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

Bridging the gap between two-stage and joint models: The case of tumor growth inhibition and overall survival models

Many clinical trials generate both longitudinal biomarker and time-to-event data. We might be interested in their relationship, as in the case of tumor size and overall survival in oncology drug development. Many well-established methods exist for analyzing such data either sequentially (two-stage models) or simultaneously (joint models). Two-stage modeling (2stgM) has been challenged (i) for not acknowledging that biomarkers are endogenous covariable to the survival submodel and (ii) for not propagating the uncertainty of the longitudinal biomarker submodel to the survival submodel. On the other hand, joint modeling (JM), which properly circumvents both problems, has been criticized for being time-consuming, and difficult to use in practice. In this paper, we explore a third approach, referred to as a novel two-stage modeling (N2stgM). This strategy reduces the model complexity without compromising the parameter estimate accuracy. The three approaches (2stgM, JM, and N2stgM) are formulated, and a Bayesian framework is considered for their implementation. Both real and simulated data were used to analyze the performance of such approaches. In all scenarios, our proposal estimated the parameters approximately as JM but without being computationally expensive, while 2stgM produced biased results.

The Next Horizon of Drug Development: External Control Arms and Innovative Tools to Enrich Clinical Trial Data

Conducting clinical trials (CTs) has become increasingly costly and complex in terms of designing and operationalizing. These challenges exist in running CTs on novel therapies, particularly in oncology and rare diseases, where CTs increasingly target narrower patient groups. In this study, we describe external control arms (ECA) and other relevant tools, such as virtualization and decentralized clinical trials (DCTs), and the ability to follow the clinical trial subjects in the real world using tokenization. ECAs are typically constructed by identifying appropriate external sources of data, then by cleaning and standardizing it to create an analysis-ready data file, and finally, by matching subjects in the external data with the subjects in the CT of interest. In addition, ECA tools also include subject-level meta-analysis and simulated subjects' data for analyses. By implementing the recent advances in digital health technologies and devices, virtualization, and DCTs, realigning of CTs from site-centric designs to virtual, decentralized, and patient-centric designs can be done, which reduces the patient burden to participate in the CTs and encourages diversity. Tokenization technology allows linking the CT data with real-world data (RWD), creating more comprehensive and longitudinal outcome measures. These tools provide robust ways to enrich the CT data for informed decision-making, reduce the burden on subjects and costs of trial operations, and augment the insights gained for the CT data.

An evaluation of synthetic data augmentation for mitigating covariate bias in health data

Data bias is a major concern in biomedical research, especially when evaluating large-scale observational datasets. It leads to imprecise predictions and inconsistent estimates in standard regression models. We compare the performance of commonly used bias-mitigating approaches (resampling, algorithmic, and post hoc approaches) against a synthetic data-augmentation method that utilizes sequential boosted decision trees to synthesize under-represented groups. The approach is called synthetic minority augmentation (SMA). Through simulations and analysis of real health datasets on a logistic regression workload, the approaches are evaluated across various bias scenarios (types and severity levels). Performance was assessed based on area under the curve, calibration (Brier score), precision of parameter estimates, confidence interval overlap, and fairness. Overall, SMA produces the closest results to the ground truth in low to medium bias (50% or less missing proportion). In high bias (80% or more missing proportion), the advantage of SMA is not obvious, with no specific method consistently outperforming others.

Segmentation of patients with small cell lung cancer into responders and non-responders using the optimal cross-validation technique

BACKGROUND

The timing of treating cancer patients is an essential factor in the efficacy of treatment. So, patients who will not respond to current therapy should receive a different treatment as early as possible. Machine learning models can be built to classify responders and nonresponders. Such classification models predict the probability of a patient being a responder. Most methods use a probability threshold of 0.5 to convert the probabilities into binary group membership. However, the cutoff of 0.5 is not always the optimal choice.

METHODS

In this study, we propose a novel data-driven approach to select a better cutoff value based on the optimal cross-validation technique. To illustrate our novel method, we applied it to three clinical trial datasets of small-cell lung cancer patients. We used two different datasets to build a scoring system to segment patients. Then the models were applied to segment patients into the test data.

RESULTS

We found that, in test data, the predicted responders and non-responders had significantly different long-term survival outcomes. Our proposed novel method segments patients better than the standard approach using a cutoff of 0.5. Comparing clinical outcomes of responders versus non-responders, our novel method had a p-value of 0.009 with a hazard ratio of 0.668 for grouping patients using the Cox proportion hazard model and a p-value of 0.011 using the accelerated failure time model which approved a significant difference between responders and non-responders. In contrast, the standard approach had a p-value of 0.194 with a hazard ratio of 0.823 using the Cox proportion hazard model and a p-value of 0.240 using the accelerated failure time model indicating the responders and non-responders do not differ significantly in survival.

CONCLUSION

In summary, our novel prediction method can successfully segment new patients into responders and non-responders. Clinicians can use our prediction to decide if a patient should receive a different treatment or stay with the current treatment.

Explaining in-vitro to in-vivo efficacy correlations in oncology pre-clinical development via a semi-mechanistic mathematical model

In-vitro to in-vivo correlations (IVIVC), relating in-vitro parameters like IC50 to in-vivo drug exposure in plasma and tumour growth, are widely used in oncology for experimental design and dose decisions. However, they lack a deeper understanding of the underlying mechanisms. Our paper therefore focuses on linking empirical IVIVC relations for small-molecule kinase inhibitors with a semi-mechanistic tumour-growth model. We develop an approach incorporating parameters like the compound's peak-trough ratio (PTR), Hill coefficient of in-vitro dose-response curves, and xenograft-specific properties. This leads to formulas for determining efficacious doses for tumor stasis under linear pharmacokinetics equivalent to traditional empirical IVIVC relations, but enabling more systematic analysis. Our findings reveal that in-vivo xenograft-specific parameters, specifically the growth rate (g) and decay rate (d), along with the average exposure, are generally more significant determinants of tumor stasis and effective dose than the compound's peak-trough ratio. However, as the Hill coefficient increases, the dependency of tumor stasis on the PTR becomes more pronounced, indicating that the compound is more influenced by its maximum or trough values rather than the average exposure. Furthermore, we discuss the translation of our method to predict population dose ranges in clinical studies and propose a resistance mechanism that solely relies on specific in-vivo xenograft parameters instead of IC50 exposure coverage. In summary, our study aims to provide a more mechanistic understanding of IVIVC relations, emphasizing the importance of xenograft-specific parameters and PTR on tumor stasis.

Patient and Public Willingness to Share Personal Health Data for Third-Party or Secondary Uses: Systematic Review

BACKGROUND

International advances in information communication, eHealth, and other digital health technologies have led to significant expansions in the collection and analysis of personal health data. However, following a series of high-profile data sharing scandals and the emergence of COVID-19, critical exploration of public willingness to share personal health data remains limited, particularly for third-party or secondary uses.

OBJECTIVE

This systematic review aims to explore factors that affect public willingness to share personal health data for third-party or secondary uses.

METHODS

A systematic search of 6 databases (MEDLINE, Embase, PsycINFO, CINAHL, Scopus, and SocINDEX) was conducted with review findings analyzed using inductive-thematic analysis and synthesized using a narrative approach.

RESULTS

Of the 13,949 papers identified, 135 were included. Factors most commonly identified as a barrier to data sharing from a public perspective included data privacy, security, and management concerns. Other factors found to influence willingness to share personal health data included the type of data being collected (ie, perceived sensitivity); the type of user requesting their data to be shared, including their perceived motivation, profit prioritization, and ability to directly impact patient care; trust in the data user, as well as in associated processes, often established through individual choice and control over what data are shared with whom, when, and for how long, supported by appropriate models of dynamic consent; the presence of a feedback loop; and clearly articulated benefits or issue relevance including valued incentivization and compensation at both an individual and collective or societal level.

CONCLUSIONS

There is general, yet conditional public support for sharing personal health data for third-party or secondary use. Clarity, transparency, and individual control over who has access to what data, when, and for how long are widely regarded as essential prerequisites for public data sharing support. Individual levels of control and choice need to operate within the auspices of assured data privacy and security processes, underpinned by dynamic and responsive models of consent that prioritize individual or collective benefits over and above commercial gain. Failure to understand, design, and refine data sharing approaches in response to changeable patient preferences will only jeopardize the tangible benefits of data sharing practices being fully realized.

Recommendations for the collection and annotation of biosamples for analysis of biomarkers in neurofibromatosis and schwannomatosis clinical trials

INTRODUCTION

Neurofibromatosis 1 and schwannomatosis are characterized by potential lifelong morbidity and life-threatening complications. To date, however, diagnostic and predictive biomarkers are an unmet need in this patient population. The inclusion of biomarker discovery correlatives in neurofibromatosis 1/schwannomatosis clinical trials enables study of low-incidence disease. The implementation of a common data model would further enhance biomarker discovery by enabling effective concatenation of data from multiple studies.

METHODS

The Response Evaluation in Neurofibromatosis and Schwannomatosis biomarker working group reviewed published data on emerging trends in neurofibromatosis 1 and schwannomatosis biomarker research and developed recommendations in a series of consensus meetings.

RESULTS

Liquid biopsy has emerged as a promising assay for neurofibromatosis 1/schwannomatosis biomarker discovery and validation. In addition, we review recommendations for a range of biomarkers in clinical trials, neurofibromatosis 1/schwannomatosis-specific data annotations, and common data models for data integration.

CONCLUSION

These Response Evaluation in Neurofibromatosis and Schwannomatosis consensus guidelines are intended to provide best practices for the inclusion of biomarker studies in neurofibromatosis 1/schwannomatosis clinical trials, data, and sample annotation and to lay a framework for data harmonization and concatenation between trials.

Improved estimation of overall survival and progression-free survival for state transition modeling

Aim: National Institute for Health and Care Excellence guidance (Technical Support Document 19) highlights a key challenge of state transition models (STMs) being their difficulty in achieving a satisfactory fit to the observed within-trial endpoints. Fitting poorly to data over the trial period can then have implications for long-term extrapolations. A novel estimation approach is defined in which the predicted overall survival (OS) and progression-free survival (PFS) extrapolations from an STM are optimized to provide closer estimates of the within-trial endpoints. Materials & methods: An STM was fitted to the SQUIRE trial data in non-small-cell lung cancer (obtained from Project Data Sphere). Two methods were used: a standard approach whereby the maximum likelihood was utilized for the individual transitions and the best-fitting parametric model selected based on AIC/BIC, and a novel approach in which parameters were optimized by minimizing the area between the STM-predicted OS and PFS curves and the corresponding OS and PFS Kaplan-Meier curves. Sensitivity analyses were conducted to assess uncertainty. Results: The novel approach resulted in closer estimations to the OS and PFS Kaplan-Meier for all combinations of parametric distributions analyzed compared with the standard approach. Though the uncertainty associated with the novel approach was slightly larger, it provided better estimates to the restricted mean survival time in 10 of the 12 parametric distributions analyzed. Conclusion: A novel approach is defined which provides an alternative STM estimation method enabling improved fits to modeled endpoints, which can easily be extended to more complex model structures.

Treatment of evolving cancers will require dynamic decision support

Cancer research has traditionally focused on developing new agents, but an underexplored question is that of the dose and frequency of existing drugs. Based on the modus operandi established in the early days of chemotherapies, most drugs are administered according to predetermined schedules that seek to deliver the maximum tolerated dose and are only adjusted for toxicity. However, we believe that the complex, evolving nature of cancer requires a more dynamic and personalized approach. Chronicling the milestones of the field, we show that the impact of schedule choice crucially depends on processes driving treatment response and failure. As such, cancer heterogeneity and evolution dictate that a one-size-fits-all solution is unlikely-instead, each patient should be mapped to the strategy that best matches their current disease characteristics and treatment objectives (i.e. their 'tumorscape'). To achieve this level of personalization, we need mathematical modeling. In this perspective, we propose a five-step 'Adaptive Dosing Adjusted for Personalized Tumorscapes (ADAPT)' paradigm to integrate data and understanding across scales and derive dynamic and personalized schedules. We conclude with promising examples of model-guided schedule personalization and a call to action to address key outstanding challenges surrounding data collection, model development, and integration.

Impact of body mass index and its change on survival outcomes in patients with early breast cancer: A pooled analysis of individual-level data from BCIRG-001 and BCIRG-005 trials

INTRODUCTION

The relationships between body mass index (BMI) and survival rates are complex, and have not been thoroughly investigated in breast cancer patients who received adjuvant chemotherapy.

METHODS

We collected data on 2394 patients from two randomized, phase III clinical trials that investigated adjuvant chemotherapy in breast cancer identified in Project Data Sphere. The objective was to examine the effect of baseline BMI, BMI after adjuvant chemotherapy, and BMI change from baseline to post-adjuvant chemotherapy on disease-free survival (DFS) and overall survival (OS). Restricted cubic splines were used to examine potential non-linear associations between continuous BMI value and survival. Stratified analyses involved chemotherapy regimens.

RESULTS

Severe obesity (BMI≥40.0 kg/m2) at baseline was independently associated with worse DFS (hazard ration [HR] = 1.48, 95% confidence interval [CI] 1.02-2.16, P = 0.04) and OS (HR = 1.79, 95%CI 1.17-2.74, P = 0.007) compared with underweight/normal weight (BMI≤24.9 kg/m2). A BMI loss >10% was also an independent prognostic factor for adverse OS (HR = 2.14, 95%CI 1.17-3.93, P = 0.014). Stratified analyses revealed that severe obesity adversely affected DFS (HR = 2.38, 95%CI 1.26-4.34, P = 0.007) and OS (HR = 2.90, 95%CI 1.46-5.76, P = 0.002) in the docetaxel-based group, but not in the non-docetaxel-based group. Restricted cubic splines revealed a "J-shaped" association of baseline BMI with risk of recurrence or all-cause death, and this relationship was more pronounced in the docetaxel-based group.

CONCLUSIONS

In early breast cancer patients treated with adjuvant chemotherapy, baseline severe obesity was significantly linked to worse DFS and OS, and a BMI loss over 10% from baseline to post-adjuvant chemotherapy also negatively affected OS. Moreover, the prognostic role of BMI might differ between docetaxel-based and non-docetaxel-based groups.

Quality of life outcomes for patients with metastatic castration-resistant prostate cancer and pretreatment prognostic score

BACKGROUND

A prognostic risk score (Halabi score) in metastatic castration-resistant prostate cancer (mCRPC) accurately predicts overall survival, but its association with quality of life (QOL) has not been defined. We hypothesize that a higher pretreatment Halabi score is associated with worse QOL outcomes over time in mCRPC patients.

METHODS

Patient-level data from the docetaxel plus prednisone control arm of Mainsail, a Phase 3 clinical trial in mCRPC were accessed via ProjectDataSphere. Pretreatment Halabi score included disease-related factors: metastatic site, opioid use, Eastern Cooperative Oncology Group performance status (ECOG-PS), alkaline phosphatase, albumin, hemoglobin, lactic acid dehydrogenase, and PSA, with higher score indicating worse survival. Three QOL scales were created: Functional Assessment of Cancer Therapy-Prostate (FACT-P, higher score = better QOL), Brief Pain Inventory-Short Form Severity score (BPI-SFSS, higher score = higher pain severity), and BPI-SF Interference score (BPI-SFIS, higher score = greater pain interference). Mixed linear model was used to estimate the associations between Halabi score and QOL scores assessed at different time points (baseline, 2 months, and 6 months).

RESULTS

This analysis included 412 mCRPC patients (median age = 68 years, 82% white, 5% Black, median log PSA = 4.4 ng/mL). After multivariable adjustment, Halabi score was significantly associated with QOL scores at all time points. At 6 months, multivariable adjusted FACT-P decreased by 10.0 points (worsening), BPI-SFSS increased by 0.8 points (worsening), and BPI-SFIS increased by 0.9 points (worsening) for each unit increase in Halabi risk score. In multivariable analysis of individual components, ECOG-PS, site of metastasis, and opioid use were significantly associated with worse QOL scores at baseline.

CONCLUSIONS

Chemotherapy-naïve mCRPC patients with poorer Halabi prognostic risk scores have poorer QOL and greater pain intensity and interference at baseline and during follow-up.

Asia-Inclusive Clinical Research and Development Enabled by Translational Science and Quantitative Clinical Pharmacology: Toward a Culture That Challenges the Status Quo

Access lag to innovative therapies in Asian populations continues to present a challenge to global health. Recent progressive changes in the global regulatory landscape, including newer guidelines, are enabling simultaneous global drug development and near-simultaneous global drug registration. The International Conference on Harmonization (ICH) E17 guideline outlines general principles for the design and analysis of multiregional clinical trials (MRCTs). We posit that translational research and quantitative clinical pharmacology tools are core enablers for Asia-inclusive global drug development aligned with ICH E17 principles. Assessment of ethnic sensitivity should be initiated early in the development lifecycle to inform the need for, and extent of, Asian phase I ethno-bridging data. Relevant ethno-bridging data may be generated as standalone Asian phase I trials, as part of Western First-In-Human trials, or under accelerated development settings as a lead-in phase in an MRCT. Quantitative understanding of human clearance mechanisms and pharmacogenetic factors is vital to forecasting ethnic sensitivity in drug exposure using physiologically-based pharmacokinetic models. Stratification factors to control heterogeneity in MRCTs can be identified by reverse translational research incorporating pharmacometric disease models and model-based meta-analyses. Because epidemiological variations can extend to the molecular level, quantitative systems pharmacology models may be useful in forecasting how molecular variation in therapeutic targets or pathway proteins across populations might impact treatment outcomes. Through prospective evaluation of conservation in drug- and disease-related intrinsic and extrinsic factors, a pooled East Asian region can be implemented in Asia-inclusive MRCTs to maximize efficiency in substantiating evidence of benefit-risk for the region at-large with a Totality of Evidence approach.

Polycystic Kidney Disease Drug Development: A Conference Report

Autosomal dominant polycystic kidney disease (ADPKD) is part of a spectrum of inherited diseases that also includes autosomal recessive polycystic kidney disease, autosomal dominant polycystic liver disease, and an expanding group of recessively inherited disorders collectively termed hepatorenal fibrocystic disorders. ADPKD is the most common monogenic disorder frequently leading to chronic kidney failure with an estimated prevalence of 12 million people worldwide. Currently, only one drug (tolvaptan) has been approved by regulatory agencies as disease-modifying therapy for ADPKD, but, given its mechanism of action and side effect profile, the need for an improved therapy for ADPKD remains a priority. Although significant regulatory progress has been made, with qualification of total kidney volume as a prognostic enrichment biomarker and its later designation as a reasonably likely surrogate endpoint for progression of ADPKD within clinical trials, further work is needed to accelerate drug development efforts for all forms of PKD. In May 2021, the PKD Outcomes Consortium at the Critical Path Institute and the PKD Foundation organized a PKD Regulatory Summit to spur conversations among patients, industry, academic, and regulatory stakeholders regarding future development of tools and drugs for ADPKD and autosomal recessive polycystic kidney disease. This Special Report reviews the key points discussed during the summit and provides future direction related to PKD drug development tools.

The design and evaluation of hybrid controlled trials that leverage external data and randomization

Patient-level data from completed clinical studies or electronic health records can be used in the design and analysis of clinical trials. However, these external data can bias the evaluation of the experimental treatment when the statistical design does not appropriately account for potential confounders. In this work, we introduce a hybrid clinical trial design that combines the use of external control datasets and randomization to experimental and control arms, with the aim of producing efficient inference on the experimental treatment effects. Our analysis of the hybrid trial design includes scenarios where the distributions of measured and unmeasured prognostic patient characteristics differ across studies. Using simulations and datasets from clinical studies in extensive-stage small cell lung cancer and glioblastoma, we illustrate the potential advantages of hybrid trial designs compared to externally controlled trials and randomized trial designs.

Patient-level external control data from prior clinical studies or electronic health records can be used in the design and analysis of clinical trials. Here the authors report a hybrid trial design combining the use of external control data and randomization to test experimental treatments, using small cell lung cancer and glioblastoma datasets as examples.

Early termination of pivotal trials in Alzheimer's disease-Preserving optimal value for participants and science

Participants in Alzheimer's disease late-phase clinical trials are frequently confronted with a situation of early termination. We discuss measures to protect the perceived value of study participation and to maximize the scientific value under such circumstances. A communication strategy should ensure that trial participants maintain a positive relationship with the research team and have their informational needs optimally met. Measures to maximize the scientific value may include data/sample sharing, strategies for personalized medicine, as well as scientific follow-up. Critical for the success of such a concept are networks of excellence, extending models of existing initiatives like Global Alzheimer's Platform Foundation Network (GAP-Net). These networks could fundamentally strengthen the role of clinical investigators if they decide on their involvement in trials based upon their estimation of the scientific value and benefit for the participants, actively contribute to scientific analyses, and mediate optimal communication among the relevant trial stakeholders.

Developments in proton MR spectroscopic imaging of prostate cancer

In this paper, we review the developments of 1H-MR spectroscopic imaging (MRSI) methods designed to investigate prostate cancer, covering key aspects such as specific hardware, dedicated pulse sequences for data acquisition and data processing and quantification techniques. Emphasis is given to recent advancements in MRSI methodologies, as well as future developments, which can lead to overcome difficulties associated with commonly employed MRSI approaches applied in clinical routine. This includes the replacement of standard PRESS sequences for volume selection, which we identified as inadequate for clinical applications, by sLASER sequences and implementation of 1H MRSI without water signal suppression. These may enable a new evaluation of the complementary role and significance of MRSI in prostate cancer management.

Translation of Prognostic and Pharmacodynamic Biomarkers from Trial to Non-trial Patients with Metastatic Castration-resistant Prostate Cancer Treated with Docetaxel

AIMS

We conducted a pooled analysis of four randomised controlled trials and a non-trial retrospective dataset to study the changes in serum prostate-specific antigen (PSA) concentrations during treatment and its impact on survival in men treated with docetaxel for metastatic castration-resistant prostate cancer. We also compared the outcomes and pre-treatment prognostic factors between trial and non-trial patients.

MATERIALS AND METHODS

Data were obtained from four randomised controlled trials and a non-trial cohort from a tertiary cancer centre. The PSA kinetics covariates chosen were absolute value (PSAT), best percentage change (BPCH) and tumour growth rate (K). The association between the covariates collected and overall survival was assessed within a Cox proportional hazards model. How well a covariate captured the difference between trial and non-trial patients was assessed by reporting on models with or without trial status as a covariate.

RESULTS

We reviewed individual datasets of 2282 patients. The median overall survival for trial patients was 20.4 (95% confidence interval 19.6-22.2) months and for the non-trial cohort was 12.4 (10.7-14.7) months (P < 0.001). Of the pre-treatment factors, we found that only lactate dehydrogenase fully captured the difference in prognosis between the trial and non-trial cohorts. All PSA kinetic metrics appeared to be prognostic in both the trial and non-trial patients. However, the effect size was reduced in non-trial versus trial patients (interaction P < 0.001). Of the time-dependent covariates, we found that BPCH best captured the difference between trial and non-trial patient prognosis.

CONCLUSIONS

The analysis presented here highlights how data from open-source trial databases can be combined with emerging clinical practice databases to assess differences between trial versus non-trial patients for particular treatments. These results highlight the importance of developing prognostic models using both pre-treatment and time-dependent biomarkers of new treatments.

Nano-Theranostics for the Sensing, Imaging and Therapy of Prostate Cancers

We highlight hereby recent developments in the emerging field of theranostics, which encompasses the combination of therapeutics and diagnostics in a single entity aimed for an early-stage diagnosis, image-guided therapy as well as evaluation of therapeutic outcomes of relevance to prostate cancer (PCa). Prostate cancer is one of the most common malignancies in men and a frequent cause of male cancer death. As such, this overview is concerned with recent developments in imaging and sensing of relevance to prostate cancer diagnosis and therapeutic monitoring. A major advantage for the effective treatment of PCa is an early diagnosis that would provide information for an appropriate treatment. Several imaging techniques are being developed to diagnose and monitor different stages of cancer in general, and patient stratification is particularly relevant for PCa. Hybrid imaging techniques applicable for diagnosis combine complementary structural and morphological information to enhance resolution and sensitivity of imaging. The focus of this review is to sum up some of the most recent advances in the nanotechnological approaches to the sensing and treatment of prostate cancer (PCa). Targeted imaging using nanoparticles, radiotracers and biomarkers could result to a more specialised and personalised diagnosis and treatment of PCa. A myriad of reports has been published literature proposing methods to detect and treat PCa using nanoparticles but the number of techniques approved for clinical use is relatively small. Another facet of this report is on reviewing aspects of the role of functional nanoparticles in multimodality imaging therapy considering recent developments in simultaneous PET-MRI (Positron Emission Tomography-Magnetic Resonance Imaging) coupled with optical imaging in vitro and in vivo, whilst highlighting feasible case studies that hold promise for the next generation of dual modality medical imaging of PCa. It is envisaged that progress in the field of imaging and sensing domains, taken together, could benefit from the biomedical implementation of new synthetic platforms such as metal complexes and functional materials supported on organic molecular species, which can be conjugated to targeting biomolecules and encompass adaptable and versatile molecular architectures. Furthermore, we include hereby an overview of aspects of biosensing methods aimed to tackle PCa: prostate biomarkers such as Prostate Specific Antigen (PSA) have been incorporated into synthetic platforms and explored in the context of sensing and imaging applications in preclinical investigations for the early detection of PCa. Finally, some of the societal concerns around nanotechnology being used for the detection of PCa are considered and addressed together with the concerns about the toxicity of nanoparticles–these were aspects of recent lively debates that currently hamper the clinical advancements of nano-theranostics. The publications survey conducted for this review includes, to the best of our knowledge, some of the most recent relevant literature examples from the state-of-the-art. Highlighting these advances would be of interest to the biomedical research community aiming to advance the application of theranostics particularly in PCa diagnosis and treatment, but also to those interested in the development of new probes and methodologies for the simultaneous imaging and therapy monitoring employed for PCa targeting.

Development of Balanced Whole System Value Measures for Inflammatory Bowel Disease Care in the IBD Qorus Collaborative Using a Modified Delphi Process

BACKGROUND

The IBD Qorus Collaborative aims to reduce variation and increase the value of care for the adult inflammatory bowel disease (IBD) community. To evaluate the success of the collaborative, we aimed to develop a balanced set of outcome measures that reflect a multistakeholder view of value in IBD care. To achieve this, we used the Clinical Value Compass framework and engaged a mixed-stakeholder group to conduct a modified Delphi process. The end result was a 10-measure set to assess the value of IBD care.

METHOD

The modified Delphi process included 3 iterative rounds of blinded voting and interactive webinar-style discussion. We recruited 18 participants for the Delphi panel, including clinicians, researchers, patients, Crohn's & Colitis Foundation staff, and payers. Participants first identified constructs to measure, then identified the tools to measure those constructs. A literature review and environmental scan of current measures in 4 domains were performed, and relevant measures were proposed for discussion and voting in each domain. Throughout the process, participants were invited to contribute additional measures.

CONCLUSION

The modified Delphi process led to selection of 10 value measures across 4 domains: (1) patient experience; (2) functional status; (3) clinical status; and (4) health care costs and utilization. We have successfully completed a 3-stage modified Delphi process to develop a balanced set of value measures for adult IBD care. The value measure set expands upon prior efforts that have established quality measures for IBD care by adding cost and experience of care elements. This work positions IBD Qorus to better assess, study, improve, and demonstrate value at individual, system, and population levels and will inform and empower related research, improvement, and implementation efforts.

Prognostic significance of baseline neutrophil-lymphocyte ratio in patients with non-small-cell lung cancer: a pooled analysis of open phase III clinical trial data

Aim: This study aimed to assess survival and hematological prognostic indicators of patients with non-small-cell lung cancer (NSCLC). Material & methods: Through the Project Data Sphere portal, two phase III clinical trial datasets were downloaded to analyze survival outcomes and related risk factors. Results: The median progression-free survival and overall survival of 756 patients with stage III-IV NSCLC were 6.2 and 14.2 months, respectively. In multivariate Cox analysis, high baseline neutrophil-lymphocyte ratio (NLR; ≥3.8) was associated with worse progression-free survival (hazard ratio: 1.37; p = 0.0004) and overall survival (hazard ratio: 1.65; p < 0.0001). In addition, it exerted an unfavorable impact on survival across multiple subgroups. Conclusions: NLR, a powerful inflammatory and immunologic indicator, is an independent prognostic indicator in patients with advanced NSCLC.

Data sharing and privacy issues arising with COVID-19 data and applications

The coronavirus disease 2019 (COVID-19) (2019-nCov), which was first detected in Wuhan/China in December 2019 and spread to the whole world in a short time, was explained as a new coronavirus by the World Health Organization on February 11, 2020. Countries are developing various strategies against the spread of epidemic threat. The main ones are to develop web-based or mobile applications to reduce the spread and economic damage of the epidemic by making use of COVID-19 datasets. It is seen that the existing applications developed within the framework of these expectations contain absolute location information (direct), relative location information (indirect), and characteristic data defining people. Even if these data mean a lot to the world's struggle with COVID-19, it is necessary to foresee the risks that may occur after the epidemic when the relations of the information are considered. In order to measure the privacy risk of this kind of applications containing personal data, privacy metrics have been defined in the literature. In this chapter, we give a perspective about the sharing and privacy of medical data within the scope of COVID-19. Within this context, privacy models, metrics, and approaches for selecting the appropriate model are described, in particular for COVID-19 applications, and we also propose a new metric with the entropy approach to metrics defined in the literature and effective in determining the privacy score.

Genomics governance: advancing justice, fairness and equity through the lens of the African communitarian ethic of Ubuntu

There is growing interest for a communitarian approach to the governance of genomics, and for such governance to be grounded in principles of justice, equity and solidarity. However, there is a near absence of conceptual studies on how communitarian-based principles, or values, may inform, support or guide the governance of genomics research. Given that solidarity is a key principle in Ubuntu, an African communitarian ethic and theory of justice, there is emerging interest about the extent to which Ubuntu could offer guidance for the governance of genomics research in Africa. To this effect, we undertook a conceptual analysis of Ubuntu with the goal of identifying principles that could inform equity-oriented governance of genomics research. Solidarity, reciprocity, open sharing, accountability, mutual trust, deliberative decision-making and inclusivity were identified as core principles that speak directly to the different macro-level ethical issues in genomics research in Africa such as: the exploitation of study populations and African researchers, equitable access and use of genomics data, benefit sharing, the possibility of genomics to widen global health inequities and the fair distribution of resources such as intellectual property and patents. We use the identified the principles to develop ethical guidance for genomics governance in Africa.

Semi-automated Conversion of Clinical Trial Legacy Data into CDISC SDTM Standards Format Using Supervised Machine Learning

OBJECTIVE

 This study aimed to develop a semi-automated process to convert legacy data into clinical data interchange standards consortium (CDISC) study data tabulation model (SDTM) format by combining human verification and three methods: data normalization; feature extraction by distributed representation of dataset names, variable names, and variable labels; and supervised machine learning.

MATERIALS AND METHODS

 Variable labels, dataset names, variable names, and values of legacy data were used as machine learning features. Because most of these data are string data, they had been converted to a distributed representation to make them usable as machine learning features. For this purpose, we utilized the following methods for distributed representation: Gestalt pattern matching, cosine similarity after vectorization by Doc2vec, and vectorization by Doc2vec. In this study, we examined five algorithms-namely decision tree, random forest, gradient boosting, neural network, and an ensemble that combines the four algorithms-to identify the one that could generate the best prediction model.

RESULTS

 The accuracy rate was highest for the neural network, and the distribution of prediction probabilities also showed a split between the correct and incorrect distributions. By combining human verification and the three methods, we were able to semi-automatically convert legacy data into the CDISC SDTM format.

CONCLUSION

 By combining human verification and the three methods, we have successfully developed a semi-automated process to convert legacy data into the CDISC SDTM format; this process is more efficient than the conventional fully manual process.

KMDATA: a curated database of reconstructed individual patient-level data from 153 oncology clinical trials

We created a database of reconstructed patient-level data from published clinical trials that includes multiple time-to-event outcomes such as overall survival and progression-free survival. Outcomes were extracted from Kaplan–Meier (KM) curves reported in 153 oncology Phase III clinical trial publications identified through a PubMed search of clinical trials in breast, lung, prostate and colorectal cancer, published between 2014 and 2016. For each trial that met our search criteria, we curated study-level information and digitized all reported KM curves with the software Digitizelt. We then used the digitized KM survival curves to estimate (possibly censored) patient-level time-to-event outcomes. Collections of time-to-event datasets from completed trials can be used to support the choice of appropriate trial designs for future clinical studies. Patient-level data allow investigators to tailor clinical trial designs to diseases and classes of treatments. Patient-level data also allow investigators to estimate the operating characteristics (e.g. power and type I error rate) of candidate statistical designs and methods.

Database URL: https://10.6084/m9.figshare.14642247.v1

Assessing the Feasibility of a Novel mHealth App in Hematopoietic Stem Cell Transplant Patients

Hematopoietic stem cell transplantation (HCT) is a curative treatment option for patients with hematologic conditions but presents many complications that must be managed as a complex, chronic condition. Mobile health applications (mHealth apps) may permit tracking of symptoms in HCT. In seeking strategies to manage the complexities of HCT, our team collaborated with Sicklesoft, Inc., to develop an mHealth app specifically for HCT patients to allow for daily evaluation of patient health, Technology Recordings to better Understand Bone Marrow Transplantation (TRU-BMT). The primary value of this application is that of potentially enhancing the monitoring of symptoms and general health of patients undergoing HCT, with the ultimate goal of allowing earlier detection of adverse events, earlier intervention, and improving outcomes. To first evaluate patient interest in mHealth apps, we designed and administered an interest survey to patients at the 2017 BMT-InfoNet reunion. As a follow-up to the positive feedback received, we began testing the TRU-BMT app in a Phase 1 pilot study. Thirty patients were enrolled in this single-arm study and were given the TRU-BMT mHealth app on a smartphone device in addition to a wearable activity tracker. Patients were followed for up to 180 days, all the while receiving daily app monitoring. Adherence to TRU-BMT was approximately 30% daily and 44% weekly, and greater adherence was associated with increased meal completion, decreased heart rate, and shorter hospital stay. TRU-BMT assessments of symptom severity were significantly associated with duration of hospital stay and development of chronic graft-versus-host disease. Our findings suggest that using TRU-BMT throughout HCT is feasible for patients and established a proof-of-concept for a future randomized control trial of the TRU-BMT application in HCT. © 2021 American Society for Blood and Marrow Transplantation. Published by Elsevier Inc. All rights reserved.

Effective Data Sharing as a Conduit for Advancing Medical Product Development

INTRODUCTION

Patient-level data sharing has the potential to significantly impact the lives of patients by optimizing and improving the medical product development process. In the product development setting, successful data sharing is defined as data sharing that is actionable and facilitates decision making during the development and review of medical products. This often occurs through the creation of new product development tools or methodologies, such as novel clinical trial design and enrichment strategies, predictive pre-clinical and clinical models, clinical trial simulation tools, biomarkers, and clinical outcomes assessments, and more.

METHODS

To be successful, extensive partnerships must be established between all relevant stakeholders, including industry, academia, research institutes and societies, patient-advocacy groups, and governmental agencies, and a neutral third-party convening organization that can provide a pre-competitive space for data sharing to occur.

CONCLUSIONS

Data sharing focused on identified regulatory deliverables that improve the medical product development process encounters significant challenges that are not seen with data sharing aimed at advancing clinical decision making and requires the commitment of all stakeholders. Regulatory data sharing challenges and solutions, as well as multiple examples of previous successful data sharing initiatives are presented and discussed in the context of medical product development.

Biomedical Data Sharing Among Researchers: A Study from Jordan

Background

Data sharing is an encouraged practice to support research in all fields. For that purpose, it is important to examine perceptions and concerns of researchers about biomedical data sharing, which was investigated in the current study.

Methods

This is a cross-sectional survey study that was distributed among biomedical researchers in Jordan, as an example of developing countries. The study survey consisted of questions about demographics and about respondent’s attitudes toward sharing of biomedical data.

Results

Among study participants, 46.9% (n=82) were positive regarding making their research data available to the public, whereas 53.1% refused the idea. The reasons for refusing to publicly share their data included “lack of regulations” (33.5%), “access to research data should be limited to the research team” (29.5%), “no place to deposit the data” (6.5%), and “lack of funding for data deposition” (6.0%). Agreement with the idea of making data available was associated with academic rank (P=0.003). Moreover, gender (P-value=0.043) and number of publications (P-value=0.005) were associated with a time frame for data sharing (ie, agreeing to share data before vs after publication).

Conclusion

About half of the respondents reported a positive attitude toward biomedical data sharing. Proper regulations and facilitation data deposition can enhance data sharing in Jordan.

Enhancing the analytic utility of clinical trial data to inform health disparities research

Clinical trials are often conducted among younger, healthier, and less racially diverse patient populations than the population at large. Health disparities for individuals with cancer are most apparent when there are notable differences in the occurrence, frequency, burden of cancer and mortality rates among specific population groups. Enhancing the diversity of participants in clinical trials to reflect the characteristics of cancer survivors in the U.S. population is of growing interest to better insure the safety and efficacy of resultant treatments. The Project Data Sphere® (PDS) cancer research platform is a first-of-its kind research environment that provides the research community with broad access to both de-identified patient-level clinical trial data and advanced analytic tools to enable big data-driven research. To address these analytic constraints, the data profiles in selected PDS patient-level cancer phase III clinical datasets have been augmented by linking the social, economic, and health-related characteristics of like cancer survivors from nationally representative health and health care-related survey data from the Medical Expenditure Panel Survey (MEPS). Our article shines a spotlight on this ongoing initiative to improve access to clinical trial data in support of health care disparities research initiatives.

Antibacterial Use Is Associated with an Increased Risk of Hematologic and Gastrointestinal Adverse Events in Patients Treated with Gemcitabine for Stage IV Pancreatic Cancer

BACKGROUND

Preclinical evidence has demonstrated that common intratumor bacteria metabolize the chemotherapeutic drug gemcitabine. The significance of this bacterial metabolism pathway, relative to the known metabolic pathways by host enzymes, is not known. We hypothesized that bacterial metabolism is clinically significant and that "knockdown" by antibacterial therapy has the unintended effect of increasing the effective dose of gemcitabine, thereby increasing the risk for gemcitabine-associated toxicities.

MATERIALS AND METHODS

We reanalyzed the comparator arm of the MPACT trial (NCT01442974), made available through Project Data Sphere, LLC (CEO Roundtable on Cancer's Life Sciences Consortium, Cary, NC; www.projectdatasphere.org). In this arm, 430 patients with metastatic pancreatic adenocarcinoma were treated with gemcitabine. We used the Anderson-Gill survival model to compare the risk of developing an adverse event after antibacterial prescription with time unexposed to antibacterials. Adverse events of grade 3 and greater were considered at three levels of granularity: all aggregated into one endpoint, aggregated by class, and taken individually. Antibiotic exposures were analyzed in aggregate as well as by class.

RESULTS

Antibacterial exposure was associated with an increased risk of adverse events (hazard ratio [HR]: 1.77; confidence interval [CI]: 1.46-2.14), any hematologic adverse event (HR: 1.64; CI: 1.26-2.13), and any gastrointestinal adverse event (HR: 2.14; CI: 1.12-4.10) but not a constitutional (HR: 1.33; CI: 0.611-2.90) or hepatologic adverse event (HR: 0.99; CI: 0.363-2.71). Among specific adverse events, antibacterial exposure was associated with an increased risk of anemia (HR: 3.16; CI: 1.59-6.27), thrombocytopenia (HR: 2.52; CI: 1.31-4.85), leukopenia (HR: 3.91; CI: 1.46-10.5), and neutropenia (HR: 1.53; CI: 1.07-2.17) but not any other specific adverse events.

CONCLUSION

Antibacterial exposure was associated with an increased risk of gemcitabine-associated, dose-limiting adverse events, including aggregate hematologic and gastrointestinal events, as well as four specific hematologic adverse events, suggesting that intratumor bacteria may be responsible for a clinically significant portion of gemcitabine metabolism. Alternative avenues of evidence will be necessary to confirm this preliminary finding and assess its generalizability. There is plentiful opportunity for similar analyses on other clinical trial data sets, where gemcitabine or other biomimetic small molecules were used.

IMPLICATIONS FOR PRACTICE

Patients treated with gemcitabine for metastatic pancreatic ductal adenocarcinoma have an increased rate of gemcitabine-associated toxicity during and after antibiotic therapy. This observation is consistent with preclinical evidence that intratumor bacteria metabolize gemcitabine to an inactive form. Further research is needed to determine whether this observation merits any changes in clinical practice.

Obtaining and managing data sets for individual participant data meta-analysis: scoping review and practical guide

BACKGROUND

Shifts in data sharing policy have increased researchers' access to individual participant data (IPD) from clinical studies. Simultaneously the number of IPD meta-analyses (IPDMAs) is increasing. However, rates of data retrieval have not improved. Our goal was to describe the challenges of retrieving IPD for an IPDMA and provide practical guidance on obtaining and managing datasets based on a review of the literature and practical examples and observations.

METHODS

We systematically searched MEDLINE, Embase, and the Cochrane Library, until January 2019, to identify publications focused on strategies to obtain IPD. In addition, we searched pharmaceutical websites and contacted industry organizations for supplemental information pertaining to recent advances in industry policy and practice. Finally, we documented setbacks and solutions encountered while completing a comprehensive IPDMA and drew on previous experiences related to seeking and using IPD.

RESULTS

Our scoping review identified 16 articles directly relevant for the conduct of IPDMAs. We present short descriptions of these articles alongside overviews of IPD sharing policies and procedures of pharmaceutical companies which display certification of Principles for Responsible Clinical Trial Data Sharing via Pharmaceutical Research and Manufacturers of America or European Federation of Pharmaceutical Industries and Associations websites. Advances in data sharing policy and practice affected the way in which data is requested, obtained, stored and analyzed. For our IPDMA it took 6.5 years to collect and analyze relevant IPD and navigate additional administrative barriers. Delays in obtaining data were largely due to challenges in communication with study sponsors, frequent changes in data sharing policies of study sponsors, and the requirement for a diverse skillset related to research, administrative, statistical and legal issues.

CONCLUSIONS

Knowledge of current data sharing practices and platforms as well as anticipation of necessary tasks and potential obstacles may reduce time and resources required for obtaining and managing data for an IPDMA. Sufficient project funding and timeline flexibility are pre-requisites for successful collection and analysis of IPD. IPDMA researchers must acknowledge the additional and unexpected responsibility they are placing on corresponding study authors or data sharing administrators and should offer assistance in readying data for sharing.

Performance of randomization-based causal methods with and without integrating external data sources for adjusting overall survival in case of extensive treatment switches in placebo-controlled randomized oncology phase 3 trials

In recent placebo-controlled randomized phase 3 oncology trials, evaluation of overall survival with frequent crossover is crucial for regulatory and pricing decisions. The problem is that an intention-to-treat based analysis causes a substantial loss of power to detect causal survival effect without crossover, and performance of existing methods is not satisfactory. In this article, our aims were to evaluate properties of the existing and a proposed Bayesian power prior method where data from an external trial is available. Simulation results suggested that proposed method was the most powerful under typical scenarios where patients with better prognosis are likely to crossover.

Connected Health User Willingness to Share Personal Health Data: Questionnaire Study

BACKGROUND

Connected health has created opportunities for leveraging health data to deliver preventive and personalized health care services. The increasing number of personal devices and advances in measurement technologies contribute to an exponential growth in digital health data. The practices for sharing data across the health ecosystem are evolving as there are more opportunities for using such data to deliver responsive health services.

OBJECTIVE

The objective of this study was to explore user attitudes toward sharing personal health data (PHD). The study was executed within the first year after the implementation of the new General Data Protection Regulation (GDPR) legal framework.

METHODS

The authors analyzed the results of an online questionnaire survey to explore the willingness of 8004 people using connected health services across four European countries to share their PHD and the conditions under which they would be willing to do so.

RESULTS

Our findings indicate that the majority of users are willing to share their personal PHD for scientific research (1811/8004, 22.63%). Age, education level, and occupation of the participants, in addition to the level of digitalization in their country were found to be associated with data sharing attitudes.

CONCLUSIONS

Positive attitudes toward data sharing for scientific research can be perceived as an indication of trust established between users and academia. Nevertheless, the interpretation of data sharing attitudes is a complex process, related to and influenced by various factors.

How Clinical Trial Data Sharing Platforms Can Advance the Study of Biomarkers

Although data sharing platforms host diverse data types the features of these platforms are well-suited to facilitating biomarker research. Given the current state of biomarker discovery, an innovative paradigm to accelerate biomarker discovery is to utilize platforms such as Vivli to leverage researchers' abilities to integrate certain classes of biomarkers.

Core Data Elements in Acute Myeloid Leukemia: A Unified Medical Language System-Based Semantic Analysis and Experts' Review

Background

For cancer domains such as acute myeloid leukemia (AML), a large set of data elements is obtained from different institutions with heterogeneous data definitions within one patient course. The lack of clinical data harmonization impedes cross-institutional electronic data exchange and future meta-analyses.

Objective

This study aimed to identify and harmonize a semantic core of common data elements (CDEs) in clinical routine and research documentation, based on a systematic metadata analysis of existing documentation models.

Methods

Lists of relevant data items were collected and reviewed by hematologists from two university hospitals regarding routine documentation and several case report forms of clinical trials for AML. In addition, existing registries and international recommendations were included. Data items were coded to medical concepts via the Unified Medical Language System (UMLS) by a physician and reviewed by another physician. On the basis of the coded concepts, the data sources were analyzed for concept overlaps and identification of most frequent concepts. The most frequent concepts were then implemented as data elements in the standardized format of the Operational Data Model by the Clinical Data Interchange Standards Consortium.

Results

A total of 3265 medical concepts were identified, of which 1414 were unique. Among the 1414 unique medical concepts, the 50 most frequent ones cover 26.98% of all concept occurrences within the collected AML documentation. The top 100 concepts represent 39.48% of all concepts’ occurrences. Implementation of CDEs is available on a European research infrastructure and can be downloaded in different formats for reuse in different electronic data capture systems.

Conclusions

Information management is a complex process for research-intense disease entities as AML that is associated with a large set of lab-based diagnostics and different treatment options. Our systematic UMLS-based analysis revealed the existence of a core data set and an exemplary reusable implementation for harmonized data capture is available on an established metadata repository.

Willingness to Participate in Health Information Networks with Diverse Data Use: Evaluating Public Perspectives

INTRODUCTION

Health information generated by health care encounters, research enterprises, and public health is increasingly interoperable and shareable across uses and users. This paper examines the US public's willingness to be a part of multi-user health information networks and identifies factors associated with that willingness.

METHODS

Using a probability-based sample (n = 890), we examined the univariable and multivariable relationships between willingness to participate in health information networks and demographic factors, trust, altruism, beliefs about the public's ethical obligation to participate in research, privacy, medical deception, and policy and governance using linear regression modeling.

RESULTS

Willingness to be a part of a multi-user network that includes health care providers, mental health, social services, research, or quality improvement is low (26 percent-7.4 percent, depending on the user). Using stepwise regression, we identified a model that explained 42.6 percent of the variability in willingness to participate and included nine statistically significant factors associated with the outcome: Trust in the health system, confidence in policy, the belief that people have an obligation to participate in research, the belief that health researchers are accountable for conducting ethical research, the desire to give permission, education, concerns about insurance, privacy, and preference for notification.

DISCUSSION

Our results suggest willingness to be a part of multi-user data networks is low, but that attention to governance may increase willingness. Building trust to enable acceptance of multi-use data networks will require a commitment to aligning data access practices with the expectations of the people whose data is being used.

The changing role of radium-223 in metastatic castrate-resistant prostate cancer: has the EMA missed the mark with revising the label?

Radium-223 (223Ra) is a life-prolonging treatment in symptomatic men with metastatic castrate-resistant prostate cancer (mCRPC) and bone metastases, but no visceral disease, regardless of prior treatment with docetaxel. Together with four other drugs (i.e. abiraterone, cabazitaxel, docetaxel, enzalutamide), it has been available for clinical use since 2013 and has been shown to also provide benefits in quality-of-life and societal benefits. However, in 2018 the European Medicines Agency ruled to restrict the use of radium-223 to a more advanced disease setting after at least two lines of one or the other life-prolonging agent. This decision was triggered by the results of a safety interim analysis of ERA-223, a trial investigating the combination of 223Ra and abiraterone versus abiraterone alone in patients without prior chemotherapy (with the exception of adjuvant treatment) with asymptomatic bone predominant mCRPC. That safety analysis showed an early increased risk of fracture and deaths with the combination treatment. This review critically appraises the available and emerging data with 223Ra treatment in an attempt to assess the appropriateness of the revised label of radium-223.

Exploring users' willingness to share their health and personal data under the prism of the new GDPR: implications in healthcare

At the same time healthcare undergoes a digital transformation, the implementation of the new General Data Protection Regulation (GDPR) introduces changes to internet users. Understanding users' data-sharing attitudes for four type of personal data in regards to the new GDPR can facilitate stakeholders and policy-makers in healthcare to make sense of the current landscape. Authors analyzed the results of a questionnaire survey to explore the willingness of 8.004 people across four European countries to share four types of data: health; perceived values or beliefs; consumption habits and purchases; and wealth. Our results suggest that participants are more willing to share health data and data about beliefs and values than wealth information and that GDPR has impacted the data-sharing behavior of the participants.

Pilot randomized trial of an electronic symptom monitoring and reporting intervention for hospitalized adults undergoing hematopoietic stem cell transplantation

PURPOSE

Patients undergoing a hematopoietic stem cell transplantation (HCT) have varied symptoms during their hospitalization. This study examined whether daily symptom reporting (with electronic patient-reported outcomes [PROs]) in an inpatient bone marrow transplant clinic reduced symptom burden on post-transplant days +7, +10, and +14.

METHODS

A prospective, single-institution 1:1 pilot randomized, two-arm study recruited HCT patients. HCT inpatients (N = 76) reported daily on 16 common symptoms using the PRO version of the Common Terminology for Adverse Events (PRO-CTCAE). Fisher's exact test was used to examine differences in the proportion of patients reporting individual symptoms. Multivariable linear regression modeling was used to examine group differences in peak symptom burden, while controlling for symptom burden at baseline, age, comorbidity, and transplantation type (autologous or allogeneic).

RESULTS

HCT patients receiving the PRO intervention also experienced lower peak symptom burden (average of 16 symptoms) at days +7, +10, and +14 (10.4 vs 14.5, p = 0.03).

CONCLUSIONS

Daily use of electronic symptom reporting to nurses in an inpatient bone marrow transplant clinic reduced peak symptom burden and improved individual symptoms during the 2 weeks post-transplant. A multi-site trial is warranted to demonstrate the generalizability, efficacy, and value of this intervention.

TRIAL REGISTRATION

ClinicalTrials.gov identifier: NCT02574897.

Resistance models to EGFR inhibition and chemotherapy in non-small cell lung cancer via analysis of tumour size dynamics

Purpose

Imaging time-series data routinely collected in clinical trials are predominantly explored for covariates as covariates for survival analysis to support decision-making in oncology drug development. The key objective of this study was to assess if insights regarding two relapse resistance modes, de-novo (treatment selects out a pre-existing resistant clone) or acquired (resistant clone develops during treatment), could be inferred from such data.

Methods

Individual lesion size time-series data were collected from ten Phase III study arms where patients were treated with either first-generation EGFR inhibitors (erlotinib or gefitinib) or chemotherapy (paclitaxel/carboplatin combination or docetaxel). The data for each arm of each study were analysed via a competing models framework to determine which of the two mathematical models of resistance, de-novo or acquired, best-described the data.

Results

Within the first-line setting (treatment naive patients), we found that the de-novo model best-described the gefitinib data, whereas, for paclitaxel/carboplatin, the acquired model was preferred. In patients pre-treated with paclitaxel/carboplatin, the acquired model was again preferred for docetaxel (chemotherapy), but for patients receiving gefitinib or erlotinib, both the acquired and de-novo models described the tumour size dynamics equally well. Furthermore, in all studies where a single model was preferred, we found a degree of correlation in the dynamics of lesions within a patient, suggesting that there is a degree of homogeneity in pharmacological response.

Conclusions

This analysis highlights that tumour size dynamics differ between different treatments and across lines of treatment. The analysis further suggests that these differences could be a manifestation of differing resistance mechanisms.

Electronic supplementary material

The online version of this article (10.1007/s00280-019-03840-3) contains supplementary material, which is available to authorized users.

Second-line erlotinib after failure of pemetrexed-containing chemotherapy in advanced non-small cell lung cancer (NSCLC): Real-world effectiveness, safety and tolerability

INTRODUCTION

Little data is available on patients with advanced non-squamous NSCLC treated with erlotinib specifically after failure of first-line pemetrexed-containing chemotherapy. We assessed the effectiveness, safety and tolerability of erlotinib in a real-world setting.

METHODS

Prospective single-arm, open-label, multicenter, non-interventional study of erlotinib (150mg daily) in inoperable stage III/IV NSCLC after progression on first-line pemetrexed-containing chemotherapy without EGFR-mutation selection. Patients were followed according to routine practice and response assessment was performed using RECIST 1.1. The primary end point was progression-free survival (PFS). Secondary end points included best confirmed overall response rate (ORR), disease control rate (DCR), and overall survival (OS). Adverse events were recorded. An independent dataset was used to validate the results.

RESULTS

In all, 59 patients were screened, 57 enrolled, and 54 (36 men; median age 65 years) included in the per-protocol analysis. Median PFS was 1.8 (95% CI 1.4-2.6) months, with 11% (95% CI 5-21%) alive and progression-free at 6 months. The ORR was 0.0% (97.5% CI 0.0-6.8%) and the DCR 34.6% (95% CI 21.9-49.0%). Median overall survival was 5.8 (95% CI 3.3-8.6) months with 28% (95% CI 17-42%) alive at one year. Rash occurred in 60.7% (95% CI 46.7-73.5%), with severe rash in 12.5% (95% CI 5.1-24.1%). Any grade diarrhea was observed in 42.8% (95% CI 29.7-56.8%), with grade 3 occurring in 7.1% (95% CI 1.9-17.2%). Erlotinib was stopped in 21.0% (95% CI 11.3-33.9%) of patients due to adverse events, which were treatment related in 7%.

CONCLUSION

Second-line erlotinib after pemetrexed treatment results in similar real-world outcomes as reported after non-pemetrexed containing first-line therapy. However, the overall duration of response in unselected patients remains limited and other effective treatments have in the meantime been introduced. No new safety signals were detected.

Development and Validation of a Nomogram Prognostic Model for Patients With Advanced Non-Small-Cell Lung Cancer

Importance

Nomogram prognostic models can facilitate cancer patient treatment plans and patient enrollment in clinical trials.

Objective

The primary objective is to provide an updated and accurate prognostic model for predicting the survival of advanced non-small-cell lung cancer (NSCLC) patients, and the secondary objective is to validate a published nomogram prognostic model for NSCLC using an independent patient cohort.

Design

1817 patients with advanced NSCLC from the control arms of 4 Phase III randomized clinical trials were included in this study. Data from 524 NSCLC patients from one of these trials were used to validate a previously published nomogram and then used to develop an updated nomogram. Patients from the other 3 trials were used as independent validation cohorts of the new nomogram. The prognostic performances were comprehensively evaluated using hazard ratios, integrated area under the curve (AUC), concordance index, and calibration plots.

Setting

General community.

Main outcome

A nomogram model was developed to predict overall survival in NSCLC patients.

Results

We demonstrated the prognostic power of the previously published model in an independent cohort. The updated prognostic model contains the following variables: sex, histology, performance status, liver metastasis, hemoglobin level, white blood cell counts, peritoneal metastasis, skin metastasis, and lymphocyte percentage. This model was validated using various evaluation criteria on the 3 independent cohorts with heterogeneous NSCLC populations. In the SUN1087 patient cohort, the continuous risk score output by the nomogram achieved an integrated area under the receiver operating characteristics (ROC) curve of 0.83, a log-rank P-value of 3.87e-11, and a concordance index of 0.717. In the SAVEONCO patient cohort, the integrated area under the ROC curve was 0.755, the log-rank P-value was 4.94e-6 and the concordance index was 0.678. In the VITAL patient cohort, the integrated area under the ROC curve was 0.723, the log-rank P-value was 1.36e-11, and the concordance index was 0.654. We implemented the proposed nomogram and several previously published prognostic models on an online Web server for easy user access.

Conclusions

This nomogram model based on basic clinical features and routine lab testing predicts individual survival probabilities for advanced NSCLC and exhibits cross-study robustness.

Patient data discovery platforms as enablers of biomedical and translational research: A systematic review

BACKGROUND

The global shift from paper health records to electronic ones has led to an impressive growth of biomedical digital data along the past two decades. Exploring and extracting knowledge from these data has the potential to enhance translational research and lead to positive outcomes for the population's health and healthcare.

OBECTIVE

The aim of this study was to conduct a systematic review to identify software platforms that enable discovery, secondary use and interoperability of biomedical data. Additionally, we aim evaluating the identified solutions in terms of clinical interest and main healthcare-related outcomes.

METHODS

A systematic search of the scientific literature published and indexed in Pubmed between January 2014 and September 2018 was performed. Inclusion criteria were as follows: relevance for the topic of biomedical data discovery, English language, and free full text. To increase the recall, we developed a semi-automatic and incremental methodology to retrieve articles that cite one or more of the previous set.

RESULTS

A total number of 500 candidate papers were retrieved through this methodology. Of these, 85 were eligible for abstract assessment. Finally, 37 studies qualified for a full-text review, and 20 provided enough information for the study objectives.

CONCLUSIONS

This study revealed that biomedical discovery platforms are both a current necessity and a significantly innovative agent in the area of healthcare. The outcomes that were identified, in terms of scientific publications, clinical studies and research collaborations stand as evidence.

Data Integration Innovations to Enhance Analytic Utility of Clinical Trial Content to Inform Health Disparities Research

Project Data Sphere (PDS) is a research platform that provides the research community with broad access to both de-identified patient-level data from oncology clinical trials and related analytic tools. While these data are rich in measures that characterize the clinical trials under study, data providers are required to de-identify patient-level data by removing key demographic data. To address these analytic constraints, the data profiles in selected PDS patient-level cancer phase III clinical datasets have been augmented by linking the social, economic, and health-related characteristics of like cancer survivors from nationally representative health and health care-related survey data. Using statistical linkage and model-based techniques, patient-level records in selected PDS datasets have been linked to those of comparable cancer survivors, and are thereby augmented with survey content on social, economic, and health-related characteristics. These new analytically enhanced PDS data resources enable more targeted analyses designed to examine questions such as how disparities in cancer patients' access to health care and income impact patient outcomes in specific phase III clinical trials, and what variations in patient outcomes are associated with specific demographic, socioeconomic, and health-related factors. This study provides an overview of the methodologies used to connect patient-level clinical trial data with nationally representative health-related data on cancer survivors from the national Medical Expenditure Panel Survey (MEPS). MEPS was designed to provide national population-based health care use, expenditure, and source of payment estimates in addition to measures of health status, demographic characteristics, employment, health insurance coverage, and access to health care. Study findings include probabilistic assessments of the representation of the patients in the respective clinical trials relative to the characteristics of cancer survivors in the general population. The study also demonstrates how the augmented datasets serve to enable researchers to assess the impact of socioeconomic factors added through data integration on cancer survival and related outcomes of interest.

Myocarditis Associated with Immune Checkpoint Inhibitors: An Expert Consensus on Data Gaps and a Call to Action

Immune checkpoint inhibitors (ICIs) have transformed the treatment landscape for cancer. Due to the mechanism of action of ICIs, inflammatory reactions against normal tissue were an anticipated side effect of these agents; these immune-related adverse events have been documented and are typically low grade and manageable. Myocarditis has emerged as an uncommon but potentially life-threatening adverse reaction in patients treated with ICIs. Assessment and characterization of ICI-associated myocarditis is challenging because of its low incidence and protean manifestations. Nevertheless, the seriousness of ICI-associated myocarditis justifies a coordinated effort to increase awareness of this syndrome, identify patients who may be at risk, and enable early diagnosis and appropriate treatment. The "Checkpoint Inhibitor Safety Working Group," a multidisciplinary committee of academic, industry, and regulatory partners, convened at a workshop hosted by Project Data Sphere, LLC, on December 15, 2017. This meeting aimed to evaluate the current information on ICI-associated myocarditis, determine methods to collect and share data on this adverse reaction, and establish task forces to close the identified knowledge gaps. In this report, we summarize the workshop findings and proposed steps to address the impact of ICI-associated myocarditis in patients with cancer.

Clinical Trial Participants' Views of the Risks and Benefits of Data Sharing

Background Sharing of participant-level clinical trial data has potential benefits, but concerns about potential harms to research participants have led some pharmaceutical sponsors and investigators to urge caution. Little is known about clinical trial participants' perceptions of the risks of data sharing. Methods We conducted a structured survey of 771 current and recent participants from a diverse sample of clinical trials at three academic medical centers in the United States. Surveys were distributed by mail (350 completed surveys) and in clinic waiting rooms (421 completed surveys) (overall response rate, 79%). Results Less than 8% of respondents felt that the potential negative consequences of data sharing outweighed the benefits. A total of 93% were very or somewhat likely to allow their own data to be shared with university scientists, and 82% were very or somewhat likely to share with scientists in for-profit companies. Willingness to share data did not vary appreciably with the purpose for which the data would be used, with the exception that fewer participants were willing to share their data for use in litigation. The respondents' greatest concerns were that data sharing might make others less willing to enroll in clinical trials (37% very or somewhat concerned), that data would be used for marketing purposes (34%), or that data could be stolen (30%). Less concern was expressed about discrimination (22%) and exploitation of data for profit (20%). Conclusions In our study, few clinical trial participants had strong concerns about the risks of data sharing. Provided that adequate security safeguards were in place, most participants were willing to share their data for a wide range of uses. (Funded by the Greenwall Foundation.).

Balancing Between Privacy and Patient Needs for Health Information in the Age of Participatory Health and Social Media: A Scoping Review

OBJECTIVES

 With the increased use of participatory health enabling technologies, such as social media, balancing the need for health information with patient privacy and confidentiality has become a more complex and immediate concern. The purpose of this paper produced by the members of the IMIA Participatory Health and Social Media (PHSM) working group is to investigate patient needs for health information using participatory health enabling technologies, while balancing their needs for privacy and confidentiality.

METHODS

 Six domain areas including media sharing platforms, patient portals, web-based platforms, crowdsourcing websites, medical avatars, and other mobile health technologies were identified by five members of the IMIA PHSM working group as relevant to participatory health and the balance between data sharing and patient needs for privacy and confidentiality. After identifying the relevant domain areas, our scoping review began by searching several databases such as PubMed, MEDLINE, Scopus, and Google Scholar using a variety of key search terms.

RESULTS

 A total of 1,973 studies were identified, of which 68 studies met our inclusion criteria and were included in the analysis. Results showed that challenges for balancing patient needs for information and privacy and confidentiality concerns included: cross-cultural understanding, clinician and patient awareness, de-identification of data, and commercialization of patient data. Some opportunities identified were patient empowerment, connecting participatory health enabling technologies with clinical records, open data sharing agreement, and e-consent.

CONCLUSION

 Balancing between privacy and patient needs for health information in the age of participatory health and social media offers several opportunities and challenges. More people are engaging in actively managing health through participatory health enabling technologies. Such activity often includes sharing health information and with this comes a perennial tension between balancing individual needs and the desire to uphold privacy and confidentiality. We recommend that guidelines for both patients and clinicians, in terms of their use of participatory health-enabling technologies, are developed to ensure that patient privacy and confidentiality are protected, and a maximum benefit can be realized.

Toward a Tiered Model to Share Clinical Trial Data and Samples in Precision Oncology

The recent revolution in science and technology applied to medical research has left in its wake a trial of biomedical data and human samples; however, its opportunities remain largely unfulfilled due to a number of legal, ethical, financial, strategic, and technical barriers. Precision oncology has been at the vanguard to leverage this potential of "Big data" and samples into meaningful solutions for patients, considering the need for new drug development approaches in this area (due to high costs, late-stage failures, and the molecular diversity of cancer). To harness the potential of the vast quantities of data and samples currently fragmented across databases and biobanks, it is critical to engage all stakeholders and share data and samples across research institutes. Here, we identified two general types of sharing strategies. First, open access models, characterized by the absence of any review panel or decision maker, and second controlled access model where some form of control is exercised by either the donor (i.e., patient), the data provider (i.e., initial organization), or an independent party. Further, we theoretically describe and provide examples of nine different strategies focused on greater sharing of patient data and material. These models provide varying levels of control, access to various data and/or samples, and different types of relationship between the donor, data provider, and data requester. We propose a tiered model to share clinical data and samples that takes into account privacy issues and respects sponsors' legitimate interests. Its implementation would contribute to maximize the value of existing datasets, enabling unraveling the complexity of tumor biology, identify novel biomarkers, and re-direct treatment strategies better, ultimately to help patients with cancer.