Response Stratification in the First-Line Combined Immunotherapy of Hepatocellular Carcinoma at Genomic, Transcriptional and Immune Repertoire Levels

Jiamin Cheng; Yinyin Li; Xiaohui Wang; Zheng Dong; Yan Chen; Rui Zhang; Jiagan Huang; Xueyuan Jin; Jianfei Yao; Aifang Ge; Lele Song; Yinying Lu; Zhen Zeng

doi:10.2147/JHC.S326356

Response Stratification in the First-Line Combined Immunotherapy of Hepatocellular Carcinoma at Genomic, Transcriptional and Immune Repertoire Levels

J Hepatocell Carcinoma. 2021 Oct 27:8:1281-1295. doi: 10.2147/JHC.S326356. eCollection 2021.

Authors

Jiamin Cheng^#¹, Yinyin Li^#¹, Xiaohui Wang^#², Zheng Dong¹, Yan Chen¹, Rui Zhang¹, Jiagan Huang¹, Xueyuan Jin¹, Jianfei Yao², Aifang Ge², Lele Song^#^{2

3}, Yinying Lu¹, Zhen Zeng¹

Affiliations

¹ Comprehensive Liver Cancer Department, The Fifth Medical Center of the Chinese PLA General Hospital, Beijing, 100039, People's Republic of China.
² HaploX Biotechnology, Shenzhen, Guangdong Province, People's Republic of China.
³ Department of Radiotherapy, The Eighth Medical Center of the Chinese PLA General Hospital, Beijing, 100091, People's Republic of China.

^# Contributed equally.

Abstract

Background: Immunotherapy combined with VEGF inhibitor has become the new first-line therapy for advanced or metastatic hepatocellular carcinoma (HCC). However, the biomarkers for response and prognosis stratification of HCC first-line combined immunotherapy have not been clarified.

Methods: Here, we obtained the genomic alteration data from pre-therapeutic samples of 103 HCC patients using a 605-gene NGS test, and obtained the transcriptional and T cell receptor (TCR) diversity data from 18 patients who underwent the first-line combined immunotherapy using RNAseq and TCR sequencing, respectively. Patients received sorafenib/sintilimab or lenvatinib/sintilimab combined first-line therapy and the response was assessed at 3-6 cycles of therapy.

Results: No stratification of response was found in high-frequency key driver gene mutations, including TP53 and TERT. However, significantly higher ratio of progression (PD) was found in patients carrying MDM4 amplification. Similarly, FGF/3/4/19 amplifications could also result in high ratio of PD. The mRNA and lncRNA levels of eight genes related to hepatic metabolism and immune microenvironment exhibited significant differences between PR/SD and PD group, including DGKI, TNFSF14, CHST4, ACTIN1, PFKP, SLC51B, LCK and ERN1, suggesting stratification of response. Furthermore, moderate correlation was identified between the stratification genes (CHST4, SLC51B and ERN1) and immune factors (TIGIT, CD34, ICAM1, CCL5, CXCL9 and CXCL10), suggesting potential roles of these factors in immunoregulation. Strong linear correlation was found between any two of the three indexes for TCR CDR3 diversity, including Shannon-Wiener Index, Simpson index and evenness. However, no significant difference was found in the three indexes between the PR/SD and PD group, suggesting no stratification of response by these indexes.

Conclusion: We identified several potential biomarkers for response stratification in the first-line combined immunotherapy. MDM4 was capable of predicting disease progression, and a panel mRNA and lncRNA of eight genes may also predict the response. Further validation is needed to verify these biomarkers.

Keywords: HCC; TCR; hepatocellular carcinoma; immunotherapy; lenvatinib; lncRNA; mRNA; sintilimab; sorafenib.

Grants and funding

This study was supported by the National Science and Technology Major Project “Prevention and Control of Major Infectious Diseases including AIDS and Viral Hepatitis’ Special Project “Development and Transformation of Hepatitis B-Related Hepatic Cancer Markers for Accurate Diagnosis and Treatment” (2018ZX10302205) subproject “Translational Study of Tissue Microenvironment on Metastasis, Recurrence and Intervention of Liver Cancer” (2018ZX10302205-001). This study was also supported by the Youth Project of National Natural Science Foundation of China (81702389). This study was also supported by the Special Funds for Strategic Emerging Industry Development of Shenzhen (grant number 20170922151538732), the Science and Technology Project of Shenzhen (grant number JSGG20180703164202084). All funders did not participate in the study design, study implementation, data collection, data analysis, data interpretation and manuscript writing of the study.