AUA2021 PLENARY: Highlights Summary of the State-of-the-Art Lecture on: Personalized Medicine in the Management of Prostate Cancer across the Patient Care Continuum
By: Jack R. Andrews, MD; Brian F. Chapin, MD | Posted on: 06 Dec 2021
At the conclusion of the activity, participants will be able to:
- Discuss personalized care for prostate cancer therapy including prognostic versus predictive markers, potential predictive markers and prospective trials, as well as barriers.
A personalized approach to prostate cancer therapy is on the horizon, and while much work is being done in this arena, there is still much to accomplish. As is known, prostate cancer is the most prevalent cancer among men and can present in various disease states. Each stage of this disease (boxes in fig. 1) represents a timepoint with unique and varying prognostic variables. Through the years, we have further risk stratified (compartmentalized) patients with prostate cancer by Gleason score, clinical and pathological stages, histology, and AUA/National Comprehensive Cancer Network® (NCCN®) risk categories, and more recently have begun to identify the varying biology.
As is known, not all patients with prostate cancer should be treated the same, and while risk stratification (NCCN, CAPRA etc) allows for improved prognostication, it has its limitations. Risk stratification also allows for improved balance in clinical trials and more accurate comparisons in retrospective studies with statistical balancing, such as matching or propensity score analysis. While compartmentalization/risk stratification can be helpful it can also generate bias. While a patient with Gleason score 10 prostate cancer and a patient with Gleason score 7 prostate cancer should obviously be managed differently, the prognosis and management can vary equally between 2 men with Gleason 7 prostate cancers with different underlying biology. Risk stratification is a helpful tool but alone is not granular enough to usher in the era of personalized medicine.
As personalization in prostate cancer becomes more and more prevalent, it is critically important to differentiate between prognostic biomarkers and predictive biomarkers. The terms are often used interchangeably, but they have very different meanings and implied consequences. The ability to interpret advances in personalization will require providers to recognize the difference between prognostic and predictive biomarkers and when/how to apply them in their practice.
A prognostic biomarker is a variable associated with favorable or unfavorable outcomes for patients in the absence of treatment. An example of a prognostic biomarker can be something as simple as prostate color (fig. 2). Over time, blue prostates have a more favorable survival than red prostates without or regardless of treatments applied. Therefore, prostate color is prognostic of survival outcome. Prostate color however cannot be used to determine whether a patient is more or less likely to respond to a specific treatment. Gleason score is an example of a real-world prognostic marker. Untreated Gleason score 7 prostate cancer will have less favorable outcomes compared to Gleason score 6 prostate cancer.
A predictive biomarker is a variable used to identify or select for patients or groups of patients most likely to benefit from a specific therapy. A theoretical example would be prostates with high genetic risk have improved survival when Drug X is added to androgen deprivation therapy (ADT), while in prostates with low genetic risk no survival benefit is seen with the addition of Drug X to ADT (fig. 3). In this scenario, high genetic risk is a predictive biomarker for the addition of Drug X to ADT. An example of a real-world predictive biomarker in prostate cancer is DNA damage repair (DDR) mutations. Patients with DDR mutations have been shown to have improved survival with PARP inhibitor therapy compared to those patients without DDR mutations.1 Therefore, a DDR mutation is predictive of a response to a PARP inhibitor but does not provide prognostic information in the absence of treatment.
Commonly, a prognostic biomarker will be used incorrectly to “predict” treatment response. For example, while genomic testing may provide additional prognostic information, it is important to know when to use a genomic test and how to interpret the results. Reflexive genomic testing is not recommended in all patients as it is often not helpful and sometimes even detrimental.2,3 For example, genomic tests on prostate biopsy specimens are associated with pathological features on radical prostatectomy and may not be applicable to predicting eligibility for active surveillance.4 When used incorrectly, genomic testing may be detrimental to patients by leading to overtreatment. Given the complexity of genomic screening indications, inappropriate reflexive genomic testing may further confuse patients and complicate treatment decisions unnecessarily. Additionally, to date no randomized trials in prostate cancer have demonstrated an improvement in patient outcomes based on genomic tests.
While it is important to utilize prognostic biomarkers and predictive biomarkers correctly, a biomarker can still be prognostic and predictive. In a 2017 JAMA Oncology manuscript by Zhao et al, the authors looked at 3,782 prostatectomy specimens and assessed PAM50 gene expression classifiers including Luminal A, Luminal B and Basal PAM50 expression.5 The authors found that prostate cancer-specific mortality (PCSM) varies between the variable PAM50 expressions with Luminal A PAM50 expression having the longest PCMS and Basal PAM50 expression have the poorest PCSM. These data illustrate the prognostic role of PAM50 expression as a prognostic biomarker. The authors also evaluated the impact of ADT vs no ADT in PAM50 gene expression. The results demonstrated that ADT provides a benefit in distant metastasis-free survival to patients with Luminal B PAM50 expression but not to those with Luminal A or Basal PAM50 expression. These data highlight the role of Luminal B PAM50 expression as a possible predictive biomarker for use of ADT to improve distant metastatic free survival.
Zhao et al also published a 2016 study looking at a 24 gene prediction score for postoperative radiation, called the Post-Operative Radiation Therapy Outcomes Score (PORTOS).6 The 24 genes were selected from 196 men and were validated in a separate cohort of 330 men with clinical endpoints of metastasis free survival with a follow up time of 10 years. Men with a high PORTOS score had significantly improved distant metastasis-free survival when treated with radiation therapy (RT) compared to those with a low PORTOS score, in which there was no improvement with radiation. These results demonstrate the use of PORTOS as a potential predictive biomarker in the setting of postoperative radiation. An exciting potential area for investigation would be the use of PORTOS in the primary treatment of prostate cancer and if a high PORTOS in localized prostate cancer would predict improved benefit with primary radiotherapy. Future trials will hope to assess this and potentially validate in a prospective trial to inform if indeed these markers can be predictive.
Currently, NRG-GU009: PREDICT-RT Trial is an active trial prospectively evaluating the Decipher® Prostate RP Genomic Classifier for a potential role as a predictive biomarker. Decipher is a 22-gene classifier used to stratify risk of metastasis based on prostatectomy specimen analysis.7 It is a prognostic biomarker and does not offer any validated prediction of treatment response. The trial design schema can be seen in figure 4 and shows the intensification/de-intensification trial design. Patients with a Decipher score in the bottom two-thirds will be de-intensified and randomized to RT+12 months of ADT or RT+24 months of ADT. Patients with a Decipher score in the top third will be intensified and randomized to RT+24 months of ADT or RT+24 months of ADT and apalutamide. This trial design is an exciting and interesting example of how to prospectively validate a potential predictive biomarker.
There are many exciting areas of development in personalized treatment of prostate cancer. While few predictive markers exist, it is realistic to think that many of the current prognostic markers could be validated as predictive markers using banked samples from prior prospective trials or with prospective evaluation. Another area of potential is to validate existing markers across earlier stages of the disease. Many markers, such as PAM50 and DDR mutations, are developed in the later stages of prostate cancer for many reasons. When markers are validated in earlier stages this clinical impact may be magnified. There are simply more patients in earlier stages of the disease and more expected longevity in which to realize the potential benefit of a personalized treatment. While personalized medicine is sure to change the management of prostate cancer, there are barriers to overcome prior to implementation. One barrier to consider when using biopsy samples is tumor heterogeneity. Prostate tumors are known to harbor multiple clonal populations and biomarker analysis based on a subdominant clone, which may mislead management. In addition to tumor heterogeneity, metastatic lesions are genetically dynamic as they respond to androgen receptor pathway drugs and chemotherapies. Because of this it may be necessary to reassess for predictive markers at multiple time points in the patient’s care and likely will require multiple samples or evaluation of circulating tumor factors.
In conclusion, significant progress has been made in personalized care for the treatment of prostate cancer, but there is much work to be done. Practice-changing personalized medicine in prostate cancer is on the horizon, and clinicians will need to be prepared to interpret and implement biomarkers into practice. To assess biomarkers, it is critical to understand the differences between a prognostic biomarker and a predictive marker and how to assimilate that marker into practice. Validation of markers within prospective trials is needed, and skeptical optimism is appropriate until validations are complete.
- de Bono J, Mateo J, Fizazi K et al: Olaparib for metastatic castration-resistant prostate cancer. N Engl J Med 2020; 382: 2091.
- Eggener SE, Rumble RB, Armstrong AJ et al: Molecular biomarkers in localized prostate cancer: ASCO guideline. J Clin Oncol 2019; 38: 1474.
- Sanda MG, Cadeddu JA, Kirkby E et al: Clinically localized prostate cancer: AUA/ASTRO/SUO guideline. Part I: risk stratification, shared decision making, and care options. J Urol 2018; 199: 683.
- Lin DW, Zheng Y, McKenney JK et al: 17-Gene genomic prostate score test results in the Canary Prostate Active Surveillance Study (PASS) cohort. J Clin Oncol 2020. 38: 1549.
- Zhao SG, Chang SL, Erho N et al: Associations of luminal and basal subtyping of prostate cancer with prognosis and response to androgen deprivation therapy. JAMA Oncol 2017; 3: 1663.
- Zhao SG, Chang SL, Spratt DE et al: Development and validation of a 24-gene predictor of response to postoperative radiotherapy in prostate cancer: a matched, retrospective analysis. Lancet Oncol 2016; 17: 1612.
- Jairath NK, Dal Pra A, Vince R Jr et al: A systematic review of the evidence for the decipher genomic classifier in prostate cancer. Eur Urol 2021; 79: 374.