On the Design of Combination Cancer Therapy.

Combination therapy programs are the hallmark of the successful treatment of all forms of human malignancies. In this issue of Cell, Palmer and Sorger present data suggesting that cell culture results indicative of synergistic anticancer drug interactions rarely translate clinically and that the results of combination therapies in mouse models or human clinical trials, even if successful, are best explained by the independent activities of the individually administered drugs.

OpenGeneMed: a portable, flexible and customizable informatics hub for the coordination of next-generation sequencing studies in support of precision medicine trials.

Trials involving genomic-driven treatment selection require the coordination of many teams interacting with a great variety of information. The need of better informatics support to manage this complex set of operations motivated the creation of OpenGeneMed. OpenGeneMed is a stand-alone and customizable version of GeneMed (Zhao et al. GeneMed: an informatics hub for the coordination of next-generation sequencing studies that support precision oncology clinical trials. Cancer Inform 2015;14(Suppl 2):45), a web-based interface developed for the National Cancer Institute Molecular Profiling-based Assignment of Cancer Therapy (NCI-MPACT) clinical trial coordinated by the NIH. OpenGeneMed streamlines clinical trial management and it can be used by clinicians, lab personnel, statisticians and researchers as a communication hub. It automates the annotation of genomic variants identified by sequencing tumor DNA, classifies the actionable mutations according to customizable rules and facilitates quality control in reviewing variants. The system generates summarized reports with detected genomic alterations that a treatment review team can use for treatment assignment. OpenGeneMed allows collaboration to happen seamlessly along the clinical pipeline; it helps reduce errors made transferring data between groups and facilitates clear documentation along the pipeline. OpenGeneMed is distributed as a stand-alone virtual machine, ready for deployment and use from a web browser; its code is customizable to address specific needs of different clinical trials and research teams. Examples on how to change the code are provided in the technical documentation distributed with the virtual machine. In summary, OpenGeneMed offers an initial set of features inspired by our experience with GeneMed, a system that has been proven to be efficient and successful for coordinating the application of next-generation sequencing in the NCI-MPACT trial.

Criteria for the use of omics-based predictors in clinical trials.

The US National Cancer Institute (NCI), in collaboration with scientists representing multiple areas of expertise relevant to 'omics'-based test development, has developed a checklist of criteria that can be used to determine the readiness of omics-based tests for guiding patient care in clinical trials. The checklist criteria cover issues relating to specimens, assays, mathematical modelling, clinical trial design, and ethical, legal and regulatory aspects. Funding bodies and journals are encouraged to consider the checklist, which they may find useful for assessing study quality and evidence strength. The checklist will be used to evaluate proposals for NCI-sponsored clinical trials in which omics tests will be used to guide therapy.

Amount, Source, and Quality of Support as Predictors of Women's Birth Evaluations.

BACKGROUND: This paper examines the separate effects of the perceived amount, source, and quality of support during labor and delivery on women's positive and negative evaluations of their birth experiences. METHODS: Data come from the Listening to Mothers I and II (LTM) surveys (n = 2,765). Women's perception of support was regressed separately onto indices of positive and negative words that women associated with their labor and delivery. RESULTS: The total number of support sources, type of support person, and quality of support all impacted women's birth evaluations across different regression models, controlling for demographics, birth interventions, and other birth characteristics. Support overall had a greater effect on increasing women's positive evaluations, but was not as protective against negative evaluations. Support from medical and birth professionals (doctors, nurses, doulas) had the greatest effect on women's positive evaluations. Good partner support was complexly related: it was associated with less positive evaluations but also appeared to have a protective effect against negative birth evaluations. DISCUSSION: Support in childbirth is a complex concept with multiple dimensions that matter for women's birth evaluations. Support from nursing staff, doctors, and doulas is important for enabling positive evaluations while support from partners is more complexly related to women's evaluations. Research on support for laboring women should more extensively address the division of labor between different sources of support.

Clinical implications of a multiparametric magnetic resonance imaging based nomogram applied to prostate cancer active surveillance.

PURPOSE: Multiparametric magnetic resonance imaging may be beneficial in the search for rational ways to decrease prostate cancer intervention in patients on active surveillance. We applied a previously generated nomogram based on multiparametric magnetic resonance imaging to predict active surveillance eligibility based on repeat biopsy outcomes. MATERIALS AND METHODS: We reviewed the records of 85 patients who met active surveillance criteria at study entry based on initial biopsy and who then underwent 3.0 Tesla multiparametric magnetic resonance imaging with subsequent magnetic resonance imaging/ultrasound fusion guided prostate biopsy between 2007 and 2012. We assessed the accuracy of a previously published nomogram in patients on active surveillance before confirmatory biopsy. For each cutoff we determined the number of biopsies avoided (ie reliance on magnetic resonance imaging alone without rebiopsy) over the full range of nomogram cutoffs. RESULTS: We assessed the performance of the multiparametric magnetic resonance imaging active surveillance nomogram based on a decision to perform biopsy at various nomogram generated probabilities. Based on cutoff probabilities of 19% to 32% on the nomogram the number of patients who could be spared repeat biopsy was 27% to 68% of the active surveillance cohort. The sensitivity of the test in this interval was 97% to 71% and negative predictive value was 91% to 81%. CONCLUSIONS: Multiparametric magnetic resonance imaging based nomograms may reasonably decrease the number of repeat biopsies in patients on active surveillance by as much as 68%. Analysis over the full range of nomogram generated probabilities allows patient and caregiver preference based decision making on the risk assumed for the benefit of fewer repeat biopsies.

Diagnostic value of biparametric magnetic resonance imaging (MRI) as an adjunct to prostate-specific antigen (PSA)-based detection of prostate cancer in men without prior biopsies.

OBJECTIVES: To determine the diagnostic yield of analysing biparametric (T2- and diffusion-weighted) magnetic resonance imaging (B-MRI) for prostate cancer detection compared with standard digital rectal examination (DRE) and prostate-specific antigen (PSA)-based screening. PATIENTS AND METHODS: Review of patients who were enrolled in a trial to undergo multiparametric-prostate (MP)-MRI and MR/ultrasound fusion-guided prostate biopsy at our institution identified 143 men who underwent MP-MRI in addition to standard DRE and PSA-based prostate cancer screening before any prostate biopsy. Patient demographics, DRE staging, PSA level, PSA density (PSAD), and B-MRI findings were assessed for association with prostate cancer detection on biopsy. RESULTS: Men with detected prostate cancer tended to be older, with a higher PSA level, higher PSAD, and more screen-positive lesions (SPL) on B-MRI. B-MRI performed well for the detection of prostate cancer with an area under the curve (AUC) of 0.80 (compared with 0.66 and 0.74 for PSA level and PSAD, respectively). We derived combined PSA and MRI-based formulas for detection of prostate cancer with optimised thresholds. (i) for PSA and B-MRI: PSA level + 6 x (the number of SPL) > 14 and (ii) for PSAD and B-MRI: 14 × (PSAD) + (the number of SPL) >4.25. AUC for equations 1 and 2 were 0.83 and 0.87 and overall accuracy of prostate cancer detection was 79% in both models. CONCLUSIONS: The number of lesions positive on B-MRI outperforms PSA alone in detection of prostate cancer. Furthermore, this imaging criteria coupled as an adjunct with PSA level and PSAD, provides even more accuracy in detecting clinically significant prostate cancer.

Comparison of MR/ultrasound fusion-guided biopsy with ultrasound-guided biopsy for the diagnosis of prostate cancer.

IMPORTANCE: Targeted magnetic resonance (MR)/ultrasound fusion prostate biopsy has been shown to detect prostate cancer. The implications of targeted biopsy alone vs standard extended-sextant biopsy or the 2 modalities combined are not well understood. OBJECTIVE: To assess targeted vs standard biopsy and the 2 approaches combined for the diagnosis of intermediate- to high-risk prostate cancer. DESIGN, SETTING, AND PARTICIPANTS: Prospective cohort study of 1003 men undergoing both targeted and standard biopsy concurrently from 2007 through 2014 at the National Cancer Institute in the United States. Patients were referred for elevated level of prostate-specific antigen (PSA) or abnormal digital rectal examination results, often with prior negative biopsy results. Risk categorization was compared among targeted and standard biopsy and, when available, whole-gland pathology after prostatectomy as the "gold standard." INTERVENTIONS: Patients underwent multiparametric prostate magnetic resonance imaging to identify regions of prostate cancer suspicion followed by targeted MR/ultrasound fusion biopsy and concurrent standard biopsy. MAIN OUTCOMES AND MEASURES: The primary objective was to compare targeted and standard biopsy approaches for detection of high-risk prostate cancer (Gleason score ≥ 4 + 3); secondary end points focused on detection of low-risk prostate cancer (Gleason score 3 + 3 or low-volume 3 + 4) and the biopsy ability to predict whole-gland pathology at prostatectomy. RESULTS: Targeted MR/ultrasound fusion biopsy diagnosed 461 prostate cancer cases, and standard biopsy diagnosed 469 cases. There was exact agreement between targeted and standard biopsy in 690 men (69%) undergoing biopsy. Targeted biopsy diagnosed 30% more high-risk cancers vs standard biopsy (173 vs 122 cases, P < .001) and 17% fewer low-risk cancers (213 vs 258 cases, P < .001). When standard biopsy cores were combined with the targeted approach, an additional 103 cases (22%) of mostly low-risk prostate cancer were diagnosed (83% low risk, 12% intermediate risk, and 5% high risk). The predictive ability of targeted biopsy for differentiating low-risk from intermediate- and high-risk disease in 170 men with whole-gland pathology after prostatectomy was greater than that of standard biopsy or the 2 approaches combined (area under the curve, 0.73, 0.59, and 0.67, respectively; P < .05 for all comparisons). CONCLUSIONS AND RELEVANCE: Among men undergoing biopsy for suspected prostate cancer, targeted MR/ultrasound fusion biopsy, compared with standard extended-sextant ultrasound-guided biopsy, was associated with increased detection of high-risk prostate cancer and decreased detection of low-risk prostate cancer. Future studies will be needed to assess the ultimate clinical implications of targeted biopsy. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT00102544.

Accuracy of multiparametric magnetic resonance imaging in confirming eligibility for active surveillance for men with prostate cancer.

BACKGROUND: Active surveillance (AS) is an attempt to avoid overtreatment of clinically insignificant prostate cancer (PCa); however, patient selection remains controversial. Multiparametric prostate magnetic resonance imaging (MP-MRI) may help better select AS candidates. METHODS: We reviewed a cohort of men who underwent MP-MRI with MRI/Ultrasound fusion-guided prostate biopsy and selected potential AS patients at entry using Johns Hopkins criteria. MP-MRI findings were assessed, including number of lesions, dominant lesion diameter, total lesion volume, prostate volume, and lesion density (calculated as total lesion volume/prostate volume). Lesions were assigned a suspicion score for cancer by MRI. AS criteria were reapplied based on the confirmatory biopsy, and accuracy of MP-MRI in predicting AS candidacy was assessed. Logistic regression modeling and chi-square statistics were used to assess associations between MP-MRI interpretation and biopsy results. RESULTS: Eighty-five patients qualified for AS with a mean age of 60.2 years and mean prostate-specific antigen level of 4.8 ng/mL. Of these, 25 patients (29%) were reclassified as not meeting AS criteria based on confirmatory biopsy. Number of lesions, lesion density, and highest MRI lesion suspicion were significantly associated with confirmatory biopsy AS reclassification. These MRI-based factors were combined to create a nomogram that generates a probability for confirmed AS candidacy. CONCLUSION: As clinicians counsel patients with PCa, MP-MRI may contribute to the decision-making process when considering AS. Three MRI-based factors (number of lesions, lesion suspicion, and lesion density) were associated with confirmatory biopsy outcome and reclassification. A nomogram using these factors has promising predictive accuracy for which future validation is necessary. Cancer 2013;119:3359-66. Published 2013. This article is a U.S. Government work and is in the public domain in the USA.

Criteria for the use of omics-based predictors in clinical trials: explanation and elaboration.

High-throughput 'omics' technologies that generate molecular profiles for biospecimens have been extensively used in preclinical studies to reveal molecular subtypes and elucidate the biological mechanisms of disease, and in retrospective studies on clinical specimens to develop mathematical models to predict clinical endpoints. Nevertheless, the translation of these technologies into clinical tests that are useful for guiding management decisions for patients has been relatively slow. It can be difficult to determine when the body of evidence for an omics-based test is sufficiently comprehensive and reliable to support claims that it is ready for clinical use, or even that it is ready for definitive evaluation in a clinical trial in which it may be used to direct patient therapy. Reasons for this difficulty include the exploratory and retrospective nature of many of these studies, the complexity of these assays and their application to clinical specimens, and the many potential pitfalls inherent in the development of mathematical predictor models from the very high-dimensional data generated by these omics technologies. Here we present a checklist of criteria to consider when evaluating the body of evidence supporting the clinical use of a predictor to guide patient therapy. Included are issues pertaining to specimen and assay requirements, the soundness of the process for developing predictor models, expectations regarding clinical study design and conduct, and attention to regulatory, ethical, and legal issues. The proposed checklist should serve as a useful guide to investigators preparing proposals for studies involving the use of omics-based tests. The US National Cancer Institute plans to refer to these guidelines for review of proposals for studies involving omics tests, and it is hoped that other sponsors will adopt the checklist as well.

Using cross-validation to evaluate predictive accuracy of survival risk classifiers based on high-dimensional data.

Developments in whole genome biotechnology have stimulated statistical focus on prediction methods. We review here methodology for classifying patients into survival risk groups and for using cross-validation to evaluate such classifications. Measures of discrimination for survival risk models include separation of survival curves, time-dependent ROC curves and Harrell's concordance index. For high-dimensional data applications, however, computing these measures as re-substitution statistics on the same data used for model development results in highly biased estimates. Most developments in methodology for survival risk modeling with high-dimensional data have utilized separate test data sets for model evaluation. Cross-validation has sometimes been used for optimization of tuning parameters. In many applications, however, the data available are too limited for effective division into training and test sets and consequently authors have often either reported re-substitution statistics or analyzed their data using binary classification methods in order to utilize familiar cross-validation. In this article we have tried to indicate how to utilize cross-validation for the evaluation of survival risk models; specifically how to compute cross-validated estimates of survival distributions for predicted risk groups and how to compute cross-validated time-dependent ROC curves. We have also discussed evaluation of the statistical significance of a survival risk model and evaluation of whether high-dimensional genomic data adds predictive accuracy to a model based on standard covariates alone.

Gendered contexts: masculinity, knowledge, and attitudes toward biotechnology.

Research into the public understanding of science has revealed that the effect of scientific knowledge on attitudes toward science is contextualized by gender; however there has not yet been an account of how gender contextualizes knowledge. This paper investigates how gender identification affects attitudes toward science, and tempers the effects of scientific knowledge on attitudes toward science. Utilizing data from the Eurobarometer 52.1, it is predicted that when males perceive a threat to their masculinity (as measured by a perceived threat to their financial security) they will become more likely to be aversive to biotechnology, and that increased levels of perceived threat will reduce the effects of knowledge on attitudes toward biotechnology. Moreover, it is predicted that these effects will be smaller than their female counterparts. All predictions are affirmed.

Gender differences in knowledge and attitude towards biotechnology.

The relationship between gender, knowledge of biotechnology, attitudes toward biotechnology, and various socio-demographic variables was investigated using the Eurobarometer 52.1. It was found that neither socio-demographics, nor differing levels of scientific knowledge could explain females' greater probability of being pessimistic toward biotechnology. After running separate models for males and females, it was discovered that, for males, more knowledge of biotechnology decreased their probability of being pessimistic about science, but for females more knowledge of biotechnology actually lead to a greater probability of being pessimistic. Further, a gender-education interaction was discovered that revealed that, for males, education and knowledge of biotechnology have independent effects on attitudes, but for females education has no effect on attitudes towards biotechnology when knowledge is controlled. The results for females complicate the deficit model of social support for science, which posits that more knowledge of science always leads to more positive attitudes.

Phase 2 study of pembrolizumab in patients with advanced rare cancers.

BACKGROUND: Patients with advanced rare cancers have poor prognosis and few treatment options. As immunotherapy is effective across multiple cancer types, we aimed to assess pembrolizumab (programmed cell death 1 (PD-1) inhibitor) in patients with advanced rare cancers. METHODS: In this open-label, phase 2 trial, patients with advanced rare cancers whose tumors had progressed on standard therapies, if available, within the previous 6 months were enrolled in nine tumor-specific cohorts and a 10th cohort for other rare histologies. Pembrolizumab 200 mg was administered intravenously every 21 days. The primary endpoint was non-progression rate (NPR) at 27 weeks; secondary endpoints were safety and tolerability, objective response rate (ORR), and clinical benefit rate (CBR). RESULTS: A total of 127 patients treated between August 15, 2016 and July 27, 2018 were included in this analysis. At the time of data cut-off, the NPR at 27 weeks was 28% (95% CI, 19% to 37%). A confirmed objective response (OR) was seen in 15 of 110 (14%) evaluable patients (complete response in one and partial response in 14). CBR, defined as the percentage of patients with an OR or stable disease ≥4 months, was 38% (n=42). Treatment was ongoing in 11 of 15 patients with OR at last follow-up. In the cohort with squamous cell carcinoma (SCC) of the skin, the NPR at 27 weeks was 36%, ORR 31%, and CBR 38%. In patients with adrenocortical carcinoma (ACC), NPR at 27 weeks was 31%, ORR 15%, and CBR 54%. In the patients with carcinoma of unknown primary (CUP), NPR at 27 weeks was 33%, ORR 23%, and CBR 54%. In the paraganglioma-pheochromocytoma cohort, NPR at 27 weeks was 43%, ORR 0%, and CBR 75%. Treatment-related adverse events (TRAEs) occurred in 66 of 127 (52%) patients, and 12 (9%) had grade ≥3 TRAEs. The most common TRAEs were fatigue (n=25) and rash (n=17). There were six deaths, all of which were unrelated to the study drug. CONCLUSIONS: The favorable toxicity profile and antitumor activity seen in patients with SCC of skin, ACC, CUP, and paraganglioma-pheochromocytoma supports further evaluation of pembrolizumab in this patient population. TRIAL REGISTRATION NUMBER: NCT02721732.

How large a training set is needed to develop a classifier for microarray data?

PURPOSE: A common goal of gene expression microarray studies is the development of a classifier that can be used to divide patients into groups with different prognoses, or with different expected responses to a therapy. These types of classifiers are developed on a training set, which is the set of samples used to train a classifier. The question of how many samples are needed in the training set to produce a good classifier from high-dimensional microarray data is challenging. EXPERIMENTAL DESIGN: We present a model-based approach to determining the sample size required to adequately train a classifier. RESULTS: It is shown that sample size can be determined from three quantities: standardized fold change, class prevalence, and number of genes or features on the arrays. Numerous examples and important experimental design issues are discussed. The method is adapted to address ex post facto determination of whether the size of a training set used to develop a classifier was adequate. An interactive web site for performing the sample size calculations is provided. CONCLUSION: We showed that sample size calculations for classifier development from high-dimensional microarray data are feasible, discussed numerous important considerations, and presented examples.

Identification of pharmacodynamic biomarkers and common molecular mechanisms of response to genotoxic agents in cancer cell lines.

PURPOSE: Genotoxic agents (GAs) including cisplatin, doxorubicin, gemcitabine, and topotecan are often used in cancer treatment. However, the response to GAs is variable among patients and predictive biomarkers are inadequate to select patients for treatment. Accurate and rapid pharmacodynamics measures of response can, thus, be useful for monitoring therapy and improve clinical outcomes. METHODS: This study focuses on integrating a database of genome-wide response to treatment (The NCI Transcriptional Pharmacodynamics Workbench) with a database of baseline gene expression (GSE32474) for the NCI-60 cell lines to identify mechanisms of response and pharmacodynamic (PD) biomarkers. RESULTS AND CONCLUSIONS: Our analysis suggests that GA-induced endoplasmic reticulum (ER) stress may signal for GA-induced cell death. Reducing the uptake of GA, activating DNA repair, and blocking ER-stress induction cooperate to prevent GA-induced cell death in the GA-resistant cells. ATF3, DDIT3, CARS, and PPP1R15A appear as possible candidate PD biomarkers for monitoring the progress of GA treatment. Further validation studies on the proposed intrinsic drug-resistant mechanism and candidate genes are needed using in vivo data from either patient-derived xenograft models or clinical chemotherapy trials.

D3Oncoprint: Stand-Alone Software to Visualize and Dynamically Explore Annotated Genomic Mutation Files.

PURPOSE: Advances in next-generation sequencing technologies have led to a reduction in sequencing costs, which has increased the availability of genomic data sets to many laboratories. Increasing amounts of sequencing data require effective analysis tools to use genomic data for biologic discovery and patient management. Available packages typically require advanced programming knowledge and system administration privileges, or they are Web services that force researchers to work on outside servers. METHODS: To support the interactive exploration of genomic data sets on local machines with no programming skills required, we developed D3Oncoprint, a standalone application to visualize and dynamically explore annotated genomic mutation files. D3Oncoprint provides links to curated variants lists from CIViC, My Cancer Genome, OncoKB, and Food and Drug Administration-approved drugs to facilitate the use of genomic data for biomedical discovery and application. D3Oncoprint also includes curated gene lists from BioCarta pathways and FoundationOne cancer panels to explore commonly investigated biologic processes. RESULTS: This software provides a flexible environment to dynamically explore one or more variant mutation profiles provided as input. The focus on interactive visualization with biologic and medical annotation significantly lowers the barriers between complex genomics data and biomedical investigators. We describe how D3Oncoprint helps researchers explore their own data without the need for an extensive computational background. CONCLUSION: D3Oncoprint is free software for noncommercial use. It is available for download from the Web site of the Biometric Research Program of the Division of Cancer Treatment and Diagnosis at the National Cancer Institute ( https://brb.nci.nih.gov/d3oncoprint ). We believe that this tool provides an important means of empowering researchers to translate information from collected data sets to biologic insights and clinical development.

Gene expression patterns and profile changes pre- and post-erlotinib treatment in patients with metastatic breast cancer.

PURPOSE: To delineate gene expression patterns and profile changes in metastatic tumor biopsies at baseline and 1 month after treatment with the epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor erlotinib in patients with metastatic breast cancer. EXPERIMENTAL DESIGN: Patients were treated with 150 mg of oral erlotinib daily. Gene expression profiles were measured with Affymetrix U133A GeneChip and immunohistochemistry was used to validate microarray findings. RESULTS: Estrogen receptor (ER) status by immunohistochemistry is nearly coincided with the two major expression clusters determined by expression of genes using unsupervised hierarchical clustering analysis. One of 10 patients had an EGFR-positive tumor detected by both microarray and immunohistochemistry. In this tumor, tissue inhibitor of metalloproteinases-3 and collagen type 1 alpha 2, which are the EGF-down-regulated growth repressors, were significantly increased by erlotinib. Gene changes in EGFR-negative tumors are those of G-protein-linked and cell surface receptor-linked signaling. Gene ontology comparison analysis pretreatment and posttreatment in EGFR-negative tumors revealed biological process categories that have more genes differentially expressed than expected by chance. Among 495 gene ontology categories, the significant differed gene ontology groups include G-protein-coupled receptor protein signaling (34 genes, P = 0.002) and cell surface receptor-linked signal transduction (74 genes, P = 0.007). CONCLUSIONS: ER status reflects the major difference in gene expression pattern in metastatic breast cancer. Erlotinib had effects on genes of EGFR signaling pathway in the EGFR-positive tumor and on gene ontology biological process categories or genes that have function in signal transduction in EGFR-negative tumors.

Interlaboratory comparability study of cancer gene expression analysis using oligonucleotide microarrays.

A key step in bringing gene expression data into clinical practice is the conduct of large studies to confirm preliminary models. The performance of such confirmatory studies and the transition to clinical practice requires that microarray data from different laboratories are comparable and reproducible. We designed a study to assess the comparability of data from four laboratories that will conduct a larger microarray profiling confirmation project in lung adenocarcinomas. To test the feasibility of combining data across laboratories, frozen tumor tissues, cell line pellets, and purified RNA samples were analyzed at each of the four laboratories. Samples of each type and several subsamples from each tumor and each cell line were blinded before being distributed. The laboratories followed a common protocol for all steps of tissue processing, RNA extraction, and microarray analysis using Affymetrix Human Genome U133A arrays. High within-laboratory and between-laboratory correlations were observed on the purified RNA samples, the cell lines, and the frozen tumor tissues. Intraclass correlation within laboratories was only slightly stronger than between laboratories, and the intraclass correlation tended to be weakest for genes expressed at low levels and showing small variation. Finally, hierarchical cluster analysis revealed that the repeated samples clustered together regardless of the laboratory in which the experiments were done. The findings indicate that under properly controlled conditions it is feasible to perform complete tumor microarray analysis, from tissue processing to hybridization and scanning, at multiple independent laboratories for a single study.

Efficiency of Prostate Cancer Diagnosis by MR/Ultrasound Fusion-Guided Biopsy vs Standard Extended-Sextant Biopsy for MR-Visible Lesions.

BACKGROUND: Use of magnetic resonance (MR) imaging to improve prostate biopsy efficiency is rapidly gaining in popularity. The aim of this study was to assess the biopsy efficiency of MR/ultrasound (MR/US) fusion-guided ("targeted") biopsies vs extended-sextant 12-core ("standard") biopsies for overall and high-grade prostate cancer detection. METHODS: From August 2007 to February 2014, 1003 men were enrolled in a prospective trial comparing the diagnostic yield of targeted and standard prostate biopsies performed during the same session. A total of 17 619 biopsy cores were reviewed. Biopsy efficiency was determined by dividing the total number of cores by the number of positive cores obtained. All statistical tests were two-sided. RESULTS: A mean of 12.3 (95% confidence interval [CI] = 12.2 to 12.3) standard and 5.3 (95% CI = 5.1 to 5.5) targeted biopsy cores were obtained from each patient. Targeted biopsy detected 461 cases of prostate cancer, of which 173 (37.5%) were high grade (Gleason score ≥ 4 + 3), while standard biopsy detected 469 cases of prostate cancer, of which 122 (26.5%) were high grade. The percentage of biopsy cores positive for prostate cancer, irrespective of grade, was statistically significantly higher for targeted than for standard biopsies (27.9% vs 13.5%, respectively, P < .001), with 11.5 targeted cores vs 26.2 standard cores utilized per diagnosis of prostate cancer. For detection of high-grade cancer, 30.7 targeted vs 100.8 standard cores were utilized per diagnosis. CONCLUSION: In men with MR-visible prostate lesions, targeted biopsy is more efficient than standard biopsy, diagnosing a similar number of cancer cases and more high-grade cases while sampling 56.1% fewer biopsy cores.