Reaching the limits of prognostication in non-small cell lung cancer: an optimized biomarker panel fails to outperform clinical parameters

Abstract

Numerous protein biomarkers have been analyzed to improve prognostication in non-small cell lung cancer, but have not yet demonstrated sufficient value to be introduced into clinical practice. Here, we aimed to develop and validate a prognostic model for surgically resected non-small cell lung cancer. A biomarker panel was selected based on (1) prognostic association in published literature, (2) prognostic association in gene expression data sets, (3) availability of reliable antibodies, and (4) representation of diverse biological processes. The five selected proteins (MKI67, EZH2, SLC2A1, CADM1, and NKX2-1 alias TTF1) were analyzed by immunohistochemistry on tissue microarrays including tissue from 326 non-small cell lung cancer patients. One score was obtained for each tumor and each protein. The scores were combined, with or without the inclusion of clinical parameters, and the best prognostic model was defined according to the corresponding concordance index (C-index). The best-performing model was subsequently validated in an independent cohort consisting of tissue from 345 non-small cell lung cancer patients. The model based only on protein expression did not perform better compared to clinicopathological parameters, whereas combining protein expression with clinicopathological data resulted in a slightly better prognostic performance (C-index: all non-small cell lung cancer 0.63 vs 0.64; adenocarcinoma: 0.66 vs 0.70, squamous cell carcinoma: 0.57 vs 0.56). However, this modest effect did not translate into a significantly improved accuracy of survival prediction. The combination of a prognostic biomarker panel with clinicopathological parameters did not improve survival prediction in non-small cell lung cancer, questioning the potential of immunohistochemistry-based assessment of protein biomarkers for prognostication in clinical practice.

Expression of scavenger receptor MARCO defines a targetable tumor‐associated macrophage subset in non‐small cell lung cancer

“…Following adjustment for gender, age at surgery, pathological stage, smoking history and performance status, there was no evidence of differences in all‐cause mortality between densities of positive macrophages for any of the four markers (Supporting Information, Table S3). As expected, pathological stage, smoking status and performance status were all independent predictors of all‐cause mortality . For patients with squamous cell carcinoma, a tendency for worse overall survival was observed for patients with high infiltration of MARCO‐positive TAMs only (Supporting Information, Fig.…”

“…As expected, pathological stage, smoking status and performance status were all independent predictors of all-cause mortality. 36 For patients with squamous cell carcinoma, a tendency for worse overall survival was observed for patients with high infiltration of MARCO-positive TAMs only (Supporting Information, Fig. S1).…”

Integrative analysis of genome-wide gene copy number changes and gene expression in non-small cell lung cancer

“…Despite of the significant association of the identified genes with survival it should be noted that their practical usefulness in clinical routine should be discussed with caution. Limitations of molecular prognostic biomarkers were recently demonstrated in a study testing the value of a panel of 5 immunohistochemical biomarkers [ 64 ] to predict NSCLC patient survival. Although the highly optimized panel showed a strong association with prognosis alone, it did not add significant prognostic information to the combination of traditional clinical factors (age, stage and performance status).…”

Highlight report: Limits of prognostication of non-small cell lung cancer

“…Recently, Patrick Micke and colleagues from Uppsala University have contributed an outstanding publication on the limitations to predict prognosis in non-small cell lung cancer (Grinberg et al, 2017[ 6 ]). For this purpose the authors used two cohorts of 354 and 357 patients, respectively, that were analyzed by immunostaining.…”

“…Next, the authors studied the association of the combined five protein factors to clinicopathological data. Interestingly, the model based on protein expression alone did not outperform the model based only on the clinicopathological parameters (Grinberg et al, 2017[ 6 ]). Combining protein expression with clinicopathological data did not lead to a significantly improved accuracy of survival prediction.…”