Seamless Designs: Current Practice and Considerations for Early-Phase Drug Development in Oncology

Roadmap for the next generation of Children's Oncology Group rhabdomyosarcoma trials

Clinical trials conducted by the Intergroup Rhabdomyosarcoma (RMS) Study Group and the Children's Oncology Group have been pivotal to establishing current standards for diagnosis and therapy for RMS. Recent advancements in understanding the biology and clinical behavior of RMS have led to more nuanced approaches to diagnosis, risk stratification, and treatment. The complexities introduced by these advancements, coupled with the rarity of RMS, pose challenges to conducting large-scale phase 3 clinical trials to evaluate new treatment strategies for RMS. Given these challenges, systematic planning of future clinical trials in RMS is paramount to address pertinent questions regarding the therapeutic efficacy of drugs, biomarkers of response, treatment-related toxicity, and patient quality of life. Herein, the authors outline the proposed strategic approach of the Children's Oncology Group Soft Tissue Sarcoma Committee to the next generation of RMS clinical trials, focusing on five themes: improved novel agent identification and preclinical to clinical translation, more efficient trial development and implementation, expanded opportunities for knowledge generation during trials, therapeutic toxicity reduction and quality of life, and patient engagement.

A Systematic Review of Adaptive Seamless Clinical Trials for Late-Phase Oncology Development

INTRODUCTION

Although oncology has seen large scientific and clinical advances over the last decade, it also has one of the lowest success rates for novel agents across therapeutic areas. Adaptive clinical trial design has been a popular option for increasing clinical trial efficiency and the chances of trial success. Seamless clinical trial design are studies in which two or more clinical trial phases are combined into a single study with a pre-specified transition between stages. This integration of phases may enhance efficiency.

METHODS

To understand the precedent for the use of seamless designs, this working group was formed to conduct a comprehensive literature search on seamless clinical trials conducted with confirmatory intent in oncology. Trial design features were extracted into a database and analyzed with descriptive statistics.

RESULTS

A literature search identified 68 clinical trials meeting inclusion and exclusion criteria. The most common design feature was a gate on treatment efficacy, where the trial would only proceed to the second stage if sufficient efficacy was observed in the first. The next most common feature was a selection of a dose or treatment regimen. Inferentially and operationally seamless designs were approximately equally represented.

DISCUSSION

Key statistical considerations for seamless phase II/III designs include optimizing design choices by evaluating and comparing operating characteristics across design alternatives as well as showing control of overall Type I error rates. Executing the transition between phases should be evaluated for issues related to accrual, drug production, and procedures to maintain trial integrity.

CONCLUSIONS

While there are unique statistical, regulatory, and operational considerations for seamless designs they are also uniquely suited to many development settings. These include, for example, addressing dose selection under FDA's Project Optimus and addressing the growing use of biomarkers and personalized medicine approaches in cancer treatment.

Lisocabtagene Maraleucel in Relapsed/Refractory Mantle Cell Lymphoma: Primary Analysis of the Mantle Cell Lymphoma Cohort From TRANSCEND NHL 001, a Phase I Multicenter Seamless Design Study

PURPOSE

To report the primary analysis results from the mantle cell lymphoma (MCL) cohort of the phase I seamless design TRANSCEND NHL 001 (ClinicalTrials.gov identifier: NCT02631044) study.

METHODS

Patients with relapsed/refractory (R/R) MCL after ≥two lines of previous therapy, including a Bruton tyrosine kinase inhibitor (BTKi), an alkylating agent, and a CD20-targeted agent, received lisocabtagene maraleucel (liso-cel) at a target dose level (DL) of 50 × 106 (DL1) or 100 × 106 (DL2) chimeric antigen receptor-positive T cells. Primary end points were adverse events (AEs), dose-limiting toxicities, and objective response rate (ORR) by independent review committee per Lugano criteria.

RESULTS

Of 104 leukapheresed patients, liso-cel was infused into 88. Median (range) number of previous lines of therapy was three (1-11) with 30% receiving ≥five previous lines of therapy, 73% of patients were age 65 years and older, 69% had refractory disease, 53% had BTKi refractory disease, 23% had TP53 mutation, and 8% had secondary CNS lymphoma. Median (range) on-study follow-up was 16.1 months (0.4-60.5). In the efficacy set (n = 83; DL1 + DL2), ORR was 83.1% (95% CI, 73.3 to 90.5) and complete response (CR) rate was 72.3% (95% CI, 61.4 to 81.6). Median duration of response was 15.7 months (95% CI, 6.2 to 24.0) and progression-free survival was 15.3 months (95% CI, 6.6 to 24.9). Most common grade ≥3 treatment-emergent AEs were neutropenia (56%), anemia (37.5%), and thrombocytopenia (25%). Cytokine release syndrome (CRS) was reported in 61% of patients (grade 3/4, 1%; grade 5, 0), neurologic events (NEs) in 31% (grade 3/4, 9%; grade 5, 0), grade ≥3 infections in 15%, and prolonged cytopenia in 40%.

CONCLUSION

Liso-cel demonstrated high CR rate and deep, durable responses with low incidence of grade ≥3 CRS, NE, and infections in patients with heavily pretreated R/R MCL, including those with high-risk, aggressive disease.

Two-year follow-up of lisocabtagene maraleucel in relapsed or refractory large B-cell lymphoma in TRANSCEND NHL 001

ABSTRACT

Lisocabtagene maraleucel (liso-cel) demonstrated significant efficacy with a manageable safety profile as third-line or later treatment for patients with relapsed or refractory (R/R) large B-cell lymphoma (LBCL) in the TRANSCEND NHL 001 study. Primary end points were adverse events (AEs), dose-limiting toxicities, and objective response rate (ORR) per independent review committee. Key secondary end points were complete response (CR) rate, duration of response (DOR), progression-free survival (PFS), and overall survival (OS). After 2-year follow-up, patients could enroll in a separate study assessing long-term (≤15 years) safety and OS. Liso-cel-treated patients (N = 270) had a median age of 63 years (range, 18-86 years) and a median of 3 prior lines (range, 1-8) of systemic therapy, and 181 of them (67%) had chemotherapy-refractory LBCL. Median follow-up was 19.9 months. In efficacy-evaluable patients (N = 257), the ORR was 73% and CR rate was 53%. The median (95% confidence interval) DOR, PFS, and OS were 23.1 (8.6 to not reached), 6.8 (3.3-12.7), and 27.3 months (16.2-45.6), respectively. Estimated 2-year DOR, PFS, and OS rates were 49.5%, 40.6%, and 50.5%, respectively. In the 90-day treatment-emergent period (N = 270), grade 3 to 4 cytokine release syndrome and neurological events occurred in 2% and 10% of patients, respectively. The most common grade ≥3 AEs in treatment-emergent and posttreatment-emergent periods, respectively, were neutropenia (60% and 7%) and anemia (37% and 6%). Liso-cel demonstrated durable remissions and a manageable safety profile with no new safety signals during the 2-year follow-up in patients with R/R LBCL. These trials were registered at www.ClinicalTrials.gov as #NCT02631044 and #NCT03435796.

The role of clinical imaging in oncology drug development: progress and new challenges

In 2008, the role of clinical imaging in oncology drug development was reviewed. The review outlined where imaging was being applied and considered the diverse demands across the phases of drug development. A limited set of imaging techniques was being used, largely based on structural measures of disease evaluated using established response criteria such as response evaluation criteria in solid tumours. Beyond structure, functional tissue imaging such as dynamic contrast-enhanced MRI and metabolic measures using [18F]flourodeoxyglucose positron emission tomography were being increasingly incorporated. Specific challenges related to the implementation of imaging were outlined including standardisation of scanning across study centres and consistency of analysis and reporting. More than a decade on the needs of modern drug development are reviewed, how imaging has evolved to support new drug development demands, the potential to translate state-of-the-art methods into routine tools and what is needed to enable the effective use of this broadening clinical trial toolset. In this review, we challenge the clinical and scientific imaging community to help refine existing clinical trial methods and innovate to deliver the next generation of techniques. Strong industry-academic partnerships and pre-competitive opportunities to co-ordinate efforts will ensure imaging technologies maintain a crucial role delivering innovative medicines to treat cancer.

Trial Design for Cancer Immunotherapy: A Methodological Toolkit

Immunotherapy with checkpoint inhibitors (CPIs) and cell-based products has revolutionized the treatment of various solid tumors and hematologic malignancies. These agents have shown unprecedented response rates and long-term benefits in various settings. These clinical advances have also pointed to the need for new or adapted approaches to trial design and assessment of efficacy and safety, both in the early and late phases of drug development. Some of the conventional statistical methods and endpoints used in other areas of oncology appear to be less appropriate in immuno-oncology. Conversely, other methods and endpoints have emerged as alternatives. In this article, we discuss issues related to trial design in the early and late phases of drug development in immuno-oncology, with a focus on CPIs. For early trials, we review the most salient issues related to dose escalation, use and limitations of tumor response and progression criteria for immunotherapy, the role of duration of response as an endpoint in and of itself, and the need to conduct randomized trials as early as possible in the development of new therapies. For late phases, we discuss the choice of primary endpoints for randomized trials, review the current status of surrogate endpoints, and discuss specific statistical issues related to immunotherapy, including non-proportional hazards in the assessment of time-to-event endpoints, alternatives to the Cox model in these settings, and the method of generalized pairwise comparisons, which can provide a patient-centric assessment of clinical benefit and be used to design randomized trials.

Early phase clinical trials in oncology: Realising the potential of seamless designs

BACKGROUND

The pharmaceutical industry's productivity has been declining over the last two decades and high attrition rates and reduced regulatory approvals are being seen. The development of oncology drugs is particularly challenging with low rates of approval for novel treatments when compared with other therapeutic areas. Reliably establishing the potential of novel treatment and the corresponding optimal dosage is a key component to ensure efficient overall development. A growing interest lies in terminating developments of poor treatments quickly while enabling accelerated development for highly promising interventions.

METHODS

One approach to reliably establish the optimal dosage and the potential of a novel treatment and thereby improve efficiency in the drug development pathway is the use of novel statistical designs that make efficient use of the data collected.

RESULTS

In this paper, we discuss different (seamless) strategies for early oncology development and illustrate their strengths and weaknesses through real trial examples. We provide some directions for good practices in early oncology development, discuss frequently seen missed opportunities for improved efficiency and some future opportunities that have yet to fully develop their potential in early oncology treatment development.

DISCUSSION

Modern methods for dose-finding have the potential to shorten and improve dose-finding and only small changes to current approaches are required to realise this potential.

Types and progress of clinical trial design for breast cancer: a narrative review

Background and Objective

In recent years, the field of breast cancer diagnosis and therapy has witnessed rapid technological advances. Concurrently, the emergence of molecular biology and novel detection methodologies has facilitated the transition of breast cancer management into the precision medicine era. The primary objective of this review is to discuss the transformation in the research and development paradigm for breast cancer therapies and strategies.

Methods

We systematically searched PubMed, EMBASE and Cochrane databases for relevant studies published over the past 20 years using keywords including "breast cancer", "clinical trial", "seamless", "master protocol", "umbrella", "basket", "platform", and "precision medicine". Articles were screened for eligibility and key data extracted. The search was limited to English-language publications.

Key Content and Findings

The review identifies three core innovations in breast cancer trial methodology: (I) in terms of research speed, the traditional three-stage drug development models are being substituted by "seamless designs" as exemplified by the immunotherapy combination study NCT0328056. (II) Addressing research breadth, "master protocols" such as basket trials (IMMU-132-01), umbrella trials (FUTURE), and platform trials (I-SPY 2) have been introduced, allowing the simultaneous assessment of multiple treatments or disease subtypes within a singular framework. (III) Pertaining to research precision, newer designs utilize biomarkers such as "enrichment" (seen in EMBRACA and OlympiA trials) and "marker stratification" (as in the SOLAR-1 trial), enabling the identification of appropriate patient subgroups and the provision of tailored therapy strategies, a stark contrast to traditional histopathology-based evaluations.

Conclusions

Clinical trial design in breast cancer research has been revolutionized, moving towards more efficient and targeted strategies. Despite the presence of ethical, logistical, and data complexities, it is anticipated that ongoing technological and regulatory enhancements will pave the way for even more refined research approaches, subsequently influencing future research, clinical practices, and policymaking in breast cancer care.

Development of consensus-driven SPIRIT and CONSORT extensions for early phase dose-finding trials: the DEFINE study

BACKGROUND

Early phase dose-finding (EPDF) trials are crucial for the development of a new intervention and influence whether it should be investigated in further trials. Guidance exists for clinical trial protocols and completed trial reports in the SPIRIT and CONSORT guidelines, respectively. However, both guidelines and their extensions do not adequately address the characteristics of EPDF trials. Building on the SPIRIT and CONSORT checklists, the DEFINE study aims to develop international consensus-driven guidelines for EPDF trial protocols (SPIRIT-DEFINE) and reports (CONSORT-DEFINE).

METHODS

The initial generation of candidate items was informed by reviewing published EPDF trial reports. The early draft items were refined further through a review of the published and grey literature, analysis of real-world examples, citation and reference searches, and expert recommendations, followed by a two-round modified Delphi process. Patient and public involvement and engagement (PPIE) was pursued concurrently with the quantitative and thematic analysis of Delphi participants' feedback.

RESULTS

The Delphi survey included 79 new or modified SPIRIT-DEFINE (n = 36) and CONSORT-DEFINE (n = 43) extension candidate items. In Round One, 206 interdisciplinary stakeholders from 24 countries voted and 151 stakeholders voted in Round Two. Following Round One feedback, one item for CONSORT-DEFINE was added in Round Two. Of the 80 items, 60 met the threshold for inclusion (≥ 70% of respondents voted critical: 26 SPIRIT-DEFINE, 34 CONSORT-DEFINE), with the remaining 20 items to be further discussed at the consensus meeting. The parallel PPIE work resulted in the development of an EPDF lay summary toolkit consisting of a template with guidance notes and an exemplar.

CONCLUSIONS

By detailing the development journey of the DEFINE study and the decisions undertaken, we envision that this will enhance understanding and help researchers in the development of future guidelines. The SPIRIT-DEFINE and CONSORT-DEFINE guidelines will allow investigators to effectively address essential items that should be present in EPDF trial protocols and reports, thereby promoting transparency, comprehensiveness, and reproducibility.

TRIAL REGISTRATION

SPIRIT-DEFINE and CONSORT-DEFINE are registered with the EQUATOR Network ( https://www.equator-network.org/ ).

Sensitivity Analyses of Clinical Trial Designs: Selecting Scenarios and Summarizing Operating Characteristics

The use of simulation-based sensitivity analyses is fundamental for evaluating and comparing candidate designs of future clinical trials. In this context, sensitivity analyses are especially useful to assess the dependence of important design operating characteristics with respect to various unknown parameters. Typical examples of operating characteristics include the likelihood of detecting treatment effects and the average study duration, which depend on parameters that are unknown until after the onset of the clinical study, such as the distributions of the primary outcomes and patient profiles. Two crucial components of sensitivity analyses are (i) the choice of a set of plausible simulation scenarios and (ii) the list of operating characteristics of interest. We propose a new approach for choosing the set of scenarios to be included in a sensitivity analysis. We maximize a utility criterion that formalizes whether a specific set of sensitivity scenarios is adequate to summarize how the operating characteristics of the trial design vary across plausible values of the unknown parameters. Then, we use optimization techniques to select the best set of simulation scenarios (according to the criteria specified by the investigator) to exemplify the operating characteristics of the trial design. We illustrate our proposal in three trial designs.

Recent innovations in adaptive trial designs: A review of design opportunities in translational research

Clinical trials are constantly evolving in the context of increasingly complex research questions and potentially limited resources. In this review article, we discuss the emergence of "adaptive" clinical trials that allow for the preplanned modification of an ongoing clinical trial based on the accumulating evidence with application across translational research. These modifications may include terminating a trial before completion due to futility or efficacy, re-estimating the needed sample size to ensure adequate power, enriching the target population enrolled in the study, selecting across multiple treatment arms, revising allocation ratios used for randomization, or selecting the most appropriate endpoint. Emerging topics related to borrowing information from historic or supplemental data sources, sequential multiple assignment randomized trials (SMART), master protocol and seamless designs, and phase I dose-finding studies are also presented. Each design element includes a brief overview with an accompanying case study to illustrate the design method in practice. We close with brief discussions relating to the statistical considerations for these contemporary designs.

Tumor-Naïve Circulating Tumor DNA as an Early Response Biomarker for Patients Treated With Immunotherapy in Early Phase Clinical Trials

PURPOSE

To evaluate early circulating tumor DNA (ctDNA) kinetics using a tumor-naïve assay and correlate it with clinical outcomes in early phase immunotherapy (IO) trials.

METHODS

Plasma samples were analyzed using a 425-gene next-generation sequencing panel at baseline and before cycle 2 (3-4 weeks) in patients with advanced solid tumors treated with investigational IO agents. Variant allele frequency (VAF) for mutations in each gene, mean VAF (mVAF) from all mutations, and change in mVAF between both time points were calculated. Hyperprogression (HyperPD) was measured using Matos and Caramella criteria.

RESULTS

A total of 162 plasma samples were collected from 81 patients with 27 different tumor types. Patients were treated in 37 different IO phase I/II trials, 72% of which involved a PD-1/PD-L1 inhibitor. ctDNA was detected in 122 plasma samples (75.3%). A decrease in mVAF from baseline to precycle 2 was observed in 24 patients (37.5%) and was associated with longer progression-free survival (hazard ratio [HR], 0.43; 95% CI, 0.24 to 0.77; P < .01) and overall survival (HR, 0.54; 95% CI, 0.3 to 0.96; P = .03) compared with an increase. These differences were more marked if there was a >50% decrease in mVAF for both progression-free survival (HR, 0.29; 95% CI, 0.13 to 0.62; P < .001) and overall survival (HR, 0.23; 95% CI, 0.09 to 0.6; P = .001). No differences in mVAF changes were observed between the HyperPD and progressive disease patients.

CONCLUSION

A decrease in ctDNA within 4 weeks of treatment was associated with treatment outcomes in patients in early phase IO trials. Tumor-naïve ctDNA assays may be useful for identifying early treatment benefits in phase I/II IO trials.

Rare molecular subtypes of lung cancer

Oncogenes that occur in ≤5% of non-small-cell lung cancers have been defined as 'rare'; nonetheless, this frequency can correspond to a substantial number of patients diagnosed annually. Within rare oncogenes, less commonly identified alterations (such as HRAS, NRAS, RIT1, ARAF, RAF1 and MAP2K1 mutations, or ERBB family, LTK and RASGRF1 fusions) can share certain structural or oncogenic features with more commonly recognized alterations (such as KRAS, BRAF, MET and ERBB family mutations, or ALK, RET and ROS1 fusions). Over the past 5 years, a surge in the identification of rare-oncogene-driven lung cancers has challenged the boundaries of traditional clinical grade diagnostic assays and profiling algorithms. In tandem, the number of approved targeted therapies for patients with rare molecular subtypes of lung cancer has risen dramatically. Rational drug design has iteratively improved the quality of small-molecule therapeutic agents and introduced a wave of antibody-based therapeutics, expanding the list of actionable de novo and resistance alterations in lung cancer. Getting additional molecularly tailored therapeutics approved for rare-oncogene-driven lung cancers in a larger range of countries will require ongoing stakeholder cooperation. Patient advocates, health-care agencies, investigators and companies with an interest in diagnostics, therapeutics and real-world evidence have already taken steps to surmount the challenges associated with research into low-frequency drivers.

Augmenting control arms with real-world data for cancer trials: Hybrid control arm methods and considerations

Background

Hybrid controlled trials with real-world data (RWD), where the control arm is composed of both trial and real-world patients, could facilitate research when the feasibility of randomized controlled trials (RCTs) is challenging and single-arm trials would provide insufficient information.

Methods

We propose a frequentist two-step borrowing method to construct hybrid control arms. We use parameters informed by a completed randomized trial in metastatic triple-negative breast cancer to simulate the operating characteristics of dynamic and static borrowing methods, highlighting key trade-offs and analytic decisions in the design of hybrid studies.

Results

Simulated data were generated under varying residual-bias assumptions (no bias: HR_RWD = 1) and experimental treatment effects (target trial scenario: HR_Exp = 0.78). Under the target scenario with no residual bias, all borrowing methods achieved the desired 88% power, an improvement over the reference model (74% power) that does not borrow information externally. The effective number of external events tended to decrease with higher bias between RWD and RCT (i.e. HR_RWD away from 1), and with weaker experimental treatment effects (i.e. HR_Exp closer to 1). All dynamic borrowing methods illustrated (but not the static power prior) cap the maximum Type 1 error over the residual-bias range considered. Our two-step model achieved comparable results for power, type 1 error, and effective number of external events borrowed compared to other borrowing methodologies.

Conclusion

By pairing high-quality external data with rigorous simulations, researchers have the potential to design hybrid controlled trials that better meet the needs of patients and drug development.

Basket Trials: Review of Current Practice and Innovations for Future Trials

Advances in biology and immunology have elucidated genetic and immunologic origins of cancer. Innovations in sequencing technologies revealed that distinct cancer histologies shared common genetic and immune phenotypic traits. Pharmacologic developments made it possible to target these alterations, yielding novel classes of targeted agents whose therapeutic potential span multiple tumor types. Basket trials, one type of master protocol, emerged as a tool for evaluating biomarker-targeted therapies among multiple tumor histologies. Conventionally conducted within the phase II setting and designed to estimate high and durable objective responses, basket trials pose challenges to statistical design and interpretation of results. This article reviews basket trials implemented in oncology studies and discusses issues related to their statistical design and analysis.

Innovative Designs and Logistical Considerations for Expedited Clinical Development of Combination Disease-Modifying Treatments for Type 1 Diabetes

It has been 100 years since the life-saving discovery of insulin, yet daily management of type 1 diabetes (T1D) remains challenging. Even with closed-loop systems, the prevailing need for persons with T1D to attempt to match the kinetics of insulin activity with the kinetics of carbohydrate metabolism, alongside dynamic life factors affecting insulin requirements, results in the need for frequent interventions to adjust insulin dosages or consume carbohydrates to correct mismatches. Moreover, peripheral insulin dosing leaves the liver underinsulinized and hyperglucagonemic and peripheral tissues overinsulinized relative to their normal physiologic roles in glucose homeostasis. Disease-modifying therapies (DMT) to preserve and/or restore functional β-cell mass with controlled or corrected autoimmunity would simplify exogenous insulin need, thereby reducing disease mortality, morbidity, and management burdens. However, identifying effective DMTs for T1D has proven complex. There is some consensus that combination DMTs are needed for more meaningful clinical benefit. Other complexities are addressable with more innovative trial designs and logistics. While no DMT has yet been approved for marketing, existing regulatory guidance provides opportunities to further "de-risk" development. The T1D development ecosystem can accelerate progress by using more innovative ways for testing DMTs for T1D. This perspective outlines suggestions for accelerating evaluation of candidate T1D DMTs, including combination therapies, by use of innovative trial designs, enhanced logistical coordination of efforts, and regulatory guidance for expedited development, combination therapies, and adaptive designs.

Drug repurposing-an emerging strategy in cancer therapeutics

Cancer is a complex disease affecting millions of people around the world. Despite advances in surgical and radiation therapy, chemotherapy continues to be an important therapeutic option for the treatment of cancer. The current treatment is expensive and has several side effects. Also, over time, cancer cells develop resistance to chemotherapy, due to which there is a demand for new drugs. Drug repurposing is a novel approach that focuses on finding new applications for the old clinically approved drugs. Current advances in the high-dimensional multiomics landscape, especially proteomics, genomics, and computational omics-data analysis, have facilitated drug repurposing. The drug repurposing approach provides cheaper, effective, and safe drugs with fewer side effects and fastens the process of drug development. The review further delineates each repurposed drug's original indication and mechanism of action in cancer. Along with this, the article also provides insight upon artificial intelligence and its application in drug repurposing. Clinical trials are vital for determining medication safety and effectiveness, and hence the clinical studies for each repurposed medicine in cancer, including their stages, status, and National Clinical Trial (NCT) identification, are reported in this review article. Various emerging evidences imply that repurposing drugs is critical for the faster and more affordable discovery of anti-cancerous drugs, and the advent of artificial intelligence-based computational tools can accelerate the translational cancer-targeting pipeline.

Bayesian and frequentist approaches to sequential monitoring for futility in oncology basket trials: A comparison of Simon's two-stage design and Bayesian predictive probability monitoring with information sharing across baskets

This article discusses and compares statistical designs of basket trial, from both frequentist and Bayesian perspectives. Baskets trials are used in oncology to study interventions that are developed to target a specific feature (often genetic alteration or immune phenotype) that is observed across multiple tissue types and/or tumor histologies. Patient heterogeneity has become pivotal to the development of non-cytotoxic treatment strategies. Treatment targets are often rare and exist among several histologies, making prospective clinical inquiry challenging for individual tumor types. More generally, basket trials are a type of master protocol often used for label expansion. Master protocol is used to refer to designs that accommodates multiple targets, multiple treatments, or both within one overarching protocol. For the purpose of making sequential decisions about treatment futility, Simon's two-stage design is often embedded within master protocols. In basket trials, this frequentist design is often applied to independent evaluations of tumor histologies and/or indications. In the tumor agnostic setting, rarer indications may fail to reach the sample size needed for even the first evaluation for futility. With recent innovations in Bayesian methods, it is possible to evaluate for futility with smaller sample sizes, even for rarer indications. Novel Bayesian methodology for a sequential basket trial design based on predictive probability is introduced. The Bayesian predictive probability designs allow interim analyses with any desired frequency, including continual assessments after each patient observed. The sequential design is compared with and without Bayesian methods for sharing information among a collection of discrete, and potentially non-exchangeable tumor types. Bayesian designs are compared with Simon's two-stage minimax design.

Clinical trials of orphan drugs in China over the decade 2012-2022: Opportunities and challenges

Rare diseases refer to diseases with very low prevalence. Along with the support of national policies and improvement of research capability, a new landscape for orphan drug is emerging in China. To identity unmet clinical needs and provide insight on the development of orphan drugs, we reviewed the changes over time of orphan drug clinical trials in China from 2012 to 2022. A total of 261 trials of 40 drugs were initiated, of which 66.3% trials were sponsored by Chinese local pharmaceutical enterprises. Among the 261 trials, chemical drugs (about 63.6%) and biological products (35.6%) account for the high proportions, and traditional Chinese medicine (0.8%) was the least; the indications mainly focused on homozygous hypercholesterolemia, hemophilia, multiple sclerosis and idiopathic pulmonary fibrosis; single-arm study design was applied to 50% of the clinical trials, with an average sample size of 52 participants. Additionally, totally 122 trials were completed by January 2022, of which the average duration time was 15.7 months for new drug and 3.5 months for generic drug, respectively. The trends over time illustrated that remarkable progress has been achieved in development of orphan drugs in China since 2012. Given the large patient pool and the rising capability of innovation, it is believed that China will contribute more to the global drug pipelines for rare diseases.

Targeted Therapies for Lung Cancer Patients With Oncogenic Driver Molecular Alterations

Lung cancer has traditionally been classified by histology. However, a greater understanding of disease biology and the identification of oncogenic driver alterations has dramatically altered the therapeutic landscape. Consequently, the new classification paradigm of non-small-cell lung cancer is further characterized by molecularly defined subsets actionable with targeted therapies and the treatment landscape is becoming increasingly complex. This review encompasses the current standards of care for targeted therapies in lung cancer with driver molecular alterations. Targeted therapies for EGFR exon 19 deletion and L858R mutations, and ALK and ROS1 rearrangements are well established. However, there is an expanding list of approved targeted therapies including for BRAF V600E, EGFR exon 20 insertion, and KRAS G12C mutations, MET exon 14 alterations, and NTRK and RET rearrangements. In addition, there are numerous other oncogenic drivers, such as HER2 exon 20 insertion mutations, for which there are emerging efficacy data for targeted therapies. The importance of diagnostic molecular testing, intracranial efficacy of novel therapies, the optimal sequencing of therapies, role for targeted therapies in early-stage disease, and future directions for precision oncology approaches to understand tumor evolution and therapeutic resistance are also discussed.

Bayesian basket trial design with false-discovery rate control

BACKGROUND

Recent advances in developing "tumor agnostic" oncology therapies have identified molecular targets that define patient subpopulations in a manner that supersedes conventional criteria for cancer classification. These successes have produced effective targeted therapies that are administered to patients regardless of their tumor histology. Trials have evolved as well with master protocol designs. By blending translational and clinical science, basket trials in particular are well-suited to investigate and develop targeted therapies among multiple cancer histologies. However, basket trials intrinsically involve more complex design decisions, including issues of multiple testing across baskets, and guidance for investigators is needed.

METHODS

The sensitivity of the multisource exchangeability model to prior specification under differing degrees of response heterogeneity is explored through simulation. Then, a multisource exchangeability model design that incorporates control of the false-discovery rate is presented and a simulation study compares the operating characteristics to a design where the family-wise error rate is controlled and to the frequentist approach of treating the baskets as independent. Simulations are based on the original design of a real-world clinical trial, the SUMMIT trial, which investigated Neratinib treatment for a variety of solid tumors. The methods studied here are specific to single-arm phase II trials with binary outcomes.

RESULTS

Values of prior probability of exchangeability in the multisource exchangeability model between 0.1 and 0.3 provide the best trade-offs between gain in precision and bias, especially when per-basket sample size is below 30. Application of these calibration results to a re-analysis of the SUMMIT trial showed that the breast basket exceeded the null response rate with posterior probability of 0.999 while having low posterior probability of exchangeability with all other baskets. Simulations based on the design of the SUMMIT trial revealed that there is meaningful improvement in power even in baskets with small sample size when the false-discovery rate is controlled as opposed to the family-wise error rate. For example, when only the breast basket was active, with a sample size of 25, the power was 0.76 when the false-discovery rate was controlled at 0.05 but only 0.56 when the family-wise error rate was controlled at 0.05, indicating that impractical sample sizes for the phase II setting would be needed to achieve acceptable power while controlling the family-wise error rate in this setting of a trial with 10 baskets.

CONCLUSION

Selection of the prior exchangeability probability based on calibration and incorporation of false-discovery rate control result in multisource exchangeability model designs with high power to detect promising treatments in the context of phase II basket trials.

Immunotherapeutic early-phase clinical trials and malignant gliomas: A single-center experience and comprehensive immunophenotyping of circulating leukocytes

Background

Immunotherapeutic early-phase clinical trials (ieCTs) increasingly adopt large expansion cohorts exploring novel agents across different tumor types. High-grade glioma (HGG) patients are usually excluded from these trials.

Methods

Data of patients with recurrent HGGs treated within multicohort ieCTs between February 2014 and August 2019 (experimental group, EG) at our Phase I Unit were retrospectively reviewed and compared to a matched control group (CG) of patients treated with standard therapies. We retrospectively evaluated clinical, laboratory, and molecular parameters through univariate and multivariate analysis. A prospective characterization of circulating leukocyte subpopulations was performed in the latest twenty patients enrolled in the EG, with a statistical significance cutoff of P < .1.

Results

Thirty HGG patients were treated into six ieCTs. Fifteen patients received monotherapies (anti-PD-1, anti-CSF-1R, anti-TGFβ, anti-cereblon), fifteen patients combination regimens (anti-PD-L1 + anti-CD38, anti-PD-1 + anti-CSF-1R). In the EG, median progression-free survival and overall survival (OS) from treatment initiation were 1.8 and 8.6 months; twelve patients survived more than 12 months, and two of them more than 6 years. Univariate analysis identified O⁶-methylguanine DNA methyltransferase (MGMT) promoter methylation and total protein value at six weeks as significantly correlated with a better outcome. Decreased circulating neutrophils and increased conventional dendritic cells levels lead to significantly better OS.

Conclusions

A subgroup of EG patients achieved remarkably durable disease control. MGMT promoter methylation identifies patients who benefit more from immunotherapy. Monitoring dynamic changes of innate immune cell populations may help to predict clinical outcomes.

Predicting outcomes of phase III oncology trials with Bayesian mediation modeling of tumor response

Pivotal cancer trials often fail to yield evidence in support of new therapies thought to offer promising alternatives to standards-of-care. Conducting randomized controlled trials in oncology tends to be considerably more expensive than studies of other diseases with comparable sample size. Moreover, phase III trial design often takes place with a paucity of survival data for experimental therapies. Experts have explained the failures on the basis of design flaws which produce studies with unrealistic expectations. This article presents a framework for predicting outcomes of phase III oncology trials using Bayesian mediation models. Predictions, which arise from interim analyses, derive from multivariate modeling of the relationships among treatment, tumor response, and their conjoint effects on survival. Acting as a safeguard against inaccurate pre-trial design assumptions, the methodology may better facilitate rapid closure of negative studies. Additionally the models can be used to inform re-estimations of sample size for under-powered trials that demonstrate survival benefit via tumor response mediation. The methods are applied to predict the outcomes of two colorectal cancer studies. Simulation is used to evaluate and compare models in the absence versus presence of reliable surrogate markers of survival.

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.

Dose selection and tolerability of anticancer agents evaluated by the European Medicines Agency in the period 2015-2020

Background

Novel anticancer agents are initially evaluated in a palliative setting in phase I studies. The benefit–risk applying the selected dose from these phase I studies can be considered acceptable at time of registration, however, it is unknown if the optimal dose has been selected during drug development.

Methods

The European Medicines Agency (EMA) European Public Assessment Reports (EPARs) overview was used to select anticancer agents evaluated between 2015 and 2020. The dose selection and tolerability data of EMA assessed anticancer agents was analysed to evaluate dose selection.

Results

Sixty EPARs were included for analysis. A dose–response relation was identified in five dossiers (8%). The maximum tolerated dose (MTD) was the selected dose for 15 anticancer agents (25%). The MTD was not determined in 27 out of 60 cases (59%). When the MTD was determined but not applied as final dose, the most frequently used dose selection criteria were the combination of toxicity, exposure response, pharmacokinetic data and pharmacodynamic data (in 7 out of 18 cases). Data on tolerability were analysed separately for protein kinase inhibitors and monoclonal antibodies as the dosing interval and mitigation of adverse events (AEs) differs. The median discontinuation, dose reduction and dose interruption rates due to AEs of protein kinase inhibitors were 10%, 26% and 45% for monotherapy and 13%, 47% and 55% for combination therapy, respectively. The median discontinuation rates due to AEs for monoclonal antibodies were 8% for monotherapy and 26% for combination therapy.

Conclusion

The dose–response relationship has not been established for the majority of the registered anticancer agents. The selected posology is often poorly tolerable as reflected by the high discontinuation and dose reduction rates. Due to the absence of dose–response data, it is often unknown if the optimal dose has been selected for anticancer agents.

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The dose–response relationship has not been established for the majority of the anticancer agents.

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The posology of anticancer agents is often poorly tolerable, reflected by the high discontinuation and dose reduction rates.

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Due to the absence of dose–response data it is often unknown if the optimal dose has been selected for anticancer agents.

A survival mediation model with Bayesian model averaging

Determining the extent to which a patient is benefiting from cancer therapy is challenging. Criteria for quantifying the extent of "tumor response" observed within a few cycles of treatment have been established for various types of solids as well as hematologic malignancies. These measures comprise the primary endpoints of phase II trials. Regulatory approvals of new cancer therapies, however, are usually contingent upon the demonstration of superior overall survival with randomized evidence acquired with a phase III trial comparing the novel therapy to an appropriate standard of care treatment. With nearly two-thirds of phase III oncology trials failing to achieve statistically significant results, researchers continue to refine and propose new surrogate endpoints. This article presents a Bayesian framework for studying relationships among treatment, patient subgroups, tumor response, and survival. Combining classical components of a mediation analysis with Bayesian model averaging, the methodology is robust to model misspecification among various possible relationships among the observable entities. A posterior inference is demonstrated via an application to a randomized controlled phase III trial in metastatic colorectal cancer. Moreover, the article details posterior predictive distributions of survival and statistical metrics for quantifying the extent of direct and indirect, or tumor response mediated treatment effects.

Systematic review of combinations of targeted or immunotherapy in advanced solid tumors

With rapid advances in our understanding of cancer, there is an expanding number of potential novel combination therapies, including novel-novel combinations. Identifying which combinations are appropriate and in which subpopulations are among the most difficult questions in medical research. We conducted a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-guided systematic review of trials of novel-novel combination therapies involving immunotherapies or molecular targeted therapies in advanced solid tumors. A MEDLINE search was conducted using a modified Cochrane Highly Sensitive Search Strategy for published clinical trials between July 1, 2017, and June 30, 2020, in the top-ranked medical and oncology journals. Trials were evaluated according to a criterion adapted from previously published Food and Drug Administration guidance and other key considerations in designing trials of combinations. This included the presence of a strong biological rationale, the use of a new established or emerging predictive biomarker prospectively incorporated into the clinical trial design, appropriate comparator arms of monotherapy or supportive external data sources and a primary endpoint demonstrating a clinically meaningful benefit. Of 32 identified trials, there were 11 (34%) trials of the novel-novel combination of anti-programmed death 1 (PD-1)/programmed death ligand 1 (PD-L1) and anti-cytotoxic T lymphocyte-associated antigen-4 (CTLA-4) therapy, and 10 (31%) trials of anti-PD-1/PD-L1 and anti-vascular endothelial growth factor (VEGF) combination therapy. 20 (62.5%) trials were phase II trials, while 12 (37.5%) were phase III trials. Most (72%) trials lacked significant preclinical evidence supporting the development of the combination in the given indication. A majority of trials (69%) were conducted in biomarker unselected populations or used pre-existing biomarkers within the given indication for patient selection. Most studies (66%) were considered to have appropriate comparator arms or had supportive external data sources such as prior studies of monotherapy. All studies were evaluated as selecting a clinically meaningful primary endpoint. In conclusion, designing trials to evaluate novel-novel combination therapies presents numerous challenges to demonstrate efficacy in a comprehensive manner. A greater understanding of biological rationale for combinations and incorporating predictive biomarkers may improve effective evaluation of combination therapies. Innovative statistical methods and increasing use of external data to support combination approaches are potential strategies that may improve the efficiency of trial design. Designing trials to evaluate novel-novel combination therapies presents numerous challenges to demonstrate efficacy in a comprehensive manner. A greater understanding of biological rationale for combinations and incorporating predictive biomarkers may improve effective evaluation of combination therapies. Innovative statistical methods and increasing use of external data to support combination approaches are potential strategies that may improve the efficiency of trial design.

Moving Beyond 3+3: The Future of Clinical Trial Design

Misgivings have been raised about the operating characteristics of the canonical 3+3 dose-escalation phase I clinical trial design. Yet, the traditional 3+3 design is still the most commonly used. Although it has been implied that adhering to this design is due to a stubborn reluctance to adopt change despite other designs performing better in hypothetical computer-generated simulation models, the continued adherence to 3+3 dose-escalation phase I strategies is more likely because these designs perform the best in the real world, pinpointing the correct dose and important side effects with an acceptable degree of precision. Beyond statistical simulations, there are little data to refute the supposed shortcomings ascribed to the 3+3 method. Even so, to address the unique nuances of gene- and immune-targeted compounds, a variety of inventive phase 1 trial designs have been suggested. Strategies for developing these therapies have launched first-in-human studies devised to acquire a breadth of patient data that far exceed the size of a typical phase I design and blur the distinction between dose selection and efficacy evaluation. Recent phase I trials of promising cancer therapies assessed objective tumor response and durability at various doses and schedules as well as incorporated multiple expansion cohorts spanning a variety of histology or biomarker-defined tumor subtypes, sometimes resulting in U.S. Food and Drug Administration approval after phase I. This article reviews recent innovations in phase I design from the perspective of multiple stakeholders and provides recommendations for future trials.

Trailblazing precision medicine in Europe: A joint view by Genomic Medicine Sweden and the Centers for Personalized Medicine, ZPM, in Germany

Over the last decades, rapid technological and scientific advances have led to a merge of molecular sciences and clinical medicine, resulting in a better understanding of disease mechanisms and the development of novel therapies that exploit specific molecular lesions or profiles driving disease. Precision oncology is here used as an example, illustrating the potential of precision/personalized medicine that also holds great promise in other medical fields. Real-world implementation can only be achieved by dedicated healthcare connected centers which amass and build up interdisciplinary expertise reflecting the complexity of precision medicine. Networks of such centers are ideally suited for a nation-wide outreach offering access to precision medicine to patients independent of their place of residence. Two of these multicentric initiatives, Genomic Medicine Sweden (GMS) and the Centers for Personalized Medicine (ZPM) initiative in Germany have teamed up to present and share their views on core concepts, potentials, challenges, and future developments in precision medicine. Together with other initiatives worldwide, GMS and ZPM aim at providing a robust and sustainable framework, covering all components from technology development to clinical trials, ethical and legal aspects as well as involvement of all relevant stakeholders, including patients and policymakers in the field.

Modernizing Clinical Trial Eligibility Criteria: Recommendations of the ASCO-Friends of Cancer Research Prior Therapies Work Group

PURPOSE

Restrictive eligibility criteria induce differences between clinical trial and "real-world" treatment populations. Restrictions based on prior therapies are common; minimizing them when appropriate may increase patient participation in clinical trials.

EXPERIMENTAL DESIGN

A multi-stakeholder working group developed a conceptual framework to guide evaluation of prevailing practices with respect to using prior treatment as selection criteria for clinical trials. The working group made recommendations to minimize restrictions based on prior therapies within the boundaries of scientific validity, patient centeredness, distributive justice, and beneficence.

RECOMMENDATIONS

(i) Patients are eligible for clinical trials regardless of the number or type of prior therapies and without requiring a specific therapy prior to enrollment unless a scientific or clinically based rationale is provided as justification. (ii) Prior therapy (either limits on number and type of prior therapies or requirements for specific therapies before enrollment) could be used to determine eligibility in the following cases: a) the agents being studied target a specific mechanism or pathway that could potentially interact with a prior therapy; b) the study design requires that all patients begin protocol-specified treatment at the same point in the disease trajectory; and c) in randomized clinical studies, if the therapy in the control arm is not appropriate for the patient due to previous therapies received. (iii) Trial designers should consider conducting evaluation separately from the primary endpoint analysis for participants who have received prior therapies.

CONCLUSIONS

Clinical trial sponsors and regulators should thoughtfully reexamine the use of prior therapy exposure as selection criteria to maximize clinical trial participation.See related commentary by Giantonio, p. 2369.

The Future of Clinical Trial Design in Oncology

Clinical trials represent a fulcrum for oncology drug discovery and development to bring safe and effective medicines to patients in a timely manner. Clinical trials have shifted from traditional studies evaluating cytotoxic chemotherapy in largely histology-based populations to become adaptively designed and biomarker-driven evaluations of molecularly targeted agents and immune therapies in selected patient subsets. This review will discuss the scientific, methodological, practical, and patient-focused considerations to transform clinical trials. A call to action is proposed to establish the framework for next-generation clinical trials that strikes an optimal balance of operational efficiency, scientific impact, and value to patients. SIGNIFICANCE: The future of cancer clinical trials requires a framework that can efficiently transform scientific discoveries to clinical utility through applications of innovative technologies and dynamic design methodologies. Next-generation clinical trials will offer individualized strategies which ultimately contribute to globalized knowledge and collective learning, through the joint efforts of all key stakeholders including investigators and patients.

Clinical Design and Analysis Strategies for the Development of Gene Therapies: Considerations for Quantitative Drug Development in the Age of Genetic Medicine

Cell and gene therapies have shown enormous promise across a range of diseases in recent years. Numerous adoptive cell therapy modalities as well as systemic and direct-to-target tissue gene transfer administrations are currently in clinical development. The clinical trial design, development, reporting, and analysis of novel cell and gene therapies can differ significantly from established practices for small molecule drugs and biologics. Here, we discuss important quantitative considerations and key competencies for drug developers in preclinical requirements, trial design, and lifecycle planning for gene therapies. We argue that the unique development path of gene therapies requires practicing quantitative drug developers-statisticians, pharmacometricians, pharmacokineticists, epidemiologists, and medical and translational science leads-to exercise active collaboration and cross-functional learning across development stages.

Statistical design considerations for trials that study multiple indications

Breakthroughs in cancer biology have defined new research programs emphasizing the development of therapies that target specific pathways in tumor cells. Innovations in clinical trial design have followed with master protocols defined by inclusive eligibility criteria and evaluations of multiple therapies and/or histologies. Consequently, characterization of subpopulation heterogeneity has become central to the formulation and selection of a study design. However, this transition to master protocols has led to challenges in identifying the optimal trial design and proper calibration of hyperparameters. We often evaluate a range of null and alternative scenarios; however, there has been little guidance on how to synthesize the potentially disparate recommendations for what may be optimal. This may lead to the selection of suboptimal designs and statistical methods that do not fully accommodate the subpopulation heterogeneity. This article proposes novel optimization criteria for calibrating and evaluating candidate statistical designs of master protocols in the presence of the potential for treatment effect heterogeneity among enrolled patient subpopulations. The framework is applied to demonstrate the statistical properties of conventional study designs when treatments offer heterogeneous benefit as well as identify optimal designs devised to monitor the potential for heterogeneity among patients with differing clinical indications using Bayesian modeling.

A modular framework for early-phase seamless oncology trials

BACKGROUND

As our understanding of the etiology and mechanisms of cancer becomes more sophisticated and the number of therapeutic options increases, phase I oncology trials today have multiple primary objectives. Many such designs are now "seamless," meaning that the trial estimates both the maximum tolerated dose and the efficacy at this dose level. Sponsors often proceed with further study only with this additional efficacy evidence. However, with this increasing complexity in trial design, it becomes challenging to articulate fundamental operating characteristics of these trials, such as (1) what is the probability that the design will identify an acceptable, that is., safe and efficacious, dose level? or (2) how many patients will be assigned to an acceptable dose level on average?

METHODS

In this manuscript, we propose a new modular framework for designing and evaluating seamless oncology trials. Each module is comprised of either a dose assignment step or a dose-response evaluation, and multiple such modules can be implemented sequentially. We develop modules from existing phase I/II designs as well as a novel module for evaluating dose-response using a Bayesian isotonic regression scheme.

RESULTS

We also demonstrate a freely available R package called seamlesssim to numerically estimate, by means of simulation, the operating characteristics of these modular trials.

CONCLUSIONS

Together, this design framework and its accompanying simulator allow the clinical trialist to compare multiple different candidate designs, more rigorously assess performance, better justify sample sizes, and ultimately select a higher quality design.

Master Protocol Trial Design for Efficient and Rational Evaluation of Novel Therapeutic Oncology Devices

Historically, the gold standard for evaluation of cancer therapeutics, including medical devices, has been the randomized clinical trial. Although high-quality clinical data are essential for safe and judicious use of therapeutic oncology devices, class II devices require only preclinical data for US Food and Drug Administration approval and are often not rigorously evaluated prior to widespread uptake. Herein, we review master protocol design in medical oncology and its application to therapeutic oncology devices, using examples from radiation oncology. Unique challenges of clinical testing of radiation oncology devices (RODs) include patient and treatment heterogeneity, lack of funding for trials by industry and health-care payers, and operator dependence. To address these challenges, we propose the use of master protocols to optimize regulatory, financial, administrative, quality assurance, and statistical efficiency of trials evaluating RODs. These device-specific master protocols can be extrapolated to other devices and encompass multiple substudies with the same design, statistical considerations, logistics, and infrastructure. As a practical example, we outline our phase I and II master protocol trial of stereotactic magnetic resonance imaging–guided adaptive radiotherapy, which to the best of our knowledge is the first master protocol trial to test a ROD. Development of more efficient clinical trials is needed to promote thorough evaluation of therapeutic oncology devices, including RODs, in a resource-limited environment, allowing more practical and rapid identification of the most valuable advances in our field.

Lisocabtagene maraleucel for patients with relapsed or refractory large B-cell lymphomas (TRANSCEND NHL 001): a multicentre seamless design study

BACKGROUND

Lisocabtagene maraleucel (liso-cel) is an autologous, CD19-directed, chimeric antigen receptor (CAR) T-cell product. We aimed to assess the activity and safety of liso-cel in patients with relapsed or refractory large B-cell lymphomas.

METHODS

We did a seamless design study at 14 cancer centres in the USA. We enrolled adult patients (aged ≥18 years) with relapsed or refractory large B-cell lymphomas. Eligible histological subgroups included diffuse large B-cell lymphoma, high-grade B-cell lymphoma with rearrangements of MYC and either BCL2, BCL6, or both (double-hit or triple-hit lymphoma), diffuse large B-cell lymphoma transformed from any indolent lymphoma, primary mediastinal B-cell lymphoma, and follicular lymphoma grade 3B. Patients were assigned to one of three target dose levels of liso-cel as they were sequentially tested in the trial (50 × 10⁶ CAR⁺ T cells [one or two doses], 100 × 10⁶ CAR⁺ T cells, and 150 × 10⁶ CAR⁺ T cells), which were administered as a sequential infusion of two components (CD8⁺ and CD4⁺ CAR⁺ T cells) at equal target doses. Primary endpoints were adverse events, dose-limiting toxicities, and the objective response rate (assessed per Lugano criteria); endpoints were assessed by an independent review committee in the efficacy-evaluable set (comprising all patients who had confirmed PET-positive disease and received at least one dose of liso-cel). This trial is registered with ClinicalTrials.gov, NCT02631044.

FINDINGS

Between Jan 11, 2016, and July 5, 2019, 344 patients underwent leukapheresis for manufacture of CAR⁺ T cells (liso-cel), of whom 269 patients received at least one dose of liso-cel. Patients had received a median of three (range 1-8) previous lines of systemic treatment, with 260 (97%) patients having had at least two lines. 112 (42%) patients were aged 65 years or older, 181 (67%) had chemotherapy-refractory disease, and seven (3%) had secondary CNS involvement. Median follow-up for overall survival for all 344 patients who had leukapheresis was 18·8 months (95% CI 15·0-19·3). Overall safety and activity of liso-cel did not differ by dose level. The recommended target dose was 100 × 10⁶ CAR⁺ T cells (50 × 10⁶ CD8⁺ and 50 × 10⁶ CD4⁺ CAR⁺ T cells). Of 256 patients included in the efficacy-evaluable set, an objective response was achieved by 186 (73%, 95% CI 66·8-78·0) patients and a complete response by 136 (53%, 46·8-59·4). The most common grade 3 or worse adverse events were neutropenia in 161 (60%) patients, anaemia in 101 (37%), and thrombocytopenia in 72 (27%). Cytokine release syndrome and neurological events occurred in 113 (42%) and 80 (30%) patients, respectively; grade 3 or worse cytokine release syndrome and neurological events occurred in six (2%) and 27 (10%) patients, respectively. Nine (6%) patients had a dose-limiting toxicity, including one patient who died from diffuse alveolar damage following a dose of 50 × 10⁶ CAR⁺ T cells.

INTERPRETATION

Use of liso-cel resulted in a high objective response rate, with a low incidence of grade 3 or worse cytokine release syndrome and neurological events in patients with relapsed or refractory large B-cell lymphomas, including those with diverse histological subtypes and high-risk features. Liso-cel is under further evaluation at first relapse in large B-cell lymphomas and as a treatment for other relapsed or refractory B-cell malignancies.

FUNDING

Juno Therapeutics, a Bristol-Myers Squibb Company.

Histology-agnostic drug development - considering issues beyond the tissue

With advances in tumour biology and immunology that continue to refine our understanding of cancer, therapies are now being developed to treat cancers on the basis of specific molecular alterations and markers of immune phenotypes that transcend specific tumour histologies. With the landmark approvals of pembrolizumab for the treatment of patients whose tumours have high microsatellite instability and larotrectinib and entrectinib for those harbouring NTRK fusions, a regulatory pathway has been created to facilitate the approval of histology-agnostic indications. Negative results presented in the past few years, however, highlight the intrinsic complexities faced by drug developers pursuing histology-agnostic therapeutic agents. When patient selection and statistical analysis involve multiple potentially heterogeneous histologies, guidance is needed to navigate the challenges posed by trial design. Additionally, as new therapeutic agents are tested and post-approval data become available, the regulatory framework for acting on these data requires further optimization. In this Review, we summarize the development and testing of approved histology-agnostic therapeutic agents and present data on other agents currently under development. Finally, we discuss the challenges intrinsic to histology-agnostic drug development in oncology, including biological, regulatory, design and statistical considerations.

Design and Conduct of Early Clinical Studies of Immunotherapy: Recommendations from the Task Force on Methodology for the Development of Innovative Cancer Therapies 2019 (MDICT)

PURPOSE

To review key aspects of the design and conduct of early clinical trials (ECT) of immunotherapy agents.

EXPERIMENTAL DESIGN

The Methodology for the Development of Innovative Cancer Therapies Task Force 2019 included experts from academia, nonprofit organizations, industry, and regulatory agencies. The review focus was on methodology for ECTs testing immune-oncology therapies (IO) used in combination with other IO or chemotherapy.

RESULTS

Although early successes have been seen, the landscape continues to be very dynamic, and there are ongoing concerns regarding the capacity to test all new drugs and combinations in clinical trials.

CONCLUSIONS

Optimization of drug development methodology is required, taking into account early, late, and lower grade intolerable toxicities, novel response patterns, as well as pharmacodynamic data.

Tailoring early-phase clinical trial design to address multiple research objectives

INTRODUCTION

In contemporary oncology drug development, implementation of novel early-phase designs with the ability to address multiple research objectives is needed to better refine regimens. This paper describes an adaptive design strategy for identifying a range of optimal regimens based on two endpoints within multiple cohorts. The proposed design was developed to address objectives in an early-phase trial of cancer vaccines in combination with agonistic antibodies to CD40 and CD27.

MATERIALS AND METHODS

We describe a model-based design strategy that was developed for a trial evaluating the safety and immunogenicity of vaccination with (1) peptides plus CD40 antibody and TLR3 ligand, (2) systemic administration of an agonistic CD27 antibody, and (3) to assess immune response from (1) and (2) compared to optimal controls in participants with stage IIB-IV melanoma.

RESULTS AND CONCLUSIONS

The proposed design is a practical adaptive method for use with combined immunotherapy regimens with multiple objectives within multiple cohorts of interest. Further advances in the effectiveness of cancer immunotherapies will require new approaches that include redefining optimal strategies to take multiple regimens forward into later phases, incorporating additional endpoints in the dose selection process and testing drug combination therapies to improve efficacy and reduce toxicity. Our goal is to facilitate the acceptance and application of more novel designs in contemporary early development trials.

Prediction of Drug Approval After Phase I Clinical Trials in Oncology: RESOLVED2

PURPOSE

Drug development in oncology currently is facing a conjunction of an increasing number of antineoplastic agents (ANAs) candidate for phase I clinical trials (P1CTs) and an important attrition rate for final approval. We aimed to develop a machine learning algorithm (RESOLVED2) to predict drug development outcome, which could support early go/no-go decisions after P1CTs by better selection of drugs suitable for further development.

METHODS

PubMed abstracts of P1CTs reporting on ANAs were used together with pharmacologic data from the DrugBank5.0 database to model time to US Food and Drug Administration (FDA) approval (FDA approval-free survival) since the first P1CT publication. The RESOLVED2 model was trained with machine learning methods. Its performance was evaluated on an independent test set with weighted concordance index (IPCW).

RESULTS

We identified 462 ANAs from PubMed that matched with DrugBank5.0 (P1CT publication dates 1972 to 2017). Among 1,411 variables, 28 were used by RESOLVED2 to model the FDA approval-free survival, with an IPCW of 0.89 on the independent test set. RESOLVED2 outperformed a model that was based on efficacy/toxicity (IPCW, 0.69). In the test set at 6 years of follow-up, 73% (95% CI, 49% to 86%) of drugs predicted to be approved were approved, whereas 92% (95% CI, 87% to 98%) of drugs predicted to be nonapproved were still not approved (log-rank P < .001). A predicted approved drug was 16 times more likely to be approved than a predicted nonapproved drug (hazard ratio, 16.4; 95% CI, 8.40 to 32.2).

CONCLUSION

As soon as P1CT completion, RESOLVED2 can predict accurately the time to FDA approval. We provide the proof of concept that drug development outcome can be predicted by machine learning strategies.

Discussion of Trial Designs for Biomarker Identification and Validation Through the Use of Case Studies

With the launch of the National Cancer Institute's Precision Medicine Initiative in 2015, there has been a shift to trial designs that tailor health care solutions to individual patients by using a screening platform and by moving away from the one-trial/one-biomarker-at-a-time approach. To make precision medicine a reality, it is critical to identify and validate potential biomarkers to help select patients who will truly benefit from a targeted therapy. In this article, we discuss five trial designs: enrichment, umbrella, basket, subgroup, and window of opportunity. For each trial design, we describe the design characteristics, use ongoing or completed trials as case studies, provide any recent advances to the trial design, and discuss advantages and disadvantages of each design.

Early-Phase Platform Trials: A New Paradigm for Dose Finding and Treatment Screening in the Era of Precision Oncology

Applications in early-phase cancer trials have motivated the development of many statistical designs since the late 1980s, including dose-finding methods, futility screening, treatment selection, and early stopping rules. These methods are often proposed to address the conventional cytotoxic therapeutics for neoplastic diseases and cancer. Recent advances in precision medicine have motivated novel trial designs, most notably the idea of master protocol (eg, platform trial, basket trial, umbrella trial, N-of-1 trial), for the evaluation of molecularly targeted cancer therapies. In this article, we review the concepts and methodology of early-phase cancer trial designs with a focus on dose finding and treatment screening and put these methods in the context of platform trials of molecularly targeted cancer therapies. Because most cancer trial designs have been developed for cytotoxic agents, we will discuss how these time-tested design principles hold relevance for targeted cancer therapies, and we will delineate how a master protocol may serve as an efficient platform for safety and efficacy evaluations of novel targeted therapies.