Prognostic Value of Systemic Inflammatory Biomarkers in Patients with Metastatic Renal Cell Carcinoma.
Guilherme Nader MartaPedro Isaacsson VelhoRenata R C BonadioMirella NardoSheila F FarajManoel Carlos L de Azevedo SouzaDavid Q B MunizDiogo Assed BastosCarlos DzikPublished in: Pathology oncology research : POR (2020)
Metastatic renal cell carcinoma (mRCC) encompasses a heterogeneous group of neoplasms with distinct clinical behavior and prognoses. As a result of the increasing number of therapeutic options in the metastatic setting, it is crucial to improve prognostic stratification ability. We aimed to evaluate the prognostic value of neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and combination platelet count and neutrophil lymphocyte ratio (COP-NLR) in patients with mRCC. We evaluated a cohort of mRCC patients treated with first-line pazopanib or sunitinib. Levels of NLR, PLR and COP-NLR were measured prior to systemic treatment and evaluated as prognostic predictors. Primary endpoint was overall survival (OS). Data from 276 patients were included, of which 54.7% received first-line pazopanib and 45.3%, sunitinib. Memorial Sloan-Kettering Cancer Center risk classification was intermediate and poor in 50% and 42.6% of patients, respectively. High NLR (> 3.5) was associated with inferior OS (median 9.6 vs 17.8 months, P < 0.001). A high PLR (> 200) was associated with inferior OS (median 10.3 vs 17 months, P = 0.002). The median OS in the COP-NLR 1, 2 and 3 groups were 19.0 months (95% CI 15.3-26.0), 13.1 months (95% CI 9.8-17.0) and 7.4 months (95% CI 3.6-11.9), respectively (P < 0.001). In the multivariate analysis, high NLR and high COP-NLR were associated with inferior OS. Both high NLR and high COP-NLR were associated with poorer OS in our cohort of patients with mRCC treated with first-line pazopanib or sunitinib.
Keyphrases
- metastatic renal cell carcinoma
- end stage renal disease
- newly diagnosed
- ejection fraction
- chronic kidney disease
- machine learning
- small cell lung cancer
- squamous cell carcinoma
- peripheral blood
- renal cell carcinoma
- prognostic factors
- deep learning
- oxidative stress
- big data
- data analysis
- patient reported outcomes
- artificial intelligence
- young adults
- replacement therapy
- lymph node metastasis