Validation of the Decipher genomic classifier in patients receiving salvage radiotherapy without hormone therapy after radical prostatectomy – an ancillary study of the SAKK 09/10 randomized clinical trial
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In localized prostate cancer, tissue-based genomic classifiers primarily serve prognostic functions by refining risk estimates beyond clinicopathologic variables.
“In localized disease, tissue-based genomic classifiers primarily serve prognostic functions by refining risk estimates beyond clinicopathologic variables”
From A decision-oriented framework for genomic testing across the prostate cancer continuum (2026) · cites this paper as [6]
The Introduction (page not provided) describes Decipher as a tissue-based assay developed for prognostication and says it improves risk discrimination beyond clinicopathologic variables. The Discussion reports independent prognostic performance after salvage radiotherapy. The paper supports the claim for Decipher, but does not establish that tissue-based genomic classifiers generally serve primarily prognostic functions across localized prostate cancer. (LLM, conf=0.84)
Experimental — accuracy not yet measuredIn newly diagnosed localized disease, tissue-based genomic classifiers are generally used to refine baseline risk and guide decisions between active surveillance and definitive treatment.
“In newly diagnosed localized disease, tissue-based genomic classifiers are generally used to refine baseline risk and guide decisions between active surveillance and definitive treatment [4–7].”
From A decision-oriented framework for genomic testing across the prostate cancer continuum (2026) · cites this paper as [6]
The Introduction describes Decipher as a tissue-based assay that aids prostate cancer prognostication. But this study concerns patients receiving salvage radiotherapy after prostatectomy, and the Discussion considers decisions about adding hormone therapy—not decisions between active surveillance and definitive treatment in newly diagnosed localized disease. No page numbers are available in our copy. (LLM, conf=0.85)
Experimental — accuracy not yet measuredTissue-based genomic classifiers provide prognostic information regarding adverse pathology, biochemical recurrence, and metastatic risk but generally do not identify specific therapeutic targets.
“These assays provide prognostic information regarding adverse pathology, biochemical recurrence, and metastatic risk but generally do not identify specific therapeutic targets.”
From A decision-oriented framework for genomic testing across the prostate cancer continuum (2026) · cites this paper as [6]
The Introduction describes Decipher as a tissue-based assay for prostate cancer prognostication, and the Results report an association with biochemical progression. The paper also evaluates metastasis-related outcomes, but the cited passages do not establish the broader claims about adverse pathology or that these assays generally do not identify specific therapeutic targets. No page numbers are available in our copy. (LLM, conf=0.85)
Experimental — accuracy not yet measuredTissue-based genomic classifiers can improve risk refinement in localized disease, but their incremental utility is context dependent and not uniformly transformative.
“Tissue-based genomic classifiers can improve risk refinement in localized disease, but their incremental utility is context dependent and not uniformly transformative [4–7].”
From A decision-oriented framework for genomic testing across the prostate cancer continuum (2026) · cites this paper as [6]
The Introduction and Discussion (page not provided) say the tissue-based Decipher classifier improves prognostication beyond clinicopathologic factors and adds independent prognostic information after salvage radiotherapy. The Discussion also identifies sample-size and subgroup-analysis limitations. But this study examines a specific post-prostatectomy salvage-radiotherapy setting; it does not establish how much classifiers improve risk refinement across localized disease or compare their incremental utility across contexts. (LLM, conf=0.82)
Experimental — accuracy not yet measuredDecipher has broader validation across both biopsy and prostatectomy contexts and has demonstrated associations with metastasis and prostate cancer-specific mortality, including in randomized trial cohorts.
“Decipher has broader validation across both biopsy and prostatectomy contexts and has demonstrated associations with metastasis and prostate cancer–specific mortality, including in randomized trial cohorts [6, 7].”
From A decision-oriented framework for genomic testing across the prostate cancer continuum (2026) · cites this paper as [6]
The Introduction and Discussion describe Decipher validation in randomized trial cohorts, and Relevant Oncological Endpoints reports that the classifier was prognostic for all endpoints, including exploratory metastasis endpoints. The tissue analyzed in this study came from prostatectomy specimens; our copy does not establish validation in biopsy specimens or an association with prostate cancer–specific mortality. The indexed passages provide section labels but no page numbers. (LLM, conf=0.87)
Experimental — accuracy not yet measured
Earlier generations of molecular assays in prostate cancer included smaller candidate-gene panels and early biomarker signatures developed prior to the current emphasis on large-scale validation.
“Earlier generations of molecular assays in prostate cancer included smaller candidate-gene panels and early biomarker signatures that were developed prior to the current emphasis on large-scale validation [6, 11, 14].”
From A decision-oriented framework for genomic testing across the prostate cancer continuum (2026) · cites this paper as [6]
In biochemical recurrence, genomic classifiers may help identify patients at higher risk of metastatic progression or those who may benefit from treatment intensification in conjunction with salvage therapy.
“In biochemical recurrence, genomic classifiers may help identify patients at higher risk of metastatic progression or those who may benefit from treatment intensification in conjunction with salvage therapy [6, 7].”
From A decision-oriented framework for genomic testing across the prostate cancer continuum (2026) · cites this paper as [6]
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Cites
REporting recommendations for tumour MARKer prognostic studies (REMARK).
for the Statistics Subcommittee of the NCI-EORTC Working Group on Cancer Diagnostics, McShane, L. M., Altman, D. G., Sauerbrei, W., Taube, S. E., Gion, M., & Clark, G. M. (2005) · British Journal of Cancer · doi:10.1038/sj.bjc.6602678 · Cited by 1,394 · see citing works
Flagged first, then by citations
Research works printed but not linked: 6
- Tendulkar RD, Agrawal S, Gao T, et al: Contemporary Update of a Multi- Institutional Predictive Nomogram for Salvage Radiotherapy After Radical Prostatectomy. Journal of Clinical Oncology 34:3648-3654, 2016
- 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 79:374-383, 2021
- Gray RJ: A Class of K-Sample Tests for Comparing the Cumulative Incidence of a Competing Risk. The Annals of Statistics 16:1141-1154, 1988
- Fine JP, Gray RJ: A Proportional Hazards Model for the Subdistribution of a Competing Risk. Journal of the American Statistical Association 94:496-509, 1999
- Morgan TM, Miller DC, Dunn R, et al: Prospective randomized trial of genomic classifier impact on treatment decisions in patients at high risk of recurrence following radical prostatectomy (G-MINOR). Journal of Clinical Oncology 36:TPS154-TPS154, 2018
- Morgan TM, Okoth LA, Spratt DE, et al: Prospective randomized trial of gene expression classifier utility following radical prostatectomy (G-MINOR). Journal of Clinical Oncology 39:15-15, 2021 J o u r n a l P r e -p r o o f
Cited by
Sorted by citations
A decision-oriented framework for genomic testing across the prostate cancer continuum
Cobran, E. K., Samadder, N. J., Schaid, D. J., Conti, D. V., Haiman, C., Vargas, L., Gionfriddo, M. R., & Tilburt, J. C. (2026) · Cancer Genetics · doi:10.1016/j.cancergen.2026.06.002 · Cited by 1 · see citing works
- Authors
- Dal Pra, A., Ghadjar, P., Hayoz, S., Liu, V. Y. T., Spratt, D. E., Thompson, D. J. S., Davicioni, E., Huang, H.-C., Zhao, X., Liu, Y., Schär, C., Gut, P., Plasswilm, L., Hölscher, T., Polat, B., Hildebrandt, G., Müller, A.-C., Pollack, A., Thalmann, G. N., Zwahlen, D., & Aebersold, D. M.
- Venue
- Annals of Oncology
- Publisher
- Elsevier BV
- Published
- 2022-09
- Volume
- 33
- Issue
- 9
- Pages
- 950-958
- ISSN
- 0923-7534
- Cited by
- 52
- References
- 1
- Language
- en
- Licence
- Funders
- AstraZeneca
- Decipher Biosciences
- Astellas Pharma US
- Veracyte
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