Study on a Prognostic Blood-Based Sphingolipid Panel for Men with Localized Prostate Cancer on Active Surveillance

Academic Background

Prostate cancer is one of the most common cancers among men globally, particularly for those with low- and intermediate-risk prostate cancer, where active surveillance (AS) has become the preferred management strategy. AS involves regular monitoring of disease progression to avoid unnecessary aggressive treatments, thereby reducing treatment-related side effects and quality of life deterioration. However, although AS is clinically safe, a small proportion of patients remain at risk of disease progression, particularly Gleason Grade (GG) upgrading, which may lead to delayed radical treatment. Currently, clinicians primarily rely on invasive biopsies to monitor disease progression, but this method is burdensome for patients and lacks sufficient predictive power.

Previous studies have shown that circulating sphingolipid levels are associated with prostate cancer progression. Sphingolipids are a class of lipid molecules involved in cell membrane composition and signal transduction, and their metabolic abnormalities are linked to the progression of various cancers. Specifically, research has found that prostate cancer cells regulate sphingolipid metabolism through the Caveolin-1 (Cav-1) protein, promoting cancer cell growth and survival. Based on these findings, this study aims to validate the association between sphingolipids and GG upgrading and to develop a blood-based sphingolipid biomarker panel for identifying high-risk prostate cancer patients on AS.

Source of the Paper

This paper was authored by Justin R. Gregg et al., from The University of Texas MD Anderson Cancer Center and the University of Washington and Fred Hutchinson Cancer Research Center. The paper was published in 2024 in the journal Biomarker Research.

Research Process

Study Subjects and Samples

The study included two AS cohorts: the Canary PASS cohort and the MDACC cohort. The Canary PASS cohort consisted of 544 patients, while the MDACC cohort included 697 patients. During follow-up, 98 patients (17.7%) in the Canary PASS cohort and 133 patients (19.1%) in the MDACC cohort experienced GG upgrading. The study used mass spectrometry to quantify 87 unique sphingolipid species.

Development and Validation of the Sphingolipid Panel

The study first developed a neural network model based on 21 sphingolipids in the Canary PASS cohort to predict GG upgrading. Subsequently, patients were stratified into low-, intermediate-, and high-risk groups based on tertile thresholds, and a combined model incorporating PSA density and the rate of core positivity on diagnostic biopsy was constructed. This model was validated in the MDACC cohort, with evaluation metrics including Cox proportional hazard models, C-index, AUC, and cumulative incidence curves.

Data Analysis

The study used Cox proportional hazard models to assess the association between individual sphingolipids and disease progression, and a deep learning model (DLM) was employed to construct the sphingolipid panel. Model performance was evaluated using the C-index and AUC, and multivariate analysis was conducted incorporating clinical factors such as PSA density and the rate of core positivity.

Main Results

Results in the Canary PASS Cohort

In the Canary PASS cohort, the hazard ratio (HR) per unit standard deviation (SD) increase for the sphingolipid panel was 1.36 (95% CI: 1.07–1.70). The combined model incorporating PSA density and the rate of core positivity had an HR of 1.63 (95% CI: 1.33–2.00). Based on tertile thresholds, the high-risk group had a significantly higher risk of GG upgrading compared to the low-risk group (HR 3.17, 95% CI: 1.84–5.46).

Validation in the MDACC Cohort

In the MDACC cohort, the HR for the sphingolipid panel was 1.35 (95% CI: 1.11–1.64), and the combined model had an HR of 1.44 (95% CI: 1.25–1.66). The high-risk group had an HR of 3.65 (95% CI: 2.21–6.02) for GG upgrading, capturing 50% of the patients who experienced GG upgrading.

Conclusion

This study confirmed the independent association between the sphingolipid panel and GG upgrading and demonstrated that the sphingolipid panel combined with clinical factors can effectively stratify risk, guiding clinical management in AS. The study provides a potential non-invasive biomarker for prostate cancer patients, enabling the identification of high-risk patients and optimizing monitoring strategies.

Research Highlights

1. Innovation: The first development of a blood-based sphingolipid biomarker panel for predicting GG upgrading in prostate cancer AS.
2. Clinical Application Value: The panel, combined with clinical factors, can effectively stratify risk, helping clinicians develop personalized monitoring strategies and reducing unnecessary biopsies and treatments.
3. Multi-Cohort Validation: The model’s performance was validated in two independent cohorts (Canary PASS and MDACC), enhancing the reliability of the results.

Other Valuable Information

The study also explored the role of MRI in risk stratification, finding that the sphingolipid panel remained effective in predicting GG upgrading even in MRI-negative patients. Additionally, the study suggested that future research should further incorporate MRI and targeted biopsy results to optimize the risk stratification model.

Summary

This study demonstrates that the blood-based sphingolipid biomarker panel has significant predictive value in prostate cancer AS, helping to identify high-risk patients and optimize clinical management strategies. Future research is needed to further validate the clinical application of this panel and explore its potential in other cancers.