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When to order genomic tests: development and external validation of a model to predict high-risk prostate cancer at the genotypic level

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Abstract

Purpose

The aim of this study was to develop a model to predict high-genomic-risk prostate cancer (PCa) according to Decipher score, a validated 22 gene prognostic panel. By doing so, one might select the individuals who are likely to benefit from genomic testing and improve pre-op counseling about the need for adjuvant treatments.

Methods

We retrospectively reviewed IRB-approved databases at two institutions. All patients had preoperative magnetic resonance imaging (MRI) and Decipher prostate radical prostatectomy (RP), a validated 22 gene prognostic panel. We used binary logistic regression to estimate high-risk Decipher (Decipher score > 0.60) probability on RP specimen. Area under the curve (AUC) and calibration were used to assess the accuracy of the model in the development and validation cohort. Decision curve analysis (DCA) was performed to assess the clinical benefit of the model.

Results

The development and validation cohort included 622 and 185 patients with 283 (35%) and 80 (43%) of those with high-risk Decipher. The multivariable model included PSA density, biopsy Gleason Grade Group, percentage of positive cores and MRI extracapsular extension. AUC was 0.73 after leave-one-out cross-validation. DCA showed a clinical benefit in a range of probabilities between 15 and 60%. In the external validation cohort, AUC was 0.70 and calibration showed that the model underestimates the actual probability of the outcome.

Conclusions

The proposed model to predict high-risk Decipher score at RP is helpful to improve risk stratification of patients with PCa and to assess the need for additional testing and treatments.

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Data availability

The data that support the findings of this study are available on request from the corresponding author, [UGF].

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Acknowledgements

This research was supported by a grant of the European Urological Scholarship Programme awarded to UGF.

Funding

None.

Author information

Authors and Affiliations

  1. Department of Urology, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, 6th Floor, New York City, NY, 10029, USA

    Ugo Giovanni Falagario, Dimple Chakravarty, Anna Lantz, Parita Ratnani, Sneha Parekh, Dara Lundon, Natasha Kyprianou, Peter Wiklund & Ash Tewari

  2. Department of Urology and Organ Transplantation, University of Foggia, Foggia, Italy

    Ugo Giovanni Falagario, Luigi Cormio & Giuseppe Carrieri

  3. Department of Urology, Urological Research Institute, Vita-Salute University, San Raffaele Scientific Institute, Milan, Italy

    Alberto Martini

  4. Department of Surgery, King Hussein Cancer Center, Amman, Jordan

    Mohammed Shahait

  5. Department of Surgery, Urology Division, University of Pennsylvania, Philadelphia, USA

    Ayah El-Fahmawi

  6. Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, USA

    Ivan Jambor

  7. Department of Radiology, University of Turku, Turku, Finland

    Ivan Jambor

  8. Department of Urology, Karolinska University Hospital, Solna, Sweden

    Ugo Giovanni Falagario, Anna Lantz & Peter Wiklund

  9. Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden

    David Grannas

  10. Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, USA

    Kenneth Haines

  11. Department of Urology, Bonomo Teaching Hospital, Andria (BAT), Italy

    Luigi Cormio

  12. Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland, USA

    Michael W. Kattan

  13. Department of Urology, Glickman Urological and Kidney Institute, Cleveland, USA

    Eric A. Klein

  14. Department of Urology, University of California Irvine, Irvine, USA

    David I. Lee

Authors
  1. Ugo Giovanni Falagario
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  2. Dimple Chakravarty
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  4. Mohammed Shahait
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  5. Ayah El-Fahmawi
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  18. Peter Wiklund
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  20. Ash Tewari
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Contributions

UGF, AK, MS and AL conceived and designed the study; PR, SP, KH, VW, DC and AE acquired the data; UGF, AM and DG analyzed and interpreted the data; UGF, MS, AL, AM and IJ drafted the manuscript; AT, GC, LC, NK, PW, MWK and EAK critically revised the manuscript for important intellectual content; DL provided administrative, technical, or material support; AT, GC, DL, PW, MWK and EAK took part in supervision; and KH carried out the pathologic examination of the slides.

Corresponding author

Correspondence to Ugo Giovanni Falagario.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest related to the present study.

Ethical approval and consent to participate

The present study was approved by Mount Sinai Hospital ethical committee (IRB-19-01597) with a waiver on consent to participate.

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Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Figure 1.

Calibration plot of observed versus predicted probability of biologically significant prostate cancer (high-risk Decipher) according to the risk calculator in the development and validation cohort. Calibration plot of the development cohort is shown for the predicted probability of the model after Logistic regression (LR) and after leave-one-out cross-validation (LOOCV). (TIFF 10073 KB)

Supplementary Figure 2.

Decision curve analysis (DCA) demonstrating the net benefit associated with the use of the risk calculator-derived probability for the prediction of high-risk Decipher score in the development cohort. DCA is a statistical method evaluating the clinical benefit of a model when used for clinical decision making. In our case, the clinical decision is to perform or not a genomic test. Two reference strategies are defined: i. performing Decipher test in all and ii. performing Decipher test in no one. The third line refers to perform Decipher test using the model predicted probability. The range of probability where this line is above the reference lines defines the upper and the lower limit of probability where the model has a clinical benefit. (TIF 13928 KB)

Supplementary file3 (DOCX 19 KB)

Supplementary file4 (XLSX 13 KB)

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Falagario, U.G., Chakravarty, D., Martini, A. et al. When to order genomic tests: development and external validation of a model to predict high-risk prostate cancer at the genotypic level. World J Urol 41, 85–92 (2023). https://doi.org/10.1007/s00345-022-04240-8

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