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. 2023 Feb;14(5):497-505.
doi: 10.1111/1759-7714.14772. Epub 2023 Jan 2.

The development of a tumor-associated autoantibodies panel to predict clinical outcomes for immune checkpoint inhibitor-based treatment in patients with advanced non-small-cell lung cancer

Jing Zhao  1 , Yang Wu  2 , Yuan Yue  1 , Minjiang Chen  1 , Yan Xu  1 , Xiangning Liu  1 , Xiaoyan Liu  1 , Xiaoxing Gao  1 , Hanping Wang  1 , Xiaoyan Si  1 , Wei Zhong  1 , Xiaotong Zhang  1 , Li Zhang  1 , Mengzhao Wang  1
Affiliations

The development of a tumor-associated autoantibodies panel to predict clinical outcomes for immune checkpoint inhibitor-based treatment in patients with advanced non-small-cell lung cancer

Jing Zhao et al. Thorac Cancer. 2023 Feb.

Abstract

Background: Immune checkpoint inhibitors (ICIs) have become one important therapeutic strategy for advanced non-small-cell lung cancer (NSCLC). It remains imperative to identify reliable and convenient biomarkers to predict both the efficacy and toxicity of immunotherapy, and tumor-associated autoantibodies (TAAbs) are recognized as one of the promising candidates for this.

Patients and methods: This study enrolled 97 advanced NSCLC patients with ICI-based immunotherapy treatment, who were divided into a training cohort (n = 48) and a validation cohort (n = 49), and measured for the serum level of 35 TAAbs. According to the statistical association between the serum positivity and clinical outcome of each TAAb in the training cohort, a TAAb panel was developed to predict the progression-free survival (PFS), and further examined in the validation cohort and in different subgroups. Similarly, another TAAb panel was derived to predict the occurrence of immune-related adverse events (irAEs).

Results: In the training cohort, a 7-TAAb panel composed of p53, CAGE, MAGEA4, GAGE7, UTP14A, IMP2, and PSMC1 TAAbs was derived to predict PFS (median PFS [mPFS] 9.9 vs. 4.3 months, p = 0.043). The statistical association between the panel positivity and longer PFS was confirmed in the validation cohort (mPFS 11.1 vs. 4.8 months, p = 0.015) and in different subgroups of patients. Moreover, another 4-TAAb panel of BRCA2, MAGEA4, ZNF768, and PARP TAAbs was developed to predict the occurrence of irAEs, showing higher risk in panel-positive patients (71.43% vs. 28.91%, p = 0.0046).

Conclusions: Collectively, our study developed and validated two TAAb panels as valuable prognostic biomarkers for immunotherapy.

Keywords: immune checkpoint inhibitors; non-small-cell lung cancer; prognostic biomarker; tumor-associated autoantibody.

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Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

FIGURE 1
FIGURE 1
Identification of the 7‐TAAb panel with predictive value for progression in the discovery cohort. (a) Flow diagram showing the procedures to identify the 7‐TAAb panel. (b) Dot plot showing the OD fold change of each TAAb normalized to the cut‐off of normal control. Patients with fold change >1 were defined as positive and are indicated in red, while patients with fold change <1 were defined as negative and are indicated in blue. The number of positive patients for each TAAb is shown on the left‐hand side. (c) Bar plot showing the median progression‐free survival (mPFS) corresponding to the positive patients of each TAAb. TAAbs with mPFS >120 days are shown in red, while TAAbs with mPFS ≤120 days are shown in blue. (d) Kaplan–Meier plot showing progression‐free survival to compare panel‐positive patients vs. panel‐negative patients in the discovery cohort. The log‐rank test (two‐sided) was used and the hazard ratio (HR) is given
FIGURE 2
FIGURE 2
Evaluation of the predictive value of the 7‐TAAb panel in the validation cohort. (a) Dot plot showing OD fold change normalized to the cut‐off of the normal control of each TAAb in the 7‐TAAb panel. Patients with fold change >1 were defined as positive and are indicated in red, while patients with fold change <1 were defined as negative and are indicated in blue. The number of positive patients for each TAAb is shown on the left‐hand side. (b) Stacked bar plot showing the comparison of the 7‐TAAb panel positive rate between the discovery and validation cohorts. (c and d) Kaplan–Meier plot showing progression‐free survival to compare panel‐positive patients vs. panel‐negative patients in the validation cohort (C) and overall population (D). The log‐rank test (two‐sided) was used and the hazard ratio (HR) is given. (e) Stacked bar plot showing the comparison of patient percentages classified by different best responses between panel‐positive and panel‐negative patients. PD, progressive disease; SD, stable disease; PR, partial response. The chi‐square exact test was used to compare the difference (**p < 0.01)
FIGURE 3
FIGURE 3
Evaluation of the predictive value of the 7‐TAAb panel in different subgroups of the overall population. (a–h) Kaplan–Meier plots showing the progression‐free survival to compare panel‐positive patients vs. panel‐negative patients in subgroups classified as monotherapy (a), combination therapy (b), subsequent‐line therapy (c), first‐line therapy (d), squamous cell carcinoma (e), nonsquamous cell carcinoma (f), with driver mutations (g), and without driver mutations (h). The log‐rank test (two‐sided) was used and the hazard ratio (HR) is given. (i) Forest plot showing the summary of HR, median progression‐free survival (mPFS) and p value of each intrasubgroup comparison
FIGURE 4
FIGURE 4
Identification of a 4‐TAAb panel to predict the occurrence of irAEs. (a) Pie chart showing the distribution of all NSCLC patients with or without irAEs. (b) Bar plot showing the minus log2 p value of the chi‐square test for the statistical association between the positivity of the 4‐TAAb panel and irAE occurrence. Different combinations of 4‐TAAb panels are listed on the right‐hand side. (c) Stacked bar plot showing the comparison of patient percentage with irAE for panel‐positive and panel‐negative patients. Fisher's exact test was used to compare irAE incidence (**p < 0.01)
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References

    1. Mao Y, Yang D, He J, Krasna MJ. Epidemiology of lung cancer. Surg Oncol Clin N Am. 2016;25:439–45. - PubMed
    1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71:209–49. - PubMed
    1. Reck M, Rabe KF. Precision diagnosis and treatment for advanced non‐small‐cell lung cancer. N Engl J Med. 2017;377:849–61. - PubMed
    1. Travis WD, Brambilla E, Nicholson AG, Yatabe Y, Austin JHM, Beasley MB, et al. The 2015 World Health Organization classification of lung tumors: impact of genetic, clinical and radiologic advances since the 2004 classification. J Thorac Oncol. 2015;10:1243–60. - PubMed
    1. Shields MD, Marin‐Acevedo JA, Pellini B. Immunotherapy for advanced non–small cell lung cancer: a decade of progress. Am Soc Clin Oncol Educ Book. 2021;41:e105–27. 10.1200/EDBK_321483 - DOI - PubMed

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