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Anti-PD1/PDL1 IgG subclass distribution in ten cancer types and anti-PD1 IgG4 as biomarker for the long time survival in NSCLC with anti-PD1 therapy

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Cancer Immunology, Immunotherapy Aims and scope Submit manuscript

Abstract

Background

Antibodies targeting programmed cell death-1(PD1) and its ligand (PDL1) have revolutionized cancer therapy. However, little is known about the preexisted anti-PD1/PDL1 autoantibodies (AAbs) distribution in multiple cancer types, nor is their potential biomarker role for anti-PD1 therapy.

Method

Plasma anti-PD1/PDL1 AAb IgG and subclasses (IgG1-4) were detected by enzyme-linked immune sorbent assay (ELISA) in 190 cancer patients, covering 10 cancer types (lung, breast, esophageal, colorectal, liver, prostatic, cervical, ovarian, gastric cancers and lymphoma), the comprehensive correlation of AAbs with multiple clinical parameters was analyzed. We further tested these AAbs in 76 non-small cell lung cancer (NSCLC) samples receiving anti-PD1 therapy, the association of AAbs level with survival was analyzed and validated in an independent cohort (n = 32).

Results

Anti-PD1/PDL1 AAb IgG were globally detected in 10 types of cancer patients. IgG1 and IgG2 were the major subtypes for anti-PD1/PDL1 AAbs. Correlation analysis revealed a distinct landscape between various cancer types. The random forest model indicated that IgG4 subtype was mostly associated with cancer. In discovery cohort of 76 NSCLC patients, high anti-PD1 IgG4 was associated with a reduced overall survival (OS, p = 0.019), not progression-free survival (PFS, p = 0.088). The negative association of anti-PD1 IgG4 with OS was validated in 32 NSCLC patients (p = 0.032).

Conclusion

This study reports for the first time the distribution of preexisted anti-PD1/PDL1 AAb IgG and subclasses across 10 cancer types. Moreover, the anti-PD1 AAb IgG4 subclass was identified to associate with OS, which may serve as a potential biomarker for anti-PD1 therapeutic survival benefit in NSCLC patients.

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Acknowledgements

This work was supported by the China National Major Project for New Drug Innovation (2017ZX09304015, 2019ZX09201-002), Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (CIFMS)( 2021-1-I2M-003) and National Natural Science Foundation of China(81972805).

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Author notes

  1. Qiaoyun Tan and Liyuan Dai contribute equally to this work.

Authors and Affiliations

  1. Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study On Anticancer Molecular Targeted Drugs, Beijing, 100021, China

    Qiaoyun Tan, Liyuan Dai, Yanrong Wang, Shuxia Liu, Rongrong Luo, Shasha Wang, Ning Lou, Haizhu Chen, Yu Zhou, Qiaofeng Zhong, Jianliang Yang, Puyuan Xing, Xingsheng Hu, Yutao Liu, Shengyu Zhou, Jiarui Yao, Di Wu, Zhishang Zhang, Le Tang & Yuankai Shi

  2. State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences-Beijing (PHOENIX Center), Beijing Institute of Lifeomics, Beijing, 102206, China

    Te Liang & Xiaobo Yu

  3. Clinical Pharmacology Research Center, Peking Union Medical College Hospital, State Key Laboratory of Complex Severe and Rare Diseases, NMPA Key Laboratory for Clinical Research and Evaluation of Drug, Beijing Key Laboratory of Clinical PK & PD Investigation for Innovative Drugs, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100032, China

    Xiaohong Han

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  1. Qiaoyun Tan
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Contributions

YKS and XHH concepted, designed and supervised the study. YKS, XHH, LYD, QYT, YRW, SXL, RRL, SSW, NL, HZC, YXT, YZ, QFZ, JLY, PYX, XSH, YTL, SYZ, JRY, DW, ZSZ and LT collected the plasma samples and clinical information. QYT, YRW and TL collected the data. QYT, LYD, YKS, XHH and XBY analyzed and interpreted the data. QYT, LYD, TL and NL did the statistical analysis. TQY and LYD wrote the manuscript, YKS and XHH revised the manuscript. All authors reviewed the manuscript and approved the final version to submission.

Corresponding authors

Correspondence to Xiaohong Han or Yuankai Shi.

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Tan, Q., Dai, L., Wang, Y. et al. Anti-PD1/PDL1 IgG subclass distribution in ten cancer types and anti-PD1 IgG4 as biomarker for the long time survival in NSCLC with anti-PD1 therapy. Cancer Immunol Immunother 71, 1681–1691 (2022). https://doi.org/10.1007/s00262-021-03106-z

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