Online first
Research paper
Published online: 2024-04-08

open access

Page views 106
Article views/downloads 47
Get Citation

Connect on Social Media

Connect on Social Media

Higher neoantigen load correlates with better overall survival in Chinese lung adenocarcinoma patients

Xuemei Liu1, Yongfeng Yu1, Wenhui Song2, Xiao Deng2, Kaili Gu2, Mengdi Yuan2, Zeyu Jiang2, Yu Wang2, Yafei Zhang2

Abstract

Introduction. Neoantigen load (NAL) has been extensively studied as a promising biomarker for immunotherapy. Recently it was also reported that NAL is associated with lung cancer patient survival, but the results were not consistent. 

Material and methods. To further evaluate the prognostic value of NAL in lung cancer, we analyzed NAL in a cohort of 96 lung adenocarcinoma (AD) and 83 lung squamous cell carcinoma (SQ) patients from the Cancer Genome Atlas (TCGA). We found that high NAL correlates with better overall survival (OS) of AD patients but with worse OS of SQ patients. Next, we collected a total of 25 NSCLC patient samples and explored whole exome sequencing (WES) and a large targeted gene panel (Med1CDx panel containing 579 genes) for NAL and tumor mutation burden (TMB) analysis. 

Results. We found that patients with both higher NAL and TMB, who underwent chemotherapy combined with immunotherapy, showed better OS and progression-free survival (PFS) in both AD and SQ subgroups. We also compared the concordance of NAL and TMB between WES and the Med1CDx panel. The R2 for concordance of NAL and TMB prediction by WES and our Med1CDx panel was 0.81 and 0.86, respectively. 

Conclusions. In this study, we showed that NAL is a useful biomarker for lung cancer OS prediction at least in the AD cohort. Furthermore, considering the high cost ofWES, large targeted gene-panel-based NAL and TMB analysis could be a good alternative in clinical practical settings. 

Article available in PDF format

View PDF Download PDF file

References

  1. Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021; 71(3): 209–249.
  2. Chan BA, Hughes BGM. Targeted therapy for non-small cell lung cancer: current standards and the promise of the future. Transl Lung Cancer Res. 2015; 4(1): 36–54.
  3. Majeed U, Manochakian R, Zhao Y, et al. Targeted therapy in advanced non-small cell lung cancer: current advances and future trends. J Hematol Oncol. 2021; 14(1): 108.
  4. Blass E, Ott PA. Advances in the development of personalized neoantigen-based therapeutic cancer vaccines. Nat Rev Clin Oncol. 2021; 18(4): 215–229.
  5. Jiang T, Shi T, Zhang H, et al. Tumor neoantigens: from basic research to clinical applications. J Hematol Oncol. 2019; 12(1): 93.
  6. Šutić M, Vukić A, Baranašić J, et al. Diagnostic, Predictive, and Prognostic Biomarkers in Non-Small Cell Lung Cancer (NSCLC) Management. J Pers Med. 2021; 11(11).
  7. Thakur MK, Gadgeel SM. Predictive and Prognostic Biomarkers in Non-Small Cell Lung Cancer. Semin Respir Crit Care Med. 2016; 37(5): 760–770.
  8. Gubin MM, Artyomov MN, Mardis ER, et al. Tumor neoantigens: building a framework for personalized cancer immunotherapy. J Clin Invest. 2015; 125(9): 3413–3421.
  9. Fancello L, Gandini S, Pelicci PG, et al. Tumor mutational burden quantification from targeted gene panels: major advancements and challenges. J Immunother Cancer. 2019; 7(1): 183.
  10. Büttner R, Longshore JW, López-Ríos F, et al. Implementing TMB measurement in clinical practice: considerations on assay requirements. ESMO Open. 2019; 4(1): e000442.
  11. Zou XL, Li XB, Ke H, et al. Prognostic Value of Neoantigen Load in Immune Checkpoint Inhibitor Therapy for Cancer. Front Immunol. 2021; 12: 689076.
  12. Wang P, Chen Y, Wang C. Beyond Tumor Mutation Burden: Tumor Neoantigen Burden as a Biomarker for Immunotherapy and Other Types of Therapy. Front Oncol. 2021; 11: 672677.
  13. Devarakonda S, Rotolo F, Tsao MS, et al. Tumor Mutation Burden as a Biomarker in Resected Non-Small-Cell Lung Cancer. J Clin Oncol. 2018; 36(30): 2995–3006.
  14. Talvitie EM, Vilhonen H, Kurki S, et al. High tumor mutation burden predicts favorable outcome among patients with aggressive histological subtypes of lung adenocarcinoma: A population-based single-institution study. Neoplasia. 2020; 22(9): 333–342.
  15. Jiang T, Shi J, Dong Z, et al. Genomic landscape and its correlations with tumor mutational burden, PD-L1 expression, and immune cells infiltration in Chinese lung squamous cell carcinoma. J Hematol Oncol. 2019; 12(1): 75.
  16. Yu H, Chen Z, Ballman KV, et al. Correlation of PD-L1 Expression with Tumor Mutation Burden and Gene Signatures for Prognosis in Early-Stage Squamous Cell Lung Carcinoma. J Thorac Oncol. 2019; 14(1): 25–36.
  17. Schumacher TN, Schreiber RD. Neoantigens in cancer immunotherapy. Science. 2015; 348(6230): 69–74.
  18. Gong L, He R, Xu Y, et al. Neoantigen load as a prognostic and predictive marker for stage II/III non‐small cell lung cancer in Chinese patients. Thorac Cancer. 2021; 12(15): 2170–2181.
  19. McGranahan N, Furness AJS, Rosenthal R, et al. Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science. 2016; 351(6280): 1463–1469.
  20. Garofalo A, Sholl L, Reardon B, et al. The impact of tumor profiling approaches and genomic data strategies for cancer precision medicine. Genome Med. 2016; 8(1): 79.
  21. Thorsson V, Gibbs DL, Brown SD, et al. Cancer Genome Atlas Research Network, Cancer Genome Atlas Research Network. The Immune Landscape of Cancer. Immunity. 2018; 48(4): 812–830.e14.
  22. Chalmers ZR, Connelly CF, Fabrizio D, et al. Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden. Genome Med. 2017; 9(1): 34.
  23. Van Allen EM, Miao D, Schilling B, et al. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science. 2015; 350(6257): 207–211.
  24. Zhu Y, Meng X, Ruan X, et al. Characterization of Neoantigen Load Subgroups in Gynecologic and Breast Cancers. Front Bioeng Biotechnol. 2020; 8: 702.
  25. Lyu Q, Lin A, Cao M, et al. Alterations in TP53 Are a Potential Biomarker of Bladder Cancer Patients Who Benefit From Immune Checkpoint Inhibition. Cancer Control. 2020; 27(1): 1073274820976665.
  26. Zhang L, Han X, Shi Y. Association of MUC16 Mutation With Response to Immune Checkpoint Inhibitors in Solid Tumors. JAMA Netw Open. 2020; 3(8): e2013201.
  27. Kim N, Hong Y, Kwon D, et al. Somatic mutaome profile in human cancer tissues. Genomics Inform. 2013; 11(4): 239–244.
  28. Rizvi NA, Hellmann MD, Snyder A, et al. Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science. 2015; 348(6230): 124–128.
  29. Chae YK, Anker JF, Bais P, et al. Mutations in DNA repair genes are associated with increased neo-antigen load and activated T cell infiltration in lung adenocarcinoma. Oncotarget. 2018; 9(8): 7949–7960.