A Novel Loci-Selected Method For Microsatellite Instability Detection Validated On A Large Chinese Cohort

Purpose: Microsatellite instability (MSI) was the FDA-approved companion diagnostic biomarker for using pembrolizumab in solid tumors and the biomarker for Lynch Syndrome. Next-generation sequencing (NGS) based method was recommended by NCCN and ESMO for detecting MSI status due to rich mutation information it could provide. However, current NGS based methods could be discordant on some cases, triggering confusion in clinical practice. To address this concern, we developed a novel and highly accurate MSI detection method, 3D-MSI, with a set of well-selected loci and compared its performance with other 3 public methods (MANTIS, MSISensor2, mSINGS) Materials and Methods: 100 MSI loci were selected from 2539 candidates and then sequenced on 145 FFPE samples as the training dataset. A binomial distribution model was built on each loci to predict PCR-validated MSI statuses of all samples in the training dataset and then all models were ordered by their prediction accuracy. 3D-MSI method determined MSI status by the percentage of top 30 loci models which predict the sample as MSI-H (\u003e40%: MSI-H; otherwise MSS). Performances of 3D-MSI and other 3 methods were compared on 485 MSI-validated sample. Their discordance were further investigated on a large cohort of 10399 Chinese cancer patients. Detection limits of these 4 methods were determined by 4 serial-diluted tumor samples. We also observed concordance between 3D-MSI detected statuses and immunotherapy response on 27 advanced colorectal cancer (CRC) patients. Results: In the dataset of 485 samples, 3D-MSI had the highest accuracy (All: 96.7%, Colorectal cancer CRC: 98.9%, Gastric cancer GC: 97.0%, other cancer: 86.4%), sensitivity (All: 93.2%, CRC: 97.0%, GC: 94.4%, Other: 80.8%) and specificity (All: 98.2%, CRC: 99.5%, GC: 98.8%, Other: 90.0%) among all 4 methods. It could detect correct MSI status under low tumor content down to 5%, but other methods required much higher tumor content to work (MSISensor2: ≥15%, mSINGS: ≥20%, MANTIS: ≥40%). Study on 10399 Chinese cancer patients confirmed superiority of 3D-MSI: compared with other 3 methods, it yielded the fewest discordant and unreliable MSI statuses questioned by PCR (3D-MSI: 1, MSISensor2: 36, mSINGS: 36, MANTIS: 50). Loci selection was the key point for 3D-MSI to outrival other 3 methods, revealed by comparing their scoring distribution. 3D-MSI detection predicted significantly prolonged PFS (log-rank P Conclusions: Our study offered an improved and clinically validated method for NGS-based MSI detection and stressed importance of loci selection in MSI detection, which would have crucial implications for future research and clinical management. Citation Format: Jian Li, Zhenghang Wang, Bo Yang, Bin Li, Yunjie Song, Bei Zhang, Xiaochen Zhao, Yifan Zhou, Zhenghua Xie, Xiaofang Li, Hao Chen, Hua Dong, Yuezong Bai, Lei Xiong, Fugen Li, Jing Gao, Lin Shen. A novel loci-selected method for microsatellite instability detection validated on a large Chinese cohort [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 5477.