Refined Analysis of Prostate-specific Antigen Kinetics to Predict Prostate Cancer Active Surveillance Outcomes

Cooperberg, Matthew R.; Brooks, James D.; Faino, Anna; Newcomb, Lisa F.; Kearns, James T.; Carroll, Peter R.; Dash, Atreya; Etzioni, Ruth; Fabrizio, Michael D.; Gleave, Martin; Morgan, Todd M.; Nelson, Peter S.; Thompson, Ian M.; Wagner, Andrew A.; Lin, Daniel W.; Zheng, Yingye

doi:10.1016/j.eururo.2018.01.017

Cited by 35 publications

(41 citation statements)

References 20 publications

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“…Multivariate nomograms have been tested with AS patients to track CaP progression, however average AUCs reported were 0.59 [ 30 ]. Some groups have also attempted to identify serum, and urinary clinical biomarkers to determine the risk of progression in disease using protein expression, and tumor morphology [ 12 , 15 , 16 ]. For example, PCA3 and pro-PSA have been studied as predictors of more aggressive disease.…”

Section: Discussionmentioning

confidence: 99%

“…Several groups identified serum, urinary, and tissue biomarkers linked to aggressive CaP morphology in AS-eligible patients [ 12 ]. However, these markers have yet to be directly correlated with patients who progress or are unable to determine the risk when the patient is initially placed in AS [ 4 , 13 , 14 , 15 , 16 ]. Therefore, there is a need for an accurate, quantifiable method that can determine early whether a patient is at an increased risk of significant progression in CaP.…”

Section: Introductionmentioning

confidence: 99%

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Computer Extracted Features from Initial H&E Tissue Biopsies Predict Disease Progression for Prostate Cancer Patients on Active Surveillance

Chandramouli

Leo

Lee

et al. 2020

Cancers

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In this work, we assessed the ability of computerized features of nuclear morphology from diagnostic biopsy images to predict prostate cancer (CaP) progression in active surveillance (AS) patients. Improved risk characterization of AS patients could reduce over-testing of low-risk patients while directing high-risk patients to therapy. A total of 191 (125 progressors, 66 non-progressors) AS patients from a single site were identified using The Johns Hopkins University’s (JHU) AS-eligibility criteria. Progression was determined by pathologists at JHU. 30 progressors and 30 non-progressors were randomly selected to create the training cohort D1 (n = 60). The remaining patients comprised the validation cohort D2 (n = 131). Digitized Hematoxylin & Eosin (H&E) biopsies were annotated by a pathologist for CaP regions. Nuclei within the cancer regions were segmented using a watershed method and 216 nuclear features describing position, shape, orientation, and clustering were extracted. Six features associated with disease progression were identified using D1 and then used to train a machine learning classifier. The classifier was validated on D2. The classifier was further compared on a subset of D2 (n = 47) against pro-PSA, an isoform of prostate specific antigen (PSA) more linked with CaP, in predicting progression. Performance was evaluated with area under the curve (AUC). A combination of nuclear spatial arrangement, shape, and disorder features were associated with progression. The classifier using these features yielded an AUC of 0.75 in D2. On the 47 patient subset with pro-PSA measurements, the classifier yielded an AUC of 0.79 compared to an AUC of 0.42 for pro-PSA. Nuclear morphometric features from digitized H&E biopsies predicted progression in AS patients. This may be useful for identifying AS-eligible patients who could benefit from immediate curative therapy. However, additional multi-site validation is needed.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Computer Extracted Features from Initial H&E Tissue Biopsies Predict Disease Progression for Prostate Cancer Patients on Active Surveillance

Chandramouli

Leo

Lee

et al. 2020

Cancers

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“…4,5) Treatment with surgery or radiation is standard local therapy, but about one-third of patients will develop biochemical recurrence, 6) and many are living with urinary or sexual function problems afterward. 7) As a result, active surveillance is now recognized as a preferred strategy in patients with low-risk localized prostate cancer as an alternative to the immediate radical treatment. 6,8) For patients with metastatic prostate cancer, androgen deprivation therapy (ADT) is the standard of care.…”

Section: Introductionmentioning

confidence: 99%

“…7) As a result, active surveillance is now recognized as a preferred strategy in patients with low-risk localized prostate cancer as an alternative to the immediate radical treatment. 6,8) For patients with metastatic prostate cancer, androgen deprivation therapy (ADT) is the standard of care. However, most patients progress to castration-resistant prostate cancer (CRPC).…”

Section: Introductionmentioning

confidence: 99%

Telomerase Inhibition, Telomere Shortening, and Cellular Uptake of the Perylene Derivatives PM2 and PIPER in Prostate Cancer Cells

Kaewtunjai

Summart

Wongnoppavich

et al. 2019

Biological & Pharmaceutical Bulletin

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Prostate cancer is the second most common cancer among men worldwide, and it is ranked first in the United States and Europe. Since prostate cancer is slow-growing, active surveillance for low-risk cancer has been increasingly supported by various guidelines. Most prostate cancers reactivate telomerase to circumvent the replicative senescence caused by the end replication problem; therefore, telomerase inhibition is potentially useful for the suppression of prostate cancer progression during this active surveillance or for the prevention of cancer recurrence after conventional therapies. In this study, we demonstrated that the perylene derivatives, PM2 and PIPER, could suppress human telomerase reverse transcriptase (hTERT) expression and telomerase activity in the short-term treatment of androgen-dependent prostate cancer cell line LNCaP and the androgen-independent prostate cancer cell line PC3 prostate cancer cells. Long-term treatment with subcytotoxic doses of these compounds in both prostate cancer cells showed telomere shortening and a significant increase in senescent cells. Although the acute cytotoxicity of PM2 was about 30 times higher than that of PIPER in both prostate cancer cells, the cellular uptake of both compounds was comparable as determined by flow cytometry and fluorescent microscopy.

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“…However, there are still limitations, especially at a lower concentration (4-10 ng/mL). The PSA value has a rather weak diagnostic e ciency [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

5 Gleason score-associated gene signatures serve as novel biomarkers for identifying early recurring events and contributing to early diagnosis for Prostate Adenocarcinoma

Zhang¹,

Liu²,

Liu³

et al. 2020

Preprint

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Background: Prostate cancer (PCa) recurrence leads to much higher mortality than those without recurring events. Early and accurate laboratory diagnosis is particularly important for early identification of patients at high risk of recurrence and to benefit from additional systemic intervention. This study aimed to develop efficient and accurate Prostate Cancer diagnostic and prognostic biomarkers for the identification of initial tumor new events. Methods: Gene Expression Omnibus (GEO) datasets and The Cancer Genome Atlas (TCGA) data portal were utilized to obtain differentially expressed genes (DEGs) and clinical trait information in PCa. WGCNA analysis obtained the most relevant clinical traits and genes enriched in several modules. Univariate Cox regression analysis and multivariate Cox proportional hazards (Cox-PH) model was employed to candidate gene signatures related to Disease-Free Interval (DFI). Internal and external cohort was utilized to test and validate the validity, accuracy, and clinical utility of prognostic models.Results: We constructed and optimized a valid and credible model for predicting patient outcomes, based on 5 Gleason score-associated gene signatures (ZNF695, CENPA, TROAP, BIRC5, KIF20A). Furthermore, ROC and Kaplan-Meier analysis revealed higher diagnostic efficiency for PCa and predictive effectiveness in tumor recurrence and metastasis. Calibration curve also revealed high prediction accuracy in internal TCGA cohort and external GEO cohort. The model was prognostically significant in the stratified cohort, including TNM classification and Gleason score, and was deemed to be an independent PCa prognostic factor, and superioring to other clinicopathological characteristics. On the other hand, we also measured the correlation between gene signatures’ expression and inflammation landscape. 5 gene signatures were significantly positively correlated with tumor purity and negatively correlated with the immersion levels of CD8+ T cells. Conclusions: Our study identified and validated 5 gene signatures as biomarkers for prostate cancer diagnosis, providing an assessment of DFI while predicting tumor progression, possibly providing novel theories for the treatment of prostate cancer.

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Refined Analysis of Prostate-specific Antigen Kinetics to Predict Prostate Cancer Active Surveillance Outcomes

Cited by 35 publications

References 20 publications

Computer Extracted Features from Initial H&E Tissue Biopsies Predict Disease Progression for Prostate Cancer Patients on Active Surveillance

Computer Extracted Features from Initial H&E Tissue Biopsies Predict Disease Progression for Prostate Cancer Patients on Active Surveillance

Telomerase Inhibition, Telomere Shortening, and Cellular Uptake of the Perylene Derivatives PM2 and PIPER in Prostate Cancer Cells

5 Gleason score-associated gene signatures serve as novel biomarkers for identifying early recurring events and contributing to early diagnosis for Prostate Adenocarcinoma

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