Microenvironment-Inferred Genotyping: An Exclusionary Classifier for EGFR Amplification When DNA Testing Fails

Abstract

EGFR amplification occurs in approximately 40-50% of glioblastoma (GBM) cases and is critical for treatment selection [1]. However, GBM tissue samples frequently yield insufficient material for comprehensive molecular testing due to extensive necrosis and tissue quality limitations [2]. This affects thousands of patients annually in the United States [4]. We developed a microenvironment-inferred genotyping approach, enabling molecular classification by measuring the "oligodendrocyte desert" effect when direct genetic testing is impossible. Using single-cell RNA-seq data from 102 GBM patients (1.47M cells) [19], we identified oligodendrocyte exclusion patterns associated with EGFR amplification. We developed a 13-feature machine learning classifier and computationally validated it across an independent external cohort (CPTAC, n=96) and cross-pipeline technical validation using TCGA data processed through three distinct bioinformatic pipelines (n=148 patients) [20,21,22,23]. EGFR-amplified tumors created detectable oligodendrocyte deserts (60-70% depletion, p<0.001). Our exclusionary classifier achieved AUC 0.845 in discovery cohorts and 0.756 average across validation analyses (n=244 unique patients). With a positive predictive value of 94%, this tool identifies high-confidence candidates for EGFR-targeted therapies who would otherwise be excluded from treatment. To our knowledge, this is the first algorithm enabling microenvironment-inferred genotyping from routine RNA-seq data, providing rescue diagnosis for EGFR classification when DNA testing fails.