Abstract
Early-phase clinical trials (EPCTs) have been increasingly adopted as the pivotal trial to support US Food and Drug Administration (FDA) approval of novel anticancer drugs. Among EPCT designs, dose-escalation and -expansion cohort (DEEC) substantially reduces the time and resources that are required in the traditional three-phase paradigm. DEEC facilitates expedited approvals of investigational drugs, particularly those targeting novel mechanisms, helping to address the pressing needs of patients with cancer. From 2012 to 2023, DEECs provided pivotal trial evidence that supported the FDA approval for 46 indications across 36 targeted anticancer drugs. Dose escalation uses 3 + 3, Bayesian optimal interval design, or continual reassessment method to explore the optimal dose level, whereas expansion cohorts directly incorporate dose-escalation cohort at the recommended phase II dose level. Expansion cohorts adopt flexible designs such as basket, umbrella, and platform trials. In addition, each expansion cohort in DEEC often uses adaptive approaches, such as Simon's two-stage design. To avoid the bias of end point assessment, conducting DEEC trials requires end point adjudication, often by an independent review committee. The design, conduct, and analysis of DEEC trials each have distinct characteristics. However, these characteristics were often overlooked in DEEC reporting. We reviewed the structural domains and items in trial design and conduct and discussed the strengths and limitations of DEEC studies, aiming to enhance the utilization of this trial design to generate higher-quality clinical evidence and ultimately contribute to better outcomes for patients with cancer.
INTRODUCTION
In the past decade, the landscape of pivotal clinical trials supporting anticancer drug approvals has undergone significant transformation.1-3 Of all pivotal trials supporting the US Food and Drug Administration (FDA) approval of targeted anticancer drugs, nearly 60% were early-phase clinical trials (EPCTs).4 EPCT is defined as the trials before phase III, primarily consisting of phase I, phase II, and seamless studies that bridge these phases.5,6 Dose-escalation and expansion cohort (DEEC), as an important EPCT, has experienced the fastest growth because of its smaller sample sizes and ease of conduct compared with randomized controlled trials (RCTs), supporting many of the FDA-accelerated approvals (AAs).7 A variety of small-molecule kinase inhibitors (SMKIs), especially for those targeting cancer driver genes (CDGs) such as osimertinib,8 ceritinib,9 and pralsetinib,10 used DEEC evidence in new drug application. In addition, several blockbuster antibody drugs such as pembrolizumab,11 nivolumab,12 trastuzumab deruxtecan,13,14 and durvalumab15 received initial approval based on DEEC evidence. To date, DEECs serving as pivotal trials of targeted anticancer drugs have achieved in providing robust clinical evidence, enabling high efficiency in drug development.4
APPROVED INDICATIONS OF TARGETED ANTICANCER DRUGS BASED ON DEECs
Totally, there were 46 indications for 36 targeted anticancer drugs approved by the FDA based on DEEC trials (Appendix Fig A1). The first use of DEEC as a pivotal trial for the FDA market approval was the bosutinib trial (ClinicalTrials.gov identifier: NCT00261846; Appendix Table A1).16 In this trial, bosutinib demonstrated compelling efficacy, achieving a 77% complete hematologic response, along with an acceptable safety profile.16 These results led to the FDA's AA of bosutinib in 2012 for patients with chronic myeloid leukemia who were resistant or intolerant to previous tyrosine kinase inhibitor therapies. Since then, DEEC was increasingly used as the pivotal trial of targeted anticancer drugs. Furthermore, the method of DEEC design has been continuously refined, particularly incorporating adaptive designs to enhance trial efficiency. For instance, pembrolizumab and larotrectinib DEECs used cohort studies with adaptive designs, exhibiting a reduced but sufficient sample size.17,18 These DEECs, leveraging adaptive and basket designs, have proven to be efficient in generating preliminary evidence, particularly for drugs addressing CDG-defined cancer subtypes.
The number of DEEC pivotal trials was growing steadily. From 2012 to 2014, only four anticancer indications' approvals were based on DEEC evidence (Appendix Table A1; Fig 1). Between 2018 and 2020, DEECs experienced a significant surge, accommodating the development of the drugs for 19 indications such as neurotrophic receptor tyrosine kinase (NTRK) gene fusion–positive cancers and KRAS G12C–mutant cancers.18-20 By 2023, DEEC had served as the pivotal trial design for targeted anticancer drugs for over a decade.
FIG 1.
Timeline of targeted anticancer drug approvals based on pivotal trials of DEEC. The timeline depicts the dates and disease settings of the approvals by the FDA on the basis of DEEC evidence. AdvSM, adult patients with advanced systemic mastocytosis; ALCL, anaplastic large cell lymphoma; ALK, anaplastic lymphoma kinase; CLL/SLL, chronic lymphocytic leukemia or small lymphocytic lymphoma; CML, chronic myelogenous leukemia; CSCC, cutaneous squamous cell carcinoma; DEEC, dose-escalation and expansion cohort; DLBCL, diffuse large B-cell lymphoma; EGFR, epidermal growth factor receptor; EZH2, enhancer of zeste homolog 2; FDA, US Food and Drug Administration; FGFR2, fibroblast growth factor receptor 2; FL, follicular lymphoma; GIST, GI stromal tumor; HCC, hepatocellular carcinoma; iCCA, intrahepatic cholangiocarcinoma; IDH1, isocitrate dehydrogenase-1; IDH2, isocitrate dehydrogenase-2; IMT, inflammatory myofibroblastic tumor; MCL, mantle cell lymphoma; MM, multiple myeloma; MTC, medullary thyroid cancer; mTNBC, metastatic triple-negative breast cancer; NSCLC, non–small cell lung cancer; PDGFRA, platelet-derived growth factor receptor alpha; R/R, relapsed and/or refractory; SCCHN, squamous cell carcinoma of head and neck; SCLC, small cell lung cancer; UC, urothelial carcinoma.
The molecular targets of these 36 drugs are involved in a wide range of cell signal pathways, encompassing both common and rare oncogenic mechanisms (Appendix Table A2). Among these drugs, SMKIs dominate, representing more than half of 36 drugs. These SMKIs include inhibitors targeting receptor tyrosine kinases such as inhibitors of epidermal growth factor receptor (eg, osimertinib), anaplastic lymphoma kinase (ALK; eg, crizotinib, lorlatinib), and MEK (eg, trametinib), as well as other kinases. Enzyme inhibitors have also gained traction, particularly with isocitrate dehydrogenase-1 and isocitrate dehydrogenase-2 inhibitors like ivosidenib21 and enasidenib22 for AML and enhancer of zeste homolog 2 (EZH2) inhibitors like tazemetostat23 for follicular lymphoma (FL). Mono-specific antibodies and antibody-drug conjugates represent another substantial category, notably with PD-1/PD-L1 inhibitors (eg, pembrolizumab and nivolumab) and novel bispecific antibodies targeting CD20 and CD3 (eg, mosunetuzumab24) for hematologic malignancies. These wide distributions of therapeutic targets and drug types highlight the effectiveness of DEEC studies for a wide array of mechanisms and drug types—spanning kinases, enzymes, and immune checkpoint targets and encompassing both small molecules and large antibody-based agents (Appendix Table A2).
DEEC'S STRUCTURE AND DESIGN
Framework of DEECs in Oncologic Studies
The framework of DEECs originates from the concept of the seamless design.25 After dose escalation determines the optimal treatment dose (also called the recommended phase II dose [RP2D]), the expansion cohort transitions seamlessly to this RP2D level.7 It then recruits qualified participants to form one or multiple indication-matched cohorts, specifically designed to explore drug efficacy within targeted conditions7,26 (Table 1).
TABLE 1.
Glossary of Key Items for DEEC Characteristics
No. | Item | Definition |
---|---|---|
1 | EPCT | EPCTs refer to phase I, phase II, and seamless studies that bridge these phases which aim to determine the safety and preliminary efficacy of the investigational drug before conducting the phase III clinical trial4,5,7 |
2 | Seamless trial | Seamless trial combines two or more trials of a traditional drug development process into a single, continuous study. It allows for a smooth transition between stages, without the need to stop the trial and reinitiate a new phase. In oncology, for example, a seamless trial integrates the dose-escalation and dose-expansion phases into one study to evaluate both safety and preliminary efficacy at the recommended dose4,25 |
3 | Master protocol | Master protocol uses an overall trial structure (such as basket, umbrella, or platform design) to address multiple objectives. In early-phase oncologic trial, it also incorporates both dose-escalation and dose-expansion studies into a seamless trial27,28 |
4 | Dose escalation | Dose escalation is typically a phase I trial, where incremental increases in drug dosage are administered to small groups of participants to determine the MTD and establish a safe dosage range29 |
5 | DEC | DEC is defined as “one or more additional eligible patient cohorts seamlessly following dose-escalation cohort on RP2D dose level with objectives to explore preliminary efficacy and safety in several specific disease conditions”7,30 |
Abbreviations: DEC, dose-expansion cohort; DEEC, dose-escalation and expansion cohort; EPCT, early-phase clinical trial; MTD, maximum tolerated dose; RP2D, recommended phase II dose.
The DEEC structure is typically featured by open-label, nonrandomized, multiple cohorts in different indications or conditions, and each cohort without a parallel control group.5 As a seamless design, the entire trial is conducted under a single master protocol covering dose-finding, safety, and efficacy assessment (Fig 2).25 The DEEC strategy that proceeds from dose-escalation to expansion cohort as soon as possible offers advantages by enabling the generation of clinical evidence with relatively lower time and cost, while also reducing the complexity of conducting separate studies. For example, both selpercatinib31 and pralsetinib10 took less than 3 years from the start of clinical trials to receiving approval.
FIG 2.
A master protocol for DEECs. Dose escalation includes several typical methodologies designed to determine the optimal dosage. The traditional 3 + 3 and interval 3 + 3 design is widely used, involving the enrollment of three patients at an initial dose level, with the possibility of adding another three if DLTs occur. Advanced adaptive model–based methods, like BOIN, provide a more statistically rigorous framework, assessing dose levels based on predefined safety intervals. Another sophisticated approach, CRM, uses Bayesian modeling to update dose selections in real time, allowing for more accurate and efficient dose optimization. Together, these dose-escalation strategies offer flexibility and precision in identifying safe and effective doses in phase I trials, supporting the seamless transition to dose-expansion cohorts. The DEEC study often uses basket, umbrella, and platform design strategies to efficiently explore a drug's efficacy across multiple cancer subsets. Basket designs focus on a single therapy tested across different cancers or biomarker-defined subgroups, allowing researchers to assess activity in distinct indications simultaneously. By contrast, umbrella designs test multiple therapies within a single type of cancer, with each targeting different molecular alterations, to determine the most effective approach for that cancer. Platform trials further expand this approach by continuously evaluating multiple treatments or combinations against a shared control arm, enabling the seamless addition or removal of therapies based on interim results. These flexible designs facilitate broader exploration of therapeutic potential within dose-expansion cohorts, accelerating the identification of effective targeted treatments for diverse patient populations. BOIN, Bayesian optimal interval design; CDG, cancer drive gene; CRM, continual reassessment method; DEC, dose-expansion cohort; DEEC, dose-escalation and expansion cohort; DLT, dose-limiting toxicity; PK, pharmacokinetics; RP2D, recommended phase II dose.
Designing Strategy for Dose-Escalation Part
A master protocol for DEEC trials incorporates both dose-escalation and dose-expansion cohorts.30 In dose-escalation parts of DEECs supporting above 46 indications for 36 targeted drugs, the 3 + 3 design—including both traditional and interval 3 + 3 design—was frequently used across both antibodies and small compounds such as nivolumab12 and lenvatinib.32
Unlike 3 + 3 methods, adaptive design using a Bayesian framework such as Bayesian optimal interval design (BOIN) and continual reassessment method (CRM) to guide decisions at each dose level aims to keep the probability of dose-limiting toxicity within a predefined optimal range, which requires a more rapid convergence on the maximum tolerated dose (MTD).33-35 BOIN and CRM designs provide more flexibility and statistical efficiency. CRM, for example, adjusts dosing based on accumulating patient response data, often allowing faster and potentially more accurate identification of the MTD.36 The BOIN method combines rule-based and model-based principles, comparing observed toxicity rates with prespecified bounds to streamline escalation and de-escalation decisions, thus potentially reducing the number of participants treated at suboptimal doses.
Although each dose-escalation method has distinct characteristics, the choice of methods primarily depends on the operating preferences of the research center and the sponsor's preferred strategy of dose finding.34 In practice, the choice is not absolute, and different approaches can replace one another. For example, both selpercatinib and pralsetinib are SMKIs targeting rearranged during transfection (RET) gene fusions and were studied in first-in-human trials. Selpercatinib used traditional 3 + 3 design, enrolling 49 participants in dose escalation.31 Differently, pralsetinib used BOIN design, involving 53 participants in dose escalation.10
Designing Strategy for Dose-Expansion Part
In DEECs supporting anticancer drug indications, many used the basket design that enables researchers to gather multicohort data across different indications with the same genetic alteration, thereby surpassing the traditional paradigm that one trial supports only one indication.37 In addition, a smaller subset of DEECs adopted an umbrella design, evaluating different treatments or combinations within the same cancer subtype.37 For example, assessing multiple dosing regimens for a specific cancer helps optimize therapeutic outcomes and reduce adverse effects. The Lung-MAP trial uses an umbrella design to screen patients with lung cancer for specific genetic mutations or biomarkers, grouping them into subgroups based on these characteristics.38 Each subgroup then receives tested treatments tailored to its specific mutation. This design enables multiple therapies to be tested within a single trial framework, offering a more efficient way to assess efficacy of the investigational drugs in new treatments or combination.38
An opinion of “One drug to treat multiple diseases” is increasingly applied in oncology when targeting the same oncogenic alterations across different cancer types. It becomes feasible to explore a targeted therapy across diverse cancer types exhibiting the same genetic mutations.39,40 To efficiently evaluate this therapy, DEECs with basket designs are implemented under a single master protocol. This approach enables simultaneous assessment of drug efficacy in multiple cancer types with the same CDG mutation, such as ALK, ROS1, or RET, accelerating data collection on therapeutic impact and safety across diverse patient populations.40 For example, trials for drugs like larotrectinib18 and entrectinib,41 targeting NTRK gene fusions across various cancers, leveraged basket designs for establishing multidisease cohorts in a master protocol, enabling us to obtain data from different cancer types.
Finally, platform design that is a combination of basket, umbrella, and other trial designs reflects an optimal strategy that leverages each design's strengths to meet specific clinical objectives across a spectrum of cancer types and genetic contexts. For example, the I-SPY2 trial is an adaptive, biomarker-driven platform study designed to evaluate multiple investigational therapies, allowing dynamic entry and exit of treatment arms based on interim efficacy results.42 This integrated approach allowed the study to address diverse trial objectives, such as evaluating different dosages, exploring biomarker-driven subgroups, and assessing combination treatments within a single overarching protocol.42
Designing Each Cohort Within an DEEC Trial
Simon's two-stage design is commonly used in single-arm trials to minimize patient exposure to potentially ineffective treatments.43 Its key feature is a two-step process that allows for early termination if the treatment demonstrates insufficient efficacy in the first stage. An interim analysis was adopted in this design, enabling an optimal sample size determination and reducing the risk of unnecessary exposure44 (Fig 3). For instance, if an initial cohort of Simon's two-stage design does not reach the prespecified efficacy criteria in interim analysis, the trial can be terminated early, conserving resources and sparing patients from ineffective treatments. Conversely, if efficacy criteria are met, the study proceeds to the second stage, often involving an expanded cohort, to validate and strengthen the evidence.
FIG 3.
Simon's two-stage design consists of two distinct stages. Stage I (blue segment): a fixed number of patients are enrolled initially to test the treatment. The primary outcome, such as response rate, is evaluated after this stage. If the results meet a predefined efficacy threshold, the trial progresses to stage II. Otherwise, the trial is stopped early to avoid unnecessary patient exposure to ineffective treatments. Interim analysis (red box): Conducted after stage I to assess whether the trial should continue. This decision point determines if the study advances to stage II or is terminated early. Stage II (yellow segment): If the treatment shows promise, additional patients are recruited. Data from both stages are combined to make a final determination about the treatment's efficacy. Final decision point (arrow): The final analysis includes all enrolled patients to decide whether the treatment warrants further investigation or clinical use. The design aims to balance ethical considerations and resource efficiency by stopping early for futility or proceeding only with promising treatments. It is particularly advantageous in oncology and rare disease studies, where patient populations are limited.
DEEC TRIAL CONDUCT
Unlike RCTs, DEECs lack the blinding and a parallel control group, making them inherently open-label, nonrandomized studies. Without blinding, concurrent parallel control, and random assignment, DEEC evidence could introduce various forms of bias, including information bias (manifesting as investigator and institutional overenthusiasm or expectancy effects), patient selection bias, and confounding bias44,45 (Fig 4).
FIG 4.
Efficacy and safety end points adjudicated by various expert review committees. The CRC is usually part of the study team and possesses a thorough understanding of the protocol and collected data. The IRC, on the other hand, operates independently, reviewing data and reaching decisions through consensus. The BIRC focuses on reviewing medical images without any knowledge of the patient or treatment group. The SRC in adverse event adjudication relies on a multidisciplinary team to monitor and assess the evolving safety profile comprehensively. Nevertheless, a small SA entity, compared with the SRC, offers the advantage of quicker, more efficient adjudication of individual adverse events. BIRC, blinded independent review committee; CRC, central review committee; IRC, independent review committee; SA, safety assessment; SAC, safety assessment committee; SRC, safety review committee.
Moreover, personal investigator evaluation inherently introduces intrareader and inter-reader variability in end point assessments.46 To reduce these risks, it is essential to develop approaches such as centralized, independent, or blinded end point adjudication, where applicable, to enhance the reliability of trial outcomes.47 Typically, it is recommended to establish a committee of multiple professionals to adjudicate the results, ensuring a more accurate and objective assessment. Based on the adjudication levels, committees can be categorized into three types: blinded independent review committee (BIRC), independent review committee (IRC), and central review committee (CRC48; Table 2; Fig 4). The CRC is typically composed of study team members who are directly involved in the trial and familiar with the protocol and patient conditions. Its main advantages include ease of organization, real-time assessment, and relatively low cost. However, the CRC is susceptible to potential information bias stemming from investigator or institutional overenthusiasm. By contrast, an IRC comprises external experts who perform consensus-based assessments independent of the study team. The IRC is often requested by regulatory agencies to ensure more objectivity, transparency, and reduced information bias compared with the CRC. Nevertheless, the IRC involves higher operational costs and requires longer preparation for reviewer training and normalization using end point adjudication manuals or charts. The BIRC further strengthens this independence by maintaining strict blinding to treatment allocations and patient identities, thereby minimizing bias in efficacy evaluation. Compared with the IRC, the BIRC is more costly and requires a longer training period for reviewers. The choice among these committees should be based on the trial design, the nature of the end points, and regulatory expectations as each offers a different balance between contextual understanding, operational feasibility, and methodological rigor.48
TABLE 2.
Glossary of Key Items for DEEC Conduct
No. | Item | Definition |
---|---|---|
1 | CRC | The CRC consists of several radiologists or pathologists to provide professional image end point adjudication and set up quality control for the tissue-based clinical study. Compared with the local review processed on dispersed sites, central review usually provides uniform reader training and ongoing standard management to ensure that the adjudicated end point is accurate with minimized bias49 |
2 | IRC | In an IRC, the radiologic or pathologic reviewers provide an end point adjudication of patient imaging or clinicopathologic measures of responses independent of on-site clinical trial investigators, involving a separate efficacy assessment among a geographically dispersed team of pathologists or radiologists48,49 |
3 | BIRC | In a BIRC, radiology or pathology reviewers are blinded to the patient information and treatment information. The BIRC could set end point adjudication with a double-read model that the medical images are reviewed by two independent readers who are blinded to the investigator assessment, the study treatment, and some predefined clinical information47 |
4 | SRC | The SRC consists of the sponsor clinical study team, a clinical operation representative, and the principal investigators from each active clinical site contributing patients to that cohort. The SRC performs ongoing review and adjudication of SAEs and other safety events throughout the conduct of the study48 |
Abbreviations: BIRC, blinded independent review committee; CRC, central review committee; DEEC, dose-escalation and expansion cohort; IRC, independent review committee; SAEs, serious adverse events; SRC, safety review committee.
Because of their mechanisms of action, anticancer drugs can cause a range of adverse events, often including the inhibition of cell proliferation or the triggering of autoimmune responses through checkpoint blockade.50 When an investigational drug is administered to humans for the first time, evaluating adverse events becomes particularly critical to ensure safety. Thus, this strengthened the identification of adverse events before granting market approval. The safety review committee (SRC) conducts regular reviews of serious safety events and performs additional verification reviews as needed51 (Fig 4). It provides recommendations to the sponsor to help determine whether a group of events or a serious event meets the criteria for safety reporting.52 In addition, adverse events, even for individual cases, can be promptly adjudicated by a small safety assessment (SA) entity that ensures timely evaluation.48,51 Compared with the SRC, the major advantage of a small SA entity is its ability to convene efficiently and quickly, enabling faster analysis and adjudication of outcomes.51 Each type of committee has its own strengths and limitations, and the selection should be based on the specific requirements and circumstances of the trial and the type of safety event being evaluated (Fig 4). Incorporating professional adjudication of efficacy and safety end points enhances the reliability and accuracy of clinical trial evidence for oncology drugs.48
END POINTS IN THE DEEC STUDY TO SUPPORT TARGETED ANTICANCER DRUG APPROVALS
Anticancer drug efficacy can be displayed through the trial end points, with survival time extension being regarded as the gold standard for this purpose.53 Nevertheless, in oncologic trials, surrogate end points are frequently used as the clinical evidence.2 Metrics such as response rate (RR; ie, the percentage of patients whose tumors shrink beyond a defined baseline) and progression-free survival (PFS) are commonly reported as primary end points to support the FDA approval.2,54 In most cases, overall response rate (ORR) and PFS are reasonably likely to predict overall survival (OS). Nevertheless, it is worth noting that anticancer drugs approved based on surrogate end points should supply RCTs with OS outcomes during the postmarket study.1
Among above 46 indications approved based on DEEC evidence, the majority of end points was RR, and the median RR was 46%, exceeding the commonly referenced 30% (Appendix Fig A2).2
REPORTING OF THE DEEC STUDY
There was no reporting guideline for DEEC studies yet. Although DEEC was conducted under a single protocol, reporting practices varied among investigators. Some published both dose escalation and dose expansion in one article, whereas others published them separately. In addition, many trials use a basket design, where a single protocol encompasses different disease cohorts, leading to separate publications for them.31,55 To collect DEEC data, it is essential to refer to the trial protocol and National Clinical Trial (NCT) numbers. In some cases, several indications share the same NCT number as they originate from a single protocol. Despite the flexibility in reporting formats, the underlying design principles and methodologies of these DEEC trials remain consistent.
EXAMPLES OF DEEC-SUPPORTED ANTICANCER DRUGS: SELPERCATINIB AND PRALSETINIB
Selpercatinib31,55 and pralsetinib10,56 as RET inhibitors obtained the FDA AA supported by DEEC studies. The ORR outcome in DEECs supported the approvals of the two drugs for treating RET fusion–positive non–small cell lung cancer (NSCLC).10,31 Both pivotal trials of the two drugs adopted the master protocol of DEEC design, combining the basket approach for NSCLC and medullary thyroid cancer with RET alternation.55,56 Although the two trial design strategies were consistent, there were preferences in specific techniques. During the dose-escalation phase, selpercatinib used a traditional 3 + 3 design.31 Differently, pralsetinib used a BOIN design.10 To determine each cohort design and sample size, selpercatinib used Fleming's two-stage design, whereas pralsetinib used Simon's two-stage adaptive design.10,31 Simon's two-stage design is widely used in oncologic EPCTs to minimize the number of patients exposed to ineffective treatments.43,57 It focuses on early stopping for futility: if a predefined minimal number of responses are not observed in the first stage, the trial is terminated early.43,58 The main goal is to reduce patient exposure to inactive agents. Fleming's two-stage design differs in that it allows for early stopping for both futility and efficacy. It has stricter statistical control and provides a more flexible testing framework by controlling both type I and type II error rates across the two stages.43,58 Fleming's design often requires a slightly larger sample size but enables more robust conclusions. In addition, Simon's designs have two stages, and Fleming's designs can have two or more stages.58 Despite these differences, both approaches were rooted from adaptive conception. For efficacy assessment, selpercatinib used an IRC, whereas pralsetinib used a BIRC. Both studies were very efficient and were conducted over a concise timeframe from 2017 to 2020, with relatively small sample sizes (<150 patients), yet yielding high-quality data sufficient for AA10,31 (Appendix Table A3).
STRENGTHS AND LIMITATION OF DEEC STUDIES
It has been reported that approximately one third of the initial approvals for targeted cancer therapies between 2012 and 2021 were based on DEECs.4 Despite their notable advantages, such as the ability to support the approval of multiple indications within a single study, saving time and resources, DEEC also has inherent limitations. One significant shortcoming is the potential bias in safety and efficacy assessments, which can lead to unreliable results.59 As a result, more than one tenth of the approved indications based on DEEC evidence (five of 46) have been withdrawn from the market (Appendix Table A1 and Fig A3). Some drugs have failed to demonstrate clinical benefit during postmarket studies, leading to their removal from the market.1 The application of DEECs should emphasize leveraging their strengths, while addressing their limitations, particularly the challenge of supplying comprehensive safety and efficacy data. This strategy will help minimize risks and maximize the benefits of DEEC designs.
Strengths in Expedited Clinical Development of Targeted Anticancer Drugs
DEEC-supported approvals of targeted anticancer drugs provide significant benefits, primarily by conserving time and resources and facilitating early access to innovative treatments for the patients. DEEC trials, like those for selpercatinib and pralsetinib, were completed in approximately 3 years.10,31 Four factors may contribute to this efficiency. First, small sample sizes: each indication typically involves 30-200 participants, enabling quick trial completion.7,30 Adaptive design and interim analysis in DEECs allow the study to conclude once predefined end points are met, thereby avoiding unnecessary use of additional participants.60 Second, time saving by seamless design: seamless trial integration eliminates time-consuming processes such as ending one phase and restarting another. Third, multiple indications approved based on one trial: the basket design enables researchers to evaluate a drug's efficacy across multiple cancer types with the same genomic target in a single DEEC trial, thereby minimizing time and resource costs. Completion of a DEEC trial often results in the approval of multiple indications. For instance, BTK mutations are found across various hematologic malignancies.61 This same gene mutation across cancer types underpins the concept of “one drug for multiple diseases,” providing a rationale for the use of targeted therapies across different cancer types that share the same genetic mutations.37 Fourth, trial conducting flexibility: basket trials allow for staggered execution in different indication's cohorts rather than requiring simultaneous progress. This approach enables sponsors to focus resources on securing approval for one indication first before proceeding with others, optimizing resource allocation.
Above all, DEEC's strengths in saving resources have helped alleviate the challenges of clinical research for targeted anticancer drugs. Globally, the fulfillment of a clinical trial after the traditional phases I, II, and III for an anticancer drug often meets hard challenges. The difficulty of conducting these trials is increasing, particularly as the number of investigational drugs continues to rise, while the number of eligible patients for each trial becomes smaller. Particularly, CDGs are found and used as biomarkers to divide patients with cancer into more, but smaller subgroups, rendering recruitment of sufficient eligible participants for sufficiently powered RCTs much more challenging.4 For example, NSCLC patients with RET fusion are only 1.5% of all NSCLCs, leading to RET fusion NSCLC being regarded as rare cancer.31 The number of these recategorized cancer patient populations is small and could make it difficult to match the traditional phase I, phase II, and phase III study designs as before.
Strengths for Promoting Orphan Drug Approvals
In the case of rare cancers, DEEC-supported approval overcomes challenges such as small patient populations and heterogeneous disease biology and addresses the need for rapid, high-quality clinical data. For example, tazemetostat is the first-in-class selective inhibitor of EZH2.23 The pivotal trial (study E7438-G000-101; ClinicalTrials.gov identifier: NCT01897571) for tazemetostat performed an open-label, multicenter, phase I/II study using a DEEC design.23 The approval by the FDA was based on two open-label cohorts (cohort 4: EZH2-mutated FL and cohort 5: EZH2 wild-type FL) in patients with histologically confirmed FL after at least two previous systemic therapies. Efficacy was based on RR and duration of response (DOR) assessed by an IRC.23 The RR in 42 patients with EZH2-mutant FL was 69% (95% CI, 53 to 82), with 12% complete responses and 57% partial response. The median DOR in these patients was 10.9 months. The RR in 53 patients with EZH2 wild-type FL was 34% (95% CI, 22 to 48), with 4% complete responses and 30% partial responses. The median DOR was 13 months.23 Through the DEEC trial, tazemetostat was approved for two indications in rare cancer, with a total sample size of fewer than 100 patients.23
Limitation in SA
DEEC trials typically involve small sample sizes, determined by the efficacy of surrogate end point estimation with sufficient statistical power, often fewer than 100 patients. Many drugs have a low incidence of severe adverse reactions, meaning that some serious adverse events may not be detected during the DEEC trial because of the limited number of participants. Furthermore, safety event rates in DEEC cohorts are assessed only within the treatment group, without a concurrent control group, as in RCTs. Since patients with cancer in trials can experience various safety events, even in placebo groups, it becomes challenging to assess safety, particularly when it comes to identifying adverse drug reactions and determining causality. Cancer drugs approved based on DEEC, such as mobocertinib, were withdrawn by the FDA because of safety concerns.59
Limitation in Predictive Ability of Surrogate End Points
DEEC-supported approvals normally rely on surrogate end points that were deemed reasonably to predict OS or quality of life. The predictive ability of surrogate end points can vary depending on the drug's mechanism of action and the type of cancer being treated.62 In most cases, surrogate end points like ORR strongly correlate with OS, meaning that a higher ORR often predicts a better OS outcome. However, there are exceptions. For drugs targeting immune pathways, such as immune checkpoint inhibitors, surrogate end points like PFS may not always correlate strongly with OS.63 Tivantinib at a dose of 240 mg twice daily was found to improve time to progression in MET-high hepatocellular carcinoma.64,65 However, this positive finding was not supported in the phase III RCT trials. Similar phenomena have been observed with olaratumab in trials for sarcomas.66,67 This highlights the importance of predictive ability of surrogate end points in DEECs. Only a validated surrogate end point could be adopted.
In conclusion, DEECs, such as ASCEND-1,9 ARROW,10 CHECKMATE-040,12 and KEYNOTE-012,11 demonstrated the anticancer activity of investigational drugs, providing critical evidence to support the FDA AA. DEEC trials significantly streamlined the drug development process, saving both time and resources while expediting patient access to innovative therapies. Despite this, growing concerns have emerged regarding the optimal use of DEEC trials for targeted cancer drugs. Unlike RCTs which have reporting guidance and a well-established framework, DEEC lacks reporting guidance and its methodology remains immature. Highlighting the conceptual and structural elements within DEEC design and framework may help clinical investigators improve the quality of DEEC studies and report them more clearly and comprehensively in future research. This review outlines the framework and key characteristics of DEEC design and conduct, addressing a gap in the literature as no study specifically tailored to DEEC structures has been published. After our review of DEEC reporting over the past decade, we recommend that researchers pay particular attention to the following items when reporting DEEC studies:
Specify the dose-escalation methods, such as rule-based (eg, 3 + 3 or interval 3 + 3) or model-assisted (eg, BOIN).
Indicate the structure and rationale of dose-expansion cohorts (eg, basket, umbrella, or platform design).
Explain the multistage cohort design and justify the sample size.
Report the number of interim analyses and stopping rules.
Specify end point adjudication methods, such as the use of an IRC or a BIRC.
Detail safety data monitoring, such as the use of a SRC.
These items are critical for enhancing transparency and reproducibility of DEEC studies. Finally, it is hoped that this study will contribute to the development of a mature methodological framework and reporting guidance in DEECs, ultimately supporting the needs of clinical investigators.
APPENDIX
TABLE A1.
Basic Information of DEEC Data-Based Approvals of Targeted Anticancer Drugs
No. | Drugs | Approval Year | Protocol Name | NCT No. (ClinicalTrials.gov identifier) | Indications' Brief Information |
---|---|---|---|---|---|
1 | Bosutinib | 2012 | STUDY 200 | NCT00261846 | Adult patients with chronic, accelerated, or blast phase Ph+ CML with resistance or intolerance to previous therapy |
2 | Dabrafenib | 2014 | BRF113220 | NCT01072175 | In combination with trametinib, for patients with unresectable or metastatic melanoma with BRAF V600E or V600K mutations |
3 | Ceritinib | 2014 | ASCEND-1 | NCT01283516 | Patients with ALK-positive NSCLC who have progressed on or are intolerant to crizotinib |
4 | Pembrolizumab | 2014 | KEYNOTE-001 | NCT01295827 | For the treatment of patients with unresectable or metastatic melanoma and disease progression after ipilimumab and, if BRAF v600 mutation is positive, a BRAF inhibitor |
5 | Osimertinib | 2015 | AURA extension | NCT01802632 | Patients with metastatic EGFR T790M mutation–positive NSCLC, who have progressed on or after EGFR TKI therapy |
6 | Daratumumab | 2015 | SIRIUS | NCT01985126 | Patients with multiple myeloma who have received at least three previous lines of therapy including a proteasome inhibitor and an immunomodulatory agent or are double refractory to a proteasome inhibitor and an immunomodulatory agent |
7 | Pembrolizumab | 2016 | KEYNOTE-012 | NCT01848834 | For the treatment of patients with recurrent or metastatic squamous cell carcinoma of the head and neck with disease progression on or after platinum-containing chemotherapy |
8 | Durvalumab | 2017 | Study 1108 | NCT01693562 | Patients with locally advanced or metastatic urothelial carcinoma who have disease progression during or after platinum-containing chemotherapy or have disease progression within 12 months of neoadjuvant or adjuvant treatment with platinum-containing chemotherapy (withdrawn on February 19, 2021) |
9 | Nivolumab | 2017 | CHECKMATE-040 | NCT01658878 | For the treatment of adult patients with hepatocellular carcinoma who have been previously treated with sorafenib in combination with ipilimumab (withdrawn on July 23, 2021) |
10 | Enasidenib | 2017 | Study AG221-C-001 | NCT01915498 | Treatment of adult patients with relapsed or refractory AML with an IDH2 mutation as detected by an FDA-approved test |
11 | Lorlatinib | 2018 | Study B7461001 | NCT01970865 | Adult patients with metastatic NSCLC whose tumors are ALK-positive as detected by an FDA-approved test (the restriction for use only in patients whose disease has progressed on crizotinib and at least one other ALK inhibitor for metastatic disease or alectinib or ceritinib as the first ALK inhibitor therapy for metastatic disease) |
12 | Larotrectinib | 2018 | LOXO-TRK-14001, SCOUT, NAVIGATE | NCT02122913, NCT02637687, NCT02576431 | Adult and pediatric patients with solid tumors that have a NTRK gene fusion without a known acquired resistance mutation; are metastatic or where surgical resection is likely to result in severe morbidity, and have no satisfactory alternative treatments or have progressed after treatment |
13 | Cemiplimab-rwlc | 2018 | Study 1540 | NCT02760498 | Patients with metastatic CSCC or locally advanced CSCC who are not candidates for curative surgery or curative radiation |
14 | Ivosidenib | 2018 | Study AG120-C-001 | NCT02074839 | Treatment of adult patients with relapsed or refractory AML with a susceptible IDH1 mutation |
15 | Venetoclax | 2018 | Study M14-358 | NCT02203773 | In combination with azacitidine or decitabine or low-dose cytarabine for the treatment of newly diagnosed AML in adults 75 years or older or who have comorbidities that preclude the use of intensive induction chemotherapy |
16 | Lenvatinib | 2019 | KEYNOTE-146/Study 111 | NCT02501096 | In combination with pembrolizumab, for patients with advanced endometrial carcinoma who have disease progression after previous systemic therapy and are not candidates for curative surgery or radiation |
17 | Entrectinib | 2019 | ALKA, STARTRK-1, STARTRK-2 | NCT02097810, NCT02568267 | Adult patients with metastatic NSCLC whose tumors are ROS1-positive |
18 | Entrectinib | 2019 | ALKA, STARTRK-1, STARTRK-2 | NCT02097810, NCT02568267 | Adult and pediatric patients 12 years and older with solid tumors that have a NTRK gene fusion without a known acquired resistance mutation, are metastatic or where surgical resection is likely to result in severe morbidity, and have progressed after treatment or have no satisfactory alternative therapy |
19 | Pembrolizumab | 2019 | KEYNOTE-028/KEYNOTE-158 | NCT02054806, NCT02628067 | For the treatment of patients with metastatic small cell lung cancer with disease progression on or after platinum-based chemotherapy and at least one other previous line of therapy (withdrawn on March 30, 2021) |
20 | Polatuzumab vedotin | 2019 | GO29365 | NCT02257567 | In combination with bendamustine and a rituximab product for the treatment of adult patients with relapsed or refractory diffuse large B-cell lymphoma, not otherwise specified, after at least two previous therapies |
21 | Ivosidenib | 2019 | Study AG120-C-001 | NCT02074839 | For the treatment of adult patients with newly diagnosed AML, with a susceptible IDH1 mutation as detected by an FDA-approved test, who are 75 years old or who have comorbidities that preclude the use of intensive induction chemotherapy |
22 | Avapritinib | 2020 | BLU-285-1101 (NAVIGATOR) | NCT02508532 | Adults with unresectable or metastatic GIST harboring a PDGFRA exon 18 mutation, including PDGFRA D842V mutations |
23 | Selpercatinib | 2020 | LIBRETTO-001 | NCT03157128 | Patients with RET-mutant MTC who require systemic therapy |
24 | Selpercatinib | 2020 | LIBRETTO-001 | NCT03157128 | Patients with advanced or metastatic RET fusion–positive thyroid cancer who are radioactive iodine–refractory |
25 | Pralsetinib | 2020 | ARROW | NCT03037385 | Patients with advanced or metastatic RET-mutant MTC who require systemic therapy (withdrawn on July 20, 2023) |
26 | Ipilimumab | 2020 | CHECKMATE-040 | NCT01658878 | In combination with nivolumab, for the treatment of patients with HCC who have been previously treated with sorafenib |
27 | Sacituzumab govitecan | 2020 | IMMU-132-01 | NCT01631552 | Treatment of adult patients with mTNBC who have received at least two previous therapies for metastatic disease |
28 | Tazemetostat | 2020 | Study E7438-G000-101 | NCT01897571 | Adult patients with relapsed or refractory FL whose tumors are positive for an EZH2 mutation as detected by an FDA-approved test and who have received at least two previous systemic therapies |
29 | Tazemetostat | 2020 | Study E7438-G000-101 | NCT01897571 | Adult patients with relapsed or refractory FL who have no satisfactory alternative treatment options |
30 | Crizotinib | 2021 | ADVL0912 | NCT00939770 | Relapsed or refractory, systemic ALK-positive ALCL |
31 | Avapritinib | 2021 | Explorer/Pathfinder | NCT02561988, NCT03580655 | Adult patients with AdvSM, including patients with ASM and SM-AHN and MCL |
32 | Sotorasib | 2021 | CodeBreaK 100 | NCT03600883 | Treatment of adult patients with KRAS G12C–mutated locally advanced or metastatic NSCLC, who have received at least one previous systemic therapy |
33 | Mobocertinib | 2021 | AP32788-15-101 | NCT02716116 | Advanced or metastatic NSCLC with EGFR exon 20 insertion mutations, whose disease has progressed on or after platinum-based chemotherapy (withdrawn on July 15, 2024) |
34 | Amivantamab-VMJW | 2021 | CHRYSALIS | NCT02609776 | Adult patients with locally advanced or metastatic NSCLC with EGFR exon 20 insertion mutations, whose disease has progressed on or after platinum-based chemotherapy |
35 | Crizotinib | 2022 | ADVL0912 | NCT00939770 | Pediatric patients with unresectable, recurrent, or refractory IMT that is ALK-positive |
36 | Crizotinib | 2022 | Study A8081013 | NCT01121588 | Adult patients with unresectable, recurrent, or refractory IMT that is ALK-positive |
37 | Selpercatinib | 2022 | LIBRETTO-001 | NCT03157128 | Adult patients with metastatic RET fusion–positive NSCLC |
38 | Mosunetuzumab | 2022 | GO29781 | NCT02500407 | Adult patients with relapsed or refractory FL after two or more lines of systemic therapy |
39 | Adagrasib | 2022 | KRYSTAL-1 | NCT03785249 | Adult patients with KRAS G12C–mutated locally advanced or metastatic NSCLC, who have received at least one previous systemic therapy |
40 | Olutasidenib | 2022 | Study 2102-HEM-101 | NCT02719574 | Adult patients with relapsed or refractory AML with a susceptible IDH1 mutation |
41 | Teclistamab | 2022 | MajesTEC-1 | NCT03145181 | Adult patients with relapsed or refractory multiple myeloma who have received at least four previous lines of therapy, including a proteasome inhibitor, an immunomodulatory agent, and an anti-CD38 monoclonal antibody |
42 | Futibatinib | 2022 | TAS-120-101 | NCT02052778 | Adult patients with previously treated, unresectable, locally advanced, or metastatic intrahepatic cholangiocarcinoma harboring FGFR2 gene fusions or other rearrangements |
43 | Pralsetinib | 2023 | ARROW | NCT03037385 | Adult patients with metastatic RET fusion–positive NSCLC |
44 | Pirtobrutinib | 2023 | BRUIN | NCT03740529 | Adult patients with relapsed or refractory MCL after at least two lines of systemic therapy, including a BTK inhibitor |
45 | Pirtobrutinib | 2023 | BRUIN | NCT03740529 | Adult patients with CLL/SLL who have received at least two previous lines of therapy, including a BTK inhibitor and a BCL-2 inhibitor |
46 | Epcoritamab-bysp | 2023 | EPCORE NHL-1 (study GCT3013-01) | NCT03625037 | Adult patients with relapsed or refractory DLBCL, not otherwise specified, including DLBCL arising from indolent lymphoma, and high-grade B-cell lymphoma after two or more lines of systemic therapy |
Abbreviations: AdvSM, advanced systemic mastocytosis; ALCL, anaplastic large cell lymphoma; ALK, anaplastic lymphoma kinase; ASM, aggressive systemic mastocytosis; CLL/SLL, chronic lymphocytic leukemia or small lymphocytic lymphoma; CML, chronic myelogenous leukemia; CSCC, cutaneous squamous cell carcinoma; DLBCL, diffuse large B-cell lymphoma; EGFR, epidermal growth factor receptor; EZH2, enhancer of zeste homolog 2; FDA, US Food and Drug Administration; FGFR2, fibroblast growth factor receptor 2; FL, follicular lymphoma; GIST, GI stromal tumor; HCC, hepatocellular carcinoma; IDH1, isocitrate dehydrogenase-1; IDH2, isocitrate dehydrogenase-2; IMT, inflammatory myofibroblastic tumor; MCL, mantle cell lymphoma; MTC, medullary thyroid cancer; mTNBC, metastatic triple-negative breast cancer; NCT, National Clinical Trial; NSCLC, non–small cell lung cancer; NTRK, neurotrophic receptor tyrosine kinase; PDGFRA, platelet-derived growth factor receptor alpha; RET, rearranged during transfection; SM-AHN, systemic mastocytosis with an associated hematologic neoplasm; TKI, tyrosine kinase inhibitor.
TABLE A2.
Molecular Target Distribution of Targeted Anticancer Drugs Approved Based on DEEC Evidence
Category | Molecular Target and Drug |
---|---|
SMKIs: 19 drugs |
ALK inhibitors: ceritinib, crizotinib, entrectinib, lorlatinib BRAF inhibitors: dabrafenib BTK inhibitors: pirtobrutinib EGFR inhibitors: afatinib, mobocertinib, osimertinib KRAS G12C inhibitors: adagrasib, sotorasib MEK inhibitors: trametinib NTRK inhibitors: larotrectinib RET inhibitors: pralsetinib, selpercatinib Other kinases: avapritinib, bosutinib, futibatinib, lenvatinib |
Enzyme inhibitors: five drugs |
BCL-2 protein inhibitor: venetoclax EZH2 inhibitor: tazemetostat IDH1 inhibitor: ivosidenib, olutasidenib IDH2 inhibitor: enasidenib |
mAbs: six drugs | PD-1/PD-L1 blockers: cemiplimab-rwlc, durvalumab, nivolumab, pembrolizumab CD38 inhibitor: daratumumab CTLA-4 blocker: ipilimumab |
ADCs: two drugs | CD79b inhibitor: polatuzumab vedotin-piiq Trop-2 inhibitor: sacituzumab govitecan-hziy |
Bispecific antibodies: four drugs |
BCMA and CD3 inhibitors: teclistamab-cqyv CD20 and CD3 inhibitors: EGFR and MET inhibitor: amivantamab-VMJW, epcoritamab-bysp, mosunetuzumab-axgb |
Abbreviations: ADCs, antibody-drug conjugates; ALK, anaplastic lymphoma kinase; DEEC, dose-escalation and expansion cohort; EGFR, epidermal growth factor receptor; EZH2, enhancer of zeste homolog 2; IDH1, isocitrate dehydrogenase-1; IDH2, isocitrate dehydrogenase-2; mAbs, mono-specific antibodies; NTRK, neurotrophic receptor tyrosine kinase; RET, rearranged during transfection; SMKIs, small molecular kinase inhibitors.
TABLE A3.
DEEC Trials for Selpercatinib and Pralsetinib in RET Fusion NSCLC Indication
Drug | Selpercatinib | Pralsetinib |
---|---|---|
Target | Ret alternation | |
Trial name | LIBRETTO-001 (ClinicalTrials.gov identifier: NCT03157128) | ARROW (ClinicalTrials.gov identifier: NCT03037385) |
Trial design | Dose-escalation and dose-expansion cohort trial | |
Dose escalation | 3 + 3 | BOIN |
Master protocol | Basket design for patients with NSCLC and MTC | Basket design for patients with NSCLC and MTC |
Each cohort design | Fleming's two-stage design | Simon's two-stage adaptive design |
Indication | RET fusion NSCLC | |
Primary end point | ORR | ORR |
Efficacy assessment | IRC | Blinded IRC |
Safety assessment | Safe review committee | Safe review committee |
Trial during time | From 2017 to 2020 | From 2017 to 2020 |
Approval pathway | Accelerated approval | Accelerated approval |
Line of therapy | First-line therapy | First-line therapy |
Sample size | 39 treatment-naïve patients 105 previously treated patients |
27 treatment-naïve patients 92 previously treated patients |
ORR by IRC in RET fusion NSCLC | 85% in treatment-naïve patients 64% in previously treated patients |
70% in treatment-naïve patients 61% in previously treated patients |
Abbreviations: BOIN, Bayesian optimal interval design; DEEC, dose-escalation and expansion cohort; IRC, independent review committee; MTC, medullary thyroid cancer; NSCLC, non–small cell lung cancer; ORR, overall response rate; RET, rearranged during transfection.
FIG A1.
The FDA approved targeted cancer drug indications on the basis of DEEC. Information of the original and supplemental applications was searched from the Drugs@FDA database.68 Only the indications of targeted cancer drugs were included. The full PI and the FDA medical reviews were then examined to determine whether each targeted cancer drug indication was approved on the basis of the study design of DEEC by two reviewers (T.Z. and J.Z.). Discrepancies were resolved by consensus. Using the NCT numbers listed in the PI, ClinicalTrials.gov and PubMed were searched by a third reviewer (Y.H.) to cross-verify the study design of the supporting pivotal trials of each indication. We excluded the indications approved on the basis of randomized controlled trial or single-arm trials that were not seamlessly integrated with dose escalation. Finally, a total of 46 indications approved on the basis of DEEC were identified. DEEC, dose-escalation and expansion cohort; FDA, US Food and Drug Administration; NCT, National Clinical Trial; PI, prescribing information.
FIG A2.
RRs of FDA-approved anticancer drugs based on DEEC evidence. The chart shows the RR percentages for 46 indications, with a median RR of 46% (indicated by the red line). These data highlight the robust efficacy of targeted therapies and immune checkpoint inhibitors approved through DEECs. ALCL, anaplastic large cell lymphoma; DEEC, dose-escalation and expansion cohort; EZH2, enhancer of zeste homolog 2; FDA, US Food and Drug Administration; FL, follicular lymphoma; IMT, inflammatory myofibroblastic tumor; MTC, medullary thyroid cancer; NSCLC, non–small cell lung cancer; RR, response rate; SCCHN, squamous cell carcinoma of head and neck; SCLC, small cell lung cancer.
FIG A3.
The regulatory status of anticancer drugs approved by the FDA on the basis of DEEC evidence. Column 1 (left) lists the names of specific drugs approved based on DEEC. Column 2 categorizes drugs based on their mechanism of action, such as SMKI and ADC. Column 3 indicates the type of cancer or disease treated. Column 4: The regulatory status of drug approval. “Regular approval”: drugs that successfully went through the standard approval process from the beginning (without undergoing AA); “AA converted”: drugs that successfully transitioned from AA to regular approval; “AA not converted”: drugs that remain under AA without transition; “AA withdrawn”: drugs whose AA was withdrawn. AA, accelerated approval; ADC, antibody-drug conjugate; DEEC, dose-escalation and expansion cohort; ESCC, esophageal squamous cell carcinoma; FDA, US Food and Drug Administration; GEJ, gastroesophageal junction; GIST, GI stromal tumor; HCC, hepatocellular carcinoma; NSCLC, non–small cell lung cancer; SCCHN, squamous cell carcinoma of head and neck; SCLC, small cell lung cancer; SMKI, small-molecule kinase inhibitors.
SUPPORT
Supported by the National Natural Science Foundation of China (82104133).
DATA SHARING STATEMENT
The data sets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
AUTHOR CONTRIBUTIONS
Conception and design: Yafang Huang
Collection and assembly of data: Yafang Huang, Ting Zhu, Jinjia Zhong
Data analysis and interpretation: All authors
Manuscript writing: All authors
Final approval of manuscript: All authors
Accountable for all aspects of the work: All authors
AUTHORS' DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST
The following represents disclosure information provided by authors of this manuscript. All relationships are considered compensated unless otherwise noted. Relationships are self-held unless noted. I = Immediate Family Member, Inst = My Institution. Relationships may not relate to the subject matter of this manuscript. For more information about ASCO's conflict of interest policy, please refer to www.asco.org/rwc or ascopubs.org/po/author-center.
Open Payments is a public database containing information reported by companies about payments made to US-licensed physicians (Open Payments).
No potential conflicts of interest were reported.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data sets used and/or analyzed during the current study are available from the corresponding author on reasonable request.