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

Area of Science:

• Oncology
• Genomics
• Biostatistics

Background:

• Prostate cancer biomarker identification for indolent outcomes is a clinical challenge.
• Accurate prediction of low metastatic potential is crucial for patient management.
• Current methods require improvement for specificity in identifying indolent disease.

Purpose of the Study:

• To apply a novel statistical method for identifying tumor transcriptomic biomarkers.
• To optimally predict patients with low prostate cancer metastatic potential.
• To validate these biomarkers using independent patient cohorts.

Main Methods:

• Utilized whole-transcriptome data from the Health Professionals Follow-up Study (HPFS) and Physicians' Health Study (PHS).
• Compared patients with lethal (metastases or death) vs. indolent (>8 years metastasis-free) prostate cancer.
• Applied weighted logistic lasso regression optimizing partial area under the curve (pAUC) for indolent cases.

Main Results:

• Identified a 40-gene transcriptome signature for indolent prostate cancer.
• The signature significantly improved pAUC (1.72-fold) and overall AUC (0.85 to 0.93) compared to Gleason grade groups.
• Demonstrated high discrimination for intermediate Gleason 7 tumors (AUC 0.88).

Conclusions:

• Incorporating pAUC optimization enhances prognostic signature development for low-metastatic-risk prostate tumors.
• Clinical application can reduce overtreatment and overdiagnosis of indolent prostate cancers.
• The pAUC method shows potential relevance beyond prostate cancer prognostication.