Project Data Sphere Research Articles

Serum-based biomarkers and liver metastases in metastatic castration-resistant prostate cancer (mCRPC).

372 Background: Metastatic castrate-resistant prostate cancer patients with liver metastases have a poor prognosis. No large studies to date have investigated the clinical and biochemical parameters associated with liver metastases in this population. Methods: Patient data was obtained from 1,281 men with mCRPC that were enrolled on to three phase III clinical trials for the treatment of their disease. This clinical trial data was obtained from Project Data Sphere (www.projectdatasphere.org), an initiative of the CEO Roundtable on Cancer's Life Sciences Consortium. Multiple logistic regression was performed on clinical and biochemical baseline variables to test their association with the presence of liver metastases on baseline CT/MRI imaging. Variables assessed included prior docetaxel exposure, ECOG performance status, albumin, alkaline phosphatase (ALP), alanine transaminase (ALT), aspartate transaminase (AST), hemoglobin, lactate dehydrogenase (LDH), prostate specific antigen (PSA), and total bilirubin. The investigation included lab variables when treated as both dichotomous (based on normal-abnormal ranges) and continuous. Results: Multiple variable analysis demonstrated that an abnormally elevated serum AST and/or LDH was associated with an increased likelihood of having documented radiographic liver metastases (p < 0.0001). The predicted probability of having liver metastases for an abnormally elevated AST or LDH was 13.90% (95% CI 9.12 – 20.59) and 16.80% (95% CI 13.19 – 21.16), respectively. Patients with both an elevated AST and LDH had a 31.31% (95% CI 24.0 – 39.73) probability of having liver metastases as compared to only 6.67% (95% CI 5.17 – 8.57) for those with normal values. The ROC curve for the final model (AST + LDH) containing dichotomous predictors was 0.6683 (p < 0.0001), which was not significantly different when treating the predictors as continuous (p = 0.4858). Conclusions: Our analysis demonstrated a significant association between the presence of liver metastases and elevated serum levels of AST and LDH. More research is needed to validate these biomarkers and determine their application in the clinical setting.

Effect of Breast Irradiation on Cardiac Disease in Women Enrolled in BCIRG-001 at 10-Year Follow-Up

To investigate cardiac toxicity associated with breast radiation therapy (RT) at 10-year follow-up in BCIRG-001, a phase 3 trial comparing adjuvant anthracycline chemotherapy (fluorouracil, doxorubicin, and cyclophosphamide) with anthracycline-taxane chemotherapy (docetaxel, doxorubicin, and cyclophosphamide) in women with lymph node-positive early breast cancer. Prospective data from all 746 patients in the control arm (fluorouracil, doxorubicin, and cyclophosphamide) of BCIRG-001 at 10-year follow-up were obtained from Project Data Sphere. Cardiac toxicities examined included myocardial infarction (MI), heart failure (HF), arrhythmias, and relative and absolute left ventricular ejection fraction decrease of >20% from baseline. Toxicities were compared between patients who received RT versus no RT, left-sided RT versus no RT, and internal mammary nodal RT versus no RT. Of the 746 patients, 559 (75%) received RT to a median dose of 50Gy. Myocardial infarction occurred in 3 RT patients (0.5%) versus 6 no-RT patients (3%) (P=.01). Heart failure was seen in 15 RT patients (2.7%) versus 3 no-RT patients (1.6%) (P=.6). Among these, 35 RT patients (18%) had a left ventricular ejection fraction relative decrease of >20% baseline versus 7 (10%) who did not receive RT (P=.1). Arrhythmias were more common in RT patients (3.2%) versus no-RT patients (0%) (P=.01). On univariable and multivariable analysis HF was not significantly associated with RT, and MI was negatively associated with RT. In this retrospective analysis of prospective toxicity outcomes, there is an increased risk of arrhythmias but no clear evidence of significantly increased risk of MI or HF at 10years in lymph node-positive women treated with breast RT and uniform adjuvant doxorubicin-based chemotherapy. Given the low incidence of these outcomes, studies with larger numbers are needed to confirm our findings.

The application of electronic medical records (EMRs) as a virtual comparator arm in a lung cancer clinical trial: A case study.

e18098 Background: Single-arm and open-label clinical studies are common in oncology drug development, leading to a frequent need for contextualization of results and provision of other means of comparison for cost-effectiveness models. The utility of current approaches such as naïve comparisons, historical control benchmarking, and match-adjusted interventional controls is limited. An alternative approach is the use of routinely collected data from Electronic Medical Records (EMRs). This study sought to determine if overall survival (OS) in advanced non-small cell lung cancer patients from a clinical trial comparator arm could be replicated using EMR data. Methods: De-identified EMR-derived patient data from Flatiron Health were matched via propensity scores to Project Data Sphere clinical trial data for study NCT00457392 of sunitinib plus erlotinib versus erlotinib alone to compare overall survival outcomes. A Cox regression model stratified for dataset was used to estimate the hazard ratio and 95% confidence interval for OS. Results: The Kaplan Meier curves were comparable for the first period after start of treatment, but differed after median survival was reached, with the Flatiron dataset achieving longer median OS. This deviation was strongly associated with differences in post-erlotinib treatment options introduced between the clinical trial period and the EMR period. Splitting the Flatiron data in two, related to the period from which the data were obtained (pre- or post-2013), resulted in a better match for the period closer to the trial [HR: 1.12 (0.93,1.35), P = 0.219] than for the later period [HR: 1.23 (1.01,1.5), P = 0.039]. Conclusions: Our results demonstrate that the time period in which EMR data are collected is important when matching to trial data. EMR data can contextualize the OS benefit for single-arm or uncontrolled trials run in the same time period. Further exploration of time-varying effects on OS is warranted.

Assessment of a prognostic model, PSA metrics and toxicities in metastatic castrate resistant prostate cancer using data from Project Data Sphere (PDS).

BackgroundPrognostic models in metastatic castrate resistant prostate cancer (mCRPC) may have clinical utility. Using data from PDS, we aimed to 1) validate a contemporary prognostic model (Templeton et al., 2014) 2) evaluate prognostic impact of concomitant medications and PSA decrease 3) evaluate factors associated with docetaxel toxicity.MethodsWe accessed data on 2,449 mCRPC patients in PDS. The existing model was validated with a continuous risk score, time-dependent receiver operating characteristic (ROC) curves, and corresponding time-dependent Area under the Curve (tAUC). The prognostic effects of concomitant medications and PSA response were assessed by Cox proportional hazards models. One year tAUC was calculated for multivariable prognostic model optimized to our data. Conditional logistic regression models were used to assess associations with grade 3/4 adverse events (G3/4 AE) at baseline and after cycle 1 of treatment.ResultsDespite limitations of the PDS data set, the existing model was validated; one year AUC, was 0.68 (95% CI 95% CI, .66 to .71) to 0.78 (95%CI, .74 to .81) depending on the subset of datasets used. A new model was constructed with an AUC of .74 (.72 to .77). Concomitant medications low molecular weight heparin and warfarin were associated with poorer survival, Metformin and Cox2 inhibitors were associated with better outcome. PSA response was associated with survival, the effect of which was greatest early in follow-up. Age was associated with baseline risk of G3/4 AE. The odds of experiencing G3/4 AE later on in treatment were significantly greater for subjects who experienced a G3/4 AE in their first cycle (OR 3.53, 95% CI 2.53–4.91, p < .0001).ConclusionDespite heterogeneous data collection protocols, PDS provides access to large datasets for novel outcomes analysis. In this paper, we demonstrate its utility for validating existing models and novel model generation including the utility of concomitant medications in outcome analyses, as well as the effect of PSA response on survival and toxicity prediction.

Estimation of tumour regression and growth rates during treatment in patients with advanced prostate cancer: a retrospective analysis

We applied mathematical models to clinical trial data available at Project Data Sphere LLC (Cary, NC, USA), a non-profit universal access data-sharing warehouse. Our aim was to assess the rates of cancer growth and regression using the comparator groups of eight randomised clinical trials that enrolled patients with metastatic castration-resistant prostate cancer. In this retrospective analysis, we used data from eight randomised clinical trials with metastatic castration-resistant prostate cancer to estimate the growth (g) and regression (d) rates of disease burden over time. Rates were obtained by applying mathematical models to prostate-specific antigen levels as the representation of tumour quantity. Rates were compared between study interventions (prednisone, mitoxantrone, and docetaxel) and off-treatment data when on-study treatment had been discontinued to understand disease behaviour during treatment and after discontinuation. Growth (g) was examined for association with a traditional endpoint (overall survival) and for its potential use as an endpoint to reduce sample size in clinical trials. Estimates for g, d, or both were obtained in 2353 (88%) of 2678 patients with data available for analysis; g differentiated docetaxel (a US Food and Drug Administration-approved therapy) from prednisone and mitoxantrone and was predictive of overall survival in a landmark analysis at 8 months. A simulated sample size analysis, in which g was used as the endpoint, compared docetaxel data with mitoxantrone data and showed that small sample sizes were sufficient to achieve 80% power (16, 47, and 25 patients, respectively, in the three docetaxel comparator groups). Similar results were found when the mitoxantrone data were compared with the prednisone data (41, 39, and 41 patients in the three mitoxantrone comparator groups). Finally, after discontinuation of docetaxel therapy, median tumour growth (g) increased by nearly five times. The application of mathematical models to existing clinical data allowed estimation of rates of growth and regression that provided new insights in metastatic castration-resistant prostate cancer. The availability of clinical data through initiatives such as Project Data Sphere, when combined with innovative modelling techniques, could greatly enhance our understanding of how cancer responds to treatment, and accelerate the productivity of clinical development programmes. None.

Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial data

Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest-namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trial-ENTHUSE M1-in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone. 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0·791; Bayes factor >5) and surpassed the reference model (iAUC 0·743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3·32, 95% CI 2·39-4·62, p<0·0001; reference model: 2·56, 1·85-3·53, p<0·0001). The new model was validated further on the ENTHUSE M1 cohort with similarly high performance (iAUC 0·768). Meta-analysis across all methods confirmed previously identified predictive clinical variables and revealed aspartate aminotransferase as an important, albeit previously under-reported, prognostic biomarker. Novel prognostic factors were delineated, and the assessment of 50 methods developed by independent international teams establishes a benchmark for development of methods in the future. The results of this effort show that data-sharing, when combined with a crowdsourced challenge, is a robust and powerful framework to develop new prognostic models in advanced prostate cancer. Sanofi US Services, Project Data Sphere.

The Effect of Breast Irradiation on Cardiac Disease in Women Enrolled on BCIRG-001 at 10-Year Follow-up

Radiation therapy (RT) for breast cancer has been shown to increase the risk of long term cardiac morbidity. Modern techniques such as 3D-CRT and IMRT decrease cardiac dose from RT. This study investigated cardiac toxicity associated with breast RT at 10 year follow-up in BCIRG-001, a phase III trial comparing adjuvant anthracycline chemotherapy (FAC) with anthracycline-taxane chemotherapy (TAC) in women with lymph node (LN) positive early breast cancer. Prospectively collected data from all 746 patients in the control arm (FAC) of BCIRG-001 at 10 year follow up was analyzed from Project Data Sphere. Cardiac toxicities examined were grade 3 or 4 congestive heart failure (CHF), myocardial infarction (MI), and LVEF decrease of >20% from baseline. Toxicities were compared between patients who did and did not receive RT, left sided versus right sided RT, left sided versus no RT, and left internal mammary nodal (IMN) versus no RT. Statistical comparisons of categorical data were completed using the chi-square test and Fisher exact test, and multivariate analysis was performed using binomial logistic regression in the R statistical package. Of the 746 patients, 538 (72%) received RT to a median dose of 50 Gy (25-65 Gy). Among the RT patients, 266 (49%) received left sided RT, 393 (69%) received regional nodal irradiation (RNI), and 51 (13% of RNI group) were treated to the IMNs (31% left-sided). There was no significant difference in age, KPS, menopausal status, ER/PR, or HER2 status, tumor grade, history of diabetes, MI, obesity, hyperlipidemia, hypertension, or arrhythmia, between patients receiving RT and those that did not. Per protocol, nearly all patients who underwent lumpectomy received RT; 241/439 (55%) of patients who underwent mastectomy received RT. There was thus fewer RT patients (versus non-RT patients) who underwent mastectomy (55% vs. 95%, P < 0.001) and more RT patients with >3 positive LN (42% vs 28%, P = 0.001). MI occurred in 6 RT patients versus 8 patients non-RT patients (1% vs 4%, P = 0.8). No patient treated with left sided or left IMN RT experienced MI. Grade 3/4 CHF was seen in 14 RT patients versus 3 non-RT patients (3% vs. 1.5%, P = 0.9). Four of 14 RT patients (29%) died of complications of CHF versus no non-RT patients. Evaluable baseline echocardiograms were available in 270 patients. Among these, 35/198 RT patients had LVEF decrease of >20% baseline versus 6/72 who did not receive RT (18% vs. 8.3%, P = 0.09). There was no difference between LVEF decreases in right-sided versus left-sided RT. On multivariate logistic regression, MI is significantly associated with previous history of MI, obesity, and older age. There is no significant increase in risk of MI at 10 years in LN positive women treated with breast RT and uniform adjuvant doxorubicin-based chemotherapy in this modern prospective study with long-term cardiac outcomes. The higher incidence of CHF morbidity and mortality warrants further study with a larger randomized cohort.

Thoughts About Greg Curt

The life of Greg Curt is recollected through personal and professional memories. He is remembered for a distinguished career, including the creation of Project Data Sphere, and for his diplomatic skills, bringing together government, industry partners, and academia to initiate collaborative trials.

Individual Patient Data Analysis of Randomized Clinical Trials: Impact of Black Race on Castration-resistant Prostate Cancer Outcomes

BackgroundPopulation data suggest that black men have a higher risk of dying from prostate cancer (PCa) than other racial ethnicities. ObjectiveTo examine the impact of black race on progression-free survival (PFS) and overall survival (OS) among men with metastatic castration-resistant PCa (mCRPC) enrolled in randomized controlled trials (RCTs). Design, setting, and participantsA pooled analysis was performed on individual patient data from five modern PCa RCTs available from Project Data Sphere. Outcome measurements and statistical analysisAdjusted hazard ratios (HRs) were calculated to compare black and white race regarding PFS and OS. Subgroup analyses of mCRPC trials were performed based on the control arm treatments (mitoxantrone or docetaxel). Relevant covariates were used for adjustment in all analyses. Results and limitationsA total of 1613 patients were included; 77 were black (4.7%). No significant differences between black and white men's baseline characteristics were noted regarding age, performance status, or pretreatment prostate-specific antigen. The pooled HRs for black race for OS and PFS were 1.01 (95% confidence interval [CI], 0.73–1.35) and 1.29 (95% CI, 0.95–1.76), respectively. The median OS for black compared with white men was 254 versus 238 d (p=0.92), respectively, with mitoxantrone and 581 versus 546 d (p=0.53), respectively, with docetaxel. The primary limitation was the relatively small number of black men enrolled in mCRPC clinical trials. ConclusionsIn the context of RCTs, in which patients receive generally uniform treatment, a significant difference in OS for black men could not be detected in mCRPC. Black men continue to be dramatically underrepresented in RCTs, and efforts are needed to increase minority accrual to these trials. Patient summaryWe looked at the outcomes of men treated in randomized controlled trials to determine the impact of black race on survival. We found that in the context of modern clinical trials, there does not appear to be a significant difference in survival between black and white races; however, a trend for greater progression in black men was noted.

Use of crowdsourced research to develop a prognostic model for first-line metastatic castrate resistant prostate cancer (mCRPC).

180 Background: Project Data Sphere, LLC (PDS) and Sage Bionetworks/DREAM have completed the “Prostate Cancer DREAM Challenge” (Challenge), a crowdsourced competition, using historical prostate cancer clinical trial data from PDS. The Challenge aimed to improve prognostic models for overall survival (OS) and to explore predictive models for treatment toxicity in mCRPC patients. Methods: Control arms of 4 randomized phase III trials (total 2,070 patients) were used as training and validation data sets for the Challenge: ASCENT2, MAINSAIL, VENICE and ENTHUSE33. All subjects were first line mCRPC patients receiving docetaxel treatment. Curated baseline clinical covariates (demographics, comorbidity, prior treatment, laboratory, lesion and vital signs) were modeled along with raw clinical data tables. The primary purpose of the Challenge was to develop a prognostic model for OS (SubChallenge 1). The models were scored using concordance index and integrated area under receiver operator curve (iAUC) from 6-30 months. The published mCRPC OS model of Halabi, et al., JCO, 2014, was used as the benchmark. Results: The Challenge attracted over 160 active participants who formed 50 teams that submitted final models for SubChallenge 1. Median iAUC was 0.76 (0.67-0.78) with a maximum score of 0.792. Over half (n = 35) of these models exceeded the published benchmark (0.743 iAUC). Teams explored new methodologies such as model-based imputation and machine learning techniques to develop the best performing models. Many leveraged raw clinical data sets to create their own covariates and expanded beyond existing prognostic models. Conclusions: The Challenge externally validated Halabi’s first line prognostic model. New prognostic models were proposed and validated with significant improvements over the benchmark. Further analyses are needed to examine the winning models for new prognostic factors and to validate them using additional trial data from PDS. The Challenge drove interest from cross-disciplinary teams of global experts to explore and enhance their technical abilities using real clinical data whilst serving as a vehicle to accelerate medical innovation.

Abstract B204: A DREAM Challenge to improve prognostic models in patients with metastatic castrate-resistant prostate cancer

Abstract Background: Crowdsourced competitions or “challenges” have proven effective at drawing large cross-disciplinary teams of experts to solve complex problems. However, no challenges to date have been conducted using cancer clinical trial data due to privacy, legal concerns, and the restricted availability of data. Project Data Sphere, LLC (PDS) and Sage Bionetworks/DREAM have recently completed the “Prostate Cancer DREAM Challenge” (Challenge) using historical comparator arm phase III clinical trial data from PDS. The goals of this Challenge were to advance understanding of disease progression and treatment toxicity in order to improve clinical management for patients with metastatic, castrate resistant prostate cancer (mCRPC). Method: Clinical variables, outcomes and lab test data from the control arms of four phase III clinical trial data sets in mCRPC - over 2,000 patients - were curated by PDS to create training and validation data sets for the Challenge. A team of prostate cancer clinicians and scientists selected 2 Challenge questions: prediction of overall survival (OS), and prediction of treatment discontinuation from standard-of-care (docetaxel). The Challenge opened in March, 2015, lasted over 4 months, and included multiple leaderboard rounds with a final validation round to assess overall winners. Subchallenge 1 scored participants' models using concordance index and an integrated area under receiver operator curve (iAUC) from 0-24 months; Subchallenge 2 scored models using area under precision-recall curve (AUPRC). A recently published mCRPC OS model (Halabi, et al., JCO, 2014) was used as the baseline benchmark for Subchallenge 1. All Challenge participants were required to provide complete documentation of their methods including reproducible code. Challenge details can be found at: www.synapse.org/#!Synapse:syn2813558. Results: The Challenge had over 160 active participants submitting a total of 852 models for leaderboard scoring, with 50 and 30 teams submitting final models for Subchallenges 1 and 2, respectively. Median iAUC for Subchallenge 1 was 0.76 (0.67-0.78) with a maximum score of 0.792 (scores &amp;gt; 0.5 are better than random). Over half (n = 35) of these models exceeded the existing benchmark (.743 iAUC). For Subchallenge 2, median AUPRC was 0.13 (0.09-0.17) with a maximum score of 0.19 (scores &amp;gt; 0.13 are better than random). Successful teams utilized techniques for careful imputation of missing data, and ensemble methods for aggregation of multiple machine learning models. Conclusion: The Prostate Cancer DREAM Challenge successfully engaged a diverse community of experts focusing on two clinically relevant questions in mCRPC. New models for predicting OS were proposed and validated with significant improvements over a prior benchmark. Also, clinical factors related to prediction of treatment discontinuation due to adverse events were uncovered, thereby establishing a novel benchmark and providing a strong rationale for follow-up studies. Importantly, this Challenge demonstrates the benefits of crowdsourcing important biomedical questions in cancer using clinical trial data. Citation Format: Justin Guinney, Tao Wang, Chris Bare, Thea Norman, Brian Bot, Liji Shen, Kimberly Winner, Stephen Friend, Kald Abdallah, Gustavo Stolovitzky, Yang Xie, Fang Liz Zhou, James C. Costello. A DREAM Challenge to improve prognostic models in patients with metastatic castrate-resistant prostate cancer. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2015 Nov 5-9; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl 2):Abstract nr B204.

Overcoming barriers to develop a broad access data sharing platform.

e17690 Background: Analysis of Phase 3 clinical trial data is uncommon beyond the pre-defined protocol. Project Data Sphere, LLC (PDS), an independent initiative of the non-profit CEO Roundtable on...

Project Data Sphere: A first look at prostate cancer including concomitant medication use, prognosis, and toxicity.

5022 Background: PDS (www.projectdatasphere.org) enables patient-level analyses of control arms of cancer trials. We aimed to include the use of concomitant medications (CM) to (i) validate and imp...

Project Data Sphere initiative: Results from first 6 months of broad access to oncology trials data.

e17691 Background: Analysis of Phase 3 data from any clinical trial is uncommon beyond the pre-defined protocol. Aggregation of data from multiple trials make new analyses possible. Project Data Sp...

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

Background. Inthispaper,weprovidebackgroundandcontext regarding the potential for a new data-sharing platform, the ProjectDataSphere(PDS)initiative,fundedbyfinancialandinkind contributions from the CEO Roundtable on Cancer, to transform cancer research and improve patient outcomes. Giventherelativelymodestdeclineincancerdeath ratesover 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 patientstreatedwithstandardofcaretherapies(i.e.,controls). Both nationally and internationally, a variety of stakeholders have pursued data-sharing efforts to make individual patientlevel clinical trial data available to the scientific research community.

Project data sphere (PDS) in prostate cancer: A first look including concomitant medication use.

204 Background: PDS enables patient-level analyses of control arms of cancer trials. The interface (www.projectdatasphere.org) allows for both web-based and download-based analyses. We aimed to validate established prostate cancer prognostic models and explore the effect of concomitant medications on survival in mCRPC. Methods: Data was obtained for 2,747 control subjects with mCRPC from 7 Phase III clinical trials with 1962 subjects available for OS analyses from 5 studies. Overall survival was estimated using the Kaplan-Meier Method. Cox-proportional hazards models, stratified by trial, were used to estimate hazard ratios. Results: Metastatic site was significant for overall survival (Median: Node only 23.69m, Bone 18.17m, Lung 14.72m, Liver 9.43m; p &lt; 0.001). Of the 23 types of medication examined, after adjusting for metastatic site, patients taking proton pump inhibitors (HR: 1.155, p=0.017) and Erythropoietin (HR: 1.49, p-value&lt;.001) had worse overall survival whilst patients taking fish oil (HR:0.68, p-value=0.033) and non-lipophilic statins (HR:0.69, p=.00277) had improved overall survival. Within the limits of available data, we validated the prognostic models for overall survival proposed by Templeton et al. and Sonpavde et al individually and after inclusion of concomitant medication where patients taking metformin (HR=0.729, p=.0082) and Cox 2 inhibitors (HR=0.708, p=.015) had improved OS whilst those taking low molecular weight heparin (HR=1.352, p=.004) had worse OS. Conclusions: As a first project utilizing open-source PDS data in prostate cancer, we validated two prostate cancer prognostic models and illuminated the ability to undertake novel analyses such as the association of concomitant medications with outcome. Limitations of the data relate to incomplete and inconsistent data entry. Future expansion of patient trials and numbers will help to facilitate future analyses.

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

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

Project Data Sphere to Make Cancer Clinical Trial Data Publicly Available

and proprietary issues that have hampered efforts to share data. He developed Project Data Sphere, which within weeks will go live with cancer trial data from an expected five to seven pharmaceutical companies. The goal, according to Hugh-Jones, is to have Project Data Sphere grow into an interactive platform where investigators can learn from the collective successes and failures of previous trials with the help of powerful analytic tools, which the statistical software giant SAS Institute offered to the project free. “The reality is that we have to do better,” said Hugh-Jones. “You’ve got to get beyond a 5% success rate in bringing new oncology drugs to market. But it’s an economic issue as well. People are realizing there just isn’t the money to keep pumping into trials either at a commercial or at a governmental level.”

Data Sphere Shares Clinical Trial Information

The CEO Roundtable on Cancer's Project Data Sphere aims to accelerate cancer research by establishing a neutral platform for publicly sharing clinical trial data from industry and academia.