The Brave New World of clinical cancer research: Adaptive biomarker-driven trials integrating clinical practice with clinical research.
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ABSTRACT:
SUBMITTER: Berry DA
PROVIDER: S-EPMC4516171 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Over the past three decades, the pathological classification of lymphoma has substantially improved. The early Rappaport classification included a handful of subtypes that did not reflect the cell of origin and, not surprisingly, resulted in diagnostic inaccuracies. The WHO currently classifies lymphoma into 30 major distinctive types. While this classification improved the accuracy and consistency of the histological diagnosis of lymphoma, it had little impact on advancing drug development or improving the cure rate of this disease. One reason for this lack of improvement is that recent developments in cancer genomics show these histopathological subtypes to be heterogeneous. Basing treatment decisions on histopathological subtypes is inefficient as it groups different underlying molecular characteristics into one category. Such a strategy exposes many patients to potentially toxic drugs without providing benefits. The recent approval of two new cancer drugs with companion diagnostics to allow selection and treatment of patients with melanoma and non-small-cell lung cancer has raised hope that a similar approach may also expedite successful drug development in lymphoma. We review the current status of biomarker development in lymphoma, and discuss novel biomarker-directed clinical trial designs for lymphoma.
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Project description:BackgroundThe Biomarker Strategy Design has been proposed for trials assessing the value of a biomarker in guiding treatment in oncology. In such trials, patients are randomised to either receive the standard chemotherapy treatment or a biomarker-directed treatment arm, in which biomarker status is used to guide treatment.MethodsMotivated by a current trial, we consider an adaptive design in which two biomarkers are assessed. The trial is conducted in two stages. In the first stage, patients in the biomarker-guided arm are assessed using a standard and an alternative cheaper biomarker, with the standard biomarker guiding treatment. An analysis comparing biomarker results is then used to choose the biomarker to use for the remainder of the trial. The new biomarker is used if the results for the two biomarkers are sufficiently similar.ResultsWe show that in practical situations the first-stage results can be used to adapt the trial without type I error rate inflation. We also show that there can be considerable cost gains with only a small loss in power in the case where the alternative biomarker is highly concordant with the standard one.ConclusionsAdaptive designs have an important role in reducing the cost and increasing the clinical utility of trials evaluating biomarker-guided treatment strategies.
| S-EPMC3992506 | biostudies-literature
Project description:Clinical trials with adaptive designs use data that accumulate during the course of the study to modify study elements in a prespecified manner. The goal is to provide flexibility such that a trial can serve as a definitive test of its primary hypothesis, preferably in a shorter time period, involving fewer human subjects, and at lower cost. Elements that may be modified include the sample size, end points, eligible population, randomization ratio, and interventions. Accumulating data used to drive these modifications include the outcomes, subject enrollment (including factors associated with the outcomes), and information about the application of the interventions. This review discusses the types of adaptive designs for clinical trials, emphasizing their advantages and limitations in comparison with conventional designs, and opportunities for applying these designs to healthcare epidemiology research, including studies of interventions to prevent healthcare-associated infections, combat antimicrobial resistance, and improve antimicrobial stewardship.
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Project description:Adaptive designs have the potential to improve efficiency in the evaluation of new medical treatments in comparison to traditional fixed sample size designs. However, they are still not widely used in practice in clinical research. Little research has been conducted to investigate what adaptive designs are being undertaken. This review highlights the current state of registered adaptive designs and their characteristics. The review looked at phase II, II/III and III trials registered on ClinicalTrials.gov from 29 February 2000 to 1 June 2014, supplemented with trials from the National Institute for Health Research register and known adaptive trials. A range of adaptive design search terms were applied to the trials extracted from each database. Characteristics of the adaptive designs were then recorded including funder, therapeutic area and type of adaptation. The results in the paper suggest that the use of adaptive designs has increased. They seem to be most often used in phase II trials and in oncology. In phase III trials, the most popular form of adaptation is the group sequential design. The review failed to capture all trials with adaptive designs, which suggests that the reporting of adaptive designs, such as in clinical trials registers, needs much improving. We recommend that clinical trial registers should contain sections dedicated to the type and scope of the adaptation and that the term 'adaptive design' should be included in the trial title or at least in the brief summary or design sections.
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Project description:PURPOSE OF REVIEW:In this review, we will discuss (i) how the recent advancements in digital technology and computational engineering are currently applied to nephropathology in the setting of clinical research, trials, and practice; (ii) the benefits of the new digital environment; (iii) how recognizing its challenges provides opportunities for transformation; and (iv) nephropathology in the upcoming era of kidney precision and predictive medicine. RECENT FINDINGS:Recent studies highlighted how new standardized protocols facilitate the harmonization of digital pathology database infrastructure and morphologic, morphometric, and computer-aided quantitative analyses. Digital pathology enables robust protocols for clinical trials and research, with the potential to identify previously underused or unrecognized clinically useful parameters. The integration of digital pathology with molecular signatures is leading the way to establishing clinically relevant morpho-omic taxonomies of renal diseases. SUMMARY:The introduction of digital pathology in clinical research and trials, and the progressive implementation of the modern software ecosystem, opens opportunities for the development of new predictive diagnostic paradigms and computer-aided algorithms, transforming the practice of renal disease into a modern computational science.
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Project description:Interventional pulmonology is a dynamic and evolving field in respiratory medicine. Advances have improved the ability to diagnose and manage diseases of the airways. A shift toward early detection of malignant disease has generated a focus on innovative diagnostic techniques. With patient populations living longer with malignant and benign diseases, the role for interventional bronchoscopy has grown. In cancer groups, novel immunotherapies have improved the prospects of clinical outcomes and reignited a focus on optimizing patient performance status to enable access to anticancer therapy. This review discusses current and emerging diagnostic modalities and therapeutic approaches available to manage airway diseases.
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Project description:PurposeTo assess the feasibility of using Clinical Practice Research Datalink (CPRD) data for identifying populations of patients with chronic obstructive pulmonary disease (COPD) eligible for a hypothetical pragmatic trial.MethodsA retrospective multidatabase cohort study using CPRD primary care and linked secondary care data to describe the characteristics of populations of patients with COPD. Patients' demographic and lifestyle factors, comorbidity profile, spirometry measurements and treatment changes were evaluated, as was the distribution of follow-up time and types of losses during follow-up. Characteristics were evaluated using descriptive statistics.ResultsA total of 322 991 patients from 1148 primary care practices in the United Kingdom across two CPRD primary care databases, CPRD GOLD and CPRD Aurum, were potentially eligible to participate in a hypothetical trial using CPRD, starting on 31 December 2017. Patients with COPD in CPRD GOLD and CPRD Aurum were comparable in terms of age (median age 70 vs. 68 years), gender (50% vs. 52% male), disease severity (e.g., 25% vs. 24% Medical Research Council [MRC] dyspnoea score grades 3-5) and history of respiratory conditions (e.g., 43% vs. 38% asthma). High proportions of patients with COPD in CPRD GOLD and CPRD Aurum were available on 31 December 2012 for follow-up at 1, 2, and 5 years (92%, 85% and 67%, respectively).ConclusionsPatients and data from CPRD GOLD and CPRD Aurum were comparable across key aspects relevant to COPD trials. A pragmatic trial using CPRD to recruit patients with COPD is scientifically feasible.
| S-EPMC7986187 | biostudies-literature
Project description:BackgroundThe Institute of Medicine has called for approaches to help maximize the return on investments (ROI) in cancer clinical trials. Value of Research (VOR) is a health economics technique that estimates ROI and can inform research prioritization. Our objective was to evaluate the impact of using VOR analyses on the clinical trial proposal review process within the SWOG cancer clinical trials consortium.MethodsWe used a previously developed minimal modeling approach to calculate VOR estimates for 9 phase II/III SWOG proposals between February 2015 and December 2016. Estimates were presented to executive committee (EC) members (N = 12) who determine which studies are sent to the National Cancer Institute for funding consideration. EC members scored proposals from 1 (best) to 5 based on scientific merit and potential impact before and after receiving VOR estimates. EC members were surveyed to assess research priorities, proposal evaluation process satisfaction, and the VOR process.ResultsValue of Research estimates ranged from -$2.1B to $16.46B per proposal. Following review of VOR results, the EC changed their score for eight of nine proposals. Proposal rankings were different in pre- vs postscores (P value: 0.03). Respondents had mixed views of the ultimate utility of VOR for their decisions with most supporting (42%) or neutral (41%) to the idea of adding VOR to the evaluation process.ConclusionsThe findings from this pilot study indicate use of VOR analyses may be a useful adjunct to inform proposal reviews within NCI Cooperative Clinical Trials groups.
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Project description:Prostate cancer (PCa) is a common malignant tumor with increasing incidence and high heterogeneity among males worldwide. In the era of big data and artificial intelligence, the paradigm of biomarker discovery is shifting from traditional experimental and small data-based identification toward big data-driven and systems-level screening. Complex interactions between genetic factors and environmental effects provide opportunities for systems modeling of PCa genesis and evolution. We hereby review the current research frontiers in informatics for PCa clinical translation. First, the heterogeneity and complexity in PCa development and clinical theranostics are introduced to raise the concern for PCa systems biology studies. Then biomarkers and risk factors ranging from molecular alternations to clinical phenotype and lifestyle changes are explicated for PCa personalized management. Methodologies and applications for multi-dimensional data integration and computational modeling are discussed. The future perspectives and challenges for PCa systems medicine and holistic healthcare are finally provided.
| S-EPMC7060655 | biostudies-literature
Project description:In this precision oncology era, where molecular profiling at the individual patient level becomes increasingly accessible and affordable, more and more clinical trials are now driven by biomarkers, with an overarching objective to optimize and personalize disease management. As compared with the conventional clinical development paradigms, where the key is to evaluate treatment effects in histology-defined populations, the choices of biomarker-driven clinical trial designs and analysis plans require additional considerations that are heavily dependent on the nature of biomarkers (eg, prognostic or predictive, integral or integrated) and the credential of biomarkers' performance and clinical utility. Most recently, another major paradigm change in biomarker-driven trials is to conduct multi-agent and/or multihistology master protocols or platform trials. These trials, although they may enjoy substantial infrastructure and logistical advantages, also face unique operational and conduct challenges. Here we provide a concise overview of design options for both the setting of single-biomarker/single-disease and the setting of multiple-biomarker/multiple-disease types. We focus on explaining the trial design and practical considerations and rationale of when to use which designs, as well as how to incorporate various adaptive design components to provide additional flexibility, enhance logistical efficiency, and optimize resource allocation. Lessons learned from real trials are also presented for illustration.
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