The project data sphere initiative: accelerating cancer research by sharing data

Background. In this paper, we provide background and context regarding the potential for a new data-sharing platform, the Project Data Sphere (PDS) initiative, funded by financial and in-kind contributions from the CEO Roundtable on Cancer, to transform cancer research and improve patient outcomes. Given the relatively modest decline in cancer death rates over the past several years, a new research paradigm is needed to accelerate therapeutic approaches for oncologic diseases. Phase III clinical trials generate large volumes of potentially usable information, often on hundreds of patients, including patients treated with standard of care therapies (i.e., controls). Both nationally and internationally, a variety of stakeholders have pursued data-sharing efforts to make individual patient-level clinical trial data available to the scientific research community. Potential Benefits and Risks of Data Sharing. For researchers, shared data have the potential to foster a more collaborative environment, to answer research questions in a shorter time frame than traditional randomized control trials, to reduce duplication of effort, and to improve efficiency. For industry participants, use of trial data to answer additional clinical questions could increase research and development efficiency and guide future projects through validation of surrogate end points, development of prognostic or predictive models, selection of patients for phase II trials, stratification in phase III studies, and identification of patient subgroups for development of novel therapies. Data transparency also helps promote a public image of collaboration and altruism among industry participants. For patient participants, data sharing maximizes their contribution to public health and increases access to information that may be used to develop better treatments. Concerns about data-sharing efforts include protection of patient privacy and confidentiality. To alleviate these concerns, data sets are deidentified to maintain anonymity. To address industry concerns about protection of intellectual property and competitiveness, we illustrate several models for data sharing with varying levels of access to the data and varying relationships between trial sponsors and data access sponsors. The Project Data Sphere Initiative. PDS is an independent initiative of the CEO Roundtable on Cancer Life Sciences Consortium, built to voluntarily share, integrate, and analyze comparator arms of historical cancer clinical trial data sets to advance future cancer research. The aim is to provide a neutral, broad-access platform for industry and academia to share raw, deidentified data from late-phase oncology clinical trials using comparator-arm data sets. These data are likely to be hypothesis generating or hypothesis confirming but, notably, do not take the place of performing a well-designed trial to address a specific hypothesis. Prospective providers of data to PDS complete and sign a data sharing agreement that includes a description of the data they propose to upload, and then they follow easy instructions on the website for uploading their deidentified data. The SAS Institute has also collaborated with the initiative to provide intrinsic analytic tools accessible within the website itself. As of October 2014, the PDS website has available data from 14 cancer clinical trials covering 9,000 subjects, with hopes to further expand the database to include more than 25,000 subject accruals within the next year. PDS differentiates itself from other data-sharing initiatives by its degree of openness, requiring submission of only a brief application with background information of the individual requesting access and agreement to terms of use. Data from several different sponsors may be pooled to develop a comprehensive cohort for analysis. In order to protect patient privacy, data providers in the U.S. are responsible for deidentifying data according to standards set forth by the Privacy Rule of the U.S. Health Insurance Portability and Accountability Act of 1996. Using Data Sharing to Improve Outcomes in Cancer: The “Prostate Cancer Challenge.” Control-arm data of several studies among patients with metastatic castration-resistant prostate cancer (mCRPC) are currently available through PDS. These data sets have multiple potential uses. The “Prostate Cancer Challenge” will ask the cancer research community to use clinical trial data deposited in the PDS website to address key research questions regarding mCRPC. General themes that could be explored by the cancer community are described in this article: prognostic models evaluating the influence of pretreatment factors on survival and patient-reported outcomes; comparative effectiveness research evaluating the efficacy of standard of care therapies, as illustrated in our companion article comparing mitoxantrone plus prednisone with prednisone alone; effects of practice variation in dose, frequency, and duration of therapy; level of patient adherence to elements of trial protocols to inform the design of future clinical trials; and age of subjects, regional differences in health care, and other confounding factors that might affect outcomes. Potential Limitations and Methodological Challenges. The number of data sets available and the lack of experimental arm data limit the potential scope of research using the current PDS. The number of trials is expected to grow exponentially over the next year and may include multiple cancer settings, such as breast, colorectal, lung, hematologic malignancy, and bone marrow transplantation. Other potential limitations include the retrospective nature of the data analyses performed using PDS and its generalizability, given that clinical trials are often conducted among younger, healthier, and less racially diverse patient populations. Methodological challenges exist when combining individual patient data from multiple clinical trials; however, advancements in statistical methods for secondary database analysis offer many tools for reanalyzing data arising from disparate trials, such as propensity score matching. Despite these concerns, few if any comparable data sets include this level of detail across multiple clinical trials and populations. Conclusion. Access to large, late-phase, cancer-trial data sets has the potential to transform cancer research by optimizing research efficiency and accelerating progress toward meaningful improvements in cancer care. This type of platform provides opportunities for unique research projects that can examine relatively neglected areas and that can construct models necessitating large amounts of detailed data.The full potential of PDS will be realized only when multiple tumor types and larger numbers of data sets are available through the website

Alternate Metabolic Programs Define Regional Variation of Relevant Biological Features in Renal Cell Carcinoma Progression

ccRCC has recently been redefined as a highly heterogeneous disease. In addition to genetic heterogeneity, the tumor displays risk variability for developing metastatic disease, therefore underscoring the urgent need for tissue-based prognostic strategies applicable to the clinical setting. We’ve recently employed the novel positron emission tomography/magnetic resonance (PET/MR) image modality to enrich our understanding of how tumor heterogeneity can relate to gene expression and tumor biology to assist in defining individualized treatment plans

Roadmap for the development of the University of North Carolina at Chapel Hill Genitourinary OncoLogy Database—UNC GOLD

The management of genitourinary malignancies requires a multidisciplinary care team composed of urologists, medical oncologists and radiation oncologists. A genitourinary (GU) oncology clinical database is an invaluable resource for patient care and research. Although electronic medical records provide a single web-based record used for clinical care, billing and scheduling, information is typically stored in a discipline-specific manner and data extraction is often not applicable to a research setting. A GU oncology database may be used for the development of multidisciplinary treatment plans, analysis of disease-specific practice patterns, and identification of patients for research studies. Despite the potential utility, there are many important considerations that must be addressed when developing and implementing a discipline-specific database

Metachronous Bilateral Testicular Leydig-Like Tumors Leading to the Diagnosis of Congenital Adrenal Hyperplasia (Adrenogenital Syndrome)

A 33-year-old male with a history of left testis Leydig cell tumor (LCT), 3-month status after left radical orchiectomy, presented with a rapidly enlarging (0.6 cm to 3.7 cm) right testicular mass. He underwent a right radical orchiectomy, sections interpreted as showing a similar Leydig cell-like oncocytic proliferation, with a differential diagnosis including metachronous bilateral LCT and metachronous bilateral testicular tumors associated with congenital adrenal hyperplasia (a.k.a. “testicular adrenal rest tumors” (TARTs) and “testicular tumors of the adrenogenital syndrome” (TTAGS)). Additional workup demonstrated a markedly elevated serum adrenocorticotropic hormone (ACTH) and elevated adrenal precursor steroid levels. He was diagnosed with congenital adrenal hyperplasia, 3β-hydroxysteroid dehydrogenase deficiency (3BHSD) type, and started on treatment. Metachronous bilateral testicular masses in adults should prompt consideration of adult presentation of CAH. Since all untreated CAH patients are expected to have elevated serum ACTH, formal exclusion of CAH prior to surgical resection of a testicular Leydig-like proliferation could be accomplished by screening for elevated serum ACTH

Comparative Effectiveness of Mitoxantrone Plus Prednisone Versus Prednisone Alone in Metastatic Castrate‐Resistant Prostate Cancer After Docetaxel Failure

BACKGROUND. Mitoxantrone was approved for use in metastatic castrate-resistant prostate cancer (mCRPC) based on pain palliation without observed survival benefit in a small phase III trial in 1996. To re-evaluate for possible survival benefits in a larger contemporary sample and to demonstrate analytic uses of the newly available Project Data Sphere online resource, we used data from control arms of completed clinical trials to compare survival and toxicity among patients with postdocetaxel mCRPC treated with mitoxantrone and prednisone. PATIENTS AND METHODS. Control arm data from two phase III randomized control trials, SUN 1120 and TROPIC, were used to examine the efficacy of mitoxantrone plus prednisone (n = 305) versus prednisone alone (n = 257) among patients with postdocetaxel mCRPC. Propensity score matching was used to balance patient characteristics between the separate trials, conditioned on age and key prognostic variables of survival. The primary outcome was overall survival. Secondary endpoints evaluated safety. RESULTS. Median survival was similar among patients receiving mitoxantrone plus prednisone versus prednisone alone (385 days vs. 336 days; deceleration factor = 0.04; 95% confidence interval: −0.12 to 0.22). Prevalence of several any-grade toxicity, including fatigue, back pain, and peripheral neuropathy, was increased among patients who received mitoxantrone. CONCLUSION. There was no significant survival benefit for mitoxantrone plus prednisone over prednisone alone among men with mCRPC after docetaxel therapy. This finding is consistent with prior studies showing no survival advantage with mitoxantrone in the predocetaxel setting. Furthermore, our data suggest that mitoxantrone may be associated with increased toxicity compared with prednisone alone

Alternate Metabolic Programs Define Regional Variation of Relevant Biological Features in Renal Cell Carcinoma Progression

PURPOSE: ccRCC has recently been redefined as a highly heterogeneous disease. In addition to genetic heterogeneity, the tumor displays risk variability for developing metastatic disease, therefore underscoring the urgent need for tissue-based prognostic strategies applicable to the clinical setting. We’ve recently employed the novel positron emission tomography/magnetic resonance (PET/MR) image modality to enrich our understanding of how tumor heterogeneity can relate to gene expression and tumor biology to assist in defining individualized treatment plans. EXPERIMENTAL DESIGN: ccRCC patients underwent PET/MR imaging, and these images subsequently used to identify areas of varied intensity for sampling. Samples from eight patients were subjected to histological, immunohistochemical, and microarray analysis. RESULTS: Tumor subsamples displayed a range of heterogeneity for common features of HIF expression and microvessel density, as well as for features closely linked to metabolic processes, such as GLUT1 and FBP1. In addition, gene signatures linked with disease risk (ccA and ccB) also demonstrated variable heterogeneity, with most tumors displaying a dominant panel of features across the sampled regions. Intriguingly, the ccA and ccB-classified samples corresponded with metabolic features and functional imaging levels. These correlations further linked a variety of metabolic pathways (i.e. the pentose phosphate and mTOR pathways) with the more aggressive, and glucose avid ccB subtype. CONCLUSIONS: Higher tumor dependency on exogenous glucose accompanies the development of features associated with the poor risk ccB subgroup. Linking these panels of features may provide the opportunity to create functional maps to enable enhanced visualization of the heterogeneous biological processes of an individual’s disease

Neoadjuvant pazopanib and molecular analysis of tissue response in renal cell carcinoma

BACKGROUND Surgery remains the frontline therapy for patients with localized clear cell renal cell carcinoma (ccRCC); however, 20%–40% recur. Angiogenesis inhibitors have improved survival in metastatic patients and may result in responses in the neoadjuvant setting. The impact of these agents on the tumor genetic heterogeneity or the immune milieu is largely unknown. This phase II study was designed to evaluate safety, response, and effect on tumor tissue of neoadjuvant pazopanib.METHODS ccRCC patients with localized disease received pazopanib (800 mg daily; median 8 weeks), followed by nephrectomy. Five tumors were examined for mutations by whole exome sequencing from samples collected before therapy and at nephrectomy. These samples underwent RNA sequencing; 17 samples were available for posttreatment assessment.RESULTS Twenty-one patients were enrolled. The overall response rate was 8 of 21 (38%). No patients with progressive disease. At 1-year, response-free survival and overall survival was 83% and 89%, respectively. The most frequent grade 3 toxicity was hypertension (33%, 7 of 21). Sequencing revealed strong concordance between pre- and posttreatment samples within individual tumors, suggesting tumors harbor stable core profiles. However, a reduction in private mutations followed treatment, suggesting a selective process favoring enrichment of driver mutations.CONCLUSION Neoadjuvant pazopanib is safe and active in ccRCC. Future genomic analyses may enable the segregation of driver and passenger mutations. Furthermore, tumor infiltrating immune cells persist during therapy, suggesting that pazopanib can be combined with immune checkpoint inhibitors without dampening the immune response.FUNDING Support was provided by Novartis and GlaxoSmithKline as part of an investigator-initiated study

Roadmap for the development of the University of North Carolina at Chapel Hill Genitourinary OncoLogy Database—UNC GOLD

BACKGROUND: The management of genitourinary malignancies requires a multidisciplinary care team composed of urologists, medical oncologists and radiation oncologists. A genitourinary (GU) oncology clinical database is an invaluable resource for patient care and research. Although electronic medical records provide a single web-based record used for clinical care, billing and scheduling, information is typically stored in a discipline-specific manner and data extraction is often not applicable to a research setting. A GU oncology database may be used for the development of multidisciplinary treatment plans, analysis of disease-specific practice patterns, and identification of patients for research studies. Despite the potential utility, there are many important considerations that must be addressed when developing and implementing a discipline-specific database. METHODS AND MATERIALS: The creation of the GU oncology database including prostate, bladder and kidney cancers with the identification of necessary variables was facilitated by meetings of stakeholders in medical oncology, urology, and radiation oncology at the University of North Carolina (UNC) at Chapel Hill with a template data dictionary provided by the Department of Urologic Surgery at Vanderbilt University Medical Center. Utilizing Research Electronic Data Capture (REDCap, version 4.14.5), the UNC Genitourinary OncoLogy Database (UNC GOLD) was designed and implemented. RESULTS: The process of designing and implementing a discipline-specific clinical database requires many important considerations. The primary consideration is determining the relationship between the database and the Institutional Review Board (IRB) given the potential applications for both clinical and research uses. Several other necessary steps include ensuring information technology security and federal regulation compliance; determination of a core complete data set; creation of standard operating procedures; standardizing entry of free text fields; use of data exports, queries, and de-identification strategies; inclusion of individual investigators’ data; and strategies for prioritizing specific projects and data entry. CONCLUSIONS: A discipline-specific database requires a buy-in from all stakeholders, meticulous development, and data entry resources in order to generate a unique platform for housing information that may be used for clinical care and research with IRB approval. The steps and issues identified in the development of UNC GOLD provide a process map for others interested in developing a GU oncology database