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Researchers identify three novel intrinsic subtypes of prostate cancer
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Three distinct molecular subtypes of prostate cancer exist and correlate with distant metastasis–free survival, according to research presented at the ASTRO Annual Meeting.
The identification of these subtypes can help clinicians predict a patient’s response to treatment, according to the researchers.
“Currently, the risk grouping systems we use for prostate cancer rely on imperfect tools, like a digital rectal exam, or simply measuring the patient’s blood to check their PSA or just the histologic appearance of their tumor under a microscope,” Daniel E. Spratt, MD, chief of the genitourinary radiotherapy program at University of Michigan, said during a press conference. “These systems do not give us information about the biology of a patient’s disease and, therefore, they do not always give us accurate information about a patient’s risk for recurrence. Further, they don’t tell us which patient will specifically benefit from the treatments we have.”
Spratt and colleagues sought to identify the intrinsic molecular subtypes of primary prostate cancer via tumor sequencing. They also evaluated the prognostic impact of these subtypes on a patient’s risk for recurrence, and whether the subtypes could identify men most likely to benefit from postoperative radiation.
The analysis included data from 4,236 tumor samples from nine cohorts, which included seven institution-based, retrospective cohorts (n = 1,626) and two prospective cohorts — The Cancer Genome Atlas (n = 497) and Decipher GRID (n = 2,113). Researchers analyzed RNA expression patterns from high-density microarrays and transcriptome sequencing in the samples. All men had been treated with radical prostatectomy for localized prostate cancer.
When researchers first evaluated the training dataset using one of the retrospective cohorts (n = 182), they identified clustering that was strongly influenced by stromal genes, as well as by warm ischemia, which could have been related to the elapsed time between specimen harvest and fixation.
After researchers removed these extrinsic factors, they identified three distinct subgroups based on the presence of 100 genes, or factors “intrinsic” to the tumor itself.
Researchers then evaluated the presence of these subtypes in a validation set that consisted of the six remaining retrospective cohorts (n = 1,444) and the two prospective cohorts.
In general, the subtypes appeared linked to expression of androgen receptor activity and ERG oncogene expression — which are known drivers of prostate cancer — but not mutations or genetic arrangements.
“As a clinician, it’s very important for us to know not only that this biology exists, but what it means for patients,” Spratt said. “So, we also evaluated the prognostic impact of these subtypes.”
Patients in subtype A appeared to have the most favorable prognosis. The 10-year rate of distant metastasis–free survival was 73.6% for subtype A, 64.4% for subtype B and 57.1% for subtype C.
Compared with patients with subtype A, the risk for 10-year distant metastasis significantly increased among patients with subtype B (HR = 1.31; 95% CI, 1.04-1.66) and subtype C (HR = 1.65; 95% CI, 1.29-2.1). These associations persisted in analyses adjusted for clinicopathologic variables, such as PSA, Gleason score and surgical margins (HR for subtype B vs. A = 1.31; 95% CI, 1.03-1.65; HR for subtype C vs. A = 1.33; 95% CI, 1.04-1.71).
“Not only do subtype-A patients maintain the most favorable prognosis, but these molecular subtypes independently predict a patient’s risk for distant metastasis, even over these other standard clinicopathologic features,” Spratt said.
To determine whether the subtypes predicted which men would benefit most from postoperative radiation, researchers then evaluated outcomes related to subtype along 919 men who did not receive radiation and 346 men who did. Overall, men with subtype-B and -C disease appeared significantly more likely to have a response to postoperative radiotherapy (HR for group A vs. B and C = 0.44; P = .002).
“What this suggests is that this may be one of the first predictive biomarkers to tell us which patients are benefiting most from radiation therapy,” Pratt said.
“It will be very important for us in our field to use these molecular subtypes to help us personalize care for each prostate cancer patient to help inform us of their risk for recurrence and their potential benefit from radiation therapy,” he added. – by Alexandra Todak
Reference:
Spratt DE, et al. Abstract 6. Presented at: ASTRO Annual Meeting; Sept. 25-28, 2016; Boston.
Disclosure: Spratt reports a research grant from Prostate Cancer Foundation. Please see the abstract for a list of all other researchers’ relevant financial disclosures.
Perspective
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Colleen Lawton, MD, FASTRO
This is very exciting work, and certainly on the cusp of what we need in prostate cancer. We have these rough PSA levels and Gleason scores, how aggressive the tumor looks under the microscope and how the tumor feels when you do a rectal exam. But, each of these things are not perfect, and this is the direction we absolutely need to head — understanding the genes, what they mean, how they interact with surgery and how they interact with radiation. I can’t wait to see this type of data incorporated into future trials.
Perspective
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Mark A. Hallman, MD, PhD
We live in the new era of personalized medicine led by the field of oncology. Many early developments in personalized cancer treatment — eg, erlotinib (Tarceva; Genentech, Astellas Oncology), imatinib (Gleevec, Novartis) and trastuzumab (Herceptin, Genentech) — were limited to identification of unique mutations and expression patterns of a few oncogenes. Due to the limited number of genes that could be analyzed and the sometimes relatively uncommon susceptible mutations, personalized therapies have been limited to those with uniquely aggressive cancer phenotypes.
In addition to this, we have learned to predict response to standard cancer therapies analyzing expression of single genes (eg, ERCC1, thymidylate synthase, RRM1 and MGMT) or a relatively limited panel of genes (Oncotype DX [Genomic Health]) have enabled oncologists to select therapy based upon response to standard therapies and/or metastatic potential. With new high-throughput methods of gene and protein analyses — such as next generation sequencing and microarrays — we are learning to understand and predict the biology of cancer based upon global patterns of gene expression of numerous genes. These developments promise to improve cancer risk stratification, provide personalized medicine to a far broader range of patients, select the most effective therapies, and drive the discovery of novel targeted therapies.
At the ASTRO Annual Meeting, Daniel E. Spratt, MD, and multi-institutional collaborators presented important work advancing personalized medicine in the treatment of prostate cancer. Using high-throughput RNA microarray and transcriptome sequencing analysis, called the PC100, they analyzed specimens of 4,236 primary prostate tumors from nine prostatectomy cohorts, which included a prospective validation set of 2,113 men. Researchers used distant metastasis–free survival (DMFS) to characterize expression patterns of 100 genes.
Interestingly, the samples clustered into three patterns correlating to 10-year DMFS rates of 73.6%, 64.4% and 57.1%. These risk groups appeared more predictive than risk factors used in current practice. In addition, the cohorts also predicted for response to post-prostatectomy radiation therapy.
These data are quite exciting. Greater personalization of therapy will be particularly useful in treatment of prostate cancer in which there are multiple seemingly equivalent modalities of therapy. The relatively high and long-term survival of patients with prostate cancer also has potential to magnify and prolong the adverse effects of treatment upon quality of life. Therefore, this work represents a rather meaningful step toward the goal of personalized cancer care. For example, we could better select those at greatest potential to develop metastatic disease to receive androgen-deprivation therapy. Among prostatectomy patients, we will have better means to test and select the most appropriate adjuvant therapies including radiation, ADT or further observation. Equally important, we can appropriately select patients to receive less intense therapies and fewer interventions to spare the side effects of treatment among those who will not derive benefit.
Such advances in personalized medicine as the PC100 are a perfect example of tools that will increasingly help us to maximize the effectiveness and value of care for our future patients.
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