretchen L. Gierach. Application of convolutional neural networks to breast biopsies to uncover tissue correlates of mammographic breast density [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4235. doi:10.1158/1538-7445.AM2017-4235

Abstract Background: Serous tubal intraepithelial carcinoma (STIC) is hypothesized to give rise to many cancers that have been historically classified as ovarian primaries. Although STIC is gaining status as a diagnostic entity, similar appearing lesions would likely have been reported as fallopian tubal carcinoma in-situ (FT-CIS) in the past. Accordingly to characterize patterns of reporting and behavior for tubal cancers, we analyzed population-based data for incidence rate trends, co-occurrence with cancer at other sites and survival of FT-CIS and invasive fallopian tube carcinoma (FT-Inv). Methods: We analyzed data for FT-CIS and FT-Inv included within 33 registries using the Cancer Incidence in North America Deluxe Analytical Files provided by the North American Association of Central Cancer Registries (1995-2012). Primary site of invasive carcinomas were defined according to the International Classification of Diseases for Oncology (3rd edition) topography codes (ovarian (C56.9), fallopian tube (C57.0), and primary peritoneal cancers (C48.1-.2, 48.8)). We excluded cancers with non-epithelial histology. Total counts, incidence rates per 1 million adjusted to the 2000 standard US population and age-standardized stage-specific survival were computed. Temporal incidence rate patterns were analyzed by joinpoint regression with estimates of annual percentage change (APC). Results: Cases in which the first cancer diagnosis was FT-CIS, included 98 FT-CIS alone; 27 FT-CIS followed by another cancer diagnosis within 1 year and 172 cases in which FT-CIS was diagnosed concurrently with another cancer as compared with 5513 invasive tubal carcinomas over the same period. The incidence rate of FT-CIS was stable from 1995-2002, then significantly increased from 2002-2012 [APC (95% CI) = 16.2% (10.9-21.7)]. Rates of early stage high-grade serous FT-Inv increased significantly from 2002-2012 [10.4% (6.1-14.9)] and rates of late stage high-grade serous FT-Inv rose sharply from 2002-2012 [20.0% (17.5-22.6)]. Five-year age-standardized survival for women with FT-CIS only was 75.6% overall, reflecting 89.1% survival for women less than 50 years and 70.9% for women 50 years or older; survival for high-grade early stage serous FT-Inv was 77.8%. Conclusions: Diagnoses of FT-CIS and FT-Inv have increased dramatically, likely reflecting changes in diagnostic pathology practice. Developing standardized reporting for FT-CIS (and in the future, STIC) when present with concurrent invasive cancer are needed, given that this occurs often. Based on limited data, 5-year survival for FT-CIS and early stage FT-Inv are similar, but future studies to evaluate data for STIC will be required, which will likely be achievable only through national registries that are large enough to capture sufficient cases. Citation Format: Mark E. Sherman, Sally B. Coburn, Hannah Yang, William Anderson, Philip Rosenberg, Gretchen Gierach, Nicolas Wentzensen, Kathy Cronin, Britton Trabert. Incidence rates and outcomes of fallopian tube carcinomas: Data from the North American Association of Central Cancer Registries [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 260. doi:10.1158/1538-7445.AM2017-260

SummaryPregnancy‐associated plasma protein‐A (PAPP‐A) knockout (KO) mice, generated through homologous recombination in embryonic stem cells, have a significantly increased lifespan compared to wild‐type littermates. However, it is unknown whether this longevity advantage would pertain to PAPP‐A gene deletion in adult animals. In the present study, we used tamoxifen (Tam)‐inducible Cre recombinase‐mediated excision of the floxed PAPP‐A (fPAPP‐A) gene in mice at 5 months of age. fPAPP‐A mice, which were either positive (pos) or negative (neg) for Tam‐Cre, received Tam treatment with quarterly boosters. Only female mice could be used with this experimental design. fPAPP‐A/neg and fPAPP‐A/pos mice had similar weights at the start of the experiment and showed equivalent weight gain. We found that fPAPP‐A/pos mice had a significant extension of life span (P = 0.005). The median life span was increased by 21% for fPAPP‐A/pos compared to fPAPP‐A/neg mice. Analysis of mortality in life span quartiles indicated that the proportion of deaths of fPAPP‐A/pos mice were lower than fPAPP‐A/neg mice at young adult ages (P = 0.002 for 601–800 days) and higher than fPAPP‐A/neg mice at older ages (P = 0.004 for >1000 days). Thus, survival curves and age‐specific mortality indicate that female mice with knockdown of PAPP‐A gene expression as adults have an extended healthy life span.

Key Points Complete or partial response at 1 year without secondary systemic treatment provides clinical benefit in patients with chronic GVHD. Success defined by this endpoint is currently observed in fewer than 20% of patients after initial systemic treatment of chronic GVHD.

We described physical activity measures and hourly patterns in patients with chronic obstructive pulmonary disease (COPD) after stratification for generic and COPD-specific characteristics and, based on multiple physical activity measures, we identified clusters of patients. In total, 1001 patients with COPD (65% men; age, 67 years; forced expiratory volume in the first second [FEV 1 ], 49% predicted) were studied cross-sectionally. Demographics, anthropometrics, lung function and clinical data were assessed. Daily physical activity measures and hourly patterns were analysed based on data from a multisensor armband. Principal component analysis (PCA) and cluster analysis were applied to physical activity measures to identify clusters. Age, body mass index (BMI), dyspnoea grade and ADO index (including age, dyspnoea and airflow obstruction) were associated with physical activity measures and hourly patterns. Five clusters were identified based on three PCA components, which accounted for 60% of variance of the data. Importantly, couch potatoes (i.e. the most inactive cluster) were characterised by higher BMI, lower FEV 1 , worse dyspnoea and higher ADO index compared to other clusters ( p < 0.05 for all). Daily physical activity measures and hourly patterns are heterogeneous in COPD. Clusters of patients were identified solely based on physical activity data. These findings may be useful to develop interventions aiming to promote physical activity in COPD.