Jian Xiao1, Wenyun Li2, Yan Huang3, Mengli Huang4, Shanshan Li1, Xiaohui Zhai1, Jing Zhao4, Chan Gao4, Wenzhuan Xie4, Hao Qin5, Shangli Cai4, Yuezong Bai4, Ping Lan6, Yifeng Zou7. 1. Department of Medical Oncology, The Sixth Affiliated hospital of Sun Yat-sen University, Guangzhou, China. 2. Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China. 3. Department of Pathology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China. 4. The Medical Department, 3D Medicines Inc., Shanghai, China. 5. Research and Development Institute of Precision Medicine, 3D Medicines Inc., Shanghai, China. 6. Department of Colorectal Surgery, The Sixth Affiliated hospital of Sun Yat-sen University, 26 Yuancun Er Heng Road, Guangzhou, 510655, Guangdong, China. lanping@mail.sysu.edu.cn. 7. Department of Colorectal Surgery, The Sixth Affiliated hospital of Sun Yat-sen University, 26 Yuancun Er Heng Road, Guangzhou, 510655, Guangdong, China. zouyif@mail.sysu.edu.cn.
Abstract
BACKGROUND: Mismatch repair (MMR)/microsatellite instability (MSI) and tumor mutational burden (TMB) are independent biomarkers that complement each other for predicting immune checkpoint inhibitors (ICIs) efficacy. Here we aim to establish a strategy that integrates MSI and TMB determination for colorectal cancer (CRC) in one single assay. METHODS: Surgical or biopsy specimens retrospectively collected from CRC patients were subjected to NGS analysis. Immunohistochemistry (IHC) and polymerase chain reaction (PCR) were also used to determine MMR/MSI for those having enough tissues. The NGS-MSI method was validated against IHC and PCR. The MSI-high (MSI-H) or microsatellite stable (MSS) groups were further stratified based on tumor mutational burden, followed by validation using the The Cancer Genome Atlas (TCGA) CRC dataset. Immune microenvironment was evaluated for each subgroup be profiling the expression of immune signatures. RESULTS: Tissues from 430 CRC patients were analyzed using a 381-gene NGS panel. Alterations in KRAS, NRAS, BRAF, and HER2 occurred at a significantly higher incidence among MSI-H tumors than in MSS patients (83.6% vs. 58.4%, p = 0.0003). A subset comprising 98 tumors were tested for MSI/MMR using all three techniques, where NGS proved to be 99.0 and 93.9% concordant with PCR and IHC, respectively. Four of the 7 IHC-PCR discordant cases had low TMB (1.1-8.1 muts/Mb) and were confirmed to have been misdiagnosed by IHC. Intriguingly, 4 of the 66 MSS tumors (as determined by NGS) were defined as TMB-high (TMB-H) using a cut-off of 29 mut/Mb. Likewise, 15 of the 456 MSS tumors in the TCGA CRC cohort were also TMB-H with a cut-off of 9 muts/Mb. Expression of immune signatures across subgroups (MSS-TMB-H, MSI-H-TMB-H, and MSS-TMB-L) confirmed that the microenvironment of the MSS-TMB-H tumors was similar to that of the MSI-H-TMB-H tumors, but significantly more immune-responsive than that of the MSS-TMB-L tumors, indicating that MSI combined with TMB may be more precise than MSI alone for immune microenvironment prediction. CONCLUSION: This study demonstrated that NGS panel-based method is both robust and tissue-efficient for comprehensive molecular diagnosis of CRC. It also underscores the importance of combining MSI and TMB information for discerning patients with different microenvironment.
BACKGROUND: Mismatch repair (MMR)/microsatellite instability (MSI) and tumor mutational burden (TMB) are independent biomarkers that complement each other for predicting immune checkpoint inhibitors (ICIs) efficacy. Here we aim to establish a strategy that integrates MSI and TMB determination for colorectal cancer (CRC) in one single assay. METHODS: Surgical or biopsy specimens retrospectively collected from CRCpatients were subjected to NGS analysis. Immunohistochemistry (IHC) and polymerase chain reaction (PCR) were also used to determine MMR/MSI for those having enough tissues. The NGS-MSI method was validated against IHC and PCR. The MSI-high (MSI-H) or microsatellite stable (MSS) groups were further stratified based on tumor mutational burden, followed by validation using the The Cancer Genome Atlas (TCGA) CRC dataset. Immune microenvironment was evaluated for each subgroup be profiling the expression of immune signatures. RESULTS: Tissues from 430 CRCpatients were analyzed using a 381-gene NGS panel. Alterations in KRAS, NRAS, BRAF, and HER2 occurred at a significantly higher incidence among MSI-H tumors than in MSS patients (83.6% vs. 58.4%, p = 0.0003). A subset comprising 98 tumors were tested for MSI/MMR using all three techniques, where NGS proved to be 99.0 and 93.9% concordant with PCR and IHC, respectively. Four of the 7 IHC-PCR discordant cases had low TMB (1.1-8.1 muts/Mb) and were confirmed to have been misdiagnosed by IHC. Intriguingly, 4 of the 66 MSS tumors (as determined by NGS) were defined as TMB-high (TMB-H) using a cut-off of 29 mut/Mb. Likewise, 15 of the 456 MSS tumors in the TCGA CRC cohort were also TMB-H with a cut-off of 9 muts/Mb. Expression of immune signatures across subgroups (MSS-TMB-H, MSI-H-TMB-H, and MSS-TMB-L) confirmed that the microenvironment of the MSS-TMB-H tumors was similar to that of the MSI-H-TMB-H tumors, but significantly more immune-responsive than that of the MSS-TMB-L tumors, indicating that MSI combined with TMB may be more precise than MSI alone for immune microenvironment prediction. CONCLUSION: This study demonstrated that NGS panel-based method is both robust and tissue-efficient for comprehensive molecular diagnosis of CRC. It also underscores the importance of combining MSI and TMB information for discerning patients with different microenvironment.
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