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Background <p>Early and late hepatocellular carcinoma (HCC) recurrences, which are driven by residual and&#xa0;de novo&#xa0;tumors, respectively, differ in biology and potential treatment susceptibility. The effectiveness of adjuvant PD-1 inhibitors (aPD-1) across these distinct recurrence patterns remains unclear.</p> Patients and Methods <p>A total of 3436 cases were included from 9 centers in China. Among these, 1662 patients (2019–2023) were propensity score-matched to compare the effects of aPD-1 versus active surveillance on survival outcomes. Machine learning (ML) models were trained on 1774 patients (2014–2018) to predict early recurrence (≤ 2 years). The efficacy of aPD-1 was further assessed in ML-predicted subgroups, using a 2-year landmark analysis to evaluate its effect on recurrence etiology.</p> Results <p>The logistic regression model achieved the best performance, with an AUC of 0.818 in the test set. Patients classified as high risk showed significantly poorer disease-free survival (DFS) and overall survival. aPD-1 significantly improved DFS in high-risk patients (HR 0.69, 95% CI 0.57–0.84, <i>p</i> &lt; 0.001), but no benefit was observed in low-risk patients (HR 0.99, 95% CI 0.71–1.39, <i>p</i> = 0.950). Landmark analysis further demonstrated that aPD-1 preferentially reduced early recurrence in high-risk HCC (HR 0.67, 95% CI 0.54–0.83, <i>p</i> &lt; 0.001), while showing limited efficacy against late recurrence (HR 0.81, 95% CI 0.49–1.31, <i>p</i> = 0.380).</p> Conclusions <p>aPD-1 demonstrates time-dependent efficacy, primarily reducing early recurrence through controlling residual micro-metastases rather than preventing&#xa0;de novo&#xa0;tumors. A ML model accurately identified patients most likely to benefit, highlighting the importance of risk-guided adjuvant therapy.</p>

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Long-Term Survival and Beneficiaries of Adjuvant Anti-PD-1 Therapy in Resected Hepatocellular Carcinoma

  • 期刊论文

  • 发表日期：

    2025年10月25日

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  • Bin Guo,
  • Chu Luo,
  • Yuanxiang Lu,
  • Yang Wu,
  • Shenjie Xie,
  • Feng Xia,
  • Yonglin Hua,
  • Jinghan Zhu,
  • Guangzhen Cai,
  • Hong Zhu,
  • Hui Zhang,
  • Yongyi Zeng,
  • Fan Zhang,
  • Changzhen Shang,
  • Enyu Liu,
  • Guandou Yuan,
  • Wanguang Zhang

摘要

Background

Early and late hepatocellular carcinoma (HCC) recurrences, which are driven by residual and de novo tumors, respectively, differ in biology and potential treatment susceptibility. The effectiveness of adjuvant PD-1 inhibitors (aPD-1) across these distinct recurrence patterns remains unclear.

Patients and Methods

A total of 3436 cases were included from 9 centers in China. Among these, 1662 patients (2019–2023) were propensity score-matched to compare the effects of aPD-1 versus active surveillance on survival outcomes. Machine learning (ML) models were trained on 1774 patients (2014–2018) to predict early recurrence (≤ 2 years). The efficacy of aPD-1 was further assessed in ML-predicted subgroups, using a 2-year landmark analysis to evaluate its effect on recurrence etiology.

Results

The logistic regression model achieved the best performance, with an AUC of 0.818 in the test set. Patients classified as high risk showed significantly poorer disease-free survival (DFS) and overall survival. aPD-1 significantly improved DFS in high-risk patients (HR 0.69, 95% CI 0.57–0.84, p < 0.001), but no benefit was observed in low-risk patients (HR 0.99, 95% CI 0.71–1.39, p = 0.950). Landmark analysis further demonstrated that aPD-1 preferentially reduced early recurrence in high-risk HCC (HR 0.67, 95% CI 0.54–0.83, p < 0.001), while showing limited efficacy against late recurrence (HR 0.81, 95% CI 0.49–1.31, p = 0.380).

Conclusions

aPD-1 demonstrates time-dependent efficacy, primarily reducing early recurrence through controlling residual micro-metastases rather than preventing de novo tumors. A ML model accurately identified patients most likely to benefit, highlighting the importance of risk-guided adjuvant therapy.