In a first-ever study, findings identified five gene sets that can predict clinical outcome in independent patient cohorts with prostate cancer, according to researchers in England.1
Combining gene copy number and gene expression data has yielded a new 100-gene risk-stratification signature for prostate cancer that appears to outperform prostate specific antigen (PSA) and Gleason scores, and might allow early detection of the most aggressive cases.
Whereas previous studies had used either gene expression or copy number data to develop molecular risk-stratification signatures for prostate cancer, this study’s authors assessed each approach and then tried something new: they combined gene copy-number and expression data.
“Combining these data provides a more powerful tool to predict outcome following surgery,” lead study author Helen Ross-Adams, PhD, told Cancer Therapy Advisor. Dr. Ross-Adams is at the Cancer Research UK Cambridge Institute at the University of Cambridge, in Cambridge, United Kingdom.
The study “is one of the most comprehensive looks at the integration of copy number and transcriptomic data in a large cohort of patients with localized prostate cancer,” noted Sumanta Kumar Pal, an ASCO Expert and an assistant professor at the Department of Medical Oncology & Therapeutics Research at City of Hope Comprehensive Cancer, in Duarte, CA.
Using healthy blood and tissue samples and prostate tumor tissue samples from discovery and validation cohorts of 125 and 103 men, respectively, the researchers searched tumor-genetics data predictive of biochemical relapse. The study confirmed prostate tumor development associations for MYC amplification, PCA3, and AMACR overexpression, and the loss of MSMB expression, and for six genes already known to be involved in prostate cancer: MAP3K7, MELK, RCBTB2, ELAC2, TPD52, and ZBTB4.
But surprisingly, the combined-data analysis also discovered new prostate cancer associations for 94 other genes—genes whose associations with prostate cancer progression “would not have been detected using either transcript or copy number data alone,” the study authors noted.
The METABRIC study2 used the same integrative method to show that breast cancer represents at least 10 distinct molecular malignancies, Dr. Ross-Adams noted.
Gene expression and copy number alterations for a total of 100 prostate cancer-associated genes allowed differentiation of five separate prognostic subgroups that consistently predicted biochemical relapse.1
The resulting genetic assay “outperforms established clinical predictors of poor prognosis like PSA and Gleason score” and the authors report that they found in the validation cohort that the 100-gene signature showed significant power to separate out a poor prognosis patient group with quicker time to recurrence, from the low-risk cohorts, with much lower chance for recurrence and slower disease progression.
The signature involved different molecular processes (such as nucleic acid processing and protein phosphorylation) than previously published gene signatures that are based on cell-cycle and lipid-metabolism genes, the researchers noted.
They next compared the 100-gene signature’s prognostic performance with that of previously published prostate cancer gene signatures and the OncoType Dx Prostate Cancer assay.
“Our 100-gene signature outperformed all other gene sets in identifying patients with early time to biochemical relapse” (P=0.0001), they reported.
“We’ve developed a tool that will help clinicians identify the most aggressive prostate cancers, with the highest risk of relapsing after surgery,” Dr. Ross-Adams explained.
That suggests their gene signature might indeed emerge “as an effective tool to outperform previously published gene signatures, or the conventional PSA or histology risk markers,” as noted by Joseph Ragaz, MD, FRCP, MRCS-LRCP, senior medical oncologist and clinical professor at the University of British Columbia’s School of Population and Public Health, in Vancouver, Canada, and an expert on cancer outcome research including cancer risk stratification.
The new study is “just a first step,” Dr. Ross-Adams is quick to acknowledge. “We’re quite a way away from this being offered routinely in clinics. The temptation is to run before we can walk, but these findings need to be confirmed in larger clinical trials.”
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Using more genes “certainly may improve the performance of a classifier, but it is also possible that it could add more ‘noise’ to the signal,” cautioned Dr. Pal.
But the study was based on biochemical relapse – elevation in blood PSA – rather than documented metastasis, Drs. Pal and Ragaz each warned.