Abstract 5311: Integrated proteomic and informatic assessment of ER+/HER2- and ER-/HER2- breast tumors
Authors: Guisong Wang, Punit Shah, Vladimir Tolstikov, Praveen-Kumar Raj-Kumar, Jianfang Liu, Lori A. Sturtz, J. Leigh Fantacone-Campbell, Rebecca Zingmark, Jeffrey A. Hooke, Mary L. Cutler, Kris Richardson, Leonardo Rodrigues, Valerie Bussberg, Rangaprasad Sarangarajan, Niven R. Narain, Hai Hu, Michael A. Kiebish, Albert J. Kovatich, Craig D. Shriver
Journal: Cancer Research (2020)

Abstract Introduction Emerging OMIC technologies enable the comprehensive molecular characterization of breast cancer to continually evolve. Published gene expression-based studies have differentiated ER-positive and ER-negative breast cancer. Herein, we have expanded these investigations utilizing a proteomics approach. Methods Clinical IHC subtyping on core biopsies was used to select available flash-frozen surgical samples for a cohort of 121 primary breast cancer patients. The cohort includes 103 ER+/HER2- and 18 ER-/PR-/HER2- cases. Mass spectrometry-based analyses were performed on these samples for proteomic, metabolomic, and lipidomic features. Comparative analysis was performed on proteomic data between the two groups of samples using Limma with stratified subsampling method and significance is reported at FDR&amp;lt;0.05 and absolute FC&amp;gt;1.5. PCA and hierarchical clustering analysis were applied on our training dataset and independent testing datasets using the identified significantly differentially expressed proteins as a signature to distinguish ER+/HER2- samples from ER-/PR-/HER2- samples. Euclidian distance was used as distance matrix and Ward was used as a linkage method in hierarchical clustering algorithm. Results The cohort was split into a training set (81 cases) and a testing set (40 cases) using stratification randomization. There were 171 differentially expressed proteins identified from the training dataset. 95% cases in our training dataset and 92.5% cases in our testing dataset were clustered correctly using hierarchical clustering method with the 171 proteins. Publicly available datasets from TCGA (RNA-seq, 846 cases) and CPTAC (proteomics, 57 cases) further validated the proteomic results from our cohort. 94.7% cases in CPTAC cohort and 93.2% cases in TCGA RNA-seq cohort were clustered correctly using the 171 proteins. KEGG pathway analysis using the differentially expressed proteins identified two significant pathways at FDR &amp;lt; 0.05: DNA replication and cysteine and methionine metabolism. Independent metabolomic and lipidomic analysis identified profiles supporting our proteomic analysis. These results confirm that ER+/HER2- and ER-/PR-/HER2- tumors are molecularly distinct which can be separated by a panel of expressed proteins. Disclaimers The contents of this publication are the sole responsibility of the author(s) and do not necessarily reflect the views, opinions or policies of Uniformed Services University of the Health Sciences, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., the Department of Defense, the Departments of the Army, Navy, or Air Force. Mention of trade names, commercial products, or organizations does not imply endorsement by the U.S. Government. Citation Format: Guisong Wang, Punit Shah, Vladimir Tolstikov, Praveen-Kumar Raj-Kumar, Jianfang Liu, Lori A. Sturtz, J. Leigh Fantacone-Campbell, Rebecca Zingmark, Jeffrey A. Hooke, Mary L. Cutler, Kris Richardson, Leonardo Rodrigues, Valerie Bussberg, Rangaprasad Sarangarajan, Niven R. Narain, Hai Hu, Michael A. Kiebish, Albert J. Kovatich, Craig D. Shriver. Integrated proteomic and informatic assessment of ER+/HER2- and ER-/HER2- breast tumors [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5311.