4.7 Article

Dynamic serum biomarkers to predict the efficacy of PD-1 in patients with nasopharyngeal carcinoma

Journal

CANCER CELL INTERNATIONAL
Volume 21, Issue 1, Pages -

Publisher

BMC
DOI: 10.1186/s12935-021-02217-y

Keywords

Biomarker; PD-1; Nasopharyngeal carcinoma; ICB; Dynamic monitor

Categories

Funding

  1. National Postdoctoral Program for Innovative Talents [BX20200399]
  2. China Postdoctoral Science Foundation [2021M693653]
  3. Natural Science Foundation of Guangdong Province [2018A030313622]
  4. Guangdong Science and Technology Program [2019A030317003]
  5. Science and Technology Planning Project of Guangzhou City [201904010153]

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This study aimed to explore reliable and minimally invasive prognostic indicators for predicting the efficacy of PD-1 inhibitors combination therapy in recurrent or metastatic nasopharyngeal carcinoma (RM-NPC). A risk score prediction model was constructed based on dynamic changes in LDH and AST/ALT, which showed predictive and prognostic value for NPC patients treated with PD-1 inhibitors. The high-risk group identified by the model had shorter progression-free survival compared to the low-risk group, demonstrating the potential utility of dynamic serum markers in predicting treatment outcomes in RM-NPC patients.
Background There is a lack of effective treatments for recurrent or metastatic nasopharyngeal carcinoma (RM-NPC). Furthermore, the response rate of NPC patients to programmed death 1 (PD-1) inhibitors is approximately 20% to 30%. Thus, we aimed to explore reliable and minimally invasive prognostic indicators to predict the efficacy of PD-1 inhibitors combination therapy in RM-NPC. Methods The serum markers of 160 RM-NPC patients were measured before and three weeks after the first anti-PD-1 treatment. The least absolute shrinkage and selection operator (LASSO) logistic regression was carried out to select dynamic serum indicators and construct a prediction model. Furthermore, we carried out univariate, multivariate, nomogram and survival analyses to identify independent prognostic factors that were associated with 1-year progression-free survival (PFS). Results Based on two markers that were screened by Lasso logistic regression, we constructed a risk score prediction model for the prediction of anti-PD-1 efficacy at 8-12 weeks with an AUC of 0.737 in the training cohort and 0.723 in the validation cohort. Risk score and metastases were included in the nomogram, and the Kaplan-Meier survival curves demonstrated that the high-risk group has shorter PFS compared to the low-risk group. The concordance index (C-index) of the nomogram for PFS is higher than that of the TNM stage in the training and validation cohort. Conclusion We proposed a strategy to monitor dynamic changes in the biochemistry markers and emphasized their importance as potential prognostic biomarkers for the treatment of advanced NPC treated with PD-1 inhibitors. Our risk score prediction model was based on the dynamic change of LDH and AST/ALT, which has predictive and prognostic value for NPC patients who were treated with PD-1 inhibitors.

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