A Gene Expression Tumor Signature Optimizing Partial Area-Under-the-Curve (pAUC) to Improve Specificity for Indolent...

A key clinical challenge in prostate cancer is the identification and validation of biomarkers with high specificity for indolent long-term outcomes. We applied a novel statistical method to identify tumor transcriptomic biomarkers that optimally predicted patients with low metastatic potential.

Using tumor whole-transcriptome data from the Health Professionals Follow-up Study (HPFS, discovery set) and Physicians' Health Study (PHS, validation set), we compared patients who died of prostate cancer or developed metastases ("lethal," n = 113) and patients with > 8 years of metastasis-free survival ("indolent," n = 291). Whole transcriptome tumor gene expression data were generated using an Affymetrix array. We applied a novel method for optimizing a partial area under the curve (pAUC) that up-weighted indolent cases with a predefined 80%-100% specificity. This method leverages weighted logistic lasso regression, with weights chosen via cross-validation to reduce overfitting.

Median age at cancer diagnosis was 66 years; median follow-up for outcomes was 14 years. We identified a 40-gene transcriptome signature of indolent prostate cancer, which, compared to Gleason grade groups, improved the pAUC over the predefined 80%-100% specificity range by 1.72-fold (p < 0.001) and improved overall AUC from 0.85 to 0.93 (p < 0.001). The signature improved positive predictive value for indolent tumors > 2-fold with minimal decrease in negative predictive value. Importantly, the 40-gene signature showed high discrimination among intermediate Gleason 7 tumors (Grade groups 2 and 3, AUC 0.88, 95% CI: 0.79-0.95).

Incorporating pAUC into prognostic signature development improved identification of prostate tumors with low risk of metastatic potential. Its clinical application may help reduce overtreatment and overdiagnosis of indolent prostate cancers, and the pAUC may be relevant beyond prostate cancer.