Dataset Information

Predicting high-grade cancer at ten-core prostate biopsy using four kallikrein markers measured in blood in the ProtecT study.

ABSTRACT:

Background

Many men with elevated prostate-specific antigen (PSA) levels in serum do not have aggressive prostate cancer and undergo unnecessary biopsy. Retrospective studies using cryopreserved serum suggest that four kallikrein markers can predict biopsy outcome.

Methods

Free, intact and total PSA, and kallikrein-related peptidase 2 were measured in cryopreserved blood from 6129 men with elevated PSA (?3.0ng/mL) participating in the prospective, randomized trial Prostate Testing for Cancer and Treatment. Marker levels from 4765 men providing anticoagulated plasma were incorporated into statistical models to predict any-grade and high-grade (Gleason score ?7) prostate cancer at 10-core biopsy. The models were corrected for optimism by 10-fold cross validation and independently validated using markers measured in serum from 1364 men. All statistical tests were two-sided.

Results

The four kallikreins enhanced prostate cancer detection compared with PSA and age alone. Area under the curve (AUC) for the four kallikreins was 0.719 (95% confidence interval [CI] = 0.704 to 0.734) vs 0.634 (95% CI = 0.617 to 0.651, P < .001) for PSA and age alone for any-grade cancer, and 0.820 (95% CI = 0.802 to 0.838) vs 0.738 (95% CI = 0.716 to 0.761) for high-grade cancer. Using a 6% risk of high-grade cancer as an illustrative cutoff, for 1000 biopsied men with PSA levels of 3.0ng/mL or higher, the model would reduce the need for biopsy in 428 men, detect 119 high-grade cancers, and delay diagnosis of 14 of 133 high-grade cancers. Models exhibited excellent discrimination on independent validation among men with only serum samples available for analysis.

Conclusions

A statistical model based on kallikrein markers was validated in a large prospective study and reduces unnecessary biopsies while delaying diagnosis of high-grade cancers in few men.

SUBMITTER: Bryant RJ

PROVIDER: S-EPMC4554254 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature

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Publications

Predicting high-grade cancer at ten-core prostate biopsy using four kallikrein markers measured in blood in the ProtecT study.

Bryant Richard J RJ Sjoberg Daniel D DD Vickers Andrew J AJ Robinson Mary C MC Kumar Rajeev R Marsden Luke L Davis Michael M Scardino Peter T PT Donovan Jenny J Neal David E DE Lilja Hans H Hamdy Freddie C FC

Journal of the National Cancer Institute 20150411 7

<h4>Background</h4>Many men with elevated prostate-specific antigen (PSA) levels in serum do not have aggressive prostate cancer and undergo unnecessary biopsy. Retrospective studies using cryopreserved serum suggest that four kallikrein markers can predict biopsy outcome.<h4>Methods</h4>Free, intact and total PSA, and kallikrein-related peptidase 2 were measured in cryopreserved blood from 6129 men with elevated PSA (≥3.0ng/mL) participating in the prospective, randomized trial Prostate Testing ...[more]

PMID: 25863334

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Project description:BackgroundDiagnosis of Gleason 6 prostate cancer can leave uncertainty about the presence of undetected aggressive disease.ObjectiveTo evaluate the utility of a four kallikrein (4K) panel in predicting the presence of high-grade cancer in men on active surveillance.Design, setting, and participantsPlasma collected before the first and subsequent surveillance biopsies was assessed for 718 men prospectively enrolled in the multi-institutional Canary PASS trial. Biopsy data were split 2:1 into training and test sets. We developed statistical models that included clinical information and either the 4Kpanel or serum prostate-specific antigen (PSA).Outcome measurements and statistical analysisThe endpoint was reclassification to Gleason ≥7. We used receiver operating characteristic (ROC) curve analyses and area under the curve (AUC) to assess discriminatory capacity, and decision curve analysis (DCA) to report clinical net benefit.Results and limitationsSignificant predictors for reclassification were 4Kpanel (odds ratio [OR] 1.54, 95% confidence interval [CI] 1.31-1.81) or PSA (OR 2.11, 95% CI 1.53-2.91), ≥20% cores positive (OR 2.10, 95% CI 1.33-3.32), two or more prior negative biopsies (OR 0.19, 95% CI 0.04-0.85), prostate volume (OR 0.47, 95% CI 0.31-0.70), and body mass index (OR 1.09, 95% CI 1.04-1.14). ROC curve analysis comparing 4K and base models indicated that the 4Kpanel improved accuracy for predicting reclassification (AUC 0.78 vs 0.74) at the first surveillance biopsy. Both models performed comparably for prediction of reclassification at subsequent biopsies (AUC 0.75 vs 0.76). In DCA, both models showed higher net benefit compared to biopsy-all and biopsy-none strategies. Limitations include the single cohort nature of the study and the small numbers; results should be validated in another cohort before clinical use.ConclusionsThe 4Kpanel provided incremental value over routine clinical information in predicting high-grade cancer in the first biopsy after diagnosis. The 4Kpanel did not add predictive value to the base model at subsequent surveillance biopsies.Patient summaryActive surveillance is a management strategy for many low-grade prostate cancers. Repeat biopsies monitor for previously undetected high-grade cancer. We show that a model with clinical variables, including a panel of four kallikreins, indicates the presence of high-grade cancer before a biopsy is performed.

Project description:The overall survival of patients with lower grade glioma (LGG) varies greatly, but the current histopathological classification has limitations in predicting patients' prognosis. Therefore, this study aims to find potential therapeutic target genes and establish a gene signature for predicting the prognosis of LGG. CD44 is a marker of tumor stem cells and has prognostic value in various tumors, but its role in LGG is unclear. By analyzing three glioma datasets from Gene Expression Omnibus (GEO) database, CD44 was upregulated in LGG. We screened 10 CD44-related genes via protein-protein interaction (PPI) network; function enrichment analysis demonstrated that these genes were associated with biological processes and signaling pathways of the tumor; survival analysis showed that four genes (CD44, HYAL2, SPP1, MMP2) were associated with the overall survival (OS) and disease-free survival (DFS)of LGG; a novel four-gene signature was constructed. The prediction model showed good predictive value over 2-, 5-, 8-, and 10-year survival probability in both the development and validation sets. The risk score effectively divided patients into high- and low- risk groups with a distinct outcome. Multivariate analysis confirmed that the risk score and status of IDH were independent prognostic predictors of LGG. Among three LGG subgroups based on the presence of molecular parameters, IDH-mutant gliomas have a favorable OS, especially if combined with 1p/19q codeletion, which further confirmed the distinct biological pattern between three LGG subgroups, and the gene signature is able to divide LGG patients with the same IDH status into high- and low- risk groups. The high-risk group possessed a higher expression of immune checkpoints and was related to the activation of immunosuppressive pathways. Finally, this study provided a convenient tool for predicting patient survival. In summary, the four prognostic genes may be therapeutic targets and prognostic predictors for LGG; this four-gene signature has good prognostic prediction ability and can effectively distinguish high- and low-risk patients. High-risk patients are associated with higher immune checkpoint expression and activation of the immunosuppressive pathway, providing help for screening immunotherapy-sensitive patients.

Dataset Information

Predicting high-grade cancer at ten-core prostate biopsy using four kallikrein markers measured in blood in the ProtecT study.

Background

Methods

Results

Conclusions

Publications

Predicting high-grade cancer at ten-core prostate biopsy using four kallikrein markers measured in blood in the ProtecT study.

Similar Datasets

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