Dataset Information

Identification of fluorescence in situ hybridization assay markers for prediction of disease progression in prostate cancer patients on active surveillance.

ABSTRACT: Prostate Cancer (PCa) is the second most prevalent cancer among U.S. males. In recent decades many men with low risk PCa have been over diagnosed and over treated. Given significant co-morbidities associated with definitive treatments, maximizing patient quality of life while recognizing early signs of aggressive disease is essential. There remains a need to better stratify newly diagnosed men according to the risk of disease progression, identifying, with high sensitivity and specificity, candidates for active surveillance versus intervention therapy. The objective of this study was to select fluorescence in situ hybridization (FISH) panels that differentiate non-progressive from progressive disease in patients with low and intermediate risk PCa.We performed a retrospective case-control study to evaluate FISH biomarkers on specimens from PCa patients with clinically localised disease (T1c-T2c) enrolled in Watchful waiting (WW)/Active Surveillance (AS). The patients were classified into cases (progressed to clinical intervention within 10 years), and controls (did not progress in 10 years). Receiver Operating Characteristic (ROC) curve analysis was performed to identify the best 3-5 probe combinations. FISH parameters were then combined with the clinical parameters ? National Comprehensive Cancer Network (NNCN) risk categories ? in the logistic regression model.Seven combinations of FISH parameters with the highest sensitivity and specificity for discriminating cases from controls were selected based on the ROC curve analysis. In the logistic regression model, these combinations contributed significantly to the prediction of PCa outcome. The combination of NCCN risk categories and FISH was additive to the clinical parameters or FISH alone in the final model, with odds ratios of 5.1 to 7.0 for the likelihood of the FISH-positive patients in the intended population to develop disease progression, as compared to the FISH-negative group.Combinations of FISH parameters discriminating progressive from non-progressive PCa were selected based on ROC curve analysis. The combination of clinical parameters and FISH outperformed clinical parameters alone, and was complimentary to clinical parameters in the final model, demonstrating potential utility of multi-colour FISH panels as an auxiliary tool for PCa risk stratification. Further studies with larger cohorts are planned to confirm these findings.

SUBMITTER: Pestova K

PROVIDER: S-EPMC5749018 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of fluorescence in situ hybridization assay markers for prediction of disease progression in prostate cancer patients on active surveillance.

Pestova Katerina K Koch Adam J AJ Quesenberry Charles P CP Shan Jun J Zhang Ying Y Leimpeter Amethyst D AD Blondin Beth B Sitailo Svetlana S Buckingham Lela L Du Jing J Fei Huixin H Van Den Eeden Stephen K SK

BMC cancer 20180102 1

<h4>Background</h4>Prostate Cancer (PCa) is the second most prevalent cancer among U.S. males. In recent decades many men with low risk PCa have been over diagnosed and over treated. Given significant co-morbidities associated with definitive treatments, maximizing patient quality of life while recognizing early signs of aggressive disease is essential. There remains a need to better stratify newly diagnosed men according to the risk of disease progression, identifying, with high sensitivity and ...[more]

PMID: 29291731

Similar Datasets

Project description:Serial MRI is an essential assessment tool in prostate cancer (PCa) patients enrolled on active surveillance (AS). However, it has only moderate sensitivity for predicting histopathological tumour progression at follow-up, which is in part due to the subjective nature of its clinical reporting and variation among centres and readers. In this study, we used a long short-term memory (LSTM) recurrent neural network (RNN) to develop a time series radiomics (TSR) predictive model that analysed longitudinal changes in tumour-derived radiomic features across 297 scans from 76 AS patients, 28 with histopathological PCa progression and 48 with stable disease. Using leave-one-out cross-validation (LOOCV), we found that an LSTM-based model combining TSR and serial PSA density (AUC 0.86 [95% CI: 0.78-0.94]) significantly outperformed a model combining conventional delta-radiomics and delta-PSA density (0.75 [0.64-0.87]; p = 0.048) and achieved comparable performance to expert-performed serial MRI analysis using the Prostate Cancer Radiologic Estimation of Change in Sequential Evaluation (PRECISE) scoring system (0.84 [0.76-0.93]; p = 0.710). The proposed TSR framework, therefore, offers a feasible quantitative tool for standardising serial MRI assessment in PCa AS. It also presents a novel methodological approach to serial image analysis that can be used to support clinical decision-making in multiple scenarios, from continuous disease monitoring to treatment response evaluation. KEY POINTS: •LSTM RNN can be used to predict the outcome of PCa AS using time series changes in tumour-derived radiomic features and PSA density. •Using all available TSR features and serial PSA density yields a significantly better predictive performance compared to using just two time points within the delta-radiomics framework. •The concept of TSR can be applied to other clinical scenarios involving serial imaging, setting out a new field in AI-driven radiology research.

Project description:BackgroundCurrently, follow-up protocols are applied equally to men on active surveillance (AS) for prostate cancer (PCa) regardless of findings at their initial follow-up biopsy. To determine whether less intensive follow-up is suitable following negative biopsy findings, we assessed the risk of converting to active treatment, any subsequent upgrading, volume progression (>33% positive cores), and serious upgrading (grade group >2) for negative compared with positive findings on initial follow-up biopsy.Methods13,161 men from 24 centres participating in the Global Action Plan Active Surveillance Prostate Cancer [GAP3] consortium database, with baseline grade group ≤2, PSA ≤ 20 ng/mL, cT-stage 1-2, diagnosed after 1995, and ≥1 follow-up biopsy, were included in this study. Risk of converting to treatment was assessed using multivariable mixed-effects survival regression. Odds of volume progression, any upgrading and serious upgrading were assessed using mix-effects binary logistic regression for men with ≥2 surveillance biopsies.Results27% of the cohort (n = 3590) had no evidence of PCa at their initial biopsy. Over 50% of subsequent biopsies in this group were also negative. A negative initial biopsy was associated with lower risk of conversion (adjusted hazard ratio: 0.45; 95% confidence interval [CI]: 0.42-0.49), subsequent upgrading (adjusted odds ratio [OR]: 0.52; 95%CI: 0.45-0.62) and serious upgrading (OR: 0.74; 95%CI: 0.59-92). Radiological progression was not assessed due to limited imaging data.ConclusionDespite heterogeneity in follow-up schedules, findings from this global study indicated reduced risk of converting to treatment, volume progression, any upgrading and serious upgrading among men whose initial biopsy findings were negative compared with positive. Given the low risk of progression and high likelihood of further negative biopsy findings, consideration should be given to decreasing follow-up intensity for this group to reduce unnecessary invasive biopsies.

Dataset Information

Identification of fluorescence in situ hybridization assay markers for prediction of disease progression in prostate cancer patients on active surveillance.

Publications

Identification of fluorescence in situ hybridization assay markers for prediction of disease progression in prostate cancer patients on active surveillance.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets