PD-L1 Expression in Non-Small Cell Lung Cancer: Data from a Referral Center in Spain.
Karmele Saez de GordoaIngrid LopezMarta MarginetBerta ColomaGerard FrigolaNaiara VegaDaniel MartinezCristina TeixidóPublished in: Diagnostics (Basel, Switzerland) (2021)
Anti-programmed cell death (PD1)/ligand-1 (PD-L1) checkpoint inhibitors have improved the survival of non-small cell lung cancer (NSCLC) patients. Additionally, PD-L1 has emerged as a predictive biomarker of response. Our goal was to examine the histological features of all PD-L1 cases of NSCLC analyzed in our center between 2017 and 2020, as well as to correlate the expression values of the same patient in different tested samples. PD-L1 immunohistochemistry (IHC) was carried out on 1279 external and internal samples: 482 negative (tumor proportion score, TPS < 1%; 37.7%), 444 low-expression (TPS 1-49%; 34.7%) and 353 high-expression (TPS ≥ 50%; 27.6%). Similar results were observed with samples from our institution (N = 816). Significant differences were observed with respect to tumor histological type (p = 0.004); squamous carcinoma was positive in a higher proportion of cases than other histological types. There were also differences between PD-L1 expression and the type of sample analyzed (surgical, biopsy, cytology; p < 0.001), with a higher frequency of negative cytology. In addition, there were cases with more than one PD-L1 determination, showing heterogeneity. Our results show strong correlation with the literature data and reveal heterogeneity between tumors and samples from the same patient, which could affect eligibility for treatment with immunotherapy.
Keyphrases
- poor prognosis
- high grade
- small cell lung cancer
- fine needle aspiration
- single cell
- end stage renal disease
- ultrasound guided
- binding protein
- chronic kidney disease
- systematic review
- ejection fraction
- newly diagnosed
- cell cycle
- prognostic factors
- primary care
- long non coding rna
- low grade
- cell proliferation
- genome wide
- peritoneal dialysis
- oxidative stress
- gene expression
- machine learning
- combination therapy
- simultaneous determination