Abstract P6-06-09: Evaluation of laser microdissected primary breast tumors for RNA Seq over bulk processing and validated with cohort control
Authors: Praveen-Kumar Raj-Kumar, Lori A. Sturtz, Albert J. Kovatich, Brenda Deyarmin, Jeffrey A. Hooke, Leigh Fantacone-Campbell, Anupama Praveen-Kumar, Jianfang Liu, James Craig, Leonid Kvecher, Jennifer Kane, Jennifer Melley, Stella Somiari, Stephen C. Benz, Justin Golovato, Shahrooz Rabizadeh, Patrick Soon-Shiong, Richard Mural, Craig D. Shriver, Hai Hu
Journal: Cancer Research (2020)

Abstract Introduction: Laser microdissection (LMD) is a valuable method to isolate target populations of cells for molecular analysis. LMD of breast tumor samples can isolate breast tumor cells whereas bulk processing of tumor tissue will incorporate surrounding non-cancerous cells and bias tumor expression profiling. Here, we evaluated the advantage of using LMD breast tumors for RNA-Seq over bulk processing. Methods: Tissue samples for the in-house dataset were from breast cancer patients consented by a HIPAA-compliant, IRB-approved protocol of the Clinical Breast Care Project. A total of 118 primary breast tumors embedded in OCT (Optimum Cutting Temperature) were selected and processed by LMD. Total RNA and protein were extracted using the illustra triplePrep kit. Paired-end RNA sequencing of 118 cases was performed using the Illumina HiSeq platform and the reads were preprocessed using a PERL-based pipeline involving PRINSEQ, GSNAP and HTSeq. The Cancer Genome Atlas (TCGA) primary breast cancer RNA-Seq data for 1097 tumors, bulk processed was downloaded. Differential expression of genes (DEG) was assessed using DESeq2. Significance was described for DEG with fold change &amp;gt;2 and p-adjusted value of 0.05. Results: A total of 24,518 genes with a mean expression of ≥ 10 (~9%) raw counts across 118 tumor samples were identified in the in-house LMD dataset. In TCGA breast cancer RNA-Seq, 14,281 genes with a mean expression of ≥ 100 (~9%) raw counts across 1097 tumor samples were identified. The conventional PAM50 classifier was used for intrinsic subtyping of in-house data, yielding 36 Basal-like, 14 HER2-enriched, 43 Luminal A, 22 Luminal B and 3 Normal-like calls. The provided PAM50 calls for TCGA were 192 Basal-like, 82 HER2-enriched, 566 Luminal A, 217 Luminal B and 40 Normal-like calls. Within commonly expressed 13,165 genes, LMD (in-house) and bulk (TCGA) processing exhibited approximately 40-78% non-overlap in significantly differentially expressed genes (SDEG) among the conventional intrinsic subtypes. 21 unique stromal genes were present in SDEG unique to TCGA whereas there were only 5 SDEG unique to in-house dataset. We validated the results with 34 patients that had both LMD and bulk processing RNA-Seq data and found the non-overlap genes percentage to be even greater from 46-85%. The observed percentages of non-overlapping genes in the whole datasets were also validated in the 34 overlapping cases when using IHC subtypes. Overall high positive correlation is observed among the stromal genes present in SDEG unique to TCGA suggesting strong stromal contribution in bulk processing. Pathway analysis of SDEG unique to LMD data suggested alterations in known cancer pathways (B-cell immune response, RNA metabolism and splicing, phagocytosis, and signaling components). Conclusion: Analysis of The Cancer Genome Atlas breast cancer RNA-Seq data set (based on bulk tissue processing) suggested contribution of stromal signature genes and important differences from LMD specimens. Thus, tumor selection via LMD may allow us to unveil signals that are more specific to cancer cells. Disclaimer: 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 (USUHS), The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., the Department of Defense (DoD), 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: Praveen-Kumar Raj-Kumar, Lori A. Sturtz, Albert J. Kovatich, Brenda Deyarmin, Jeffrey A. Hooke, Leigh Fantacone-Campbell, Anupama Praveen-Kumar, Jianfang Liu, James Craig, Leonid Kvecher, Jennifer Kane, Jennifer Melley, Stella Somiari, Stephen C. Benz, Justin Golovato, Shahrooz Rabizadeh, Patrick Soon-Shiong, Richard Mural, Craig D. Shriver, Hai Hu. Evaluation of laser microdissected primary breast tumors for RNA Seq over bulk processing and validated with cohort control [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P6-06-09.