A Gene Expression Tumor Signature Optimizing Partial Area‐Under‐the‐Curve (pAUC) to Improve Specificity for Indolent Prostate Cancer

作者：Travis Gerke, Svitlana Tyekucheva, Konrad H. Stopsack, Christopher J Sweeney, Rosina T. Lis, Elizabeth N Westling, Massimo Loda, M I Stampfer, Kathryn L Penney, Giovanni Parmigiani, Lorelei A Mucci · 发表于：The Prostate · 年份：2026 · DOI：10.1002/pros.70189 · 研究领域：Prostate Cancer Diagnosis and Treatment、Prostate Cancer Treatment and Research、Ferroptosis and cancer prognosis

PURPOSE: 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. METHODS: 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. RESULTS: 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. Im...