Evaluation of the prostate health index (PHI) as a novel biomarker in active surveillance of prostate cancer.

Authors

null

Andrew Eichholz

University College London Hospitals NHS Foundation Trust, London, United Kingdom

Andrew Eichholz , Frank McCarthy , Nening Dennis , Karen Thomas , Tim Howlett , Jhangir Iqbal , Jan Amin , Mildred Tan , Mausam Singhera , Elizabeth Selvadurai , Robert Anthony Huddart , David Paul Dearnaley , Chris Parker

Organizations

University College London Hospitals NHS Foundation Trust, London, United Kingdom, The Institue of Cancer Research, The Royal Marsden NHS Foundation Trust, Sutton, United Kingdom, The Royal Marsden NHS Foundation Trust, Sutton, United Kingdom, Institute of Cancer Research, Sutton, United Kingdom, The Institue of Cancer Research, London, United Kingdom, The Royal Marsden NHS Foundation Trust and The Institute of Cancer Research, Sutton, United Kingdom

Research Funding

No funding sources reported

Background: PHI is calculated from serum PSA, free/total (f/t) PSA and [-2]proPSA using the Beckman Coulter assay kit, and has been approved for use in patient selection for diagnostic prostate biopsy. We hypothesized that phi might also predict outcome of active surveillance. Methods: From 2002, we have done a prospective cohort study of active surveillance for men with T1/2, Gleason <= 3+4, PSA < 15ng/ml prostate cancer. Serum was banked at baseline. Monitoring included 6 monthly PSA and 2-yearly repeat biopsy. Treatment was indicated for PSA velocity > 1ng/ml/yr or Gleason >= 4+3 on repeat biopsy. We analyzed baseline phi with respect to time to treatment. A multivariate model was fitted using total PSA, PSA velocity, PSA density, Gleason score, % biopsy cores positive, T stage, and maximum % cancer in any biopsy core. The fit of this model was then compared with the addition of % f/t PSA and PHI. Results: 370 patients were evaluable with a median follow-up of 5 years. The table shows the association between baseline PHI and time to treatment. On multivariate analysis, the model with % f/t PSA was a significant improvement over base model (change in fit 41.1, p<0.001), and the model with % f/t PSA and phi was a significantly better fit than % f/t PSA alone (change in fit 11.1, p=0.001). Conclusions: In men with favorable risk prostate cancer, PHI at diagnosis was a significant predictor of the outcome of active surveillance. The data require validation, but suggest that active surveillance is particularly attractive to men with a low PHI.

PHI value N Events Median time to treatment,
years (95% CI)
Patients free from treatment
at 5 years, % (95% CI)
Up to 31.4 (quartile 1) 94 6 Not estimable 95.1 (90.3, 99.9)
31.5 to 42.9 (quartile 2) 91 17 11.4 (not estimable) 81.6 (72.8, 90.5)
43.0 to 58.5 (quartile 3) 93 40 6.4 (4.1, 8.7) 58.9 (48.1, 69.7)
Over 58.5 (quartile 4) 93 52 5.1 (3.2, 7.1) 54.1 (43.7, 64.5)

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Abstract Details

Meeting

2014 ASCO Annual Meeting

Session Type

Poster Session

Session Title

Genitourinary (Prostate) Cancer

Track

Genitourinary Cancer

Sub Track

Prostate Cancer

Citation

J Clin Oncol 32:5s, 2014 (suppl; abstr 5071)

DOI

10.1200/jco.2014.32.15_suppl.5071

Abstract #

5071

Poster Bd #

200

Abstract Disclosures

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