Dataset Information

Risk Stratification of Prostate Cancer Through Quantitative Assessment of PTEN Loss (qPTEN).

ABSTRACT:

Background

Phosphatase and tensin homolog (PTEN) loss has long been associated with adverse findings in early prostate cancer. Studies to date have yet to employ quantitative methods (qPTEN) for measuring of prognostically relevant amounts of PTEN loss in postsurgical settings and demonstrate its clinical application.

Methods

PTEN protein levels were measured by immunohistochemistry in radical prostatectomy samples from training (n = 410) and validation (n = 272) cohorts. PTEN loss was quantified per cancer cell and per tissue microarray core. Thresholds for identifying clinically relevant PTEN loss were determined using log-rank statistics in the training cohort. Univariate (Kaplan-Meier) and multivariate (Cox proportional hazards) analyses on various subpopulations were performed to assess biochemical recurrence-free survival (BRFS) and were independently validated. All statistical tests were two-sided.

Results

PTEN loss in more than 65% cancer cells was most clinically relevant and had statistically significant association with reduced BRFS in training (hazard ratio [HR] = 2.48, 95% confidence interval [CI] = 1.59 to 3.87; P < .001) and validation cohorts (HR = 4.22, 95% CI = 2.01 to 8.83; P < .001). The qPTEN scoring method identified patients who recurred within 5.4 years after surgery (P < .001). In men with favorable risk of biochemical recurrence (Cancer of the Prostate Risk Assessment - Postsurgical scores <5 and no adverse pathological features), qPTEN identified a subset of patients with shorter BRFS (HR = 5.52, 95% CI = 2.36 to 12.90; P < .001) who may be considered for intensified monitoring and/or adjuvant therapy.

Conclusions

Compared with previous qualitative approaches, qPTEN improves risk stratification of postradical prostatectomy patients and may be considered as a complementary tool to guide disease management after surgery.

SUBMITTER: Jamaspishvili T

PROVIDER: S-EPMC7669224 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Publications

Risk Stratification of Prostate Cancer Through Quantitative Assessment of PTEN Loss (qPTEN).

Jamaspishvili Tamara T Patel Palak G PG Niu Yi Y Vidotto Thiago T Caven Isabelle I Livergant Rachel R Fu Winnie W Kawashima Atsunari A How Nathan N Okello John B JB Guedes Liana B LB Ouellet Veronique V Picanço Clarissa C Koti Madhuri M Reis Rodolfo B RB Saad Fred F Mes-Masson Anne-Marie AM Lotan Tamara L TL Squire Jeremy A JA Peng Yingwei P YP Siemens D Robert DR Berman David M DM

Journal of the National Cancer Institute 20201101 11

<h4>Background</h4>Phosphatase and tensin homolog (PTEN) loss has long been associated with adverse findings in early prostate cancer. Studies to date have yet to employ quantitative methods (qPTEN) for measuring of prognostically relevant amounts of PTEN loss in postsurgical settings and demonstrate its clinical application.<h4>Methods</h4>PTEN protein levels were measured by immunohistochemistry in radical prostatectomy samples from training (n = 410) and validation (n = 272) cohorts. PTEN los ...[more]

PMID: 32129857

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Project description:BackgroundProstate cancer (PCa) ranks as the most common malignancy and the second leading cause of cancer-related death among males worldwide. The essential role of autophagy in the progression of PCa and treatment resistance has been preliminarily revealed. However, comprehensive molecular elucidations of the correlation between PCa and autophagy are rare.MethodWe obtained transcription information and corresponding clinicopathological profiles of PCa patients from TCGA, MSKCC, and GEO datasets. LAASO analysis was employed to select gene signatures and estimate the autophagy score for each patient. Correlations between the signature and prognosis of PCa were investigated by K-M and multivariate Cox regression analyses. A nomogram was established on the basis of the above results. Further validations relied on ROC, calibration analysis, decision curve analysis, and external cohorts. Variable activated signaling pathways were revealed using GSVA algorithms, and the genetic alteration landscape was elucidated via the oncodrive module from the "maftools" R package. In addition, we also examined the therapeutic role of the signature based on phenotype data from GDSC 2016.ResultSix autophagy-related genes were eventually selected to establish the signature, including ULK1, CAPN10, FKBP5, UBE2T, NLRC4, and BNIP3L. We used these genes and corresponding coefficients to calculate an autophagy score (AutS) for each patient in this study. A high AutS group and a low AutS group were divided on the mean AutS of the patients. Longer overall survival, higher Gleason score and PSA, and better response to ADT were observed in patients with high AutS. Meanwhile, we found that high AutS PCa was related to more proliferation-associated signaling activation and higher genetic mutation frequencies, manifesting a poor prognosis. A nomogram was constructed based on GS, T stage, PSA, and AutS as covariates. Its discriminative efficacy and clinical value were validated using robust statistical methods. Finally, we tested its prognostic value through two external cohorts and six published signatures.ConclusionThe autophagy-related gene signature is a highly discriminative model for risk stratification and drug therapy in PCa, and a nomogram incorporating AutS might be a promising tool for precision medicine.

Dataset Information

Risk Stratification of Prostate Cancer Through Quantitative Assessment of PTEN Loss (qPTEN).

Background

Methods

Results

Conclusions

Publications

Risk Stratification of Prostate Cancer Through Quantitative Assessment of PTEN Loss (qPTEN).

Similar Datasets

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