Analysis of expression of the PD-1/PD-L1 immune checkpoint system and its prognostic impact in gastroenteropancreatic neuroendocrine tumors.
Miguel Sampedro-NúñezAna Serrano-SomavillaMagdalena AdradosJosé M Cameselle-TeijeiroConcepción Blanco-CarreraJosé Manuel Cabezas-AgricolaRebeca Martínez-HernándezElena Martín-PérezJosé Luis Muñoz de NovaJosé Ángel DíazRogelio García-CentenoJavier Caneiro-GómezIhab AbdulkaderRoberto González-AmaroMónica MarazuelaPublished in: Scientific reports (2018)
The immune checkpoint based therapy targeting the programmed death-1 (PD-1) receptor and its PD-L1 ligand has recently been approved for the therapy of different malignant conditions, but not yet for gastroenteropancreatic neuroendocrine tumors (GEP-NETs). In this context, we evaluated the expression of PD-1 and PD-L1 in GEP-NETs and its potential correlations with clinical outcomes. Expression of PD-1/PD-L1 was analyzed by immunohistochemistry in 116 GEP-NETs and 48 samples of peritumoral tissue. In addition, the expression of these molecules was assessed by flow cytometry in peripheral blood mononuclear cells (PBMC) from patients with GEP-NETs (n = 32) and healthy controls (n = 32) and in intratumoral mononuclear cells (TMCs) (n = 3). Expression of PD-L1 and PD-1 was detected by immunohistochemistry in 6% and 1% of tumor tissue samples, respectively, and in 8% of peritumoral tissue samples, for both markers. We also observed that PD-1 expression by TMCs was associated with metastatic disease at diagnosis, and the levels of circulating PD-1+ PBMCs were associated with progressive disease upon follow-ups. In addition, circulating PD-1+ PBMCs were significantly correlated with PD-L1 expression by tumor cells. Our data suggest that PD-1/PD-L1 is expressed in 1 to 8% of GEP-NETs, and that this feature is significantly associated with disease evolution (p < 0.01).
Keyphrases
- poor prognosis
- neuroendocrine tumors
- binding protein
- long non coding rna
- squamous cell carcinoma
- multiple sclerosis
- small cell lung cancer
- machine learning
- signaling pathway
- induced apoptosis
- deep learning
- electronic health record
- cell proliferation
- drug delivery
- oxidative stress
- big data
- artificial intelligence
- peripheral blood
- smoking cessation
- replacement therapy
- cell therapy