Intratumoral heterogeneity characterized by pretreatment PET in non-small cell lung cancer patients predicts progression-free survival on EGFR tyrosine kinase inhibitor

Abstract

Intratumoral heterogeneity has been suggested to be an important resistance mechanism leading to treatment failure. We hypothesized that radiologic images could be an alternative method for identification of tumor heterogeneity. We tested heterogeneity textural parameters on pretreatment FDG-PET/CT in order to assess the predictive value of target therapy. Recurred or metastatic non-small cell lung cancer (NSCLC) subjects with an activating EGFR mutation treated with either gefitinib or erlotinib were reviewed. An exploratory data set (n = 161) and a validation data set (n = 21) were evaluated, and eight parameters were selected for survival analysis. The optimal cutoff value was determined by the recursive partitioning method, and the predictive value was calculated using Harrell’s C-index. Univariate analysis revealed that all eight parameters showed an increased hazard ratio (HR) for progression-free survival (PFS). The highest HR was 6.41 (P<0.01) with co-occurrence (Co) entropy. Increased risk remained present after adjusting for initial stage, performance status (PS), and metabolic volume (MV) (aHR: 4.86, P<0.01). Textural parameters were found to have an incremental predictive value of early EGFR tyrosine kinase inhibitor (TKI) failure compared to that of the base model of the stage and PS (C-index 0.596 vs. 0.662, P = 0.02, by Co entropy). Heterogeneity textural parameters acquired from pretreatment FDG-PET/CT are highly predictive factors for PFS of EGFR TKI in EGFR-mutated NSCLC patients. These parameters are easily applicable to the identification of a subpopulation at increased risk of early EGFR TKI failure. Correlation to genomic alteration should be determined in future studies.

HOTAIR lncRNA SNPs rs920778 and rs1899663 are associated with smoking, male gender, and squamous cell carcinoma in a Chinese lung cancer population

“…Squamous cell carcinoma generally occurs in male and smoking patients. However the driver genes involved remain unclear [3][4][5][6]. Efficacious targeted therapy was achieved in lung adenocarcinoma via the specific targeting of driver genes, such as EGFR and ALK.…”

What can artificial intelligence teach us about the molecular mechanisms underlying disease?

“…With EGFR, findings are conflicting, with some studies predominantly showing high FDG uptake in EGFRmutated tumours, reflecting increased glycolysis through AKT signalling [47], and others showing lower uptake [48]. As well as standard features, such as SUVmax, heterogeneity parameters also show associations with EGFR mutation status [49,50] and value in predicting response and survival following treatment with TKIs [51,52]. Less specific associations between FDG PET radiomics and genetic heterogeneity, but not tumour mutational burden, have been shown in SCLC but not in NSCLC [53].…”

Chemoresistance of Lung Cancer Cells: 2D and 3D In Vitro Models for Anticancer Drug Screening

“…Both genotypic and phenotypic heterogeneity are associated with chemoresistance that have a profound impact on the clinical outcome of lung cancer patients. For instance, at the clinical level, it has been recently suggested that increased metabolic heterogeneity should be considered as a high-risk subpopulation for early EGFR TKI failure [4].…”