Improving Clinical Trial Efficiency: Thinking outside the Box

Non-invasive multiple cancer screening using trained detection canines and artificial intelligence: a prospective double-blind study

The specificity and sensitivity of a simple non-invasive multi-cancer screening method in detecting breast, lung, prostate, and colorectal cancer in breath samples were evaluated in a double-blind study. Breath samples of 1386 participants (59.7% males, median age 56.0 years) who underwent screening for cancer using gold-standard screening methods, or a biopsy for a suspected malignancy were collected. The samples were analyzed using a bio-hybrid platform comprising trained detection canines and artificial intelligence tools. According to cancer screening/biopsy results, 1048 (75.6%) were negative for cancer and 338 (24.4%) were positive. Among the 338 positive samples, 261 (77.2%) were positive for one of the four cancer types that the bio-hybrid platform was trained to detect, with an overall sensitivity and specificity of 93.9% (95% confidence interval [CI] 90.3-96.2%) and 94.3% (95% CI 92.7%-95.5%), respectively. The sensitivity of each cancer type was similar; breast: 95.0% (95% CI 87.8-98.0%), lung: 95.0% (95% CI 87.8-98.0%), colorectal: 90.0% (95% CI 74.4-96.5%), prostate: 93.0% (95% CI 84.6-97.0%). The sensitivity of 14 other malignant tumors that the bio-hybrid platform was not trained to detect, but identified, was 81.8% (95% CI 71.8%-88.8%). Early cancer (0-2) detection sensitivity was 94.8% (95% CI 91.0%-97.1%). This bio-hybrid multi-cancer screening platform demonstrated high sensitivity and specificity and enables early-stage cancer detection.

Target-Driven Tissue-Agnostic Drug Approvals-A New Path of Drug Development

The regulatory approvals of tumor-agnostic therapies have led to the re-evaluation of the drug development process. The conventional models of drug development are histology-based. On the other hand, the tumor-agnostic drug development of a new drug (or combination) focuses on targeting a common genomic biomarker in multiple cancers, regardless of histology. The basket-like clinical trials with multiple cohorts allow clinicians to evaluate pan-cancer efficacy and toxicity. There are currently eight tumor agnostic approvals granted by the Food and Drug Administration (FDA). This includes two immune checkpoint inhibitors, and five targeted therapy agents. Pembrolizumab is an anti-programmed cell death protein-1 (PD-1) antibody that was the first FDA-approved tumor-agnostic treatment for unresectable or metastatic microsatellite instability-high (MSI-H) or deficient mismatch repair (dMMR) solid tumors in 2017. It was later approved for tumor mutational burden-high (TMB-H) solid tumors, although the TMB cut-off used is still debated. Subsequently, in 2021, another anti-PD-1 antibody, dostarlimab, was also approved for dMMR solid tumors in the refractory setting. Patients with fusion-positive cancers are typically difficult to treat due to their rare prevalence and distribution. Gene rearrangements or fusions are present in a variety of tumors. Neurotrophic tyrosine kinase (NTRK) fusions are present in a range of pediatric and adult solid tumors in varying frequency. Larotrectinib and entrectinib were approved for neurotrophic tyrosine kinase (NTRK) fusion-positive cancers. Similarly, selpercatinib was approved for rearranged during transfection (RET) fusion-positive solid tumors. The FDA approved the first combination therapy of dabrafenib, a B-Raf proto-oncogene serine/threonine kinase (BRAF) inhibitor, plus trametinib, a mitogen-activated protein kinase (MEK) inhibitor for patients 6 months or older with unresectable or metastatic tumors (except colorectal cancer) carrying a BRAFV600E mutation. The most recent FDA tumor-agnostic approval is of fam-trastuzumab deruxtecan-nxki (T-Dxd) for HER2-positive solid tumors. It is important to identify and expeditiously develop drugs that have the potential to provide clinical benefit across tumor types.

High-Sensitivity Cardiac Troponin T and Cardiovascular Risk After Ischemic Stroke or Transient Ischemic Attack

Background

High-sensitivity cardiac troponin T (hs-cTnT) is associated with cardiovascular disease (CVD) risk in general and various high-risk populations.

Objectives

The purpose of this study was to precisely characterize the association of hs-cTnT with CVD risk in patients following acute ischemic stroke or transient ischemic attack.

Methods

We conducted post hoc analyses of data from the STROKE-CARD trial (NCT02156778), a pragmatic randomized controlled trial of a disease management program in patients with acute ischemic stroke or transient ischemic attack (ABCD2 score ≥3). We measured hs-cTnT on admission (Roche Elecsys, detection limit 5 ng/L) and quantified HRs for a composite CVD outcome (ie, stroke, myocardial infarction, CVD death) adjusted for age, sex, prior coronary heart disease, prior heart failure, diabetes, smoking, systolic blood pressure, and low- and high-density-lipoprotein cholesterol.

Results

Among 1,687 patients (mean age, 69.3 ± 13.7 years; 40.7% female), hs-cTnT was detectable in 80.7%. Median hs-cTnT was 10 ng/L (IQR: 6-18 ng/L). Over a median follow-up of 12.1 months, 110 patients had a CVD event. The association of hs-cTnT level with CVD risk was of log-linear shape, with a multivariable-adjusted HR of 1.40 (95% CI: 1.15-1.70; P < 0.001) per 1-SD higher log-transformed hs-cTnT value. The strength of association was similar when further adjusted for other potential confounders and across clinically relevant subgroups. Corresponding outcome-specific HRs were 1.33 (95% CI: 1.06-1.68; P = 0.016) for stroke, 1.28 (95% CI: 0.69-2.37; P = 0.430) for myocardial infarction, 1.98 (95% CI: 1.43-2.73; P < 0.001) for CVD death, and 1.93 (95% CI: 1.54-2.41; P < 0.001) for all-cause death.

Conclusions

High hs-cTnT is associated with increased CVD risk in ischemic stroke and transient ischemic attack patients.

Application of radiomics in lung immuno-oncology

Radiomics is a rapidly evolving field of research that extracts and analyzes quantitative features within medical images. Those features are termed as radiomic features that can characterize a tumor in a comprehensive and quantitative manner with regard to its internal structure and heterogeneity. Radiomic features can be used, alone or in combination with demographic, histological, genomic, or proteomic data, for predicting prognosis or treatment response. Immunotherapy, or immune-oncology, is the study of cancer treatment by taking advantage of the body's immune system to prevent, control, and eliminate cancer. In this review, we first provide a brief introduction to both radiomics and immune-oncology in lung cancer. Then, we discuss the need for developing immune-oncology biomarkers, and the advantages of radiomics in identifying biomarkers related to immunotherapy. We also discuss potential areas in and out of tumors, such as the intra-tumoral hypoxic region and tumor microenvironment, where radiomic markers might be extracted, as well as a potential application of radiomic biomarkers in clinical lung cancer management. Finally, we present radiation and immune modulation in non-small cell lung cancer, clinical trials and their design to incorporate radiomic biomarkers, and radiomics-guided precision radiation therapy.

Application of Bayesian methods to accelerate rare disease drug development: scopes and hurdles

Background

Design and analysis of clinical trials for rare and ultra-rare disease pose unique challenges to the practitioners. Meeting conventional power requirements is infeasible for diseases where sample sizes are inherently very small. Moreover, rare disease populations are generally heterogeneous and widely dispersed, which complicates study enrollment and design. Leveraging all available information in rare and ultra-rare disease trials can improve both drug development and informed decision-making processes.

Main text

Bayesian statistics provides a formal framework for combining all relevant information at all stages of the clinical trial, including trial design, execution, and analysis. This manuscript provides an overview of different Bayesian methods applicable to clinical trials in rare disease. We present real or hypothetical case studies that address the key needs of rare disease drug development highlighting several specific Bayesian examples of clinical trials. Advantages and hurdles of these approaches are discussed in detail. In addition, we emphasize the practical and regulatory aspects in the context of real-life applications.

Conclusion

The use of innovative trial designs such as master protocols and complex adaptive designs in conjunction with a Bayesian approach may help to reduce sample size, select the correct treatment and population, and accurately and reliably assess the treatment effect in the rare disease setting.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13023-022-02342-5.

Characteristics of Early Phase Clinical Trials for Rare Cancers: Insights From Interviews With Stakeholders

Background: Rare cancers occur with an incidence of no more than six cases per 100,000 people according to the definition used by the Surveillance of Rare Cancers in Europe project. For a variety of reasons (low prevalence, cytotoxicity), it is challenging to perform the necessary clinical studies to investigate the safety and efficacy of investigational medicines against such rare malignancies, reformulating even at the earliest stages of the drug development process. This article investigates the differences between phase I rare cancer trials performed in commercial (companies) and non-commercial settings (academic hospitals).

Materials and Methods: The differences were explored through the conduct of semi-structured interviews with three different stakeholder groups: representatives from academia (n = 7), representatives from companies (n = 4) and representatives from patient organizations (n = 4). All the interviews were transcribed verbatim and analyzed in NVivo using the framework method.

Results: According to the interviewees, the academic and commercial stakeholders collaborate in the majority of phase I rare cancer trials. In general, the commercial partner finances the trial, whereas academia is responsible for the execution of the study procedures. The average cost of undertaking these trials is difficult to estimate because it depends on what is specifically requested during the trial. The 3 + 3 study design remains the most widely used design and the use of expansion cohorts is controversial. With regard to the regulatory aspects of phase I rare cancer trials, it was expressed that a good regulatory framework facilitates the conduct of these studies, but that increased regulation and oversight also has drawbacks, e.g., differences in standards between different ethics committees, over interpretation of the rules, insufficient availability of qualified personnel and higher workloads. The patient organization representatives claimed that patients experience no differences in terms of accommodation, compensation and paperwork between the academic and commercial settings or the degree of follow-up. They also believed that the direct input of patients can bring added value to such studies not only with regard to the recruitment process and the feasibility of the study but also the legibility of the informed consent forms.

Conclusion: The growing need for first-in-man trials in rare malignancies needs to be highlighted, as difficult as they are to undertake and to co-develop, not only because rare cancer patients deserve an appropriate treatment, but also because these medicines represent the future of cancer therapy in the precision medicine era. Cooperation of commercial and academic sites are needed. Patient organizations need to be educated to take part in this process.

Network pharmacology: curing causal mechanisms instead of treating symptoms

For complex diseases, most drugs are highly ineffective, and the success rate of drug discovery is in constant decline. While low quality, reproducibility issues, and translational irrelevance of most basic and preclinical research have contributed to this, the current organ-centricity of medicine and the 'one disease-one target-one drug' dogma obstruct innovation in the most profound manner. Systems and network medicine and their therapeutic arm, network pharmacology, revolutionize how we define, diagnose, treat, and, ideally, cure diseases. Descriptive disease phenotypes are replaced by endotypes defined by causal, multitarget signaling modules that also explain respective comorbidities. Precise and effective therapeutic intervention is achieved by synergistic multicompound network pharmacology and drug repurposing, obviating the need for drug discovery and speeding up clinical translation.

Master Protocols for Precision Medicine in Oncology: Overcoming Methodology of Randomized Clinical Trials

Randomized clinical trials are considered the milestones of clinical research in oncology, and guided the development and approval of new compounds so far. In the last few years, however, molecular and genomic profiling led to a change of paradigm in therapeutic algorithms of many cancer types, with the spread of different biomarker-driven therapies (or targeted therapies). This scenario of "personalized medicine" revolutionized therapeutic strategies and the methodology of the supporting clinical research. New clinical trial designs are emerging to answer to the unmet clinical needs related to the development of these targeted therapies, overcoming the "classical" structure of randomized studies. Innovative trial designs able to evaluate more than one treatment in the same group of patients or many groups of patients with the same treatment (or both) are emerging as a possible future standard in clinical trial methodology. These are identified as "master protocols", and include umbrella, basket and platform trials. In this review, we described the main characteristics of these new trial designs, focusing on the opportunities and limitations of their use in the era of personalized medicine.

Basket clinical trial design for targeted therapies for cancer: a French National Authority for Health statement for health technology assessment

During the past decade, health technology assessment bodies have faced new challenges in establishing the benefits of new drugs for individuals and health-care systems. A topic of increasing importance to the field of oncology is the so-called agnostic regulatory approval of targeted therapies for cancer (independent of tumour location and histology) granted on the basis of basket trials. Basket trials in oncology offer the advantage of simultaneously evaluating treatments for multiple tumours, even rare cancers, in a single clinical trial. To address the novel challenges introduced by these trials, an interdisciplinary panel was convened on behalf of the Transparency Committee of the French National Authority for Health to clarify an approach designed to guarantee a transparent, reproducible, and fair assessment of histology-agnostic treatments for reimbursement by the French National Health Insurance Fund. The requirements of this approach include the need for randomisation, clinically relevant endpoints, appropriate correction for multiple significance testing, characterisation of subgroup heterogeneity, and validation of underlying biomarker assays. A prospectively designated external control is encouraged when the implementation of a direct comparison is deemed infeasible. We also underline the importance of recording outcomes from basket trials in a registry for use as future external controls.

A Rare Cancer Opportunity

In this issue of Cancer Epidemiology, Biomarkers & Prevention, Gallicchio and colleagues analyze recent rare-cancers research and suggest broad themes for accelerating progress in this important area. Whether the type of portfolio creation and portfolio management strategies that have worked for common cancers also work best for rare cancers warrants asking. This commentary argues for consideration of additional approaches. Incorporating principles and successes from large-scale network-based clinical trials and from advocacy-based research, and new ways to approach consortia, might accelerate the quantity and improve the quality of future rare-cancer research. Rare cancers significantly influence the overall cancer burden and cancer disparities. Creative community-based approaches to improve rare-cancers research should be considered.See related article by Gallichio et al., p. 1305.

Modern Challenges for Early-Phase Clinical Trial Design and Biomarker Discovery in Metastatic Non-Small-Cell Lung Cancer

Oncology research has changed extensively due to the possibility to categorize each cancer type into smaller subgroups based on histology and particularly on different genetic alterations due to their heterogeneity. The consequences of this heterogeneity are particularly evident in the management of metastatic non-small-cell lung cancer (NSCLC). This review will discuss the benefits and challenges of incorporating precision medicine into early- through late-phase metastatic NSCLC clinical trials, discussing examples of drug development programs in oncogene- and non-oncogene-addicted NSCLC. The experiences of clinical development of crizotinib, gefitinib and osimertinib are depicted showing that when a targeted drug is administrated in a study population not selected by any biomarker, trials could produce negative results. However, the early detection of biomarker-driven biology helps to obtain a greater benefit for a selected population and can reduce the required time for drug approval. Early clinical development programs involving nivolumab, pembrolizumab and avelumab, immune checkpoint inhibitors, taught us that, beyond safety and activity, the optimal selection of patients should be based on pre-specified biomarkers. Overall, the identification of predictive biomarkers is one of the greatest challenges of NSCLC research that should be optimized with solid methodological trial designs to maximize the clinical outcomes.

Effect size estimates from umbrella designs: Handling patients with a positive test result for multiple biomarkers using random or pragmatic subtrial allocation

Umbrella trials have been suggested to increase trial conduct efficiency when investigating different biomarker-driven experimental therapies. An overarching platform is used for patient screening and subsequent subtrial allocation according to patients’ biomarker status. Two subtrial allocation schemes for patients with a positive test result for multiple biomarkers are (i) the pragmatic allocation to the eligible subtrial with the currently fewest included patients and (ii) the random allocation to one of the eligible subtrials. Obviously, the subtrials compete for such patients which are consequently underrepresented in the subtrials. To address questions of the impact of an umbrella design in general as well as with respect to subtrial allocation and analysis method, we investigate an umbrella trial with two parallel group subtrials and discuss generalisations. First, we analytically quantify the impact of the umbrella design with random allocation on the number of patients needed to be screened, the biomarker status distribution and treatment effect estimates compared to the corresponding gold standard of an independent parallel group design. Using simulations and real data, we subsequently compare both allocation schemes and investigate weighted linear regression modelling as possible analysis method for the umbrella design. Our results show that umbrella designs are more efficient than the gold standard. However, depending on the biomarker status distribution in the disease population, an umbrella design can introduce differences in estimated treatment effects in the presence of an interaction between treatment and biomarker status. In principle, weighted linear regression together with the random allocation scheme can address this difference though it is difficult to assess if such an approach is applicable in practice. In any case, caution is required when using treatment effect estimates derived from umbrella designs for e.g. future trial planning or meta-analyses.

Stratification of patients in NASH clinical trials: A pitfall for trial success

Identifying the most effective therapeutic intervention in patients with NAFLD is challenging. Precise stratification in clinical trials is key to ensuring the inclusion of patients who will benefit (and not those who will be harmed) and/or in whom the natural history can be improved. Clinical trials in NAFLD can provide useful information about the individual components that underlie this complex metabolic disorder and the concomitant medications that could interfere with responses to an experimental intervention. However, to date, clinical trial reporting for NAFLD has been suboptimal, limiting our understanding. Frequently dysmetabolic comorbidities and/or daily habits are not reported or adequately accounted for. Herein, we suggest new strategies to integrate the spectra of comorbidities usually present in patients with NAFLD, accounting for the impact of lifestyle, to develop personalised therapeutic approaches. First, the mechanism of action of the drug being explored should be considered. Second, the same proportion of patients with relevant metabolic comorbidities should be maintained from phase II to III clinical trials, if such comorbidities are expected to impact on the treatment response. Third, innovative trial designs, such as the adaptative, umbrella or basket strategies, could be used to increase the efficiency of clinical trials, potentially benefiting patients while reducing costs and enhancing the likelihood of finding a real benefit of the therapy being studied. Finally, alcohol intake and daily exercise should be assessed objectively not only in the screening period but also during follow-up.

The Evolution of Master Protocol Clinical Trial Designs: A Systematic Literature Review

PURPOSE

Recent years have seen a change in the way that clinical trials are being conducted. There has been a rise of designs more flexible than traditional adaptive and group sequential trials which allow the investigation of multiple substudies with possibly different objectives, interventions, and subgroups conducted within an overall trial structure, summarized by the term master protocol. This review aims to identify existing master protocol studies and summarize their characteristics. The review also identifies articles relevant to the design of master protocol trials, such as proposed trial designs and related methods.

METHODS

We conducted a comprehensive systematic search to review current literature on master protocol trials from a design and analysis perspective, focusing on platform trials and considering basket and umbrella trials. Articles were included regardless of statistical complexity and classified as reviews related to planned or conducted trials, trial designs, or statistical methods. The results of the literature search are reported, and some features of the identified articles are summarized.

FINDINGS

Most of the trials using master protocols were designed as single-arm (n = 29/50), Phase II trials (n = 32/50) in oncology (n = 42/50) using a binary endpoint (n = 26/50) and frequentist decision rules (n = 37/50). We observed an exponential increase in publications in this domain during the last few years in both planned and conducted trials, as well as relevant methods, which we assume has not yet reached its peak. Although many operational and statistical challenges associated with such trials remain, the general consensus seems to be that master protocols provide potentially enormous advantages in efficiency and flexibility of clinical drug development.

IMPLICATIONS

Master protocol trials and especially platform trials have the potential to revolutionize clinical drug development if the methodologic and operational challenges can be overcome.

Bayesian cluster hierarchical model for subgroup borrowing in the design and analysis of basket trials with binary endpoints

Master protocol designs are often proposed to improve the efficiency of drug development with multiple subgroups. In the basket trial design, different subgroups can have similar biological pathogenesis pathways. Hence, a target therapy can result in similar responses. A good information sharing strategy between different subgroups can potentially improve the efficiency of evaluating treatment efficacy. In traditional hierarchical models, based on the exchangeability assumption, all subgroups are placed into the same sharing pool for cross subgroup information sharing. However, due to the heterogeneity between subgroups, there can be large differences in drug efficacy. Under such cases, strong borrowing across all subgroups is not suitable and no borrowing can be inefficient, because the treatment effect is analyzed in each subgroup separately. We propose a Bayesian cluster hierarchical model (BCHM) to improve the operating characteristics of estimating the treatment effect in multiple subgroups in basket trials. Bayesian nonparametric method is applied to dynamically calculate the number of clusters by conducting a multiple cluster classification based on subgroup outcomes. A hierarchical model is used to compute the posterior probability of the treatment effect, with the borrowing strength determined by the Bayesian nonparametric clustering and the similarities between subgroups. We apply the BCHM to clinical trials with binary endpoints. For treatment effect estimation, the BCHM yields lower mean squared error values, when compared to the independent analyses. In scenarios with a heterogeneous treatment effect, the BCHM provides lower mean squared error values compared to traditional hierarchical models. In addition, we can construct a loss function to optimize the design parameters. BCHM provides a balanced approach and smart borrowing, which yields better results in assessing the treatment effect in different scenarios compared to other conventional methods.

Reporting of master protocols towards a standardized approach: A systematic review

BACKGROUND

In September 2018 the FDA provided a draft guidance on master protocols reflecting an increased interest in these designs by industry. Master protocols refer to a single overarching protocol developed to evaluate multiple hypotheses and may be further categorized as basket, umbrella, and platform trials. However, inconsistencies in reporting persist in the literature. We conducted a systematic review to describe master protocol reporting with the goal of facilitating the further development and spread of these innovative trial designs.

METHODS

We searched MEDLINE, EMBASE, and CENTRAL from inception to April 25, 2019 for English articles on master protocols. This was supplemented by hand searches of trial registries and of the bibliographies of published reviews. We used the FDA's definitions of master protocols as references and compared them to self-reported master protocols.

RESULTS

We identified 278 master protocol publications, consisting of 228 protocols and 50 reviews. Sixty-six records provided unique definitions of master protocol types. We observed considerable heterogeneity in definitions of master protocols, and over half (54%) used oncology-specific language. The majority of self-classified master protocols (57%) were consistent with the FDA's definitions of master protocols.

CONCLUSION

The terms 'master protocol', 'basket trial', 'umbrella trial', and 'platform trial' are inconsistently described. Careful treatment of these terms and adherence to the definitions set forth by the FDA will facilitate better understanding of these trial designs and allow them to be used broadly and to their full potential in clinical research. We encourage trial methodologists to use these trial designations when applicable.

Challenges and shifting paradigms in clinical trials in oncology: the case for immunological and targeted therapies

The advent of immunotherapy has undoubtedly changed the current standard for cancer treatment. Immunotherapy offers the possibility of achieving excellent results—a new alternative for patients with advanced-stage or relapsed disease. Nowadays, the progress made in tumour biology has led to multiple advances in clinical and translational cancer research. Many oncogenic pathways responsible for tumour growth and metastases have been described and, consequently, multiple new cancer therapeutic agents have been developed and are under current investigation. Due to this rapid increase in knowledge and pharmaceutical development, traditional clinical trials designs have encountered major limitations. The pharmacological differences (in toxicity profiles and effectiveness patterns) between immunotherapy and chemotherapy have caused traditional clinical trials to evolve in order to meet this emerging need. This review focuses on the different options pertaining to clinical trial design that have arisen in the field of immuno-oncology, as well as the challenges of accurately interpreting traditional survival analyses within this novel area of cancer medicine.

Design and Endpoints of Clinical Trials, Current and Future

With the advent of several new systemic agents for the treatment of hepatocellular carcinoma and the prospect of more to come it is expected that many more clinical trials will be undertaken to establish the best treatment paradigm(s). In order to help develop the most efficient and most relevant clinical trials this review concentrates on endpoints that have been used in the past. Survival is the gold standard. None of the surrogate endpoints correspond completely with survival. In addition, alternative clinical trial designs are presented that may be more efficient than the usual phase I, II, and III clinical trial strategy that has been used in the past.

Challenges with Novel Clinical Trial Designs: Master Protocols

The 2018 Accelerating Anticancer Agent Development (AAADV) Workshop assembled a panel of experts for an in-depth discussion session to present "Challenges with Novel Clinical Trial Designs." This panel offered assessments of the challenges faced by industry, the FDA, investigators, institutional review boards, and patients. The panel focused on master protocols, which include umbrella trials, platform trials, and basket trials. Umbrella trials and platform trials share many commonalities, whereas basket trials are more distinct. Umbrella and platform trials are generally designed with multiple arms where patients of the same histology or other unifying characteristics are enrolled into different arms and multiple investigational agents are evaluated in a single protocol. In contrast, basket studies generally enroll patients with different tumor types based on the presence of a specific mutation or biomarker regardless of histology; these trials may include expansion cohorts. These novel designs offer the promise of expedited drug assessment and approval, but they also place new challenges on all the stakeholders involved in the drug development process. Only by identifying the challenges of these complex, innovative clinical trial designs and highlighting challenges from each perspective can we begin to address these challenges. The 2018 AAADV Workshop convened a panel of experts from relevant disciplines to highlight the challenges that are created by master protocols, and, where appropriate, offer strategies to address these challenges.

Master protocol trials in oncology: Review and new trial designs

In oncology, next generation sequencing and comprehensive genomic profiling have enabled the detailed classification of tumors using molecular biology. However, it is unrealistic to conduct phase I-III trials according to each sub-population based on patient molecular subtypes. Common protocols that assess the combination of several molecular markers and their targeted therapies by means of multiple sub-studies are required. These protocols are called "master protocols," and are drawing attention as a next-generation clinical trial design. Recently, several reviews of clinical trials based on the master protocol design have been published, but their definitions of these such trials, including basket, umbrella, and platform trials, were not consistent. Concurrently, the acceleration of the development of new statistical designs for master protocol trials has been underway. This article provides an overview of recent reviews for master protocols, including their statistical design methodologies in Oncology. We also introduce several examples of previous and on-going master protocol trials along with their classifications by some recent studies.

Impact of Biomarker-based Design Strategies on the Risk of False-Positive Findings in Targeted Therapy Evaluation

PURPOSE

When there is more than one potentially predictive biomarker for a new drug, the drug is often evaluated in different subpopulations defined by different biomarkers. We aim to (i) estimate the risk of false-positive findings with this approach and (ii) evaluate the cross-validated adaptive signature design (CVASD) as a potential alternative.

EXPERIMENTAL DESIGN

By using numerically simulated data, we compare the current approach and the CVASD across different settings and scenarios. We consider three strategies for CVASD. The first two CVASD strategies are different in terms of the partitioning of the overall significance level (between the population test and the subgroup test). In the third CVASD strategy, the order of the two tests is reversed, that is, the population test is realized when the prioritized subgroup test is not statistically significant.

RESULTS

The current approach results in a high risk of false-positive findings, whereas this risk is close to the nominal level of 5% once applying the CVASD, regardless of the strategy. When the treatment is equally effective to all patients, only the CVASD strategies could specify correctly the absence of a sensitive subgroup. When the treatment is only effective for some sensitive responders, the third CVASD strategy stands out by its ability to correctly identify the predictive biomarker(s).

CONCLUSIONS

The drug-biomarker coevaluation based on a series of independent enrichment trials can result in a high risk of false-positive findings. CVASD with some appropriate adjustments can be a good alternative to overcome this multiplicity issue.

Phylogenetic Copy-Number Factorization of Multiple Tumor Samples

Cancer is an evolutionary process driven by somatic mutations. This process can be represented as a phylogenetic tree. Constructing such a phylogenetic tree from genome sequencing data is a challenging task due to the many types of mutations in cancer and the fact that nearly all cancer sequencing is of a bulk tumor, measuring a superposition of somatic mutations present in different cells. We study the problem of reconstructing tumor phylogenies from copy-number aberrations (CNAs) measured in bulk-sequencing data. We introduce the Copy-Number Tree Mixture Deconvolution (CNTMD) problem, which aims to find the phylogenetic tree with the fewest number of CNAs that explain the copy-number data from multiple samples of a tumor. We design an algorithm for solving the CNTMD problem and apply the algorithm to both simulated and real data. On simulated data, we find that our algorithm outperforms existing approaches that either perform deconvolution/factorization of mixed tumor samples or build phylogenetic trees assuming homogeneous tumor samples. On real data, we analyze multiple samples from a prostate cancer patient, identifying clones within these samples and a phylogenetic tree that relates these clones and their differing proportions across samples. This phylogenetic tree provides a higher resolution view of copy-number evolution of this cancer than published analyses.

Advancements in the management of medically less-fit and older adults with newly diagnosed acute myeloid leukemia

INTRODUCTION

Treating acute myeloid leukemia (AML) in older adults remains daunting. The unique biology often renders conventional chemotherapies less effective. Accurately predicting the toxicities of treatment is another unresolved challenge. Treatment planning thus requires a good knowledge of the current trial data and familiarity with clinical tools, including formal fitness and geriatric assessments. Both obstacles - disease biology and patient fitness - might be easier overcome with specific, AML cell-targeted agents rather than traditional cytotoxic chemotherapy. This may be the future of AML therapy, but it is not our current state.

AREAS COVERED

Herein, the authors appraise the data supporting a standard induction approach, including an outline of how to predict treatment-related mortality and a review of the most up-to-date methods of geriatric assessment. They also discuss treatment expectations with less-intense therapies and highlight novel agents in development. Finally, they provide a basic approach to choosing treatment intensity.

EXPERT OPINION

In an older and/or medically less-fit patient, treatment choice should begin with a thorough disease assessment, a formal evaluation of patient fitness and frailty. There should also be a clear communication with the patient and patient's family about the risks and anticipated benefits of either an intense or nonintense treatment approach.

Signature program: a platform of basket trials

Investigating targeted therapies can be challenging due to diverse tumor mutations and slow patient accrual for clinical studies. The Signature Program is a series of 8 phase 2, agent-specific basket protocols using a rapid study start-up approach involving no predetermined study sites. Each protocol evaluated 1 agent (buparlisib, dovitinib, binimetinib, encorafenib, sonidegib, BGJ398, ceritinib, or ribociclib) in patients with solid or hematologic malignancies and an actionable mutation. The primary endpoint of each study was the clinical benefit rate (ie, complete or partial response, or stable disease) at 16 weeks. A total of 192 individual sites were opened in the United States, with a median start-up time of 3.6 weeks. The most common tumor types among the 595 treated patients were colorectal (9.2%), non-small cell lung adenocarcinoma (9.1%), and ovarian (8.4%). Frequent genetic alterations were in PIK3CA, RAS, p16, and PTEN. Overall, 30 partial or complete responses were observed with 6 compounds in 16 tumor types. The Signature Program presents a unique and successful approach for rapid signal finding across multiple tumors and allowed various agents to be evaluated in patients with rare alterations. Incorporating these program features in conventional studies could lead to improved trial efficiencies and patient outcomes.

Adaptive Designs for Clinical Trials: Application to Healthcare Epidemiology Research

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.

Advancing Diagnostics to Address Antibacterial Resistance: The Diagnostics and Devices Committee of the Antibacterial Resistance Leadership Group

Diagnostics are a cornerstone of the practice of infectious diseases. However, various limitations frequently lead to unmet clinical needs. In most other domains, diagnostics focus on narrowly defined questions, provide readily interpretable answers, and use true gold standards for development. In contrast, infectious diseases diagnostics must contend with scores of potential pathogens, dozens of clinical syndromes, emerging pathogens, rapid evolution of existing pathogens and their associated resistance mechanisms, and the absence of gold standards in many situations. In spite of these challenges, the importance and value of diagnostics cannot be underestimated. Therefore, the Antibacterial Resistance Leadership Group has identified diagnostics as 1 of 4 major areas of emphasis. Herein, we provide an overview of that development, highlighting several examples where innovation in study design, content, and execution is advancing the field of infectious diseases diagnostics.

High-Throughput Testing of Novel-Novel Combination Therapies for Cancer: An Idea Whose Time Has Come

Combination therapies are essential to address the genetic complexity, plasticity, and heterogeneity of tumors and to overcome resistance mechanisms that confound single-agent approaches, and are a paradigm that became well established in the era of conventional cytotoxic chemotherapies. Today, we are well equipped to address many of the scientific, clinical, and collaboration challenges that have existed historically; however, the pace of testing rational combinations is modest. Our analysis shows that the volume of clinical trials testing multiple investigational pipeline agents ("novel-novel" combinations) is dismally low, as out of approximately 1,500 phase I to III investigational combination trials initiated in 2014-2015, only 80 were for novel-novel combinations, and only 9 of those involved more than one company. The Collaborative Novel-Novel Combination Therapies (CoNNCT) initiative aims to alleviate this bottleneck by developing a new, faster paradigm for early investigation of scientifically informed, novel-novel drug combinations. The initiative kicked off on March 7, 2016, when representatives from top academic centers, biopharma, nonprofits, the FDA, and other groups gathered to define an actionable path forward. Cancer Discov; 6(9); 956-62. ©2016 AACR.

Precision oncology: neither a silver bullet nor a dream

Precision oncology is not an illusion, nor is it the magic bullet that will eradicate all cancers. Precision oncology is simply another weapon in our growing armament against cancer. Rather than honing in on the failures of a relatively young field, one should advocate for integrating its successes into widespread clinical practice, especially for indications, such as: ABL, ALK, BRAF, BRCA1, BRCA2, EGFR, KIT, KRAS, PDGFRA, PDGFRB, ROS1, BCR-ABL, FLT3 and ROS1, where aberrations have been shown to alter responses to US FDA approved drugs - that is, level 1 data. Moreover, to truly assess the promise of precision oncology, we must first begin by defining our expectations for this field. Importantly, we must recognize that the conception of precision oncology arose as an antithesis of the 'one-size fits all' cancer therapeutics approach. Consequently, tools used for evaluating these conventional, large-scale trials, are not directly transferable for assessing nonconventional, smaller-scale trials needed for evaluating precision oncology. Hence, a thorough vetting of precision oncology as another tool of the trade, must first begin by reassessing our expectations for this field, as well as current clinical trial designs and end point measurements. Importantly, we must recognize that most targeted therapy approaches are in their infancy, with only monotherapy approaches being assessed and combination therapies likely being necessary to fulfill the promise of precision oncology.

Clinical trial design and recommendations: collectively, we can take them

There is dynamic opportunity to advance medical research and clinical trial innovation in the USA and throughout the world. Identified problems and solutions in the clinical research and clinical trial enterprise have emerged over recent years. Strategic plans, public reports, expertise panels and international agreements have been produced, and now is the time to move forward collectively. Recommendations should be reviewed, especially because global healthcare can take them. A robust and future medical research enterprise hinges on maximized clinical trial efficiency, both in the USA and abroad.

Addressing small sample size bias in multiple-biomarker trials: Inclusion of biomarker-negative patients and Firth correction

In recent years, numerous approaches for biomarker-based clinical trials have been developed. One of these developments are multiple-biomarker trials, which aim to investigate multiple biomarkers simultaneously in independent subtrials. For low-prevalence biomarkers, small sample sizes within the subtrials have to be expected, as well as many biomarker-negative patients at the screening stage. The small sample sizes may make it unfeasible to analyze the subtrials individually. This imposes the need to develop new approaches for the analysis of such trials. With an expected large group of biomarker-negative patients, it seems reasonable to explore options to benefit from including them in such trials. We consider advantages and disadvantages of the inclusion of biomarker-negative patients in a multiple-biomarker trial with a survival endpoint. We discuss design options that include biomarker-negative patients in the study and address the issue of small sample size bias in such trials. We carry out a simulation study for a design where biomarker-negative patients are kept in the study and are treated with standard of care. We compare three different analysis approaches based on the Cox model to examine if the inclusion of biomarker-negative patients can provide a benefit with respect to bias and variance of the treatment effect estimates. We apply the Firth correction to reduce the small sample size bias. The results of the simulation study suggest that for small sample situations, the Firth correction should be applied to adjust for the small sample size bias. Additional to the Firth penalty, the inclusion of biomarker-negative patients in the analysis can lead to further but small improvements in bias and standard deviation of the estimates.

Clinical Trials and the Role of the Oncology Clinical Trials Nurse

Clinical trials are paramount to improving human health. New trial designs and informed consent issues are emerging as a result of genomic profiling and the development of molecularly targeted agents. Many groups and individuals are responsible for ensuring the protection of research participants and the quality of the data produced. The specialty role of the clinical trials nurse (CTN) is critical to clinical trials. Oncology CTNs have competencies that can help guide their practice; however, not all oncology clinical trials are supervised by a nurse. Using the process of engagement, one organization has restructured oncology CTNs under a nurse-supervised model.

Novel circulating- and imaging-based biomarkers to enhance the mechanistic understanding of human drug-induced liver injury

Liver safety biomarkers in current clinical practice are recognized to have certain shortcomings including their representation of general cell death and thus lacking in indicating the specific underlying mechanisms of injury. An informative panel of circulating- and imaging-based biomarkers, will allow a more complete understanding of the processes involved in the complex and multi-cellular disease of drug-induced liver injury; potentially preceding and therefore enabling prediction of disease progression as well as directing appropriate, existing or novel, therapeutic strategies. Several putative liver safety biomarkers are under investigation as discussed throughout this review, informing on a multitude of hepatocellular mechanisms including: early cell death (miR-122), necrosis (HMGB1, K18), apoptosis, (K18), inflammation (HMGB1), mitochondrial damage (GLDH, mtDNA), liver dysfunction (MRI, MSOT) and regeneration (CSF1). These biomarkers also hold translational value to provide important read across between in vitro-in vivo and clinical test systems. However, gaps in our knowledge remain requiring further focussed research and the ultimate qualification of key exploratory biomarkers. Relevance for patients: this novel multi-modal approach of assessing drug-induced liver injury could potentially enable better patient stratification and enhance treatment strategies. Ultimately, this could reduce unnecessary treatment, also decreasing hospital bed occupancy, whilst ensuring early and accurate identification of patients needing intervention.

Statistical controversies in clinical research: basket trials, umbrella trials, and other master protocols: a review and examples

In recent years, cancers once viewed as relatively homogeneous in terms of organ location and treatment strategy are now better understood to be increasingly heterogeneous across biomarker and genetically defined patient subgroups. This has produced a shift toward development of biomarker-targeted agents during a time when funding for cancer research has been limited; as a result, the need for improved operational efficiency in studying many agent-and-target combinations in parallel has emerged. Platform trials, basket trials, and umbrella trials are new approaches to clinical research driven by this need for enhanced efficiency in the modern era of increasingly specific cancer subpopulations and decreased resources to study treatments for individual cancer subtypes in a traditional way. In this review, we provide an overview of these new types of clinical trial designs, including discussions of motivation for their use, recommended terminology, examples, and challenges encountered in their application.

MASTERMIND: Bringing Microbial Diagnostics to the Clinic

New diagnostics are urgently needed to address emerging antimicrobial resistance. The Antibacterial Resistance Leadership Group proposes a strategy called MASTERMIND (Master Protocol for Evaluating Multiple Infection Diagnostics) for advancement of infectious diseases diagnostics. The goal of this strategy is to generate the data necessary to support US Food and Drug Administration clearance of new diagnostic tests by promoting research that might not have otherwise been feasible with conventional trial designs. MASTERMIND uses a single subject's sample(s) to evaluate multiple diagnostic tests at the same time, providing efficiencies of specimen collection and characterization. MASTERMIND also offers central trial organization, standardization of methods and definitions, and common comparators.

Combined integrated protocol/basket trial design for a first-in-human trial

BACKGROUND

Innovative trial designs are sought to streamline drug development in rare diseases. Basket- and integrated protocol designs are two of these new strategies and have been applied in a handful oncologic trials. We have taken the concept outside the realm of oncology and report about a first-in-human integrated protocol design that facilitates the transition from phase Ia in healthy volunteers to phase Ib in patients with rare complement-mediated disorders driven by the classical pathway.

RESULTS

We have been conducting a prospective, double-blind, randomized, placebo-controlled first-in-human study with TNT009, which is a humanized monoclonal antibody directed against the C1s subunit of human complement component C1. The trial consisted of three subparts, including normal healthy volunteers (part one and two) and a single cohort of patients in part three. Patients suffered from various complement-mediated diseases sharing the same pathophysiological mechanism, i.e. bullous pemphigoid, antibody-mediated rejection of organ transplants, cold agglutinin disease and warm autoimmune hemolytic anemia. Primary objective of the trial has been to evaluate the safety and tolerability of TNT009 in humans.

CONCLUSIONS

This trial provides probably the first example that basket trials may not be limited to single genetic aberrations, which is overly restrictive, but our trial design demonstrates that pathway specificity is a viable paradigm for defining baskets. This will hopefully serve as a role model that could benefit other innovative drug development programs targeting rare diseases.

Research methods to change clinical practice for patients with rare cancers

Rare cancers are a growing group as a result of reclassification of common cancers by molecular markers. There is therefore an increasing need to identify methods to assess interventions that are sufficiently robust to potentially affect clinical practice in this setting. Methods advocated for clinical trials in rare diseases are not necessarily applicable in rare cancers. This Series paper describes research methods that are relevant for rare cancers in relation to the range of incidence levels. Strategies that maximise recruitment, minimise sample size, or maximise the usefulness of the evidence could enable the application of conventional clinical trial design to rare cancer populations. Alternative designs that address specific challenges for rare cancers with the aim of potentially changing clinical practice include Bayesian designs, uncontrolled n-of-1 trials, and umbrella and basket trials. Pragmatic solutions must be sought to enable some level of evidence-based health care for patients with rare cancers.

Clinical trial designs incorporating predictive biomarkers

Development of oncologic therapies has traditionally been performed in a sequence of clinical trials intended to assess safety (phase I), preliminary efficacy (phase II), and improvement over the standard of care (phase III) in homogeneous (in terms of tumor type and disease stage) patient populations. As cancer has become increasingly understood on the molecular level, newer "targeted" drugs that inhibit specific cancer cell growth and survival mechanisms have increased the need for new clinical trial designs, wherein pertinent questions on the relationship between patient biomarkers and response to treatment can be answered. Herein, we review the clinical trial design literature from initial to more recently proposed designs for targeted agents or those treatments hypothesized to have enhanced effectiveness within patient subgroups (e.g., those with a certain biomarker value or who harbor a certain genetic tumor mutation). We also describe a number of real clinical trials where biomarker-based designs have been utilized, including a discussion of their respective advantages and challenges. As cancers become further categorized and/or reclassified according to individual patient and tumor features, we anticipate a continued need for novel trial designs to keep pace with the changing frontier of clinical cancer research.

Precision medicine for acute myeloid leukemia

The goal of precision medicine is to personalize therapy based on individual patient variation, to correctly select the right treatment, for the right patient, at the right time. Acute myeloid leukemia (AML) is a heterogeneous collection of myeloid malignancies with diverse genetic etiology and the potential for intra-patient clonal evolution over time. We discuss here how the precision medicine paradigm might be applied to the care of AML patients by focusing on the potential roles of targeting therapy by patient-specific somatic mutations and aberrant pathways, ex-vivo drug sensitivity and resistance testing, high sensitivity measurements of residual disease burden and biology along with potential clinical trial and regulatory constraints.