Abstract LB-160: Collagen I fiber signatures in breast cancer predict lymph node metastasis
Authors: Jiang Lu, Samata Kakkad, Tiffany Greenwood, Meiyappan Solaiyappan, Zaver M. Bhujwalla, Xingde Li, Kristine Glunde
Journal: Cancer Research (2014)

Abstract The extracellular matrix (ECM) in breast cancers is altered and provides new biomarkers for cancer diagnosis [1] and prognosis [2,3]. Collagen I (Col1) fibers are the major ECM component in breast tumors, and increased Col1 has been found to facilitate breast tumor formation, invasion, and metastasis [2,3]. We tested the relationship between occurrence of lymph node (LN) and Col1 fibers in primary tumors on a tissue microarray by using second harmonic generation (SHG) imaging and image analyses. A breast tissue microarray that had 50 LN positive (LN+) and 50 LN negative (LN-) status patient's primary breast tumor biopsy samples was used in this study. Z-stacks of SHG tile images were acquired on a Zeiss 710 multiphoton microscope. We analyzed the SHG images from all breast biopsies for geometric features such as fiber volume, inter-fiber distance [4], and both 2D and 3D Haralick features such as energy, entropy, and correlation, among others. K-mean clustering of a case-controlled subset (19 LN+ and 16 LN- cases with grade 2, Stage II, T2) was performed. Principal component analysis (PCA) was performed to reduce data dimensions. Supervised nonlinear support vector machine (SVM) classification of 50 LN+ and 50 LN- samples with cross validation was performed on the 3D Haralick features. The detection errors of LN+ and LN- samples based on individual geometric, Haralick texture features and their combinations, which were obtained from the analysis of the Col 1 fiber patterns of 35 case-controlled samples, were calculated. The features that best differentiate LN+ and LN- samples were 3D entropy and 3D energy in the analysis of 35 case-controlled samples. A receiver operating characteristic (ROC) curve of SVM was generated to classify 3D Haralick features of 50 LN+ and 50 LN- samples, in which a Gaussian kernel with a sigma of 2.9 was the best model and gave a false negative rate of 0.13, a false positive rate of 0.31, and an area under the ROC curve of 0.81. In this larger statistical analysis from 100 breast cancer samples, we performed supervised SVM classification with cross validation to ensure the robustness and obtained a best false positive rate of 0.3. This could be because some LN- cases may have turned out as LN+ cases later on, and no follow-up data were available to us. A larger number of breast cancer samples with patient follow-up information is currently being imaged in our lab to train a more robust statistical SVM model. SHG imaging of Col1 fibers in primary breast tumors followed by image analyses can serve as a predictive biomarker to assess LN status and thereby predict outcome of breast cancer. [1]. A. Bergamaschi et al., J. Pathol. (2008). [2] P. P. Provenzano et al., BMC Med. (2008). [3] M. W. Conklin et al., Am. J. Pathol. (2011). [4] S. Kakkad et al., J. Biomed Optics (2012). This work was supported by NIH P50 CA103175. Citation Format: Jiang Lu, Samata Kakkad, Tiffany Greenwood, Meiyappan Solaiyappan, Zaver M. Bhujwalla, Xingde Li, Kristine Glunde. Collagen I fiber signatures in breast cancer predict lymph node metastasis. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr LB-160. doi:10.1158/1538-7445.AM2014-LB-160