Comprehensive Drug Testing of Patient-derived Conditionally Reprogrammed Cells from Castration-resistant Prostate Cancer

Background Technology development to enable the culture of human prostate cancer (PCa) progenitor cells is required for the identification of new, potentially curative therapies for PCa. Objective We established and characterized patient-derived conditionally reprogrammed cells (CRCs) to assess their biological properties and to apply these to test the efficacies of drugs. Design, setting, and participants CRCs were established from seven patient samples with disease ranging from primary PCa to advanced castration-resistant PCa (CRPC). The CRCs were characterized by genomic, transcriptomic, protein expression, and drug profiling. Outcome measurements and statistical analysis The phenotypic quantification of the CRCs was done based on immunostaining followed by image analysis with Advanced Cell Classifier using Random Forest supervised machine learning. Copy number aberrations (CNAs) were called from whole-exome sequencing and transcriptomics using in-house pipelines. Dose-response measurements were used to generate multiparameter drug sensitivity scores using R-statistical language. Results and limitations We generated six benign CRC cultures which all had an androgen receptor-negative, basal/transit-amplifying phenotype with few CNAs. In three-dimensional cell culture, these cells could re-express the androgen receptor. The CRCs from a CRPC patient (HUB.5) displayed multiple CNAs, many of which were shared with the parental tumor. We carried out high-throughput drug-response studies with 306 emerging and clinical cancer drugs. Using the benign CRCs as controls, we identified the Bcl-2 family inhibitor navitoclax as the most potent cancer-specific drug for the CRCs from a CRPC patient. Other drug efficacies included taxanes, mepacrine, and retinoids. Conclusions Comprehensive cancer pharmacopeia-wide drug testing of CRCs from a CRPC patient highlighted both known and novel drug sensitivities in PCa, including navitoclax, which is currently being tested in clinical trials of CRPC. Patient summary We describe an approach to generate patient-derived cancer cells from advanced prostate cancer and apply such cells to discover drugs that could be applied in clinical trials for castration-resistant prostate cancer.Peer reviewe

Reporting Magnetic Resonance Imaging in Men on Active Surveillance for Prostate Cancer : The PRECISE Recommendations-A Report of a European School of Oncology Task Force

Background: Published data on prostate magnetic resonance imaging (MRI) during follow-up of men on active surveillance are lacking. Current guidelines for prostate MRI reporting concentrate on prostate cancer (PCa) detection and staging. A standardised approach to prostate MRI reporting for active surveillance will facilitate the robust collection of evidence in this newly developing area. Objective: To develop preliminary recommendations for reporting of individual MRI studies in men on active surveillance and for researchers reporting the outcomes of cohorts of men having MRI on active surveillance. Design, setting, and participants: The RAND/UCLA Appropriateness Method was used. Experts in urology, radiology, and radiation oncology developed a set of 394 statements relevant to prostate MRI reporting in men on active surveillance for PCa. Each statement was scored for agreement on a 9-point scale by each panellist prior to a panel meeting. Each statement was discussed and rescored at the meeting. Outcome measurements and statistical analysis: Measures of agreement and consensus were calculated for each statement. The most important statements, derived from both group discussion and scores of agreement and consensus, were used to create the Prostate Cancer Radiological Estimation of Change in Sequential Evaluation (PRECISE) checklist and case report form. Results and limitations: Key recommendations include reporting the index lesion size using absolute values at baseline and at each subsequent MRI. Radiologists should assess the likelihood of true change over time (ie, change in size or change in lesion characteristics on one or more sequences) on a 1-5 scale. A checklist of items for reporting a cohort of men on active surveillance was developed. These items were developed based on expert consensus in many areas in which data are lacking, and they are expected to develop and change as evidence is accrued. Conclusions: The PRECISE recommendations are designed to facilitate the development of a robust evidence database for documenting changes in prostateMRI findings over time ofmen on active surveillance. If used, they will facilitate data collection to distinguish-measurement error and natural variability in MRI appearances from true radiologic progression. Patient summary: Few published reports are available on how to use and interpret magnetic resonance imaging for men on active surveillance for prostate cancer. The PRECISE panel recommends that data should be collected in a standardised manner so that natural variation in the appearance and measurement of cancer over time can be distinguished from changes indicating significant tumour progression. (C) 2016 European Association of Urology. Published by Elsevier B.V. All rights reserved.Peer reviewe

The impact of imputation quality on machine learning classifiers for datasets with missing values

Background: Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial. Missing data is found in most real-world datasets and these missing values are typically imputed using established methods, followed by classification of the now complete samples. The focus of the machine learning researcher is to optimise the classifier’s performance. Methods: We utilise three simulated and three real-world clinical datasets with different feature types and missingness patterns. Initially, we evaluate how the downstream classifier performance depends on the choice of classifier and imputation methods. We employ ANOVA to quantitatively evaluate how the choice of missingness rate, imputation method, and classifier method influences the performance. Additionally, we compare commonly used methods for assessing imputation quality and introduce a class of discrepancy scores based on the sliced Wasserstein distance. We also assess the stability of the imputations and the interpretability of model built on the imputed data. Results: The performance of the classifier is most affected by the percentage of missingness in the test data, with a considerable performance decline observed as the test missingness rate increases. We also show that the commonly used measures for assessing imputation quality tend to lead to imputed data which poorly matches the underlying data distribution, whereas our new class of discrepancy scores performs much better on this measure. Furthermore, we show that the interpretability of classifier models trained using poorly imputed data is compromised. Conclusions: It is imperative to consider the quality of the imputation when performing downstream classification as the effects on the classifier can be considerable

The impact of imputation quality on machine learning classifiers for datasets with missing values.

BACKGROUND: Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial. Missing data is found in most real-world datasets and these missing values are typically imputed using established methods, followed by classification of the now complete samples. The focus of the machine learning researcher is to optimise the classifier's performance. METHODS: We utilise three simulated and three real-world clinical datasets with different feature types and missingness patterns. Initially, we evaluate how the downstream classifier performance depends on the choice of classifier and imputation methods. We employ ANOVA to quantitatively evaluate how the choice of missingness rate, imputation method, and classifier method influences the performance. Additionally, we compare commonly used methods for assessing imputation quality and introduce a class of discrepancy scores based on the sliced Wasserstein distance. We also assess the stability of the imputations and the interpretability of model built on the imputed data. RESULTS: The performance of the classifier is most affected by the percentage of missingness in the test data, with a considerable performance decline observed as the test missingness rate increases. We also show that the commonly used measures for assessing imputation quality tend to lead to imputed data which poorly matches the underlying data distribution, whereas our new class of discrepancy scores performs much better on this measure. Furthermore, we show that the interpretability of classifier models trained using poorly imputed data is compromised. CONCLUSIONS: It is imperative to consider the quality of the imputation when performing downstream classification as the effects on the classifier can be considerable

The impact of imputation quality on machine learning classifiers for datasets with missing values.

BACKGROUND: Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial. Missing data is found in most real-world datasets and these missing values are typically imputed using established methods, followed by classification of the now complete samples. The focus of the machine learning researcher is to optimise the classifier's performance. METHODS: We utilise three simulated and three real-world clinical datasets with different feature types and missingness patterns. Initially, we evaluate how the downstream classifier performance depends on the choice of classifier and imputation methods. We employ ANOVA to quantitatively evaluate how the choice of missingness rate, imputation method, and classifier method influences the performance. Additionally, we compare commonly used methods for assessing imputation quality and introduce a class of discrepancy scores based on the sliced Wasserstein distance. We also assess the stability of the imputations and the interpretability of model built on the imputed data. RESULTS: The performance of the classifier is most affected by the percentage of missingness in the test data, with a considerable performance decline observed as the test missingness rate increases. We also show that the commonly used measures for assessing imputation quality tend to lead to imputed data which poorly matches the underlying data distribution, whereas our new class of discrepancy scores performs much better on this measure. Furthermore, we show that the interpretability of classifier models trained using poorly imputed data is compromised. CONCLUSIONS: It is imperative to consider the quality of the imputation when performing downstream classification as the effects on the classifier can be considerable

The impact of imputation quality on machine learning classifiers for datasets with missing values

Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial. Missing data is found in most real-world datasets and these missing values are typically imputed using established methods, followed by classification of the now complete samples. The focus of the machine learning researcher is to optimise the classifier’s performance.Peer reviewe