Abstract 1702: Comprehensive genomic analysis of predictive biomarkers for checkpoint inhibitor therapy from a limited amount of FFPE tissue and cfDNA

Abstract PD-L1 expression and MSI status are useful predictive biomarkers for checkpoint inhibitors (CIs) in some tumor types. However, studies have demonstrated that the identification of all potential patients who may benefit from CIs will require the analysis of both tumor cell and tumor microenvironment (TME) biomarkers. For example, recent data suggests that tumor mutation burden (TMB) and tumor mutation signatures as well as inflammatory signatures and T cell clonality in the tumor microenvironment may serve as independent predictive biomarkers of CI response. The need for evaluation of multiple biomarkers using IHC, PCR and/or NGS using limited amounts of tissue poses a challenge in the clinical setting. Here we conduct a study to evaluate whether a panel of tumor and TME biomarkers can be accurately analyzed using low DNA and RNA input from FFPE specimens or using plasma-derived cfDNA. Multiple methods for assessing TMB, MSI status and mutation signatures were compared. The impact of sample quality on biomarker analysis was also evaluated. A set of late stage CRC and matched plasma samples were used in this study. DNA and RNA were co-extracted from FFPE sections using RecoverAll™ kit. cfDNA was extracted from plasma using the MagMAX™ Kit. DNA and RNA yields from FFPE ranged from 855-3875ng and 477-5200ng, respectively. cfDNA yields ranged from 8-38ng. Based on the KAPA DNA QC results, the FFPE samples were determined to be high quality. TMB and mutation signature analysis of FFPE samples were performed using Oncomine Tumor Mutation Load (TML) Assay, the Oncomine Comprehensive Assay v3 (OCAv3) and WES. MSI status in FFPE tissue and cfDNA was determined using the Promega MSI Analysis System. The mutation spectrum in the matched cfDNA samples was determined using the Oncomine PanCancer cfTNA Assay. Inflammatory (IFNG) signatures were assessed using the targeted RNASeq panel, Oncomine Immune Response Research Assay (OIRRA). The clonality of TILs was analyzed using NGS. TMB results generated from the targeted panels using only 20ng DNA input were comparable to WES results. Differences in TMB counts between the NGS assays are likely due to differences in genomic coverage and data filtering. MSI-H status and/or mutations in critical DNA repair or proofreading genes such as MSH6 and POLE correlated with high TMB. Gene expression profiling using OIRRA was successful for all FFPE RNA samples. In summary, this study demonstrates that comprehensive genomic analysis of predictive biomarkers for CI therapy can be performed from limited FFPE samples using co-extraction methods and a combination of targeted NGS and PCR-based assays. DNA and RNA quality have a significant impact on the accuracy of TMB analysis and gene expression profiling. In cases where no tumor tissue is available, the analysis of biomarkers such as MSI and mutation signatures may be possible using cfDNA. Citation Format: Xiaodong Wang, Zhenyu Yan, Quyen Vu, David Smith, Chad Galderisi, Cynthia S. Spittle, Jin Li. Comprehensive genomic analysis of predictive biomarkers for checkpoint inhibitor therapy from a limited amount of FFPE tissue and cfDNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1702.