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Blood tumor mutation burden can predict the clinical response to immune checkpoint inhibitors in advanced non-small cell lung cancer patients

  • Original Article
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Abstract

Background

Tissue tumor mutation burden (tTMB) assessed by whole-exome sequencing (WES), which has been regarded as the gold standard method of tTMB measurement, can predict the clinical benefits of immune checkpoint inhibitors (ICIs). Multiple studies have investigated the feasibility of utilizing large panels to evaluate TMB but have obtained conflicting results. Furthermore, whether blood TMB (bTMB) can also be a predictive biomarker in NSCLC has not been determined.

Methods

Fifty-six advanced NSCLC patients treated with ICIs were enrolled, including an exploratory cohort (n = 42) and a small independent validation cohort (n = 14). Next-generation sequencing was performed on tumor and plasma samples collected prior to ICI treatment using a panel consisting of 520 cancer-related genes (OncoScreen) to evaluate tTMB/bTMB. WES was also performed on tumor samples to serve as references.

Results

A positive correlation between tTMB derived from WES and OncoScreen was observed. OncoScreen-derived tTMB showed a positive correlation with OncoScreen-derived bTMB. Patients with OncoScreen-derived tTMB \(\ge\) 7 mutations/Mb (p = 0.003) or bTMB \(\ge\) 11 mutations/Mb (p = 0.0029) had superior progression-free survival (PFS). In the small validation cohort, patients with OncoScreen-derived bTMB \(\ge\) 11 mutations/Mb exhibited longer PFS (p = 0.192) with a nonsignificant difference. In all 42 patients who had available bTMB and PFS, patients with bTMB \(\ge\) 11 mutations/Mb had significantly longer PFS (p = 0.011) than those with bTMB \(<\) 11 mutations/Mb.

Conclusion

Our study confirmed the feasibility of using large panels to estimate TMB. We also demonstrated that bTMB can serve as a potential biomarker for predicting the efficacy of ICIs in NSCLC.

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Abbreviations

TMB:

Tumor mutation burden

WES:

Whole-exome sequencing

ICI:

Immune checkpoint inhibitors

NSCLC:

Non-small cell lung cancer

PFS:

Progression-free survival

DCB:

Durable clinical benefit

PD-1:

Programmed cell death-1

PD-L1:

Programmed cell death ligand-1

MSI:

Microsatellite instability

MMR:

DNA mismatch repair

RECIST:

Response Evaluation Criteria in Solid Tumors

PD:

Progressive disease

ORR:

Overall response rate

CR:

Complete response

PR:

Partial response

SD:

Stable disease

NDB:

No durable benefit

FFPE:

Formalin fixed paraffin embedded

ctDNA:

Circulating tumor DNA

AF:

Allele frequency

SNP:

Single-nucleotide polymorphism

SNV:

Single-nucleotide variant

InDels:

Insertions and deletions

ROC:

Receiver operating characteristic curves

AUC:

Area under curve

FDR:

False discovery rate

AD:

Lung adenocarcinoma

SCC:

Lung squamous cell carcinoma

CNV:

Copy number variant

ECOG PS:

Eastern Cooperative Oncology Group performance status

CI:

Confidence interval

HR:

Hazard ratio

Mb:

Megabase

OS:

Overall survival

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Funding

This work was supported by the grants from the National Key R&D Program of China (grant number: 2016YFC1303800) and the National Natural Science Foundation of China (grant number: 81972179).

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Authors and Affiliations

  1. Department of Respiratory Diseases, Thoracic Disease Diagnosis and Treatment Center, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, 310003, Zhejiang Province, China

    Xi Chen, Liangjie Fang, Yanping Zhu, Zhang Bao, Qing Wang, Rong Liu, Wenjia Sun, Jianya Zhou & Jianying Zhou

  2. Burning Rock Biotech, Guangzhou, China

    Haiwei Du, Jing Lin, Bing Yu & Songan Chen

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  13. Jianying Zhou
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Contributions

Xi Chen contributed to conceptualization, methodology, supervision, project administration, writing—original draft; Liangjie Fang contributed to methodology, data curation, resources; Yanping Zhu contributed to methodology, data curation, resources; Zhang Bao contributed to methodology, data curation, resources; Qing Wang contributed to methodology and resources; Rong Liu contributed to methodology and resources; Wenjia Sun contributed to methodology and resources; Haiwei Du contributed to writing—original draft, data curation; Jing Lin contributed to formal analysis, data curation, writing—review and editing; Bing Yu contributed to data curation, writing—review and editing; Songan Chen contributed to data curation, writing—review and editing; Jianya Zhou contributed to conceptualization, methodology, supervision, project administration, writing—review and editing; Jianying Zhou contributed to conceptualization, methodology, supervision, project administration.

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Correspondence to Jianya Zhou.

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This study was approved by the Ethics Committee of the First Affiliated Hospital of College of Medicine of Zhejiang University.

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Informed consent was obtained from each patient for the use of their peripheral blood and tumor tissue samples.

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Chen, X., Fang, L., Zhu, Y. et al. Blood tumor mutation burden can predict the clinical response to immune checkpoint inhibitors in advanced non-small cell lung cancer patients. Cancer Immunol Immunother 70, 3513–3524 (2021). https://doi.org/10.1007/s00262-021-02943-2

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  • DOI: https://doi.org/10.1007/s00262-021-02943-2

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