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Westphalen CB, Martins-Branco D, Beal JR, Cardone C, Coleman N, Schram AM, Halabi S, Michiels S, Yap C, André F, Bibeau F, Curigliano G, Garralda E, Kummar S, Kurzrock R, Limaye S, Loges S, Marabelle A, Marchió C, Mateo J, Rodon J, Spanic T, Pentheroudakis G, Subbiah V. The ESMO Tumour-Agnostic Classifier and Screener (ETAC-S): a tool for assessing tumour-agnostic potential of molecularly guided therapies and for steering drug development. Ann Oncol 2024:S0923-7534(24)01519-9. [PMID: 39187421 DOI: 10.1016/j.annonc.2024.07.730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2024] [Revised: 07/19/2024] [Accepted: 07/29/2024] [Indexed: 08/28/2024] Open
Abstract
BACKGROUND Advances in precision oncology led to approval of tumour-agnostic molecularly guided treatment options (MGTOs). The minimum requirements for claiming tumour-agnostic potential remain elusive. METHODS The European Society for Medical Oncology (ESMO) Precision Medicine Working Group (PMWG) coordinated a project to optimise tumour-agnostic drug development. International experts examined and summarised the publicly available data used for regulatory assessment of the tumour-agnostic indications approved by the US Food and Drug Administration and/or the European Medicines Agency as of December 2023. Different scenarios of minimum objective response rate (ORR), number of tumour types investigated, and number of evaluable patients per tumour type were assessed for developing a screening tool for tumour-agnostic potential. This tool was tested using the tumour-agnostic indications approved during the first half of 2024. A taxonomy for MGTOs and a framework for tumour-agnostic drug development were conceptualised. RESULTS Each tumour-agnostic indication had data establishing objective response in at least one out of five patients (ORR ≥ 20%) in two-thirds (≥4) of the investigated tumour types, with at least five evaluable patients in each tumour type. These minimum requirements were met by tested indications and may serve as a screening tool for tumour-agnostic potential, requiring further validation. We propose a conceptual taxonomy classifying MGTOs based on the therapeutic effect obtained by targeting a driver molecular aberration across tumours and its modulation by tumour-specific biology: tumour-agnostic, tumour-modulated, or tumour-restricted. The presence of biology-informed mechanistic rationale, early regulatory advice, and adequate trial design demonstrating signs of biology-driven tumour-agnostic activity, followed by confirmatory evidence, should be the principles for tumour-agnostic drug development. CONCLUSION The ESMO Tumour-Agnostic Classifier (ETAC) focuses on the interplay of targeted driver molecular aberration and tumour-specific biology modulating the therapeutic effect of MGTOs. We propose minimum requirements to screen for tumour-agnostic potential (ETAC-S) as part of tumour-agnostic drug development. Definition of ETAC cut-offs is warranted.
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Affiliation(s)
- C B Westphalen
- Comprehensive Cancer Center Munich & Department of Medicine III, University Hospital, LMU Munich, Munich; German Cancer Consortium (DKTK), partner site Munich, German Cancer Research Center (DKFZ), Heidelberg, Germany.
| | - D Martins-Branco
- Scientific and Medical Division, European Society for Medical Oncology (ESMO), Lugano, Switzerland
| | - J R Beal
- Hospital Israelita Albert Einstein, Sao Paulo, Brazil
| | - C Cardone
- Experimental Clinical Abdominal Oncology Unit, Istituto Nazionale Tumori- IRCCS-Fondazione G. Pascale, Naples, Italy
| | - N Coleman
- School of Medicine, Trinity College Dublin, Dublin; Medical Oncology Department, St. James's Hospital, Dublin; Trinity St. James's Cancer Institute, Dublin, Ireland
| | - A M Schram
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York City; Weill Cornell Medical College, New York City
| | - S Halabi
- Department of Biostatistics and Bioinformatics, Duke University, Durham; Duke Cancer Institute, Duke University, Durham, USA
| | - S Michiels
- Oncostat U1018, Inserm, Université Paris-Saclay, labeled Ligue Contre le Cancer, Villejuif; Service de Biostatistique et Epidémiologie, Gustave Roussy, Villejuif, France
| | - C Yap
- Clinical Trials and Statistics Unit, The Institute of Cancer Research, London, UK
| | - F André
- INSERM U981, Gustave Roussy, Villejuif; Department of Cancer Medicine, Gustave Roussy, Villejuif; Faculty of Medicine, Université Paris-Saclay, Kremlin Bicêtre
| | - F Bibeau
- Service d'Anatomie Pathologique, CHU Besançon, Université de Bourgogne Franche-Comté, Besançon, France
| | - G Curigliano
- Istituto Europeo di Oncologia, IRCCS, Milan; Department of Oncology and Hemato-Oncology, University of Milano, Milan, Italy
| | - E Garralda
- Vall d'Hebron Institute of Oncology (VHIO), Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - S Kummar
- Division of Hematology and Medical Oncology, Department of Medicine, Knight Cancer Institute, Oregon Health and Science University, Portland
| | - R Kurzrock
- Department of Medicine, Medical College of Wisconsin Cancer Center, Milwaukee, USA
| | - S Limaye
- Medical & Precision Oncology, Sir H. N. Reliance Foundation Hospital & Research Centre, Mumbai, India
| | - S Loges
- DKFZ-Hector Cancer Institute at the University Medical Center Mannheim, Department of Personalized Oncology, University Hospital Mannheim, Medical Faculty Mannheim, University of Heidelberg, Mannheim; Division of Personalized Medical Oncology (A420), German Cancer Research Center (DKFZ), German Center for Lung Research (DZL), German Cancer Consortium (DKTK), Heidelberg, Germany
| | - A Marabelle
- Drug Development Department (DITEP) and Laboratory for Translational Research in Immunotherapy (LRTI), Gustave Roussy, INSERM U1015 & CIC1428, Université Paris-Saclay, Villejuif, France
| | - C Marchió
- Department of Medical Sciences, University of Turin, Turin; Division of Pathology, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
| | - J Mateo
- Vall d'Hebron Institute of Oncology (VHIO), Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - J Rodon
- Department of Investigational Cancer Therapeutics, UT MD Anderson, Houston, USA
| | - T Spanic
- Europa Donna Slovenia, Ljubljana, Slovenia
| | - G Pentheroudakis
- Scientific and Medical Division, European Society for Medical Oncology (ESMO), Lugano, Switzerland
| | - V Subbiah
- Early-Phase Drug Development, Sarah Cannon Research Institute (SCRI), Nashville, USA
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2
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Kerioui M, Iasonos A, Gönen M, Arfé A. New clinical trial design borrowing information across patient subgroups based on fusion-penalized regression models. Stat Methods Med Res 2024:9622802241267355. [PMID: 39158499 DOI: 10.1177/09622802241267355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/20/2024]
Abstract
In cancer research, basket trials aim to assess the efficacy of a drug using baskets, wherein patients are organized into subgroups according to their tumor type. In this context, using information borrowing strategy may increase the probability of detecting drug efficacy in active baskets, by shrinking together the estimates of the parameters characterizing the drug efficacy in baskets with similar drug activity. Here, we propose to use fusion-penalized logistic regression models to borrow information in the setting of a phase 2 single-arm basket trial with binary outcome. We describe our proposed strategy and assess its performance via a simulation study. We assessed the impact of heterogeneity in drug efficacy, prevalence of each tumor types and implementation of interim analyses on the operating characteristics of our proposed design. We compared our approach with two existing designs, relying on the specification of prior information in a Bayesian framework to borrow information across similar baskets. Notably, our approach performed well when the effect of the drug varied greatly across the baskets. Our approach offers several advantages, including limited implementation efforts and fast computation, which is essential when planning a new trial as such planning requires intensive simulation studies.
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Affiliation(s)
- Marion Kerioui
- Department of Biostatistics, Memorial Sloan Kettering Cancer Centre, New York, NY, USA
| | - Alexia Iasonos
- Department of Biostatistics, Memorial Sloan Kettering Cancer Centre, New York, NY, USA
| | - Mithat Gönen
- Department of Biostatistics, Memorial Sloan Kettering Cancer Centre, New York, NY, USA
| | - Andrea Arfé
- Department of Biostatistics, Memorial Sloan Kettering Cancer Centre, New York, NY, USA
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3
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Thein KZ, Myat YM, Park BS, Panigrahi K, Kummar S. Target-Driven Tissue-Agnostic Drug Approvals-A New Path of Drug Development. Cancers (Basel) 2024; 16:2529. [PMID: 39061168 PMCID: PMC11274498 DOI: 10.3390/cancers16142529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2024] [Accepted: 07/06/2024] [Indexed: 07/28/2024] Open
Abstract
The regulatory approvals of tumor-agnostic therapies have led to the re-evaluation of the drug development process. The conventional models of drug development are histology-based. On the other hand, the tumor-agnostic drug development of a new drug (or combination) focuses on targeting a common genomic biomarker in multiple cancers, regardless of histology. The basket-like clinical trials with multiple cohorts allow clinicians to evaluate pan-cancer efficacy and toxicity. There are currently eight tumor agnostic approvals granted by the Food and Drug Administration (FDA). This includes two immune checkpoint inhibitors, and five targeted therapy agents. Pembrolizumab is an anti-programmed cell death protein-1 (PD-1) antibody that was the first FDA-approved tumor-agnostic treatment for unresectable or metastatic microsatellite instability-high (MSI-H) or deficient mismatch repair (dMMR) solid tumors in 2017. It was later approved for tumor mutational burden-high (TMB-H) solid tumors, although the TMB cut-off used is still debated. Subsequently, in 2021, another anti-PD-1 antibody, dostarlimab, was also approved for dMMR solid tumors in the refractory setting. Patients with fusion-positive cancers are typically difficult to treat due to their rare prevalence and distribution. Gene rearrangements or fusions are present in a variety of tumors. Neurotrophic tyrosine kinase (NTRK) fusions are present in a range of pediatric and adult solid tumors in varying frequency. Larotrectinib and entrectinib were approved for neurotrophic tyrosine kinase (NTRK) fusion-positive cancers. Similarly, selpercatinib was approved for rearranged during transfection (RET) fusion-positive solid tumors. The FDA approved the first combination therapy of dabrafenib, a B-Raf proto-oncogene serine/threonine kinase (BRAF) inhibitor, plus trametinib, a mitogen-activated protein kinase (MEK) inhibitor for patients 6 months or older with unresectable or metastatic tumors (except colorectal cancer) carrying a BRAFV600E mutation. The most recent FDA tumor-agnostic approval is of fam-trastuzumab deruxtecan-nxki (T-Dxd) for HER2-positive solid tumors. It is important to identify and expeditiously develop drugs that have the potential to provide clinical benefit across tumor types.
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Affiliation(s)
- Kyaw Z. Thein
- Division of Hematology and Medical Oncology, Comprehensive Cancer Centers of Nevada—Central Valley, 3730 S Eastern Ave, Las Vegas, NV 89169, USA
- Department of Medicine, Kirk Kerkorian School of Medicine, University of Nevada Las Vegas (UNLV), 4505 S, Maryland Pkwy, Las Vegas, NV 89154, USA
- College of Osteopathic Medicine, Touro University Nevada, Touro College and University System, 874 American Pacific Dr, Henderson, NV 89014, USA
| | - Yin M. Myat
- Belfield Campus, University College Dublin (UCD) School of Medicine, D04 V1W8 Dublin, Ireland;
- Department of Internal Medicine, One Brooklyn Health—Interfaith Medical Center Campus, 1545, Atlantic Avenue, Brooklyn, NY 11213, USA;
| | - Byung S. Park
- OHSU-PSU School of Public Health, Portland, OR 97201, USA;
- Biostatistics Shared Resource, OHSU Knight Cancer Institute, OHSU School of Medicine, Portland, OR 97239, USA
| | - Kalpana Panigrahi
- Department of Internal Medicine, One Brooklyn Health—Interfaith Medical Center Campus, 1545, Atlantic Avenue, Brooklyn, NY 11213, USA;
| | - Shivaani Kummar
- Division of Hematology & Medical Oncology, Center for Experimental Therapeutics, Knight Cancer Institute, Oregon Health & Science University, 3181 SW Sam Jackson Park Rd., Portland, OR 97239, USA;
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4
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Zhou T, Ji Y. Bayesian Methods for Information Borrowing in Basket Trials: An Overview. Cancers (Basel) 2024; 16:251. [PMID: 38254740 PMCID: PMC10813856 DOI: 10.3390/cancers16020251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 12/22/2023] [Accepted: 01/03/2024] [Indexed: 01/24/2024] Open
Abstract
Basket trials allow simultaneous evaluation of a single therapy across multiple cancer types or subtypes of the same cancer. Since the same treatment is tested across all baskets, it may be desirable to borrow information across them to improve the statistical precision and power in estimating and detecting the treatment effects in different baskets. We review recent developments in Bayesian methods for the design and analysis of basket trials, focusing on the mechanism of information borrowing. We explain the common components of these methods, such as a prior model for the treatment effects that embodies an assumption of exchangeability. We also discuss the distinct features of these methods that lead to different degrees of borrowing. Through simulation studies, we demonstrate the impact of information borrowing on the operating characteristics of these methods and discuss its broader implications for drug development. Examples of basket trials are presented in both phase I and phase II settings.
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Affiliation(s)
- Tianjian Zhou
- Department of Statistics, Colorado State University, Fort Collins, CO 80523, USA
| | - Yuan Ji
- Department of Public Health Sciences, University of Chicago, Chicago, IL 60637, USA
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5
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Subbiah V, Burris HA, Kurzrock R. Revolutionizing cancer drug development: Harnessing the potential of basket trials. Cancer 2024; 130:186-200. [PMID: 37934000 DOI: 10.1002/cncr.35085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 09/16/2023] [Accepted: 09/18/2023] [Indexed: 11/08/2023]
Abstract
The landscape of cancer therapy has been transformed by advances in clinical next-generation sequencing, genomically targeted therapies, and immunotherapies. Well designed clinical trials and efficient clinical trial conduct are crucial for advancing our understanding of cancer, improving patient outcomes, and identifying personalized treatments. Basket trials have emerged as one of the efficient modern clinical trial designs that evaluate the efficacy of these therapies across multiple cancer types based on specific molecular alterations or biomarkers, irrespective of histology or anatomic location. This review delves into the evolution of basket trials in cancer drug development, highlighting their potential prospects and current obstacles. The design of basket trials involves screening patients for specific molecular alterations or biomarkers and enrolling them in the trial to receive the targeted therapy under investigation. Statistical considerations play a crucial role in the design, analysis, and interpretation of basket trials. Several notable examples of basket trials that have led to US Food and Drug Administration approval for uncommon molecular alterations (e.g., NTRK fusions, BRAF mutations, RET and FGFR1 alterations) are discussed, including LOXO-TRK (ClinicalTrials.gov identifier NCT02122913)/SCOUT (ClinicalTrials.gov identifier NCT02637687)/NAVIGATE (ClinicalTrials.gov identifier NCT02576431)/STARTRK (ClinicalTrials.gov identifiers NT02097810, NT02568267), VE-BASKET (ClinicalTrials.gov identifier NCT01524978), ROAR Basket (ClinicalTrials.gov identifier NCT02034110), LIBRETTO-001 (ClinicalTrials.gov identifier NCT03157128), ARROW (ClinicalTrials.gov identifier NCT03037385), FIGHT-203 (ClinicalTrials.gov identifier NCT03011372), and the National Cancer Institute-Molecular Analysis for Therapy Choice trial (ClinicalTrials.gov identifier NCT02465060). Basket trials have the potential to revolutionize cancer treatment by identifying effective therapies for patients based on specific molecular alterations or biomarkers rather than traditional histology-based approaches. PLAIN LANGUAGE SUMMARY: To gain more knowledge about cancer, improve patient outcomes, and discover personalized treatments, it is crucial to conduct clinical trials efficiently. One effective type of clinical trial is called a basket trial. In basket trials, new treatments are tested on various types of cancer, regardless of their location in the body; instead, researchers focus on specific abnormalities in the cancer cells. Basket trials offer hope that we can find personalized treatments that are more effective for each individual battling cancer.
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Grants
- Boehringer Ingelheim, Debiopharm, Foundation Medicine, Genentech, Grifols, Guardant, Incyte, Konica Minolta, Medimmune, Merck Serono, Omniseq, Pfizer, Sequenom, Takeda, and TopAlliance and from the NCI
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Affiliation(s)
- Vivek Subbiah
- Sarah Cannon Research Institute, Nashville, Tennessee, USA
| | | | - Razelle Kurzrock
- Department of Medicine, Medical College of Wisconsin Cancer Center and Genome Sciences and Precision Medicine Center, Milwaukee, Wisconsin, USA
- WIN Consortium, Paris, France
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6
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Asano J, Sato H, Hirakawa A. Practical basket design for binary outcomes with control of family-wise error rate. BMC Med Res Methodol 2023; 23:52. [PMID: 36849940 PMCID: PMC9972792 DOI: 10.1186/s12874-023-01872-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 02/20/2023] [Indexed: 03/01/2023] Open
Abstract
BACKGROUND A basket trial is a type of clinical trial in which eligibility is based on the presence of specific molecular characteristics across subpopulations with different cancer types. The existing basket designs with Bayesian hierarchical models often improve the efficiency of evaluating therapeutic effects; however, these models calibrate the type I error rate based on the results of simulation studies under various selected scenarios. The theoretical control of family-wise error rate (FWER) is important for decision-making regarding drug approval. METHODS In this study, we propose a new Bayesian two-stage design with one interim analysis for controlling FWER at the target level, along with the formulations of type I and II error rates. Since the difficulty lies in the complexity of the theoretical formulation of the type I error rate, we devised the simulation-based method to approximate the type I error rate. RESULTS The proposed design enabled adjustment of the cutoff value to control the FWER at the target value in the final analysis. The simulation studies demonstrated that the proposed design can be used to control the well-approximated FWER below the target value even in situations where the number of enrolled patients differed among subpopulations. CONCLUSIONS The accrual number of patients is sometimes unable to reach the pre-defined value; therefore, existing basket designs may not ensure defined operating characteristics before beginning the trial. The proposed design that enables adjustment of the cutoff value to control FWER at the target value based on the results in the final analysis would be a better alternative.
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Affiliation(s)
- Junichi Asano
- Biostatistics Group, Center for Product Evaluation, Pharmaceuticals and Medical Devices Agency, Tokyo, Japan
- Department of Clinical Biostatistics, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Hiroyuki Sato
- Department of Clinical Biostatistics, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Akihiro Hirakawa
- Department of Clinical Biostatistics, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
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7
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Ouma LO, Grayling MJ, Wason JMS, Zheng H. Bayesian modelling strategies for borrowing of information in randomised basket trials. J R Stat Soc Ser C Appl Stat 2022; 71:2014-2037. [PMID: 36636028 PMCID: PMC9827857 DOI: 10.1111/rssc.12602] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 09/01/2022] [Indexed: 02/01/2023]
Abstract
Basket trials are an innovative precision medicine clinical trial design evaluating a single targeted therapy across multiple diseases that share a common characteristic. To date, most basket trials have been conducted in early-phase oncology settings, for which several Bayesian methods permitting information sharing across subtrials have been proposed. With the increasing interest of implementing randomised basket trials, information borrowing could be exploited in two ways; considering the commensurability of either the treatment effects or the outcomes specific to each of the treatment groups between the subtrials. In this article, we extend a previous analysis model based on distributional discrepancy for borrowing over the subtrial treatment effects ('treatment effect borrowing', TEB) to borrowing over the subtrial groupwise responses ('treatment response borrowing', TRB). Simulation results demonstrate that both modelling strategies provide substantial gains over an approach with no borrowing. TRB outperforms TEB especially when subtrial sample sizes are small on all operational characteristics, while the latter has considerable gains in performance over TRB when subtrial sample sizes are large, or the treatment effects and groupwise mean responses are noticeably heterogeneous across subtrials. Further, we notice that TRB, and TEB can potentially lead to different conclusions in the analysis of real data.
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Affiliation(s)
- Luke O. Ouma
- Population Health Sciences InstituteNewcastle UniversityNewcastle upon TyneUK
| | - Michael J. Grayling
- Population Health Sciences InstituteNewcastle UniversityNewcastle upon TyneUK
| | - James M. S. Wason
- Population Health Sciences InstituteNewcastle UniversityNewcastle upon TyneUK
| | - Haiyan Zheng
- MRC Biostatistics UnitUniversity of CambridgeCambridgeUK
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Kaizer A, Zabor E, Nie L, Hobbs B. 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. PLoS One 2022; 17:e0272367. [PMID: 35917296 PMCID: PMC9345361 DOI: 10.1371/journal.pone.0272367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 07/18/2022] [Indexed: 11/18/2022] Open
Abstract
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.
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Affiliation(s)
- Alexander Kaizer
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado-Anschutz Medical Campus, Aurora, CO, United States of America
| | - Emily Zabor
- Department of Quantitative Health Sciences & Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, United States of America
| | - Lei Nie
- Division of Biometrics II, Office of Biostatistics, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, United States of America
| | - Brian Hobbs
- Department of Population Health, University of Texas-Austin, Austin, TX, United States of America
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9
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Ji Z, Wolfson J. A flexible Bayesian framework for individualized inference via adaptive borrowing. Biostatistics 2022:6506241. [PMID: 35024790 DOI: 10.1093/biostatistics/kxab051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Revised: 12/08/2021] [Accepted: 12/14/2021] [Indexed: 11/15/2022] Open
Abstract
The explosion in high-resolution data capture technologies in health has increased interest in making inferences about individual-level parameters. While technology may provide substantial data on a single individual, how best to use multisource population data to improve individualized inference remains an open research question. One possible approach, the multisource exchangeability model (MEM), is a Bayesian method for integrating data from supplementary sources into the analysis of a primary source. MEM was originally developed to improve inference for a single study by asymmetrically borrowing information from a set of similar previous studies and was further developed to apply a more computationally intensive symmetric borrowing in the context of basket trial; however, even for asymmetric borrowing, its computational burden grows exponentially with the number of supplementary sources, making it unsuitable for applications where hundreds or thousands of supplementary sources (i.e., individuals) could contribute to inference on a given individual. In this article, we propose the data-driven MEM (dMEM), a two-stage approach that includes both source selection and clustering to enable the inclusion of an arbitrary number of sources to contribute to individualized inference in a computationally tractable and data-efficient way. We illustrate the application of dMEM to individual-level human behavior and mental well-being data collected via smartphones, where our approach increases individual-level estimation precision by 84% compared with a standard no-borrowing method and outperforms recently proposed competing methods in 80% of individuals.
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Affiliation(s)
- Ziyu Ji
- Division of Biostatistics, School of Public Health, University of Minnesota, 420 Delaware St.SE, Minneapolis, MN 55455, USA
| | - Julian Wolfson
- Division of Biostatistics, School of Public Health, University of Minnesota, 420 Delaware St.SE, Minneapolis, MN 55455, USA
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10
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Pestana RC, Sen S, Hobbs BP, Hong DS. Histology-agnostic drug development - considering issues beyond the tissue. Nat Rev Clin Oncol 2020; 17:555-568. [PMID: 32528101 DOI: 10.1038/s41571-020-0384-0] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/29/2020] [Indexed: 12/25/2022]
Abstract
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.
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Affiliation(s)
- Roberto Carmagnani Pestana
- Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Centro de Oncologia e Hematologia Einstein Familia Dayan-Daycoval, Hospital Israelita Albert Einstein, São Paulo, Brazil
| | - Shiraj Sen
- Sarah Cannon Research Institute, Denver, CO, USA
| | - Brian P Hobbs
- Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA
| | - David S Hong
- Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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