1. Introduction Patients and Methods Results Conclusion Table 1. Baseline characteristics of the 108 patients included in the biomarker analysis. Objectives The aim of the present study was to identify molecular predictors of response and survival to cetuximab by evaluating germline single nucleotide polymorphisms (SNPs) in genes involved in the following pathways to identify patients that benefit from this regimen: Genetic variants in immune response genes predict clinical outcome in mCRC patients treated with cetuximab-based therapy Anne M Schultheis 1, Nico B Volz 2, Wu Zhang 2, Dongyun Yang 2, Yan Ning 2, Sebastian Stintzing 2, Takeru Wakatsuki 2, Rita El-Khoueiry 2, Joseph Li 2, Adel Kardosh 2, Afsaneh Barzi 2, Anthony El-Khoueriy 2, Heinz-Josef Lenz 2 1 Institute of Pathology, University Hospital Cologne, Cologne, Germany; 2 USC/Norris Comprehensive Cancer Center, Los Angeles, CA Patient Characteristics Abstract ID: 3567 Results Cetuximab is a monoclonal antibody targeting the EGF-receptor 1. One of its anti-tumor mechanisms is the stimulation of the immune system via ADCC (Antibody-Dependent-Cell mediated Cytotoxicity) 2. Immune response through T-cell activation may also play a role in antitumor efficacy of chemotherapy. CTLA4, PDL1, IDO1 and CD24 are inhibitory co-receptors or ligands that may down-regulate the immune system through suppression of T-cell response 3,4,5. We tested whether germline polymorphisms in these genes are involved in the cetuximab dependent immune response pathway and thus predict outcome in mCRC patients treated with cetuximab. DNA was isolated from blood of 108 patients with mCRC treated with either irinotecan+cetuximab (n=73) or single agent cetuximab (n=35). Twelve prospectively functionally relevant SNPs; IDO1(rs9657182, rs3739319, rs1010866), PD1(rs2227981, rs7421861), PDL1(rs2297137, rs2297136, rs10122089, G>C), CTLA4(rs231777, rs231775), and CD24(rs8734) were analyzed by PCR-based direct sequencing and evaluated for association with tumor response, overall survival (OS), and progression free survival (PFS). Minor allele frequency had to be higher than 10%. PCR and product sequencing were done using standard procedures. There were 65 men and 43 women with a median PFS of 3.7 (95%CI: 2.8, 4.6) months and a median OS of 10.5 (95%CI:7.7, 13.3) months. Median follow up was 16.3 (range:1.2, 42.4) months. Uni- and multivariate analyses, adjusting for age, gender, rash and racial background, were carried out. After logistic regression analyses, baseline characteristics showing a p of <0.1 were included in the multivariate analysis. Kaplan-Meier estimation with log-rank testing for differences were carried out. References Our results suggest that SNPs in genes involved in immune response may predict efficacy of cetuximab treatment in patients with mCRC. To the best of our knowledge this is the first data linking PDL1(rs2297137) to cetuximab efficacy in mCRC patients. Studies to confirm these findings are warranted. PR = partial response, SD = stable disease, PD = progressive disease Table 2. Significant polymorphisms and clinical outcome in second-second line cetuximab therapy Figure 2-5. Univariate analysis OS and PFS Kaplan-Meier curves of significant polymorphisms. Figure 1. Genes involved in the immune response Pathways 1. Graham, J., et al. (2004). "Cetuximab." Nat Rev Drug Discov 3(7): 549-550. 2. Matsuo, T., et al. (2011). "Analysis of the anti-tumor effect of cetuximab using protein kinetics and mouse xenograft models." BMC Res Notes 4: 140. 3. Kirkwood, J. M., et al. (2012). "Immunotherapy of cancer in 2012." CA Cancer J Clin 62(5): 309-335. 4. Vanneman, M. and G. Dranoff (2012). "Combining immunotherapy and targeted therapies in cancer treatment." Nat Rev Cancer 12(4): 237-251. 5. Li, O., et al. (2004). "CD24 expression on T cells is required for optimal T cell proliferation in lymphopenic host." J Exp Med 200(8): 1083-1089. Baseline characteristics (n= 108)Primary outcome data (n = 108) Age: median (range) 64 years (35 – 110) Disease control rate (DCR)62.9 % Gendermale: 60% female: 40% Progression free survival (PFS) 3.7 months (2.8 – 4.6) Overall response rate (ORR) 20.0 %Overall survival (OS) 10.5 monthts (7.7 – 13.3) Figure 2. IDO1 rs9657182 shows a significant correlation between patients carrying the AA or AG genotype and longer OS. Figure 3. IDO1 rs3739319 shows a significant correlation between patients carrying the GG genotype and longer OS. Figure 4. CD24 rs8734 shows a significant correlation between patients carrying the GA genotype and longer OS. OS SNPNMedian, mo (95%CI) P (multivariate) IDO1rs9657182 0.009 A/A2412.0 (5.7, 20.4) A/G5515.0 (7.8, 15.9) G/G258.5 (5.1, 10.8) IDO1rs3739319 0.045 A/A2710.8 (4.4, 15.0) A/G518.7 (6.3, 12.0) G/G2717.9 (8.5, 26.4) CD24rs8734 0.001 G/G547.8 (5.7, 10.5) G/A3613.1 (8.7, 17.9) A/A52.3 (1.8, 11.3) PFS SNPNMedian, mo (95%CI) P (multivariate) CD24rs8734 0.01 G/G543.6 (2.5, 4.6) G/A365.0 (3.3, 6.6) A/A51.3 (1.0, 3.3) CTLA4rs231777 0.017 C/C654.1 (3.3, 5.3) C/T342.6 (2.3, 4.4) Tumor Response SNPNPRSD+PD PDL1rs2297137 P (multivariate) = 0.029 G/G609 (16%)49 (84%) G/A367 (19%)29 (81%) A/A95 (56%)4 (44%) Figure 5. CD24 rs8734 shows a significant correlation between patients carrying the GA genotype and longer PFS.