Nichole Tucker, MA, is the Web Editor for Targeted Oncology. Tucker received her Bachelor of Arts in Mass Communications from Virginia State University and her Master of Arts in Media & International Conflict from University College Dublin.
During a debate at the 27th Annual Prostate Cancer Foundation Virtual Scientific Retreat, Daniel Spratt, MD, debated Daniel Lin, MD, on the subject of genomic classifiers in prostate cancer.
The role of genomic classifiers as an aid in precision medicine for prostate cancer is a controversial topic. Although many advances have been made with technologies that test patients for gene expressions and genetic mutations, some still doubt that the field is ready for widespread use in prostate cancer.
During a debate at the 27th Annual Prostate Cancer Foundation Virtual Scientific Retreat, Daniel Spratt, MD, debated Daniel Lin, MD, on the subject of genomic classifiers in prostate cancer. Spratt is a professor, the Laurie Snow Research Professor of Radiation Oncology, and associate chair of clinical research at the University of Michigan and chair of genitourinary clinical research and director of the spine oncology program at the Rogel Cancer Center at Michigan Medicine, both in Ann Arbor. Lin is a professor, the vice chair of research, and the chief of urologic oncology at the University of Washington in Seattle.
Ultimately, Spratt argued for the use of genomic classifiers in prostate cancer, and Lin argued against it.1
The Case for Using Genomic Classifiers in Prostate Cancer
As an opener, Spratt explained that use of biomarkers in prostate cancer is not a novel concept. The issue is that oncologists have limited understanding of biomarkers.
Factors like age, height, and weight, vital signs, prostate-specific antigen (PSA), and tumor grade have been aids for selecting which therapies to administer to patients. In today’s management of prostate cancer, the biomarkers are molecularly defined. The molecular biomarkers currently used in prostate cancer include PSA, Gleason score, D’Amico risk group, National Comprehensive Cancer Network (NCCN) risk groups, and others.
In an interview with Targeted Oncology™, Spratt elaborated on the lack of understanding in the field, stating, “When you talk about genomic classifiers [with investigators], there are a couple of different kinds they could be referring to. One of them is the gene expression classifier that’s measuring tumor mRNA with gene expression. A variety of commercial platforms and products have been created for this purpose. Then there are genomics when you refer to mutations. This involves somatic or germline testing relating to a patient’s tumor DNA. The intent of both of these is very different and underlies how we personalize our treatment. Also, I think there is a lot of misunderstanding about those differences. The field, as a whole, could be doing a better job in terms of helping patients, providers, and scientists understand the difference.”
Although different, Spratt explained that the genomic classifiers in prostate cancer all serve the same purpose, which is to improve precision medicine.
Although retrospective data have shown more than 40 biomarkers for prostate cancer, Spratt argued that many of these biomarkers lack the data. He noted 3 published clinical trials that have demonstrated the importance of molecular biomarkers in prostate cancer: TITAN (NCT02489318), SPARTAN (NCT01946204), and CHAARTED (NCT00309985).
A total of 525 patients with metastatic castration-resistant prostate cancer (mCRPC) were randomized in the phase 3 TITAN study to receive androgen deprivation therapy (ADT) in combination with apalutamide (Erleada) or with a placebo. Patients were stratified by the Gleason score biomarker. A higher Gleason score indicated a higher grade of prostate cancer that may be more aggressive. The coprimary end points of the study were radiographic progression-free survival (rPFS) and overall survival (OS).2
At baseline, 7.8% of patients in the apalutamide arm vs 7.4% in the placebo arm had a Gleason score less than 7; 25.3% in the apalutamide arm vs 24.7% in the placebo arm had a Gleason score of exactly 7; and 66.9% in the apalutamide arm vs 67.9% in the placebo arm had a Gleason score greater than 7.
According to the subgroup analysis, patients with a Gleason score of less than or equal to 7 had a higher median rPFS than those with a score greater than 7. The median rPFS was not estimable (NE) in the apalutamide arm compared with 30.5 months in the placebo arm (HR, 0.53; 95% CI, 0.36-0.78) among patients with a Gleason score less than or equal to 7. The median rPFS among patients with a Gleason score greater than 7 was NE in the apalutamide group compared with 18.6 months in the placebo group (HR, 0.48; 95% CI, 0.37-0.61).
OS favored the apalutamide arm in the overall analysis with a 24-month OS rate of 82.4% (95% CI, 78.4-85.8) in the apalutamide arm versus 73.5% (95% CI, 68.7-77.8) in the placebo arm. The median was NE in both arms and also NE in the subgroup analysis of Gleason-score impact on outcomes. In patients with a Gleason score less than or equal to 7, the HR was 0.56 (95% CI, 0.33-0.97) versus 0.73 (95% CI, 0.52-1.01) among patients with a Gleason score greater than 7.
The TITAN study authors led by Kim N. Chi, MD, concluded that apalutamide plus ADT led to a significant improvement in rPFS and OS in patients with both low-volume mCRPC and high-volume mCRPC.
The SPARTAN trial was another comparison of apalutamide and placebo but in a larger population: 1207 men with nonmetastatic CRPC (nmCRPC). In this study, patients were primarily stratified by PSA doubling time. Other stratification factors included whether patients used bone-sparing agents and classification of local or regional nodal disease.3
Comparing baseline characteristics, the median PSA doubling time was less than or equal to 6 months in 71.5% of patients who received apalutamide compared with 70.8% of those who received a placebo. The median PSA doubling time was greater than 6 months in 28.5% in the apalutamide arm versus 29.2% in the placebo arm. The majority of patients were either N0 or N1, and most patients had no prior use of a bone-sparing agent.
The results of the primary end point—metastasis-free survival (MFS)—showed a median MFS of 40.5 months in patients with a PSA doubling time of less than or equal to 6 months treated with apalutamide compared with 14.6 months in patients in the placebo arm (HR, 0.29; 95% CI, 0.23-0.36). The MFS among patients with a PSA doubling time of greater than 6 months was not reached (NR) for the apalutamide arm compared with 22.8 months for the placebo arm (HR, 0.30; 95% CI, 0.20-0.47).
In terms of the other stratification factors, it was determined that patients who did not use a bone-sparing agent had better MFS compared with those who did. This finding was true for both treatment arms. It was also shown that patients with N0 disease had longer MFS versus patients with N1 disease.
SPARTAN concluded that apalutamide prolongs MFS in patients with nmCRPC.
In the phase 3 CHAARTED trial, 790 patients with metastatic hormone-sensitive prostate cancer were randomized to receive either ADT with docetaxel (n=397) or ADT alone (n =393). OS was the primary end point. The secondary end points were time to clinical progression, time to castration-resistant prostate cancer, the proportion of patients with a complete response at 6 and 12 months, and the change in quality of life from baseline to 3 months. Patients were prospectively stratified based on the presence of high or low burden of metastatic disease.4
In the ADT/docetaxel arm, 33.8% of patients had low-volume disease at baseline, and 66.2% had high-volume disease. In the ADT-only arm, 36.4% of patients presented with low-volume disease, and 63.6% had high-volume disease.
OS in the overall population favored the ADT/docetaxel arm with a median OS of 57.6 months compared with 44.0 months among patients who received ADT alone (HR, 0.61; 95% CI, 0.47-0.80; P<.001). In the subgroup of patients with high-volume disease, the median OS was 49.2 months in patients who received ADT/docetaxel compared with 32.2 months for those who received ADT alone (HR, 0.60; 95% CI, 0.45-0.81; P<.001). Median OS was NR in either arm for patients with low-volume disease (HR, 0.60; 95% CI, 0.32-1.13; P=.11). It was noted that a benefit from treatment with docetaxel was detected in all subgroups assessed in the study.
Spratt noted that 18 other clinical trials demonstrating the importance of using genomic classifiers before treatment have either been reported or are ongoing. Moreover, studies dating back to 2013 have demonstrated the independent prognostic impact of Decipher Prostrate Cancer Classifier, a 22-gene tissue-based assay manufactured by GenomeDx Biosciences, Inc.
The Potential of Decipher Prostate Biopsy for Classifying Prognostics
For the treatment of patients with localized prostate cancer, the C-index for metastasis is 0.65 according to the NCCN and 0.70 according to the Cancer of the Prostate Risk Assessment (CAPRA). The issue faced by oncologists treating high-risk patients with prostate cancer is that all the information the PSA and Gleason score provide has already been used, making the prognostic accuracy uncertain. Genomic classifiers can be especially helpful in this high-risk population, Spratt explained.
“Decipher has been consistently [and] independently prognostic on multivariate analyses,” Spratt stated during his presentation.
A prospective study of 1000 patients showed that Decipher reclassified 67% of the patients who were originally divided into the 6-tier clinical-genomic risk groups set by the NCCN.
“There are also prospective studies that consistently show that use of genomic classifiers changes management. For every 5 patients you order a test for, you will change the management for 1 patient,” Spratt noted during his presentation.
The data Spratt shared underscored a need to change a 60-year-old tradition of how prognostics are revealed in patients with prostate cancer.
The Future Use of Genomic Classifiers in Prostate Cancer
During his interview with Targeted Oncology™, Spratt said; “I think the next big step is trying to make people understand what these tests are trying to do. It’s also important, as Dr Lin brought up, to know how to operationalize these tests. Right now, all of the companies that make these tests have missed a huge opportunity to provide clinically relevant cut points or thresholds to help inform patients and clinicians [that at] this specific score, there may be too futile a benefit from adding hormone therapy to radiation therapy. Right now, companies basically report the risk a cancer will come back or some feature of how aggressive the cancer is, but I think [what] patients and practitioners care about is how much does an additional treatment help the patient’s ultimate outcome?”
To support his point, Spratt shared data from the phase 3 RTOG-9601 trial (NCT00002874) and compared it with prospective registry data. Results from an MFS analysis showed that patients with low- to intermediate-risk prostate cancer who did not receive hormone therapy did not develop metastatic disease.6 The result begs the question of why hormone therapy is recommended if patients do not need it. Contrarily, in the RTOG-9601 study, patients with a low Decipher score who underwent early salvage radiotherapy had only a 0.4% improvement in metastasis after 12 years.6 Spratt noted that over the 12 years, patients were given high-dose bicalutamide (Casodex) that was very toxic.1
Prostate Cancer Is Not Ready for Genomic Classifiers
The key argument against the use of genomic classifiers in everyday practice is the lack of proof that these classifiers improve outcomes, explained Lin during an interview with Targeted Oncology™.
“My thinking is that if genomic markers are ready for prime time, then [they are] considered the standard of the care. It is an expected reflexive test that has been proven to improve long-term outcomes. At this point in time, the markers that we covered in our debate have not been proven to improve long-term outcomes and relevant outcomes, such as overall survival and quality of life,” Lin said. “At this point, these are not considered routine tests.”
The relevant questions Lin raised during his presentation highlighted the supporting data for biomarkers including the biologic rationale, feasibility of use, and how tests are developed, applied, and validated in studies. He also asked whether the data available for genomic markers in prostate cancer add to established models of prognostication commonly used in the field and supported with clinical and pathologic data.
Making a Case Against Genomic Classifiers
To support his argument and answer the questions posed to the audience, Lin focused on 3 tissue-based platforms: Prolaris, a 31-gene assay from Myriad Genetics; Oncotype DX Genomic Prostate Score, a 17-gene assay from Genomic Health, Inc; and Decipher.
Lin showed during his presentation that historically, these tissue-based assays were developed merely to determine risk in patients postprostatectomy. The Oncotype test specifically demonstrated validity for determining risk in this patient group, but Lin questioned whether this could be applied to prognosticate men who are initially managed with active surveillance. In addition, Decipher was originally intended to determine risk prognostics for patients with adverse pathology who were post–radical prostatectomy.
During Lin’s presentation, data from the PASS study (NCT00756665), a multicenter active surveillance cohort, were displayed. The study biopsy tissue was obtained from men undergoing active surveillance at 8 sites. These patients were assessed for the coprimary end points of adverse pathology, which were determined by patients having a Gleason Grade Group score greater than or equal to 3 and greater than or equal to pT3a. The Oncotype DX Genomic Prostate Score was utilized in this study.7
Overall, 432 patients were included in the study and followed for a median of 4.6 years. Patients (n=101) who enrolled underwent radical prostatectomy and had adverse pathology after a median of 2.1 years of surveillance. Thirty-nine percent of the patient population had an upgrade at a later biopsy. Genomic prostate score was found to significantly correlate with adverse pathology when adjusted for diagnostic Gleason grade (HR, 1.18; 95% CI, 1.04-1.44; P =.030). Lin et al concluded from this study that although the Oncotype DX Genomic Prostate Score was independently associated with adverse pathology after active surveillance, the results were not statistically significant. Also, no association between the assays and upgrading in surveillance biopsy was shown.
Lin explained that because upgrading for active surveillance is a trigger for treatment, it is important that a biomarker have a relationship with this end point.
Another study that utilized the Decipher assay evaluated men with high-risk prostate cancer who and adverse pathology factor like pT3, pN1, positive margins, or a Gleason score greater than 7.8 The patients underwent radical prostatectomy at either Johns Hopkins University in Baltimore, Maryland, Cleveland Clinic in Ohio, Mayo Clinic in Minnesota, or Durham Veteran’s Affairs Medical Center in North Carolina. A total of 561 men were enrolled and followed for a median of 3.0 years. The results showed that patients with high Gleason scores (>0.6) compared with those who had low-intermediate scores (≤0.6), the odds ratio for prostate cancer–specific mortality adjusted for CAPRA postsurgical (CAPRA-S) was 3.91 (95% CI, 2.43-6.29), with an area under the curve of0.77. Among patients with low-intermediate risk CAPRA-S, the genomic classifier (GC) further stratifies PCSM10 risk from 2.8% for GC ≤0.6 to 18% for GC >0.6. Among high risk CAPRA-S patients GC stratifies risk from 5.5% for GC ≤0.6 to 30% for GC >0.6.These data demonstrate an increase of 0.04 with Decipher compared with CAPRA-S.
Lin admitted that Decipher may be a better clinical model to use for determining outcomes for patients with prostate cancer compared with NCCN models for some analyses. However, in other studies in which the assay performed well, there were multiple other prognostic factors assessed that helped guide treatment. He stated that more research is needed in the area.1
Since Lin argued that genomic classifiers are not ready to be applied to clinical practice, he offered a few recommendations to move these biomarkers forward during his interview.
“What many want is a biomarker to tell them what to do—and tells them by itself. Most biomarkers are not like that, but they improve risk estimate and give a physician a better idea of what might happen,” Lin said. “We need to do 2 things. We have to finish the trials using biomarkers. And we have to figure out how to have models that have better predictions than we have now, and add on top of that a biomarker and see [how it improves its] ability to predict what might actually happen.”
1. Feng F, McKay R, Spratt D, Lin D. Debate: prostate cancer genomic classifiers; are we ready for prime time? Presented at: 27th Annual Prostate Cancer Foundation Virtual Scientific Retreat; October 20-23, 2020. Accessed December 17, 2020. https://www.pcf.org/scientific-retreat/video/prostate-cancer-genomic-classifiers/
2. Chi KN, Agarwal N, Bjartell A, et al; TITAN Investigators. Apalutamide for metastatic, castration-sensitive prostate cancer. N Engl J Med. 2019;381(1):13-24. doi:10.1056/NEJMoa1903307
3. Smith MR, Saad F, Chowdhury S, et al; SPARTAN Investigators. Apalutamide treatment and metastasis-free survival in prostate cancer. N Engl J Med. 2018;378(15):1408-1418. doi:10.1056/NEJMoa1715546
4. Sweeney CJ, Chen YH, Carducci M, et al. Chemohormonal therapy in metastatic hormone-sensitive prostate cancer. N Engl J Med. 2015;373(8):737-746. doi:10.1056/NEJMoa1503747
5. Shipley WU, Seiferheld W, Lukka HR, et al; NRG Oncology RTOG. Radiation with or without antiandrogen therapy in recurrent prostate cancer. N Engl J Med. 2017;376(5):417-428. doi:10.1056/NEJMoa1607529
6. Spratt DE, Zhang J, Sangtiago-Jiménez M, et al. Development and validation of a novel integrated clinical-genomic risk group classification for localized prostate cancer. J Clin Oncol. 2018;36(6)581-590. doi:10.1200/JCO.2017.74.2940
7. 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(14):1549-1557. doi:10.1200/JCO.19.02267
8. Karnes RJ, Choeurng V, Ross AE, et al. Validation of a genomic risk classifier to predict prostate cancer-specific mortality in men with adverse pathologic features. Eur Urol. 2018;73(2):168-175. doi:10.1016/j.eururo.2017.03.036