Prognostic significance of tumor-infiltrating lymphocytes in ductal carcinoma in situ of the breast

Tumor-infiltrating lymphocytes (TILs) provide prognostic value in invasive breast cancer and guidelines for their assessment have been published. This study aims to evaluate: (a) methods of TILs assessment, and (b) their prognostic significance in breast ductal carcinoma in situ (DCIS). Hematoxylin and eosin sections from two clinically annotated DCIS cohorts; a training set (n = 150 pure DCIS) and a validation set (n = 666 comprising 534 pure DCIS and 132 cases wherein DCIS and invasive breast carcinoma were co-existent) were assessed. Seven different scoring methods were applied to the training set to identify the most optimal reproducible method associated with strongest prognostic value. Among different methods, TILs touching ducts' basement membrane or away from it by one lymphocyte cell thickness provided the strongest significant association with outcome and highest concordance rate [inter-cluster correlation coefficient = 0.95]. Assessment of periductal TILs at increasing distances from DCIS (0.2 , 0.5 , and 1 mm) as well as percent of stromal TILs were practically challenging and showed lower concordance rates than touching TILs. TILs hotspots and lymphoid follicles did not show prognostic significance. Within the pure DCIS validation set, dense TILs were associated with younger age, symptomatic presentation, larger size, higher nuclear grade, comedo necrosis and estrogen receptor negativity as well as shorter recurrence-free interval (p = 0.002). In multivariate survival analysis, dense TILs were independent predictor of shorter recurrence-free interval (p = 0.002) in patients treated with breast conservation. DCIS associated with invasive carcinoma showed denser TILs than pure DCIS (p = 9.0 × 10-13). Dense TILs is an independent prognostic variable in DCIS. Touching TILs provides a reproducible method for their assessment that can potentially be used to guide management

Do serum biomarkers really measure breast cancer?

Background Because screening mammography for breast cancer is less effective for premenopausal women, we investigated the feasibility of a diagnostic blood test using serum proteins. Methods This study used a set of 98 serum proteins and chose diagnostically relevant subsets via various feature-selection techniques. Because of significant noise in the data set, we applied iterated Bayesian model averaging to account for model selection uncertainty and to improve generalization performance. We assessed generalization performance using leave-one-out cross-validation (LOOCV) and receiver operating characteristic (ROC) curve analysis. Results The classifiers were able to distinguish normal tissue from breast cancer with a classification performance of AUC = 0.82 ± 0.04 with the proteins MIF, MMP-9, and MPO. The classifiers distinguished normal tissue from benign lesions similarly at AUC = 0.80 ± 0.05. However, the serum proteins of benign and malignant lesions were indistinguishable (AUC = 0.55 ± 0.06). The classification tasks of normal vs. cancer and normal vs. benign selected the same top feature: MIF, which suggests that the biomarkers indicated inflammatory response rather than cancer. Conclusion Overall, the selected serum proteins showed moderate ability for detecting lesions. However, they are probably more indicative of secondary effects such as inflammation rather than specific for malignancy.United States. Dept. of Defense. Breast Cancer Research Program (Grant No. W81XWH-05-1-0292)National Institutes of Health (U.S.) (R01 CA-112437-01)National Institutes of Health (U.S.) (NIH CA 84955