Significance of lymph vessel invasion identified by the endothelial lymphatic marker D2-40 in node negative breast cancer.

Monoclonal antibody D2-40, a marker of lymphatic endothelium, identifies tumor emboli in lymph vessels. The aim of the study was to assess whether D2-40+ lymph vessel invasion (LVI) correlates with clinicopathologic factors including lymphovascular invasion (LVI) as assessed by haematoxylin and eosin-stained sections (H&E+ or H&E-) and to assess the prognostic significance in node-negative breast cancer. The study group consisted of 303 node-negative breast cancer patients that had a median follow-up of 7.6 years. Clinical and pathological data were retrieved from the Henrietta Banting database. Immunohistochemical staining was performed on formalin-fixed, paraffin-embedded tissue sections of the primary invasive carcinoma using D2-40. Immunostaining with CD31 was performed on the discordant cases that were H&E+/D2-40-. D2-40+ lymph vessel invasion was detected in 82/303 (27%) cases. The foci of lymphatic invasion occurred predominantly at the invasive front of the tumor. The absence of D2-40 and CD31 in 13/17 discordant cases was suggestive of retraction artefact. D2-40+ lymph vessel invasion correlated significantly with age (P=0.0003), tumor size (P=0.005), histological grade (P=0.0001), H&E+ (P=<0.0001) and estrogen receptor status (P=0.005) but not with histological type or progesterone receptor status. Multivariate analysis revealed that D2-40+ lymph vessel invasion was the only significant predictor of distant recurrence. There was no significant association between D2-40 status and local recurrence (P=0.752) or regional recurrence (P=0.13). Both D2-40+lymph vessel invasion (P=0.009) and H&E+LVI cases (P=0.02) were associated with overall shorter survival in univariate analysis. These data indicate that D2-40 identifies lymphatic invasion in breast tumors and is a significant predictor of outcome in breast cancer.

Processing of chemical sensor arrays with a biologically inspired model of olfactory coding.

This paper presents a computational model for chemical sensor arrays inspired by the first two stages in the olfactory pathway: distributed coding with olfactory receptor neurons and chemotopic convergence onto glomerular units. We propose a monotonic concentration-response model that maps conventional sensor-array inputs into a distributed activation pattern across a large population of neuroreceptors. Projection onto glomerular units in the olfactory bulb is then simulated with a self-organizing model of chemotopic convergence. The pattern recognition performance of the model is characterized using a database of odor patterns from an array of temperature modulated chemical sensors. The chemotopic code achieved by the proposed model is shown to improve the signal-to-noise ratio available at the sensor inputs while being consistent with results from neurobiology.