Title: Expression status and prognostic value of PDL1, FOXP3, CD8 and Ki67 immunohistochemical expression in clear cell renal cell carcinoma

Authors: Gunes Guner1; Margaret Cocks1; Diana Taheri1; Mark W. Ball2; Stephania M. Bezerra1; Alan Meeker1; Maria del Carmen Rodriguez1; Alcides Chaux1,4; Arthur Burnett1; George Netto5;

Affiliations: Departments of 1Pathology, 2Urology and 3Oncology, The Johns Hopkins Medical Institutions, Baltimore, MD; 4Centro para el Desarrollo de la Investigación Científica (CEDIC), Asuncion, PRY; 5Department of Pathology, The University of Alabama at Birmingham, Birmingham, AL

Last update: 2017-02-07

Methodology

The dataset included 112 patients corresponding to 70 cases with primary ccRCC and 42 cases with metastatic ccRCC. Matched nontumor tissue was available for primary tumors. The analysis of PDL1, FOXP3 and CD8/Ki67 expression was carried out at the TMA level, including 694 TMA spots built from their corresponding tumors.

 

Evaluation of expression

Two tissue compartments were considered for analysis: parenchyma and stroma. For nontumor tissues, parenchymal cells included the epithelial cells, while for tumors (primary and metastatic) parenchymal cells included the tumor cells. Stroma refers to the loose connective tissue adjacent to the parenchyma. Similarly, intraparenchymal lymphocytes refers to lymphocytes permeating the nontumor epithelium or the tumor cells, depending on the parenchyma.

For each marker, the methodology for evaluation of the immunohistochemical expression was as follows:

  • PDL1: PDL1 was evaluated in parenchymal and stromal cells, noting the percentage of positive cells.
  • FOXP3, CD8, and Ki67: The expression of these markers was evaluated in intraparenchymal and stromal lymphocytes, counting the number of positive lymphocytes.

In all cases, the median value of all TMA spots was selected to summarize the expression levels of the marker under evaluation. Thus, for each patient, we had a median value per marker across all sampled tissues.

 

Data analysis

Data analysis was carried out using 3 approaches: descriptive analysis, association analysis, and outcome analysis. Data was analyzed and plots were generated using R version 3.3.2 (2016-10-31) from the R Foundation for Statistical Computing (Vienna, Austria). R packages from the tidyverse were extensively used.

 

Descriptive analysis

Categorical variables were described using frequency tables and barplots. Numerical variables were described using measurements of central tendency and dispersion, histograms, and density plots.

 

Association analysis

Marker values were compared considering clinical, pathologic, and outcome features. In this context, marker values were considered as the outcome variables and the aforementioned features as the predictor variables. Variables were described using measurements of central tendency and dispersion, boxplots, and density plots.

The association was evaluated using either the Mann-Whitney U test for the sum of ranks or the Kruskal-Wallis rank sum test, depending on the features of the predictor variables.

Patients’ age was categorized in younger vs. older age groups using the median age as the cutoff point. Similarly, tumor size was categorized in smaller vs. larger size groups using the median size as the cutoff point.

 

Outcome analysis

The outcome analysis included regression modeling and time-to-event (survival) analysis. Markers levels were categorized as high/kow expression using the median as the cutoff point. Outcome variables included tumor progression, overall mortality, and cancer-related mortality.

Odds ratios were estimated using unconditional binary logistic regression. Hazard ratios were estimated using Cox’s proportional hazards regression. Survival curves were built using the Kapplan-Meier estimator and compared using the log-rank test.