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Background <p>Postoperative pancreatic fistula (POPF) is the major driver of postoperative morbidity after pancreatoduodenectomy (PD) and a healthcare issue. In patients with pancreatic tumors the occurrence of POPF could lead to a complete failure of the oncologic strategy by delaying or annihilating the delivery of the indicated adjuvant chemotherapy. However, current preoperative prediction models lack precision. This study aimed to determine the ability of a high dimensional analysis of the patient’s peripheral immune system before PD to predict POPF.</p> Methods <p>Twenty-two patients in the prospective IMMUNOPANC trial (NCT03978702) underwent PD. Blood samples collected preoperatively were analyzed by combining single-cell mass cytometry and a sparse machine-learning pipeline, Stabl, to identify the most relevant POPF-predictive features. The logistic regression model output was evaluated using a five-fold cross-validation procedure.</p> Results <p>Eight (36%) patients experience POPF (grade B, n = 7; grade C, n = 1). The multivariable predictive model includes 11 features—six natural killer, three CD8<sup>+</sup> T, and two CD4<sup>+</sup> T lymphocyte cell clusters—revealing a preoperative POPF lymphocyte signature (Pancreatic Fistula Lymphocyte Signature, PFLS). The Stabl algorithm identifies a predictive model classifying POPF patients with high performance (area under the receiver operating characteristic curve=0.81, <i>P</i> = 2.04e-02).</p> Conclusions <p>In summary, preoperative circulating immune-cell composition can predict POPF in patients undergoing PD. The clinical application of the PFLS may enable the early identification of patients at high risk before pancreatic surgery, giving clinicians the opportunity to anticipate and mitigate POPF risk through tailored strategies in pre-, intra-, and post-operative settings.</p> <p></p>

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Preoperative lymphocyte signature predicts pancreatic fistula after pancreatoduodenectomy

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月11日

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  • Jonathan Garnier,
  • Grégoire Bellan,
  • Anaïs Palen,
  • Xavier Durand,
  • Jacques Ewald,
  • Amira Ben Amara,
  • Marie-Sarah Rouvière,
  • Benjamin Choisy,
  • Franck Verdonk,
  • Brice Gaudilliere,
  • Caroline Gouarné,
  • Olivier Turrini,
  • Daniel Olive,
  • Anne-Sophie Chrétien

摘要

Background

Postoperative pancreatic fistula (POPF) is the major driver of postoperative morbidity after pancreatoduodenectomy (PD) and a healthcare issue. In patients with pancreatic tumors the occurrence of POPF could lead to a complete failure of the oncologic strategy by delaying or annihilating the delivery of the indicated adjuvant chemotherapy. However, current preoperative prediction models lack precision. This study aimed to determine the ability of a high dimensional analysis of the patient’s peripheral immune system before PD to predict POPF.

Methods

Twenty-two patients in the prospective IMMUNOPANC trial (NCT03978702) underwent PD. Blood samples collected preoperatively were analyzed by combining single-cell mass cytometry and a sparse machine-learning pipeline, Stabl, to identify the most relevant POPF-predictive features. The logistic regression model output was evaluated using a five-fold cross-validation procedure.

Results

Eight (36%) patients experience POPF (grade B, n = 7; grade C, n = 1). The multivariable predictive model includes 11 features—six natural killer, three CD8+ T, and two CD4+ T lymphocyte cell clusters—revealing a preoperative POPF lymphocyte signature (Pancreatic Fistula Lymphocyte Signature, PFLS). The Stabl algorithm identifies a predictive model classifying POPF patients with high performance (area under the receiver operating characteristic curve=0.81, P = 2.04e-02).

Conclusions

In summary, preoperative circulating immune-cell composition can predict POPF in patients undergoing PD. The clinical application of the PFLS may enable the early identification of patients at high risk before pancreatic surgery, giving clinicians the opportunity to anticipate and mitigate POPF risk through tailored strategies in pre-, intra-, and post-operative settings.