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

Sep 22, 2026

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A key clinical challenge in prostate cancer is identifying and validating biomarkers with high specificity for indolent long-term outcomes. A novel statistical approach was applied to discover tumor transcriptomic biomarkers that optimally predict patients with low metastatic potential. Whole-transcriptome data from two cohorts compared lethal cases and long-term metastasis-free indolent cases using a weighted logistic lasso model with pAUC optimization. The study produced a 40-gene signature with stronger discrimination across risk groups, enhancing both pAUC and overall AUC.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages8

Reading Time20 min

AccessFree to download

Abstract

Purpose

Methods

Results

Conclusion

Introduction

Clinical challenge and unmet need

Risk stratification limits

The Prostate ORIGINAL ARTICLE A Gene Expression Tumor Signature Optimizing Partial Area‐Under‐the‐Curve (pAUC) to Improve Specificity for Indolent Prostate Cancer Travis A. Gerke1,2 | Svitlana Tyekucheva3,4 | Konrad H. Stopsack2,5 | Christopher J. Sweeney6 | Rosina T. Lis7 | Elizabeth N. Westling2,8 | Massimo Loda9, 10, 11 | Meir J. Stampfer2 | Kathryn L. Penney2 | Giovanni Parmigiani3,4 | Lorelei A. Mucci2 1Prostate Cancer Clinical Trials Consortium, Memorial Sloan Kettering Cancer Center, New York, New York, USA | 2Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA | 3Department of Data Science, Dana‐Farber Cancer Institute, Boston, Massachusetts, USA | 4Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA | 5Department of Epidemiologic Methods and Etiologic Research, Leibniz Institute for Prevention Research and Epidemiology – BIPS, Bremen, Germany | 6South Australian Immunogenomics Cancer Center, University of Adelaide, Adelaide, Australia | 7Department of Medical Oncology, Dana‐Farber Cancer Institute, Boston, Massachusetts, USA | 8Elson S. Floyd College of Medicine, Washington State University, Spokane, Washington, USA | 9Department of Pathology, Weill Cornell Medical Center, New York, New York, USA | 10Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK | 11Dana‐Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA Correspondence: Lorelei A. Mucci ([lmucci@hsph.harvard.edu](lmucci@hsph.harvard.edu)) Received: 12 September 2025 | Revised: 21 March 2026 | Accepted: 16 April 2026 Funding: DF/HCC Specialized Programs in Research Excellence (SPORE) in Prostate Cancer, Grant/Award Number: 2P50CA090381; National Cancer Institute, Grant/Award Number: T32 CA009001 Keywords: gene expression | indolent prostate cancer | partial area‐under‐the‐curve | prognostic biomarkers ABSTRACT 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. 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) . | Travis A. Gerke and Svitlana Tyekucheva contributed equally to first authorship.
Kathryn L. Penney, Giovanni Parmigiani, and Lorelei A. Mucci contributed equally to senior authorship. | | --- | | This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License, which permits use and distribution in any medium, provi

FAQs

What problem does the study address in indolent prostate cancer?

It targets the difficulty of identifying biomarkers that maintain high specificity for indolent, long-term outcomes rather than merely detecting aggressive disease.

How was the predictive biomarker signature developed?

Using tumor whole-transcriptome data from two cohorts, the researchers applied a pAUC-optimized method that up-weighted indolent cases with a predefined 80%–100% specificity range, leveraging weighted logistic lasso regression and cross-validation to reduce overfitting.

What performance improvements did the 40-gene signature show?

Compared with Gleason grade groups, the signature improved pAUC within the 80%–100% specificity range by 1.72-fold and increased overall AUC from 0.85 to 0.93, with strong discrimination among intermediate Gleason 7 tumors.

Sep 22, 2026

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