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Cherlin S, Wason JMS. Cross-validated risk scores adaptive enrichment (CADEN) design. Contemp Clin Trials 2024; 144:107620. [PMID: 38977178 DOI: 10.1016/j.cct.2024.107620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 07/01/2024] [Accepted: 07/03/2024] [Indexed: 07/10/2024]
Abstract
We propose a Cross-validated ADaptive ENrichment design (CADEN) in which a trial population is enriched with a subpopulation of patients who are predicted to benefit from the treatment more than an average patient (the sensitive group). This subpopulation is found using a risk score constructed from the baseline (potentially high-dimensional) information about patients. The design incorporates an early stopping rule for futility. Simulation studies are used to assess the properties of CADEN against the original (non-enrichment) cross-validated risk scores (CVRS) design which constructs a risk score at the end of the trial. We show that when there exists a sensitive group of patients, CADEN achieves a higher power and a reduction in the expected sample size compared to the CVRS design. We illustrate the application of the design in two real clinical trials. We conclude that the new design offers improved statistical efficiency over the existing non-enrichment method, as well as increased benefit to patients. The method has been implemented in an R package caden.
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Affiliation(s)
- Svetlana Cherlin
- Population Health Sciences Institute, Newcastle University, Baddiley-Clark Building, Newcastle upon Tyne, UK.
| | - James M S Wason
- Population Health Sciences Institute, Newcastle University, Baddiley-Clark Building, Newcastle upon Tyne, UK
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2
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Ameri P, Mercurio V, Pollesello P, Anker MS, Backs J, Bayes-Genis A, Borlaug BA, Burkhoff D, Caravita S, Chan SY, de Man F, Giannakoulas G, González A, Guazzi M, Hassoun PM, Hemnes AR, Maack C, Madden B, Melenovsky V, Müller OJ, Papp Z, Pullamsetti SS, Rainer PP, Redfield MM, Rich S, Schiattarella GG, Skaara H, Stellos K, Tedford RJ, Thum T, Vachiery JL, van der Meer P, Van Linthout S, Pruszczyk P, Seferovic P, Coats AJS, Metra M, Rosano G, Rosenkranz S, Tocchetti CG. A roadmap for therapeutic discovery in pulmonary hypertension associated with left heart failure. A scientific statement of the Heart Failure Association (HFA) of the ESC and the ESC Working Group on Pulmonary Circulation & Right Ventricular Function. Eur J Heart Fail 2024; 26:707-729. [PMID: 38639017 PMCID: PMC11182487 DOI: 10.1002/ejhf.3236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 02/23/2024] [Accepted: 03/28/2024] [Indexed: 04/20/2024] Open
Abstract
Pulmonary hypertension (PH) associated with left heart failure (LHF) (PH-LHF) is one of the most common causes of PH. It directly contributes to symptoms and reduced functional capacity and negatively affects right heart function, ultimately leading to a poor prognosis. There are no specific treatments for PH-LHF, despite the high number of drugs tested so far. This scientific document addresses the main knowledge gaps in PH-LHF with emphasis on pathophysiology and clinical trials. Key identified issues include better understanding of the role of pulmonary venous versus arteriolar remodelling, multidimensional phenotyping to recognize patient subgroups positioned to respond to different therapies, and conduct of rigorous pre-clinical studies combining small and large animal models. Advancements in these areas are expected to better inform the design of clinical trials and extend treatment options beyond those effective in pulmonary arterial hypertension. Enrichment strategies, endpoint assessments, and thorough haemodynamic studies, both at rest and during exercise, are proposed to play primary roles to optimize early-stage development of candidate therapies for PH-LHF.
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Affiliation(s)
- Pietro Ameri
- Department of Internal Medicine, University of Genova, Genoa, Italy
- Cardiac, Thoracic, and Vascular Department, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Valentina Mercurio
- Department of Translational Medical Sciences, Interdepartmental Center for Clinical and Translational Research (CIRCET), and Interdepartmental Hypertension Research Center (CIRIAPA), Federico II University, Naples, Italy
| | - Piero Pollesello
- Content and Communication, Branded Products, Orion Pharma, Espoo, Finland
| | - Markus S Anker
- Deutsches Herzzentrum der Charité, Klinik für Kardiologie, Angiologie und Intensivmedizin (Campus CBF), German Centre for Cardiovascular Research (DZHK) partner site Berlin, Berlin Institute of Health Center for Regenerative Therapies (BCRT), Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Johannes Backs
- Institute of Experimental Cardiology, University Hospital Heidelberg, University of Heidelberg and DZHK (German Centre for Cardiovascular Research), Partner Site Heidelberg/Mannheim, Heidelberg, Germany
| | - Antoni Bayes-Genis
- Heart Institute, Hospital Universitari Germans Trias i Pujol, CIBERCV, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Barry A Borlaug
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, USA
- Cardiovascular Research Foundation, New York, NY, USA
| | | | - Sergio Caravita
- Department of Management, Information and Production Engineering, University of Bergamo, Dalmine (BG), Italy
- Department of Cardiology, Istituto Auxologico Italiano IRCCS Ospedale San Luca, Milan, Italy
| | - Stephen Y Chan
- Pittsburgh Heart, Lung, and Blood Vascular Medicine Institute, Division of Cardiology, Department of Medicine, University of Pittsburgh School of Medicine and UPMC, Pittsburgh, PA, USA
| | - Frances de Man
- PHEniX laboratory, Department of Pulmonary Medicine, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Cardiovascular Sciences, Pulmonary Hypertension and Thrombosis, Amsterdam, The Netherlands
| | - George Giannakoulas
- First Department of Cardiology, AHEPA University Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Aránzazu González
- Program of Cardiovascular Diseases, CIMA Universidad de Navarra and IdiSNA, Pamplona, Spain
- CIBERCV, Madrid, Spain
| | - Marco Guazzi
- University of Milan, Milan, Italy
- Cardiology Division, San Paolo University Hospital, Milan, Italy
| | - Paul M Hassoun
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Anna R Hemnes
- Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Cristoph Maack
- Comprehensive Heart Failure Center (CHFC) and Medical Clinic I, University Clinic Würzburg, Würzburg, Germany
| | | | - Vojtech Melenovsky
- Department of Cardiology, Institute for Clinical and Experimental Medicine - IKEM, Prague, Czech Republic
| | - Oliver J Müller
- Department of Internal Medicine V, University Hospital Schleswig-Holstein, and German Centre for Cardiovascular Research (DZHK), Partner site Hamburg/Kiel/Lübeck, Kiel, Germany
| | - Zoltan Papp
- Division of Clinical Physiology, Department of Cardiology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | - Soni Savai Pullamsetti
- Department of Internal Medicine and Excellence Cluster Cardio-Pulmonary Institute (CPI), Justus-Liebig University, Giessen, Germany
| | - Peter P Rainer
- Division of Cardiology, Medical University of Graz, Graz, Austria
- BioTechMed Graz, Graz, Austria
- Department of Medicine, St. Johann in Tirol General Hospital, St. Johann in Tirol, Austria
| | | | - Stuart Rich
- Division of Cardiology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Gabriele G Schiattarella
- Max-Rubner Center (CMR), Department of Cardiology, Charité-Universitätsmedizin Berlin, Berlin, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Berlin, Berlin, Germany
- Translational Approaches in Heart Failure and Cardiometabolic Disease, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- Division of Cardiology, Department of Advanced Biomedical Sciences, Federico II University, Naples, Italy
| | - Hall Skaara
- Pulmonary Hypertension Association Europe, Vienna, Austria
| | - Kostantinos Stellos
- Department of Cardiovascular Research, European Center for Angioscience (ECAS), Heidelberg University, Mannheim, Germany
- German Centre for Cardiovascular Research (Deutsches Zentrum für Herz-Kreislauf-Forschung, DZHK), Heidelberg/Mannheim Partner Site, Heidelberg and Mannheim, Germany
- Department of Cardiology, University Hospital Mannheim, Heidelberg University, Mannheim, Germany
- Biosciences Institute, Vascular Biology and Medicine Theme, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Ryan J Tedford
- Division of Cardiology, Department of Medicine, Medical University of South Carolina, Charleston, SC, USA
| | - Thomas Thum
- Institute of Molecular and Translational Therapeutic Strategies (IMTTS), Hannover Medical School, Hannover, Germany
| | - Jean Luc Vachiery
- Department of Cardiology, Hopital Universitaire de Bruxelles Erasme, Brussels, Belgium
| | - Peter van der Meer
- Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Sophie Van Linthout
- Berlin Institute of Health (BIH) at Charité, BIH Center for Regenerative Therapies, University of Medicine, Berlin, Germany
- German Center for Cardiovascular Research (DZHK, partner site Berlin), Berlin, Germany
| | - Piotr Pruszczyk
- Department of Internal Medicine and Cardiology, Medical University of Warsaw, Warsaw, Poland
| | - Petar Seferovic
- University of Belgrade Faculty of Medicine, Belgrade University Medical Center, Serbian Academy of Sciences and Arts, Belgrade, Serbia
| | | | - Marco Metra
- Cardiology. ASST Spedali Civili and Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy
| | | | - Stephan Rosenkranz
- Department of Cardiology and Cologne Cardiovascular Research Center (CCRC), Heart Center at the University Hospital Cologne, Cologne, Germany
- Center for Molecular Medicine Cologne (CMMC), Faculty of Medicine, University of Cologne, Cologne, Germany
| | - Carlo Gabriele Tocchetti
- Department of Translational Medical Sciences, Interdepartmental Center for Clinical and Translational Research (CIRCET), and Interdepartmental Hypertension Research Center (CIRIAPA), Federico II University, Naples, Italy
- Center for Basic and Clinical Immunology Research (CISI), Federico II University, Naples, Italy
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Msaouel P, Lee J, Thall PF. Interpreting Randomized Controlled Trials. Cancers (Basel) 2023; 15:4674. [PMID: 37835368 PMCID: PMC10571666 DOI: 10.3390/cancers15194674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 09/19/2023] [Accepted: 09/19/2023] [Indexed: 10/15/2023] Open
Abstract
This article describes rationales and limitations for making inferences based on data from randomized controlled trials (RCTs). We argue that obtaining a representative random sample from a patient population is impossible for a clinical trial because patients are accrued sequentially over time and thus comprise a convenience sample, subject only to protocol entry criteria. Consequently, the trial's sample is unlikely to represent a definable patient population. We use causal diagrams to illustrate the difference between random allocation of interventions within a clinical trial sample and true simple or stratified random sampling, as executed in surveys. We argue that group-specific statistics, such as a median survival time estimate for a treatment arm in an RCT, have limited meaning as estimates of larger patient population parameters. In contrast, random allocation between interventions facilitates comparative causal inferences about between-treatment effects, such as hazard ratios or differences between probabilities of response. Comparative inferences also require the assumption of transportability from a clinical trial's convenience sample to a targeted patient population. We focus on the consequences and limitations of randomization procedures in order to clarify the distinctions between pairs of complementary concepts of fundamental importance to data science and RCT interpretation. These include internal and external validity, generalizability and transportability, uncertainty and variability, representativeness and inclusiveness, blocking and stratification, relevance and robustness, forward and reverse causal inference, intention to treat and per protocol analyses, and potential outcomes and counterfactuals.
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Affiliation(s)
- Pavlos Msaouel
- Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- David H. Koch Center for Applied Research of Genitourinary Cancers, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Juhee Lee
- Department of Statistics, University of California Santa Cruz, Santa Cruz, CA 95064, USA;
| | - Peter F. Thall
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA;
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Vinnat V, Chiche JD, Demoule A, Chevret S. Simulation study for evaluating an adaptive-randomisation Bayesian hybrid trial design with enrichment. Contemp Clin Trials Commun 2023; 33:101141. [PMID: 37397429 PMCID: PMC10313856 DOI: 10.1016/j.conctc.2023.101141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 03/22/2023] [Accepted: 04/12/2023] [Indexed: 07/04/2023] Open
Abstract
Background As we enter the era of precision medicine, the role of adaptive designs, such as response-adaptive randomisation or enrichment designs in drug discovery and development, has become increasingly important to identify the treatment given to a patient based on one or more biomarkers. Tailoring the ventilation supply technique according to the responsiveness of patients to positive end-expiratory pressure is a suitable setting for such a design. Methods In the setting of marker-strategy design, we propose a Bayesian response-adaptive randomisation with enrichment design based on group sequential analyses. This design combines the elements of enrichment design and response-adaptive randomisation. Concerning the enrichment strategy, Bayesian treatment-by-subset interaction measures were used to adaptively enrich the patients most likely to benefit from an experimental treatment while controlling the false-positive rate.The operating characteristics of the design were assessed by simulation and compared to those of alternate designs. Results The results obtained allowed the detection of the superiority of one treatment over another and the presence of a treatment-by-subgroup interaction while keeping the false-positive rate at approximately 5\% and reducing the average number of included patients. In addition, simulation studies identified that the number of interim analyses and the burn-in period may have an impact on the performance of the scheme. Conclusion The proposed design highlights important objectives of precision medicine, such as determining whether the experimental treatment is superior to another and identifying wheter such an efficacy could depend on patient profile.
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Affiliation(s)
- Valentin Vinnat
- ECSTRRA team, INSERM U1153, Université Paris Cité, Paris, France
| | - Jean-Daniel Chiche
- Service de médecine intensive adulte, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Alexandre Demoule
- Sorbonne Université, INSERM, UMRS1158 Neurophysiologie Respiratoire Expérimentale et Clinique, France
- AP-HP, Groupe Hospitalier Universitaire APHP-Sorbonne Université, site Pitié-Salpêtrière, Service de Médecine Intensive et Réanimation (Département R3S), Paris, France
| | - Sylvie Chevret
- ECSTRRA team, INSERM U1153, Université Paris Cité, Paris, France
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Bansal R, Hellerstein DJ, Sawardekar S, Chen Y, Peterson BS. A randomized controlled trial of desvenlafaxine-induced structural brain changes in the treatment of persistent depressive disorder. Psychiatry Res Neuroimaging 2023; 331:111634. [PMID: 36996664 DOI: 10.1016/j.pscychresns.2023.111634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 02/27/2023] [Accepted: 03/15/2023] [Indexed: 04/01/2023]
Abstract
The anatomical changes that antidepressant medications induce in the brain and through which they exert their therapeutic effects remain largely unknown. We randomized 61 patients with Persistent Depressive Disorder (PDD) to receive either desvenlafaxine or placebo in a 12-week trial and acquired anatomical MRI scans in 42 of those patients at baseline before randomization and immediately at the end of the trial. We also acquired MRIs once in 39 age- and sex-matched healthy controls. We assessed whether the serotonin-norepinephrine reuptake inhibitor, desvenlafaxine, differentially changed cortical thickness during the trial compared with placebo. Patients relative to controls at baseline had thinner cortices across the brain. Although baseline thickness was not associated with symptom severity, thicker baseline cortices predicted greater reduction in symptom severity in those treated with desvenlafaxine but not placebo. We did not detect significant treatment-by-time effects on cortical thickness. These findings suggest that baseline thickness may serve as predictive biomarkers for treatment response to desvenlafaxine. The absence of treatment-by-time effects may be attributable either to use of insufficient desvenlafaxine dosing, a lack of desvenlafaxine efficacy in treating PDD, or the short trial duration.
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Affiliation(s)
- Ravi Bansal
- Institute for the Developing Mind, Children's Hospital Los Angeles, CA 90027, USA; Department of Pediatrics, Keck School of Medicine at the University of Southern California, Los Angeles, CA 90033, USA.
| | - David J Hellerstein
- Depression Evaluation Service, Division of Clinical Therapeutics, New York State Psychiatric Institute, New York, NY 10032, USA; Department of Psychiatry, Columbia University College of Physicians and Surgeons, New York, NY 10032, USA; New York State Psychiatric Institute, New York, NY 10032, USA
| | - Siddhant Sawardekar
- Institute for the Developing Mind, Children's Hospital Los Angeles, CA 90027, USA
| | - Ying Chen
- Depression Evaluation Service, Division of Clinical Therapeutics, New York State Psychiatric Institute, New York, NY 10032, USA; New York State Psychiatric Institute, New York, NY 10032, USA
| | - Bradley S Peterson
- Institute for the Developing Mind, Children's Hospital Los Angeles, CA 90027, USA; Department of Psychiatry, Keck School of Medicine at the University of Southern California, Los Angeles, CA, USA 90033, USA
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Shi S, Wei S, Pan X, Zhang L, Zhang S, Wang X, Shi S, Lin W. Identification of early biomarkers of transcriptomics in alveolar macrophage for the prognosis of intubated ARDS patients. BMC Pulm Med 2022; 22:334. [PMID: 36056346 PMCID: PMC9440545 DOI: 10.1186/s12890-022-02130-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 08/30/2022] [Indexed: 11/10/2022] Open
Abstract
Background Currently, the rate of morbidity and mortality in acute respiratory distress syndrome (ARDS) remains high. One of the potential reasons for the poor and ineffective therapies is the lack of early and credible indicator of risk prediction that would help specific treatment of severely affected ARDS patients. Nevertheless, assessment of the clinical outcomes with transcriptomics of ARDS by alveolar macrophage has not been performed. Methods The expression data GSE116560 was obtained from the Gene Expression Omnibus databases (GEO) in NCBI. This dataset consists of 68 BAL samples from 35 subjects that were collected within 48 h of ARDS. Differentially expressed genes (DEGs) of different outcomes were analyzed using R software. The top 10 DEGs that were up- or down-regulated were analyzed using receiver operating characteristic (ROC) analysis. Kaplan–Meier survival analysis within two categories according to cut-off and the value of prediction of the clinical outcomes via DEGs was verified. GO enrichment, KEGG pathway analysis, and protein–protein interaction were also used for functional annotation of key genes. Results 24,526 genes were obtained, including 235 up-regulated and 292 down-regulated DEGs. The gene ADORA3 was chosen as the most obvious value to predict the outcome according to the ROC and survival analysis. For functional annotation, ADORA3 was significantly augmented in sphingolipid signaling pathway, cGMP-PKG signaling pathway, and neuroactive ligand-receptor interaction. Four genes (ADORA3, GNB1, NTS, and RHO), with 4 nodes and 6 edges, had the highest score in these clusters in the protein–protein interaction network. Conclusions Our results show that the prognostic prediction of early biomarkers of transcriptomics as identified in alveolar macrophage in ARDS can be extended for mechanically ventilated critically ill patients. In the long term, generalizing the concept of biomarkers of transcriptomics in alveolar macrophage could add to improving precision-based strategies in the ICU patients and may also lead to identifying improved strategy for critically ill patients. Supplementary Information The online version contains supplementary material available at 10.1186/s12890-022-02130-8.
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Affiliation(s)
- Songchang Shi
- Department of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital South Branch, Fujian Provincial Hospital, Fuzhou, 350001, People's Republic of China
| | - Shuo Wei
- Department of Infectious Disease, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou, 350001, People's Republic of China
| | - Xiaobin Pan
- Department of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital South Branch, Fujian Provincial Hospital, Fuzhou, 350001, People's Republic of China
| | - Lihui Zhang
- Department of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital South Branch, Fujian Provincial Hospital, Fuzhou, 350001, People's Republic of China
| | - Shujuan Zhang
- Department of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital South Branch, Fujian Provincial Hospital, Fuzhou, 350001, People's Republic of China
| | - Xincai Wang
- Department of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital South Branch, Fujian Provincial Hospital, Fuzhou, 350001, People's Republic of China
| | - Songjing Shi
- Department of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou, 350001, People's Republic of China.
| | - Wei Lin
- Department of Endocrinology, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou, 350001, People's Republic of China.
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Hoffmann OI, Regenauer M, Czogalla B, Brambs C, Burges A, Mayer B. Interpatient Heterogeneity in Drug Response and Protein Biomarker Expression of Recurrent Ovarian Cancer. Cancers (Basel) 2022; 14:cancers14092279. [PMID: 35565408 PMCID: PMC9103312 DOI: 10.3390/cancers14092279] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Revised: 04/24/2022] [Accepted: 04/29/2022] [Indexed: 12/10/2022] Open
Abstract
Recurrent ovarian-cancer patients face low 5-year survival rates despite chemotherapy. A variety of guideline-recommended second-line therapies are available, but they frequently result in trial-and-error treatment. Alterations and adjustments are common in the treatment of recurrent ovarian cancer. The drug response of 30 lesions obtained from 22 relapsed ovarian cancer patients to different chemotherapeutic and molecular agents was analyzed with the patient-derived ovarian-cancer spheroid model. The profile of druggable biomarkers was immunohistochemically assessed. The second-line combination therapy of carboplatin with gemcitabine was significantly superior to the combination of carboplatin with PEGylated liposomal doxorubicin (p < 0.0001) or paclitaxel (p = 0.0007). Except for treosulfan, all nonplatinum treatments tested showed a lesser effect on tumor spheroids compared to that of platinum-based therapies. Treosulfan showed the highest efficacy of all nonplatinum agents, with significant advantage over vinorelbine (p < 0.0001) and topotecan (p < 0.0001), the next best agents. The comparative testing of a variety of treatment options in the ovarian-cancer spheroid model resulted in the identification of more effective regimens for 30% of patients compared to guideline-recommended therapies. Recurrent cancers obtained from different patients revealed profound interpatient heterogeneity in the expression pattern of druggable protein biomarkers. In contrast, different lesions obtained from the same patient revealed a similar drug response and biomarker expression profile. Biological heterogeneity observed in recurrent ovarian cancers might explain the strong differences in the clinical drug response of these patients. Preclinical drug testing and biomarker profiling in the ovarian-cancer spheroid model might help in optimizing treatment management for individual patients.
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Affiliation(s)
| | - Manuel Regenauer
- Department of General, Visceral and Transplant Surgery, Ludwig-Maximilians-University Munich, Marchioninistraße 15, 81377 Munich, Germany;
| | - Bastian Czogalla
- Department of Obstetrics and Gynecology, Klinikum der Universität München, Ludwig-Maximilians-University Munich, Marchioninistraße 15, 81377 Munich, Germany; (B.C.); (A.B.)
| | - Christine Brambs
- Department of Obstetrics and Gynecology, Klinikum Rechts der Isar, Technical University Munich, Ismaninger Straße 22, 81675 Munich, Germany;
| | - Alexander Burges
- Department of Obstetrics and Gynecology, Klinikum der Universität München, Ludwig-Maximilians-University Munich, Marchioninistraße 15, 81377 Munich, Germany; (B.C.); (A.B.)
| | - Barbara Mayer
- SpheroTec GmbH, Am Klopferspitz 19, 82152 Martinsried, Germany;
- Department of General, Visceral and Transplant Surgery, Ludwig-Maximilians-University Munich, Marchioninistraße 15, 81377 Munich, Germany;
- German Cancer Consortium (DKTK), Partner Site Munich, Pettenkoferstraße 8a, 80336 Munich, Germany
- Correspondence: ; Tel.: +49-89-4400-76438
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Chaft JE, Shyr Y, Sepesi B, Forde PM. Preoperative and Postoperative Systemic Therapy for Operable Non-Small-Cell Lung Cancer. J Clin Oncol 2022; 40:546-555. [PMID: 34985966 PMCID: PMC8853628 DOI: 10.1200/jco.21.01589] [Citation(s) in RCA: 91] [Impact Index Per Article: 45.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 08/13/2021] [Accepted: 09/13/2021] [Indexed: 12/22/2022] Open
Abstract
Cisplatin-based adjuvant chemotherapy remains the standard of care for patients with resected stage II or III non-small-cell lung cancer. However, biomarker-informed clinical trials are starting to push the management of early-stage lung cancer beyond cytotoxic chemotherapy. This review explores recent and ongoing studies focused on improving cytotoxic chemotherapy and incorporating targeted and immunotherapies in the management of early-stage, resectable lung cancer. Adjuvant osimertinib for patients with EGFR-mutant tumors, preoperative chemoimmunotherapy, and adjuvant immunotherapy could improve outcomes for selected patients with resectable lung cancer, and ongoing or planned studies leveraging biomarkers, immunotherapy, and targeted therapy may further improve survival. We also discuss the unique barriers associated with clinical trials of early-stage lung cancer and the need for innovative trial designs to overcome these challenges.
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Affiliation(s)
- Jamie E. Chaft
- Thoracic Oncology, Department of Medicine, Memorial Sloan Kettering Cancer Center and Weill Cornell Medical Center, New York, NY
| | - Yu Shyr
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN
| | - Boris Sepesi
- Department of Thoracic and Cardiovascular Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Patrick M. Forde
- Department of Medicine, Sidney Kimmel Cancer Center, Johns Hopkins University, Baltimore, MD
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Wang X, Piantadosi S, Le-Rademacher J, Mandrekar SJ. Statistical Considerations for Subgroup Analyses. J Thorac Oncol 2021; 16:375-380. [PMID: 33373692 PMCID: PMC7920926 DOI: 10.1016/j.jtho.2020.12.008] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 12/08/2020] [Accepted: 12/12/2020] [Indexed: 12/21/2022]
Affiliation(s)
- Xiaofei Wang
- Department of Biostatistics & Bioinformatics, Duke University, Durham, North Carolina.
| | - Steven Piantadosi
- Department of Surgery, Brigham and Women's Hospital, Boston, Massachusetts
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10
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Wang T, Wang X, George SL, Zhou H. Design and analysis of biomarker-integrated clinical trials with adaptive threshold detection and flexible patient enrichment. J Biopharm Stat 2020; 30:1060-1076. [PMID: 33175640 PMCID: PMC7954851 DOI: 10.1080/10543406.2020.1832110] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Accepted: 09/12/2020] [Indexed: 10/23/2022]
Abstract
We propose a new adaptive threshold detection and enrichment design in which the biomarker threshold is adaptively estimated and updated by optimizing a trade-off between the size of the biomarker positive population and the magnitude of the treatment effect in that population. Enrichment is based on an enrollment criterion that accounts for the uncertainty in estimation of the threshold. Early termination for futility is allowed based on predictive success probability. Valid testing and estimation techniques for the treatment effect overall and inpatient subgroups are studied. Simulations and an example demonstrate advantages of the proposed design over existing designs.
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Affiliation(s)
- Ting Wang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, U.S.A
| | - Xiaofei Wang
- Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, U.S.A
| | - Stephen L George
- Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, U.S.A
| | - Haibo Zhou
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, U.S.A
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Costa LJ, Derman BA, Bal S, Sidana S, Chhabra S, Silbermann R, Ye JC, Cook G, Cornell RF, Holstein SA, Shi Q, Omel J, Callander NS, Chng WJ, Hungria V, Maiolino A, Stadtmauer E, Giralt S, Pasquini M, Jakubowiak AJ, Morgan GJ, Krishnan A, Jackson GH, Mohty M, Mateos MV, Dimopoulos MA, Facon T, Spencer A, Miguel JS, Hari P, Usmani SZ, Manier S, McCarthy P, Kumar S, Gay F, Paiva B. International harmonization in performing and reporting minimal residual disease assessment in multiple myeloma trials. Leukemia 2020; 35:18-30. [DOI: 10.1038/s41375-020-01012-4] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 07/27/2020] [Accepted: 07/29/2020] [Indexed: 12/24/2022]
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Xia F, George SL, Ning J, Li L, Huang X. A Signature Enrichment Design with Bayesian Adaptive Randomization. J Appl Stat 2020; 48:1091-1110. [PMID: 34024982 DOI: 10.1080/02664763.2020.1757048] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
Clinical trials in the era of precision cancer medicine aim to identify and validate biomarker signatures which can guide the assignment of individually optimal treatments to patients. In this article, we propose a group sequential randomized phase II design, which updates the biomarker signature as the trial goes on, utilizes enrichment strategies for patient selection, and uses Bayesian response-adaptive randomization for treatment assignment. To evaluate the performance of the new design, in addition to the commonly considered criteria of type I error and power, we propose four new criteria measuring the benefits and losses for individuals both inside and outside of the clinical trial. Compared with designs with equal randomization, the proposed design gives trial participants a better chance to receive their personalized optimal treatments and thus results in a higher response rate on the trial. This design increases the chance to discover a successful new drug by an adaptive enrichment strategy, i.e., identification and selective enrollment of a subset of patients who are sensitive to the experimental therapies. Simulation studies demonstrate these advantages of the proposed design. It is illustrated by an example based on an actual clinical trial in non-small-cell lung cancer.
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Affiliation(s)
- Fang Xia
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center
| | - Stephen L George
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine
| | - Jing Ning
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center
| | - Liang Li
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center
| | - Xuelin Huang
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center
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Wang T, Wang X, Zhou H, Cai J, George SL. Auxiliary variable-enriched biomarker-stratified design. Stat Med 2018; 37:4610-4635. [PMID: 30221368 DOI: 10.1002/sim.7938] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2018] [Revised: 06/04/2018] [Accepted: 07/15/2018] [Indexed: 12/18/2022]
Abstract
Clinical trials in the era of precision medicine require assessment of biomarkers to identify appropriate subgroups of patients for targeted therapy. In a biomarker-stratified design (BSD), biomarkers are measured on all patients and used as stratification variables. However, such a trial can be both inefficient and costly, especially when the prevalence of the subgroup of primary interest is low and the cost of assessing the biomarkers is high. Efficiency can be improved and costs reduced by using enriched biomarker-stratified designs, in which patients of primary interest, typically the biomarker-positive patients, are oversampled. We consider a special type of enrichment design, an auxiliary variable-enriched design (AEBSD), in which enrichment is based on some inexpensive auxiliary variable that is positively correlated with the true biomarker. The proposed AEBSD reduces the total cost of the trial compared with a standard BSD when the prevalence rate of true biomarker positivity is small and the positive predictive value (PPV) of the auxiliary biomarker is larger than the prevalence rate. In addition, for an AEBSD, we can immediately randomize the patients selected in the screening process without waiting for the result of the true biomarker test, reducing the treatment waiting time. We propose an adaptive Bayesian method to adjust the assumed PPV while the trial is ongoing. Numerical studies and an example illustrate the approach. An R package is available.
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Affiliation(s)
- Ting Wang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Xiaofei Wang
- Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina
| | - Haibo Zhou
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Jianwen Cai
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Stephen L George
- Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina
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