Urinary Peptidomic Signatures Predict Overall and Progression-Free Survival in Bladder Cancer.

Area of Science:

• Biochemistry
• Oncology
• Proteomics

Background:

• Current bladder cancer (BC) prognostic tools offer limited accuracy and fail to capture molecular details.
• There is a need for non-invasive methods to accurately assess BC prognosis and guide treatment decisions.

Purpose of the Study:

• To identify urinary peptide signatures associated with bladder cancer (BC) patient survival using capillary electrophoresis-mass spectrometry (CE-MS).
• To develop and validate a novel urine-based classifier for non-invasive BC risk stratification.

Main Methods:

• Capillary electrophoresis-mass spectrometry (CE-MS) was employed to analyze urine samples from BC patients.
• A support vector machine (SVM) classifier (BC110) was developed using 110 survival-associated peptides identified in a discovery cohort.
• The BC110 classifier's performance was validated in an independent cohort.

Main Results:

• A total of 114 survival-associated peptides were identified, leading to the development of the BC110 classifier.
• The BC110 classifier demonstrated an AUC of 0.78 (p = 0.03) in the validation cohort, indicating significant prognostic accuracy.
• Functional analysis revealed that the peptide panel is enriched in extracellular matrix (ECM) remodeling, collagen pathways, coagulation, and complement activation, suggesting active tumor-stroma interactions.

Conclusions:

• The urine-based BC110 classifier provides a non-invasive method for bladder cancer (BC) risk stratification.
• This classifier may enhance current prognostic tools by identifying high-risk patients for adjuvant therapy and low-risk patients for reduced surveillance.
• The findings suggest a potential reduction in the reliance on invasive procedures like cystoscopy for patient management.