RNA Sequencing Approaches to Identify Novel Biomarkers for Venous Thromboembolism (VTE) in Lung Cancer Tamara A. Sussman MD1, Mohamed Abazeed MD PhD1,

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RNA Sequencing Approaches to Identify Novel Biomarkers for Venous Thromboembolism (VTE) in Lung Cancer Tamara A. Sussman MD1, Mohamed Abazeed MD PhD1, Keith McCrae MD1, Alok A. Khorana MD1 1Taussig Cancer Institute, Cleveland Clinic, Cleveland, Ohio Abstract #554

Background Venous thromboembolism (VTE) significantly contributes to morbidity and a worse overall prognosis in patients with cancer Although cancer patients have a higher incidence of VTE (5-10%), the majority of patients do not experience VTE A validated risk tool for general oncology patients exists Novel biomarkers with greater sensitivity and specificity are needed

Objective Utilize a comprehensive RNA sequencing strategy for lung cancer to identify novel biomarkers of cancer-associated VTE

Methods RNA was sequenced using Illumina HiSeq For gene level analyses, expression values of zero were set to overall minimum value All data were log2 transformed Log2 fold-change and adjusted p-values (using Benjamini-Hochberg procedure) were calculated using linear models with moderated t-statistic Association between single-sample gene set enrichment analysis (GSEA) profiles for each gene set and binary variables were determined using an information-based similarity metric (RNMI)

Results Patient Characteristics All Patients N = 12 Age, years (median [range]) 67 (48-80) Gender (n [%]) Male 8 (67%) Race (n [%]) White 10 (83%) Stage (n [%]) IA-IIB IIIA IV 2 (16%) 3 (25%) 7 (58%) Histology (n [%]) Adenocarcinoma Squamous Cell Carcinoma Mixed Other 1 (8%) Chemotherapy Received (n [%]) Carboplatin doublet therapy Immunotherapy None Length of Follow Up, months (median [range]) 18 (1-69) Results

Results Site of VTE Lung Cancer Patients with VTE N = 6 (%) Pulmonary Embolism 3 (50%) Lower Extremity DVT 2 (33%) Catheter-Associated Thrombus 1 (17%)

Differential Gene Expression Results Genes over-expressed in patients with VTE Log2 fold change Genes under-expressed in patients with VTE Normalized Counts

Genes Over-Expressed in Lung Cancer Patients with VTE Results Genes Over-Expressed in Lung Cancer Patients with VTE Gene Log (fold-change) Q value Mechanism MIAT 4.8 0.000003 Myocardial infarction transcript SHC4 4.6 0.00003 Ras activating pathway CR1 4.2 0.0004 Complement receptor NLRP14 3.8 0.0001 Activates and regulates inflammatory response IL5RA 3.6 0.006 CLNK 0.004 Regulates inflammatory response

Genes Under-Expressed in Lung Cancer Patients with VTE Results Genes Under-Expressed in Lung Cancer Patients with VTE Gene Log (fold change) Q value Mechanism EN1 -4.12 0.0008 Cell development and differentiation DSG1 -3.48 0.009 Component of desmosome TGM1 -3.40 0.005 Cross linking of proteins IRX4 -3.35 0.01 Cell differentiation, heart development, multicellular organism development EPHB6 -3.29 0.008 Cell adhesion and migration

Results: GSEA Associated Gene Set Pathways Enrichment Score FDR q Value IL2-STAT5 Signaling 0.3026 0.4401 Allograft Rejection 0.2752 0.2585 IL6-JAK-STAT3 Signaling 0.2698 0.6826 Interferon-gamma Response 0.2592 0.6489 K-RAS Signaling Up 0.2290 0.6795 Complement 0.2044 0.675 Inflammatory Response 0.2002 0.7792 K-RAS Signaling Down -0.2279 1 Apoptosis -0.3061 0.7323

Conclusions and Future Directions To our knowledge, first comprehensive RNA sequencing strategy to identify thrombosis genomic markers in cancer patients Notably, complement, inflammatory, and KRAS1 pathways are upregulated in lung cancer patients with VTE Differentially expressed genes and pathways provide novel biologic insight and therapeutic targets for cancer-associated VTE 1Ades et al. Tumor oncogene (KRAS) status and risk of venous thrombosis in patients with metastatic CRC. JTH 2015; 13: 998-1003.