1
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Chon E, Hendricks W, White M, Rodrigues L, Haworth D, Post G. Precision Medicine in Veterinary Science. Vet Clin North Am Small Anim Pract 2024; 54:501-521. [PMID: 38212188 DOI: 10.1016/j.cvsm.2023.12.006] [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] [Indexed: 01/13/2024]
Abstract
Precision medicine focuses on the clinical management of the individual patient, not on population-based findings. Successes from human precision medicine inform veterinary oncology. Early evidence of success for canines shows how precision medicine can be integrated into practice. Decreasing genomic profiling costs will allow increased utilization and subsequent improvement of knowledge base from which to make better informed decisions. Utility of precision medicine in canine oncology will only increase for improved cancer characterization, enhanced therapy selection, and overall more successful management of canine cancer. As such, practitioners are called to interpret and leverage precision medicine reports for their patients.
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Affiliation(s)
- Esther Chon
- Vidium Animal Health, 7201 East Henkel Way, Suite 210, Scottsdale, AZ 85255, USA
| | - William Hendricks
- Vidium Animal Health, 7201 East Henkel Way, Suite 210, Scottsdale, AZ 85255, USA
| | - Michelle White
- OneHealthCompany, Inc, 530 Lytton Avenue, 2nd Floor, Palo Alto, CA 94301, USA
| | - Lucas Rodrigues
- OneHealthCompany, Inc, 530 Lytton Avenue, 2nd Floor, Palo Alto, CA 94301, USA
| | - David Haworth
- Vidium Animal Health, 7201 East Henkel Way, Suite 210, Scottsdale, AZ 85255, USA
| | - Gerald Post
- OneHealthCompany, Inc, 530 Lytton Avenue, 2nd Floor, Palo Alto, CA 94301, USA.
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2
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Cao W, Zhu H, Wang L, Zhang L, Yu J. Doubly adaptive biased coin design to improve Bayesian clinical trials with time-to-event endpoints. Stat Med 2024; 43:1743-1758. [PMID: 38387866 DOI: 10.1002/sim.10047] [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: 04/24/2023] [Revised: 02/05/2024] [Accepted: 02/12/2024] [Indexed: 02/24/2024]
Abstract
Clinical trialists often face the challenge of balancing scientific questions with other design features, such as improving efficiency, minimizing exposure to inferior treatments, and simultaneously comparing multiple treatments. While Bayesian response adaptive randomization (RAR) is a popular and effective method for achieving these objectives, it is known to have large variability and a lack of explicit theoretical results, making its use in clinical trials a subject of concern. It is desirable to propose a design that targets the same allocation proportion as Bayesian RAR and achieves the above objectives but addresses the concerns over Bayesian RAR. We propose the frequentist doubly adaptive biased coin designs (DBCD) targeting ethical allocation proportions from the Bayesian framework to satisfy different objectives in clinical trials with time-to-event endpoints. We derive the theoretical properties of the proposed adaptive randomization design and show through comprehensive numerical simulations that it can achieve ethical objectives without sacrificing efficiency. Our combined theoretical and numerical results offer a strong foundation for the practical use of RAR in real clinical trials.
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Affiliation(s)
- Wenhao Cao
- Division of Biostatistics and Health Data Science, University of Minnesota, Minneapolis, Minnesota, USA
| | - Hongjian Zhu
- Statistical Innovation Group, AbbVie Inc., Virtual Office, Sugar Land, Texas, USA
| | - Li Wang
- Statistical Innovation Group, AbbVie Inc., North Chicago, Illinois, USA
| | - Lixin Zhang
- Center for Data Science and School of Mathematical Sciences, Zhejiang University, Hangzhou, China
| | - Jun Yu
- Medical Affairs and Health Technology Assessment Statistics, AbbVie Inc., Virtual Office, Sugar Land, Texas, USA
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3
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Xiong W, Roy J, Liu H, Hu L. Leveraging machine learning: Covariate-adjusted Bayesian adaptive randomization and subgroup discovery in multi-arm survival trials. Contemp Clin Trials 2024; 142:107547. [PMID: 38688389 DOI: 10.1016/j.cct.2024.107547] [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: 12/08/2023] [Revised: 04/17/2024] [Accepted: 04/25/2024] [Indexed: 05/02/2024]
Abstract
Clinical trials evaluate the safety and efficacy of treatments for specific diseases. Ensuring these studies are well-powered is crucial for identifying superior treatments. With the rise of personalized medicine, treatment efficacy may vary based on biomarker profiles. However, researchers often lack prior knowledge about which biomarkers are linked to varied treatment effects. Fixed or response-adaptive designs may not sufficiently account for heterogeneous patient characteristics, such as genetic diversity, potentially reducing the chance of selecting the optimal treatment for individuals. Recent advances in Bayesian nonparametric modeling pave the way for innovative trial designs that not only maintain robust power but also offer the flexibility to identify subgroups deriving greater benefits from specific treatments. Building on this inspiration, we introduce a Bayesian adaptive design for multi-arm trials focusing on time-to-event endpoints. We introduce a covariate-adjusted response adaptive randomization, updating treatment allocation probabilities grounded on causal effect estimates using a random intercept accelerated failure time BART model. After the trial concludes, we suggest employing a multi-response decision tree to pinpoint subgroups with varying treatment impacts. The performance of our design is then assessed via comprehensive simulations.
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Affiliation(s)
- Wenxuan Xiong
- Department of Biostatistics and Epidemiology, Rutgers University School of Public Health, Piscataway, NJ, USA.
| | - Jason Roy
- Department of Biostatistics and Epidemiology, Rutgers University School of Public Health, Piscataway, NJ, USA
| | - Hao Liu
- Department of Biostatistics and Epidemiology, Rutgers University School of Public Health, Piscataway, NJ, USA; Cancer Institute of New Jersey, Rutgers University, New Brunswick, NJ, USA
| | - Liangyuan Hu
- Department of Biostatistics and Epidemiology, Rutgers University School of Public Health, Piscataway, NJ, USA
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4
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Hendrixson M, Gladkiy Y, Thyagarajan A, Sahu RP. Efficacy of Sorafenib-Based Therapies for Non-Small Cell Lung Cancer. Med Sci (Basel) 2024; 12:20. [PMID: 38651414 PMCID: PMC11036230 DOI: 10.3390/medsci12020020] [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: 02/16/2024] [Revised: 03/27/2024] [Accepted: 03/30/2024] [Indexed: 04/25/2024] Open
Abstract
Lung cancer remains the leading cause of cancer-related deaths, with a poor prognosis. Of the two types, non-small cell lung cancer (NSCLC) is the major and most prevalent type and associated with low response rates to the current treatment options. Sorafenib, a multitargeted tyrosine kinase inhibitor used for various malignancies, gained attention for its potential efficacy in NSCLC. This review paper focuses on the findings of recent in vitro, in vivo, and clinical studies regarding the efficacy of sorafenib. Overall, sorafenib has shown definitive therapeutic potential in NSCLC cell lines, xenografts, and human subjects. Novel approaches to sorafenib delivery may improve its efficacy and should be the focus of further studies.
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Affiliation(s)
- Morgann Hendrixson
- Boonshoft School of Medicine, Wright State University, Dayton, OH 45435, USA; (M.H.); (Y.G.)
| | - Yevgeniy Gladkiy
- Boonshoft School of Medicine, Wright State University, Dayton, OH 45435, USA; (M.H.); (Y.G.)
| | - Anita Thyagarajan
- Department of Pharmacology and Toxicology, Boonshoft School of Medicine, Wright State University, Dayton, OH 45435, USA;
| | - Ravi P. Sahu
- Department of Pharmacology and Toxicology, Boonshoft School of Medicine, Wright State University, Dayton, OH 45435, USA;
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5
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van der Maas NG, Versluis J, Nasserinejad K, van Rosmalen J, Pabst T, Maertens J, Breems D, Manz M, Cloos J, Ossenkoppele GJ, Floisand Y, Gradowska P, Löwenberg B, Huls G, Postmus D, Pignatti F, Cornelissen JJ. Bayesian interim analysis for prospective randomized studies: reanalysis of the acute myeloid leukemia HOVON 132 clinical trial. Blood Cancer J 2024; 14:56. [PMID: 38538587 PMCID: PMC10973506 DOI: 10.1038/s41408-024-01037-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 03/06/2024] [Accepted: 03/08/2024] [Indexed: 04/07/2024] Open
Abstract
Randomized controlled trials (RCTs) are the gold standard to establish the benefit-risk ratio of novel drugs. However, the evaluation of mature results often takes many years. We hypothesized that the addition of Bayesian inference methods at interim analysis time points might accelerate and enforce the knowledge that such trials may generate. In order to test that hypothesis, we retrospectively applied a Bayesian approach to the HOVON 132 trial, in which 800 newly diagnosed AML patients aged 18 to 65 years were randomly assigned to a "7 + 3" induction with or without lenalidomide. Five years after the first patient was recruited, the trial was negative for its primary endpoint with no difference in event-free survival (EFS) between experimental and control groups (hazard ratio [HR] 0.99, p = 0.96) in the final conventional analysis. We retrospectively simulated interim analyses after the inclusion of 150, 300, 450, and 600 patients using a Bayesian methodology to detect early lack of efficacy signals. The HR for EFS comparing the lenalidomide arm with the control treatment arm was 1.21 (95% CI 0.81-1.69), 1.05 (95% CI 0.86-1.30), 1.00 (95% CI 0.84-1.19), and 1.02 (95% CI 0.87-1.19) at interim analysis 1, 2, 3 and 4, respectively. Complete remission rates were lower in the lenalidomide arm, and early deaths more frequent. A Bayesian approach identified that the probability of a clinically relevant benefit for EFS (HR < 0.76, as assumed in the statistical analysis plan) was very low at the first interim analysis (1.2%, 0.6%, 0.4%, and 0.1%, respectively). Similar observations were made for low probabilities of any benefit regarding CR. Therefore, Bayesian analysis significantly adds to conventional methods applied for interim analysis and may thereby accelerate the performance and completion of phase III trials.
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Affiliation(s)
- Niek G van der Maas
- Department of Hematology, Erasmus Medical Center Cancer Institute, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Jurjen Versluis
- Department of Hematology, Erasmus Medical Center Cancer Institute, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Kazem Nasserinejad
- Department of Hematology, Erasmus Medical Center Cancer Institute, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Joost van Rosmalen
- Department of Biostatistics, Erasmus MC, Rotterdam, the Netherlands
- Department of Epidemiology, Erasmus MC, Rotterdam, the Netherlands
| | - Thomas Pabst
- University Hospital, Inselspital, Bern, Switzerland
- Swiss Group for Clinical Cancer Research (SAKK), Bern, Switzerland
| | | | | | - Markus Manz
- Swiss Group for Clinical Cancer Research (SAKK), Bern, Switzerland
- University Hospital Zurich, Zurich, Switzerland
| | - Jacqueline Cloos
- Amsterdam UMC, location VUMC, Cancer Center Amsterdam, Amsterdam, the Netherlands
| | - Gert J Ossenkoppele
- Amsterdam UMC, location VUMC, Cancer Center Amsterdam, Amsterdam, the Netherlands
| | | | - Patrycja Gradowska
- Department of Hematology, Erasmus Medical Center Cancer Institute, Erasmus University Medical Center, Rotterdam, the Netherlands
- HOVON Foundation, Rotterdam, the Netherlands
| | - Bob Löwenberg
- Department of Hematology, Erasmus Medical Center Cancer Institute, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Gerwin Huls
- University Medical Center, University Groningen, Groningen, the Netherlands
| | - Douwe Postmus
- Oncology and Hematology Office, European Medicines Agency, Amsterdam, the Netherlands
| | - Francesco Pignatti
- Oncology and Hematology Office, European Medicines Agency, Amsterdam, the Netherlands
| | - Jan J Cornelissen
- Department of Hematology, Erasmus Medical Center Cancer Institute, Erasmus University Medical Center, Rotterdam, the Netherlands.
- Oncology and Hematology Office, European Medicines Agency, Amsterdam, the Netherlands.
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6
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Duan XP, Qin BD, Jiao XD, Liu K, Wang Z, Zang YS. New clinical trial design in precision medicine: discovery, development and direction. Signal Transduct Target Ther 2024; 9:57. [PMID: 38438349 PMCID: PMC10912713 DOI: 10.1038/s41392-024-01760-0] [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: 11/30/2023] [Revised: 01/25/2024] [Accepted: 01/29/2024] [Indexed: 03/06/2024] Open
Abstract
In the era of precision medicine, it has been increasingly recognized that individuals with a certain disease are complex and different from each other. Due to the underestimation of the significant heterogeneity across participants in traditional "one-size-fits-all" trials, patient-centered trials that could provide optimal therapy customization to individuals with specific biomarkers were developed including the basket, umbrella, and platform trial designs under the master protocol framework. In recent years, the successive FDA approval of indications based on biomarker-guided master protocol designs has demonstrated that these new clinical trials are ushering in tremendous opportunities. Despite the rapid increase in the number of basket, umbrella, and platform trials, the current clinical and research understanding of these new trial designs, as compared with traditional trial designs, remains limited. The majority of the research focuses on methodologies, and there is a lack of in-depth insight concerning the underlying biological logic of these new clinical trial designs. Therefore, we provide this comprehensive review of the discovery and development of basket, umbrella, and platform trials and their underlying logic from the perspective of precision medicine. Meanwhile, we discuss future directions on the potential development of these new clinical design in view of the "Precision Pro", "Dynamic Precision", and "Intelligent Precision". This review would assist trial-related researchers to enhance the innovation and feasibility of clinical trial designs by expounding the underlying logic, which be essential to accelerate the progression of precision medicine.
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Affiliation(s)
- Xiao-Peng Duan
- Department of Medical Oncology, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Bao-Dong Qin
- Department of Medical Oncology, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Xiao-Dong Jiao
- Department of Medical Oncology, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Ke Liu
- Department of Medical Oncology, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Zhan Wang
- Department of Medical Oncology, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Yuan-Sheng Zang
- Department of Medical Oncology, Changzheng Hospital, Naval Medical University, Shanghai, China.
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7
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Bhat GR, Sethi I, Sadida HQ, Rah B, Mir R, Algehainy N, Albalawi IA, Masoodi T, Subbaraj GK, Jamal F, Singh M, Kumar R, Macha MA, Uddin S, Akil ASAS, Haris M, Bhat AA. Cancer cell plasticity: from cellular, molecular, and genetic mechanisms to tumor heterogeneity and drug resistance. Cancer Metastasis Rev 2024; 43:197-228. [PMID: 38329598 PMCID: PMC11016008 DOI: 10.1007/s10555-024-10172-z] [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: 10/01/2023] [Accepted: 01/24/2024] [Indexed: 02/09/2024]
Abstract
Cancer is a complex disease displaying a variety of cell states and phenotypes. This diversity, known as cancer cell plasticity, confers cancer cells the ability to change in response to their environment, leading to increased tumor diversity and drug resistance. This review explores the intricate landscape of cancer cell plasticity, offering a deep dive into the cellular, molecular, and genetic mechanisms that underlie this phenomenon. Cancer cell plasticity is intertwined with processes such as epithelial-mesenchymal transition and the acquisition of stem cell-like features. These processes are pivotal in the development and progression of tumors, contributing to the multifaceted nature of cancer and the challenges associated with its treatment. Despite significant advancements in targeted therapies, cancer cell adaptability and subsequent therapy-induced resistance remain persistent obstacles in achieving consistent, successful cancer treatment outcomes. Our review delves into the array of mechanisms cancer cells exploit to maintain plasticity, including epigenetic modifications, alterations in signaling pathways, and environmental interactions. We discuss strategies to counteract cancer cell plasticity, such as targeting specific cellular pathways and employing combination therapies. These strategies promise to enhance the efficacy of cancer treatments and mitigate therapy resistance. In conclusion, this review offers a holistic, detailed exploration of cancer cell plasticity, aiming to bolster the understanding and approach toward tackling the challenges posed by tumor heterogeneity and drug resistance. As articulated in this review, the delineation of cellular, molecular, and genetic mechanisms underlying tumor heterogeneity and drug resistance seeks to contribute substantially to the progress in cancer therapeutics and the advancement of precision medicine, ultimately enhancing the prospects for effective cancer treatment and patient outcomes.
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Affiliation(s)
- Gh Rasool Bhat
- Advanced Centre for Human Genetics, Sher-I-Kashmir Institute of Medical Sciences, Soura, Srinagar, Jammu and Kashmir, India
| | - Itty Sethi
- Institute of Human Genetics, University of Jammu, Jammu, Jammu and Kashmir, India
| | - Hana Q Sadida
- Department of Human Genetics-Precision Medicine in Diabetes, Obesity and Cancer Program, Sidra Medicine, Doha, Qatar
| | - Bilal Rah
- Iron Biology Group, Research Institute of Medical and Health Science, University of Sharjah, Sharjah, UAE
| | - Rashid Mir
- Department of Medical Laboratory Technology, Faculty of Applied Medical Sciences, Prince Fahad Bin Sultan Chair for Biomedical Research, University of Tabuk, Tabuk, Saudi Arabia
| | - Naseh Algehainy
- Department of Medical Laboratory Technology, Faculty of Applied Medical Sciences, Prince Fahad Bin Sultan Chair for Biomedical Research, University of Tabuk, Tabuk, Saudi Arabia
| | | | - Tariq Masoodi
- Laboratory of Cancer Immunology and Genetics, Sidra Medicine, Doha, Qatar
| | | | - Farrukh Jamal
- Dr. Rammanohar, Lohia Avadh University, Ayodhya, India
| | - Mayank Singh
- Department of Medical Oncology (Lab.), Institute of Medical Sciences (AIIMS), Dr. BRAIRCH, All India, New Delhi, India
| | - Rakesh Kumar
- School of Biotechnology, Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, India
| | - Muzafar A Macha
- Watson-Crick Centre for Molecular Medicine, Islamic University of Science and Technology, Awantipora, Jammu and Kashmir, India
| | - Shahab Uddin
- Translational Research Institute, Academic Health System, Hamad Medical Corporation, Doha, Qatar
- Laboratory Animal Research Centre, Qatar University, Doha, Qatar
| | - Ammira S Al-Shabeeb Akil
- Department of Human Genetics-Precision Medicine in Diabetes, Obesity and Cancer Program, Sidra Medicine, Doha, Qatar
| | - Mohammad Haris
- Laboratory Animal Research Centre, Qatar University, Doha, Qatar.
- Center for Advanced Metabolic Imaging in Precision Medicine, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA.
| | - Ajaz A Bhat
- Department of Human Genetics-Precision Medicine in Diabetes, Obesity and Cancer Program, Sidra Medicine, Doha, Qatar.
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8
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Scardaci R, Berlinska E, Scaparone P, Vietti Michelina S, Garbo E, Novello S, Santamaria D, Ambrogio C. Novel RAF-directed approaches to overcome current clinical limits and block the RAS/RAF node. Mol Oncol 2024. [PMID: 38362705 DOI: 10.1002/1878-0261.13605] [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: 07/24/2023] [Revised: 11/30/2023] [Accepted: 01/30/2024] [Indexed: 02/17/2024] Open
Abstract
Mutations in the RAS-RAF-MEK-ERK pathway are frequent alterations in cancer and RASopathies, and while RAS oncogene activation alone affects 19% of all patients and accounts for approximately 3.4 million new cases every year, less frequent alterations in the cascade's downstream effectors are also involved in cancer etiology. RAS proteins initiate the signaling cascade by promoting the dimerization of RAF kinases, which can act as oncoproteins as well: BRAFV600E is the most common oncogenic driver, mutated in the 8% of all malignancies. Research in this field led to the development of drugs that target the BRAFV600-like mutations (Class I), which are now utilized in clinics, but cause paradoxical activation of the pathway and resistance development. Furthermore, they are ineffective against non-BRAFV600E malignancies that dimerize and could be either RTK/RAS independent or dependent (Class II and III, respectively), which are still lacking an effective treatment. This review discusses the recent advances in anti-RAF therapies, including paradox breakers, dimer-inhibitors, immunotherapies, and other novel approaches, critically evaluating their efficacy in overcoming the therapeutic limitations, and their putative role in blocking the RAS pathway.
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Affiliation(s)
- Rossella Scardaci
- Department of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center, University of Torino, Italy
| | - Ewa Berlinska
- Department of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center, University of Torino, Italy
| | - Pietro Scaparone
- Department of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center, University of Torino, Italy
| | - Sandra Vietti Michelina
- Department of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center, University of Torino, Italy
| | - Edoardo Garbo
- Department of Oncology, University of Torino, San Luigi Hospital, Orbassano, Italy
| | - Silvia Novello
- Department of Oncology, University of Torino, San Luigi Hospital, Orbassano, Italy
| | - David Santamaria
- Centro de Investigación del Cáncer, CSIC-Universidad de Salamanca, Spain
| | - Chiara Ambrogio
- Department of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center, University of Torino, Italy
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9
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Chen J, Li XN, Lu CC, Yuan S, Yung G, Ye J, Tian H, Lin J. Considerations for master protocols using external controls. J Biopharm Stat 2024:1-23. [PMID: 38363805 DOI: 10.1080/10543406.2024.2311248] [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: 05/03/2023] [Accepted: 01/24/2024] [Indexed: 02/18/2024]
Abstract
There has been an increasing use of master protocols in oncology clinical trials because of its efficiency to accelerate cancer drug development and flexibility to accommodate multiple substudies. Depending on the study objective and design, a master protocol trial can be a basket trial, an umbrella trial, a platform trial, or any other form of trials in which multiple investigational products and/or subpopulations are studied under a single protocol. Master protocols can use external data and evidence (e.g. external controls) for treatment effect estimation, which can further improve efficiency of master protocol trials. This paper provides an overview of different types of external controls and their unique features when used in master protocols. Some key considerations in master protocols with external controls are discussed including construction of estimands, assessment of fit-for-use real-world data, and considerations for different types of master protocols. Similarities and differences between regular randomized controlled trials and master protocols when using external controls are discussed. A targeted learning-based causal roadmap is presented which constitutes three key steps: (1) define a target statistical estimand that aligns with the causal estimand for the study objective, (2) use an efficient estimator to estimate the target statistical estimand and its uncertainty, and (3) evaluate the impact of causal assumptions on the study conclusion by performing sensitivity analyses. Two illustrative examples for master protocols using external controls are discussed for their merits and possible improvement in causal effect estimation.
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Affiliation(s)
- Jie Chen
- Data Sciences, ECR Global, Shanghai, China
| | | | | | - Sammy Yuan
- Oncology Statistics, GlaxoSmithKline, Collegeville, Pennsylvania, USA
| | - Godwin Yung
- Product Development Data and Statistical Sciences, Genentech/Roche, South San Francisco, Cambridge, USA
| | - Jingjing Ye
- Global Statistics and Data Sciences, BeiGene, Fulton, Maryland, USA
| | - Hong Tian
- Global Statistics, BeiGene, Ridgefield Park, New Jersy, USA
| | - Jianchang Lin
- Statistical & Quantitative Sciences, Takeda, Cambridge, Massachusetts, USA
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10
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Silverman JB, Vega PN, Tyska MJ, Lau KS. Intestinal Tuft Cells: Morphology, Function, and Implications for Human Health. Annu Rev Physiol 2024; 86:479-504. [PMID: 37863104 DOI: 10.1146/annurev-physiol-042022-030310] [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] [Indexed: 10/22/2023]
Abstract
Tuft cells are a rare and morphologically distinct chemosensory cell type found throughout many organs, including the gastrointestinal tract. These cells were identified by their unique morphologies distinguished by large apical protrusions. Ultrastructural data have begun to describe the molecular underpinnings of their cytoskeletal features, and tuft cell-enriched cytoskeletal proteins have been identified, although the connection of tuft cell morphology to tuft cell functionality has not yet been established. Furthermore, tuft cells display variations in function and identity between and within tissues, leading to the delineation of distinct tuft cell populations. As a chemosensory cell type, they display receptors that are responsive to ligands specific for their environment. While many studies have demonstrated the tuft cell response to protists and helminths in the intestine, recent research has highlighted other roles of tuft cells as well as implicated tuft cells in other disease processes including inflammation, cancer, and viral infections. Here, we review the literature on the cytoskeletal structure of tuft cells. Additionally, we focus on new research discussing tuft cell lineage, ligand-receptor interactions, tuft cell tropism, and the role of tuft cells in intestinal disease. Finally, we discuss the implication of tuft cell-targeted therapies in human health and how the morphology of tuft cells may contribute to their functionality.
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Affiliation(s)
- Jennifer B Silverman
- Epithelial Biology Center and Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, Tennessee, USA; ,
| | - Paige N Vega
- Epithelial Biology Center and Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, Tennessee, USA; ,
| | - Matthew J Tyska
- Epithelial Biology Center and Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, Tennessee, USA; ,
| | - Ken S Lau
- Epithelial Biology Center and Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, Tennessee, USA; ,
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11
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Hayes DN, Oluoha O, Schwartz DL. For Squamous Cancers, the Streetlamps Shine on Occasional Keys, Most Baskets Are Empty, and the Umbrellas Cannot Keep Us Dry: A Call for New Models in Precision Oncology. J Clin Oncol 2024; 42:487-490. [PMID: 38190587 DOI: 10.1200/jco.23.01758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 10/06/2023] [Accepted: 11/09/2023] [Indexed: 01/10/2024] Open
Affiliation(s)
- D Neil Hayes
- University of Tennessee Health Science Center, Center for Cancer Research, Memphis, TN
| | | | - David L Schwartz
- University of Tennessee Health Science Center, Center for Cancer Research, Memphis, TN
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12
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Kasianova K, Kelbert M, Mozgunov P. Response-adaptive randomization for multiarm clinical trials using context-dependent information measures. Biom J 2023; 65:e2200301. [PMID: 37816142 DOI: 10.1002/bimj.202200301] [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: 11/01/2022] [Revised: 05/10/2023] [Accepted: 06/16/2023] [Indexed: 10/12/2023]
Abstract
Theoretical-information approach applied to the clinical trial designs appeared to bring several advantages when tackling a problem of finding a balance between power and expected number of successes (ENS). In particular, it was shown that the built-in parameter of the weight function allows finding the desired trade-off between the statistical power and number of treated patients in the context of small population Phase II clinical trials. However, in real clinical trials, randomized designs are more preferable. The goal of this research is to introduce randomization to a deterministic entropy-based sequential trial procedure generalized to multiarm setting. Several methods of randomization applied to an entropy-based design are investigated in terms of statistical power and ENS. Namely, the four design types are considered: (a) deterministic procedures, (b) naive randomization using the inverse of entropy criteria as weights, (c) block randomization, and (d) randomized penalty parameter. The randomized entropy-based designs are compared to randomized Gittins index (GI) and fixed randomization (FR). After the comprehensive simulation study, the following conclusion on block randomization is made: for both entropy-based and GI-based block randomization designs the degree of randomization induced by forward-looking procedures is insufficient to achieve a decent statistical power. Therefore, we propose an adjustment for the forward-looking procedure that improves power with almost no cost in terms of ENS. In addition, the properties of randomization procedures based on randomly drawn penalty parameter are also thoroughly investigated.
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Affiliation(s)
- Ksenia Kasianova
- Faculty of Economics, National Research University Higher School of Economics, Moscow, Russia
| | - Mark Kelbert
- Faculty of Economics, National Research University Higher School of Economics, Moscow, Russia
| | - Pavel Mozgunov
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
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13
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Huml RA, Collyar D, Antonijevic Z, Beckman RA, Quek RGW, Ye J. Aiding the Adoption of Master Protocols by Optimizing Patient Engagement. Ther Innov Regul Sci 2023; 57:1136-1147. [PMID: 37615880 DOI: 10.1007/s43441-023-00570-w] [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: 11/15/2022] [Accepted: 07/24/2023] [Indexed: 08/25/2023]
Abstract
Master protocols (MPs) are an important addition to the clinical trial repertoire. As defined by the U.S. Food and Drug Administration (FDA), this term means "a protocol designed with multiple sub-studies, which may have different objectives (goals) and involve coordinated efforts to evaluate one or more investigational drugs in one or more disease subtypes within the overall trial structure." This means we now have a unique, scientifically based MP that describes how a clinical trial will be conducted using one or more potential candidate therapies to treat patients in one or more diseases. Patient engagement (PE) is also a critical factor that has been recognized by FDA through its Patient-Focused Drug Development (PFDD) initiative, and by the European Medicines Agency (EMA), which states on its website that it has been actively interacting with patients since the creation of the Agency in 1995. We propose that utilizing these PE principles in MPs can make them more successful for sponsors, providers, and patients. Potential benefits of MPs for patients awaiting treatment can include treatments that better fit a patient's needs; availability of more treatments; and faster access to treatments. These make it possible to develop innovative therapies (especially for rare diseases and/or unique subpopulations, e.g., pediatrics), to minimize untoward side effects through careful dose escalation practices and, by sharing a control arm, to lower the probability of being assigned to a placebo arm for clinical trial participants. This paper is authored by select members of the American Statistical Association (ASA)/DahShu Master Protocol Working Group (MPWG) People and Patient Engagement (PE) Subteam. DahShu is a 501(c)(3) non-profit organization, founded to promote research and education in data science. This manuscript does not include direct feedback from US or non-US regulators, though multiple regulatory-related references are cited to confirm our observation that improving patient engagement is supported by regulators. This manuscript represents the authors' independent perspective on the Master Protocol; it does not represent the official policy or viewpoint of FDA or any other regulatory organization or the views of the authors' employers. The objective of this manuscript is to provide drug developers, contract research organizations (CROs), third party capital investors, patient advocacy groups (PAGs), and biopharmaceutical executives with a better understanding of how including the patient voice throughout MP development and conduct creates more efficient clinical trials. The PE Subteam also plans to publish a Plain Language Summary (PLS) of this publication for clinical trial participants, patients, caregivers, and the public as they seek to understand the risks and benefits of MP clinical trial participation.
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Affiliation(s)
| | | | | | - Robert A Beckman
- Departments of Oncology and of Biostatistics, Bioinformatics, & Biomathematics, Lombardi Comprehensive Cancer Center and Innovation Center for Biomedical Informatics, Georgetown University Medical Center, District of Columbia (DC), Washington, USA
| | - Ruben G W Quek
- Health Economics & Outcomes Research, Regeneron Pharmaceuticals, Tarrytown, NY, USA
| | - Jingjing Ye
- Data Science and Operational Excellent, Global Statistics and Data Sciences, BeiGene, Ltd., Washington, DC, USA
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14
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Zheng H, Grayling MJ, Mozgunov P, Jaki T, Wason JMS. Bayesian sample size determination in basket trials borrowing information between subsets. Biostatistics 2023; 24:1000-1016. [PMID: 35993875 DOI: 10.1093/biostatistics/kxac033] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 07/22/2022] [Accepted: 07/29/2022] [Indexed: 12/31/2022] Open
Abstract
Basket trials are increasingly used for the simultaneous evaluation of a new treatment in various patient subgroups under one overarching protocol. We propose a Bayesian approach to sample size determination in basket trials that permit borrowing of information between commensurate subsets. Specifically, we consider a randomized basket trial design where patients are randomly assigned to the new treatment or control within each trial subset ("subtrial" for short). Closed-form sample size formulae are derived to ensure that each subtrial has a specified chance of correctly deciding whether the new treatment is superior to or not better than the control by some clinically relevant difference. Given prespecified levels of pairwise (in)commensurability, the subtrial sample sizes are solved simultaneously. The proposed Bayesian approach resembles the frequentist formulation of the problem in yielding comparable sample sizes for circumstances of no borrowing. When borrowing is enabled between commensurate subtrials, a considerably smaller trial sample size is required compared to the widely implemented approach of no borrowing. We illustrate the use of our sample size formulae with two examples based on real basket trials. A comprehensive simulation study further shows that the proposed methodology can maintain the true positive and false positive rates at desired levels.
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Affiliation(s)
- Haiyan Zheng
- MRC Biostatistics Unit, University of Cambridge, Cambridge, CB2 0SR, UK and Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, NE2 4AX, UK
| | - Michael J Grayling
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, NE2 4AX, UK
| | - Pavel Mozgunov
- MRC Biostatistics Unit, University of Cambridge, Cambridge, CB2 0SR, UK
| | - Thomas Jaki
- MRC Biostatistics Unit, University of Cambridge, Cambridge, CB2 0SR, UK and University of Regensburg, 93040 Regensburg, Germany
| | - James M S Wason
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, NE2 4AX, UK
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15
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Yoshihara K, Lee SW, Kim YM, Enomoto T. The 1st annual meeting of the East Asian Gynecologic Oncology Trial Group (EAGOT). J Gynecol Oncol 2023; 34:e87. [PMID: 37668080 DOI: 10.3802/jgo.2023.34.e87] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 08/09/2023] [Accepted: 08/09/2023] [Indexed: 09/06/2023] Open
Affiliation(s)
- Kosuke Yoshihara
- Department of Obstetrics and Gynecology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Shin Wha Lee
- Department of Obstetrics and Gynecology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Yong-Man Kim
- Department of Obstetrics and Gynecology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Takayuki Enomoto
- Department of Obstetrics and Gynecology, Osaka University Graduate School of Medicine, Osaka, Japan.
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16
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Bteich F, Mohammadi M, Li T, Bhat MA, Sofianidi A, Wei N, Kuang C. Targeting KRAS in Colorectal Cancer: A Bench to Bedside Review. Int J Mol Sci 2023; 24:12030. [PMID: 37569406 PMCID: PMC10418782 DOI: 10.3390/ijms241512030] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 07/21/2023] [Accepted: 07/23/2023] [Indexed: 08/13/2023] Open
Abstract
Colorectal cancer (CRC) is a heterogeneous disease with a myriad of alterations at the cellular and molecular levels. Kristen rat sarcoma (KRAS) mutations occur in up to 40% of CRCs and serve as both a prognostic and predictive biomarker. Oncogenic mutations in the KRAS protein affect cellular proliferation and survival, leading to tumorigenesis through RAS/MAPK pathways. Until recently, only indirect targeting of the pathway had been investigated. There are now several KRAS allele-specific inhibitors in late-phase clinical trials, and many newer agents and targeting strategies undergoing preclinical and early-phase clinical testing. The adequate treatment of KRAS-mutated CRC will inevitably involve combination therapies due to the existence of robust adaptive resistance mechanisms in these tumors. In this article, we review the most recent understanding and findings related to targeting KRAS mutations in CRC, mechanisms of resistance to KRAS inhibitors, as well as evolving treatment strategies for KRAS-mutated CRC patients.
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Affiliation(s)
- Fernand Bteich
- Department of Medical Oncology, Montefiore Medical Center, Bronx, NY 10467, USA;
- Department of Medical Oncology, Albert Einstein College of Medicine, Bronx, NY 10461, USA; (M.M.); (T.L.); (M.A.B.); (N.W.)
| | - Mahshid Mohammadi
- Department of Medical Oncology, Albert Einstein College of Medicine, Bronx, NY 10461, USA; (M.M.); (T.L.); (M.A.B.); (N.W.)
- Department of Molecular Pharmacology, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Terence Li
- Department of Medical Oncology, Albert Einstein College of Medicine, Bronx, NY 10461, USA; (M.M.); (T.L.); (M.A.B.); (N.W.)
- Department of Molecular Pharmacology, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Muzaffer Ahmed Bhat
- Department of Medical Oncology, Albert Einstein College of Medicine, Bronx, NY 10461, USA; (M.M.); (T.L.); (M.A.B.); (N.W.)
- Department of Molecular Pharmacology, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Amalia Sofianidi
- Oncology Unit, Third Department of Internal Medicine, Sotiria General Hospital for Chest Diseases, National and Kapodistrian University of Athens, 11527 Athens, Greece;
| | - Ning Wei
- Department of Medical Oncology, Albert Einstein College of Medicine, Bronx, NY 10461, USA; (M.M.); (T.L.); (M.A.B.); (N.W.)
- Department of Molecular Pharmacology, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Chaoyuan Kuang
- Department of Medical Oncology, Montefiore Medical Center, Bronx, NY 10467, USA;
- Department of Medical Oncology, Albert Einstein College of Medicine, Bronx, NY 10461, USA; (M.M.); (T.L.); (M.A.B.); (N.W.)
- Department of Molecular Pharmacology, Albert Einstein College of Medicine, Bronx, NY 10461, USA
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17
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Robertson DS, Choodari-Oskooei B, Dimairo M, Flight L, Pallmann P, Jaki T. Point estimation for adaptive trial designs II: Practical considerations and guidance. Stat Med 2023; 42:2496-2520. [PMID: 37021359 PMCID: PMC7614609 DOI: 10.1002/sim.9734] [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: 06/25/2021] [Revised: 01/20/2023] [Accepted: 03/18/2023] [Indexed: 04/07/2023]
Abstract
In adaptive clinical trials, the conventional end-of-trial point estimate of a treatment effect is prone to bias, that is, a systematic tendency to deviate from its true value. As stated in recent FDA guidance on adaptive designs, it is desirable to report estimates of treatment effects that reduce or remove this bias. However, it may be unclear which of the available estimators are preferable, and their use remains rare in practice. This article is the second in a two-part series that studies the issue of bias in point estimation for adaptive trials. Part I provided a methodological review of approaches to remove or reduce the potential bias in point estimation for adaptive designs. In part II, we discuss how bias can affect standard estimators and assess the negative impact this can have. We review current practice for reporting point estimates and illustrate the computation of different estimators using a real adaptive trial example (including code), which we use as a basis for a simulation study. We show that while on average the values of these estimators can be similar, for a particular trial realization they can give noticeably different values for the estimated treatment effect. Finally, we propose guidelines for researchers around the choice of estimators and the reporting of estimates following an adaptive design. The issue of bias should be considered throughout the whole lifecycle of an adaptive design, with the estimation strategy prespecified in the statistical analysis plan. When available, unbiased or bias-reduced estimates are to be preferred.
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Affiliation(s)
| | - Babak Choodari-Oskooei
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, University College London, London, UK
| | - Munya Dimairo
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | - Laura Flight
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | | | - Thomas Jaki
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Faculty of Informatics and Data Science, University of Regensburg, Regensburg, Germany
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18
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Barrado LG, Burzykowski T. Over-accrual in Bayesian adaptive trials with continuous futility stopping. Clin Trials 2023; 20:252-260. [PMID: 36803007 DOI: 10.1177/17407745231154685] [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] [Indexed: 02/22/2023]
Abstract
BACKGROUND We explore frequentist operating characteristics of a Bayesian adaptive design that allows continuous early stopping for futility. In particular, we focus on the power versus sample size relationship when more patients are accrued than originally planned. METHODS We consider the case of a phase II single-arm study and a Bayesian phase II outcome-adaptive randomization design. For the former, analytical calculations are possible; for the latter, simulations are conducted. RESULTS Results for both cases show a decrease in power with an increasing sample size. It appears that this effect is due to the increasing cumulative probability of incorrectly stopping for futility. CONCLUSION The increase in cumulative probability of incorrectly stopping for futility is related to the continuous nature of the early stopping, which increases the number of interim analyses with accrual. The issue can be addressed by, for instance, delaying the start of testing for futility, reducing the number of futility tests to be performed or by setting stricter criteria for concluding futility.
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Affiliation(s)
- Leandro Garcia Barrado
- Data Science Institute and I-BioStat, Hasselt University, Hasselt, Belgium
- International Drug Development Institute (IDDI), Louvain-la-Neuve, Belgium
| | - Tomasz Burzykowski
- Data Science Institute and I-BioStat, Hasselt University, Hasselt, Belgium
- International Drug Development Institute (IDDI), Louvain-la-Neuve, Belgium
- Department of Biostatistics and Medical Informatics, Medical University of Bialystok, Bialystok, Poland
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19
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Liu W, Huo G, Chen P. Clinical benefit of pembrolizumab in treatment of first line non-small cell lung cancer: a systematic review and meta-analysis of clinical characteristics. BMC Cancer 2023; 23:458. [PMID: 37202730 DOI: 10.1186/s12885-023-10959-3] [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: 01/02/2023] [Accepted: 05/13/2023] [Indexed: 05/20/2023] Open
Abstract
OBJECTIVE Pembrolizumab has become an integral first line therapeutic agent for non-small cell lung cancer (NSCLC), but its potential predictive role in clinical and molecular characteristics remains to be clarified. Accordingly, we performed a systematic review and meta-analysis to evaluate the clinical benefit of pembrolizumab in treatment of first line NSCLC and to select individuals with the greatest potential benefit from pembrolizumab therapy, in order to obtain a more accurate treatment of NSCLC in immunotherapy. METHODS Mainstream oncology datasets and conferences were searched for randomized clinical trials (RCTs) published before August 2022. RCTs involved individuals with first line NSCLC treated with pembrolizumab monotherapy or in combination with chemotherapy. Two authors independently selected the studies, extracted data, and assessed the risk of bias. The basic characteristics of the included studies were recorded, along with 95 percent confidence intervals (CI) and hazard ratios (HR) for all patients and subgroups. The primary endpoint was overall survival (OS), and secondary endpoints was progression-free survival (PFS). Pooled treatment data were estimated using the inverse variance-weighted method. RESULTS Five RCTs involving 2,877 individuals were included in the study. Pembrolizumab-based therapy significantly improved OS (HR 0.66; CI 95%, 0.55-0.79; p < 0.00001) and PFS (HR 0.60; CI 95%, 0.40-0.91; p = 0.02) compared with chemotherapy. OS was substantially enhanced in individuals aged < 65 years (HR 0.59; CI 95%, 0.42-0.82; p = 0.002), males (HR 0.74; CI 95%, 0.65-0.83; p < 0.00001), with a smoking history (HR 0.65; CI 95%, 0.52-0.82; p = 0.0003), with PD-L1 tumor proportion score (TPS) < 1% (HR 0.55; CI 95%, 0.41-0.73; p < 0.0001) and TPS ≥ 50% (HR 0.66; CI 95%, 0.56-0.76; p < 0.00001), but not in individuals aged ≥ 75 years (HR 0.82; CI 95%, 0.56-1.21; p = 0.32), females (HR 0.57; CI 95%, 0.31-1.06; p = 0.08), never smokers (HR 0.57; CI 95%, 0.18-1.80; p = 0.34), or with TPS 1-49% (HR 0.72; CI 95%, 0.52-1.01; p = 0.06). Pembrolizumab significantly prolonged OS in NSCLC patients, regardless of histology type (squamous or non-squamous NSCLC), performance status (PS) (0 or 1), and brain metastatic status (all p < 0.05). Subgroup analysis revealed that pembrolizumab combined with chemotherapy had more favorable HR values than pembrolizumab monotherapy in improving the OS of individuals with different clinical and molecular features. CONCLUSION Pembrolizumab-based therapy is a valuable option for first line treating advanced or metastatic NSCLC. Age, sex, smoking history and PD-L1 expression status can be used to predict the clinical benefit of pembrolizumab. Cautiousness was needed when using pembrolizumab in NSCLC patients aged ≥ 75 years, females, never smokers, or in patients with TPS 1-49%. Furthermore, pembrolizumab in combination with chemotherapy may be a more effective treatment regimen.
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Affiliation(s)
- Wenjie Liu
- Department of Thoracic Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China
| | - Gengwei Huo
- Department of Thoracic Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China
| | - Peng Chen
- Department of Thoracic Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China.
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20
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Robertson DS, Lee KM, López-Kolkovska BC, Villar SS. Response-adaptive randomization in clinical trials: from myths to practical considerations. Stat Sci 2023; 38:185-208. [PMID: 37324576 PMCID: PMC7614644 DOI: 10.1214/22-sts865] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Response-Adaptive Randomization (RAR) is part of a wider class of data-dependent sampling algorithms, for which clinical trials are typically used as a motivating application. In that context, patient allocation to treatments is determined by randomization probabilities that change based on the accrued response data in order to achieve experimental goals. RAR has received abundant theoretical attention from the biostatistical literature since the 1930's and has been the subject of numerous debates. In the last decade, it has received renewed consideration from the applied and methodological communities, driven by well-known practical examples and its widespread use in machine learning. Papers on the subject present different views on its usefulness, and these are not easy to reconcile. This work aims to address this gap by providing a unified, broad and fresh review of methodological and practical issues to consider when debating the use of RAR in clinical trials.
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Affiliation(s)
- David S. Robertson
- MRC Biostatistics Unit, University of Cambridge, Forvie Site, Robinson Way, Cambridge CB2 0SR, United Kingdom
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21
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Subhan MA, Parveen F, Shah H, Yalamarty SSK, Ataide JA, Torchilin VP. Recent Advances with Precision Medicine Treatment for Breast Cancer including Triple-Negative Sub-Type. Cancers (Basel) 2023; 15:cancers15082204. [PMID: 37190133 DOI: 10.3390/cancers15082204] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/04/2023] [Accepted: 04/05/2023] [Indexed: 05/17/2023] Open
Abstract
Breast cancer is a heterogeneous disease with different molecular subtypes. Breast cancer is the second leading cause of mortality in woman due to rapid metastasis and disease recurrence. Precision medicine remains an essential source to lower the off-target toxicities of chemotherapeutic agents and maximize the patient benefits. This is a crucial approach for a more effective treatment and prevention of disease. Precision-medicine methods are based on the selection of suitable biomarkers to envision the effectiveness of targeted therapy in a specific group of patients. Several druggable mutations have been identified in breast cancer patients. Current improvements in omics technologies have focused on more precise strategies for precision therapy. The development of next-generation sequencing technologies has raised hopes for precision-medicine treatment strategies in breast cancer (BC) and triple-negative breast cancer (TNBC). Targeted therapies utilizing immune checkpoint inhibitors (ICIs), epidermal growth factor receptor inhibitor (EGFRi), poly(ADP-ribose) polymerase inhibitor (PARPi), antibody-drug conjugates (ADCs), oncolytic viruses (OVs), glucose transporter-1 inhibitor (GLUT1i), and targeting signaling pathways are potential treatment approaches for BC and TNBC. This review emphasizes the recent progress made with the precision-medicine therapy of metastatic breast cancer and TNBC.
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Affiliation(s)
- Md Abdus Subhan
- Department of Chemistry, Shahjalal University of Science and Technology, Sylhet 3114, Bangladesh
| | - Farzana Parveen
- Department of Pharmaceutics, Faculty of Pharmacy, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan
- Department of Pharmacy Services, DHQ Hospital Jhang 35200, Primary and Secondary Healthcare Department, Government of Punjab, Lahore 54000, Pakistan
| | - Hassan Shah
- Department of Pharmaceutics, Faculty of Pharmacy, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan
- CPBN, Department of Pharmaceutical Sciences, Northeastern University, Boston, MA 02115, USA
| | | | - Janaína Artem Ataide
- CPBN, Department of Pharmaceutical Sciences, Northeastern University, Boston, MA 02115, USA
- Faculty of Pharmaceutical Sciences, University of Campinas, Campinas 13083-871, SP, Brazil
| | - Valdimir P Torchilin
- CPBN, Department of Pharmaceutical Sciences, Northeastern University, Boston, MA 02115, USA
- Department of Chemical Engineering, Northeastern University, Boston, MA 02115, USA
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22
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Yan W, Quan C, Waleed M, Yuan J, Shi Z, Yang J, Lu Q, Zhang J. Application of radiomics in lung immuno‐oncology. PRECISION RADIATION ONCOLOGY 2023. [DOI: 10.1002/pro6.1191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/08/2023] Open
Affiliation(s)
- Weisi Yan
- Baptist Health System Lexington Kentucky USA
| | - Chen Quan
- City of Hope Comprehensive Cancer Center Duarte California USA
| | - Mourad Waleed
- Department of Radiation Medicine University of Kentucky Lexington Kentucky USA
| | - Jianda Yuan
- Translational Oncology at Merck & Co Kenilworth New Jersey USA
| | | | - Jun Yang
- Foshan Chancheng Hospital Foshan Guangdong China
| | - Qiuxia Lu
- Foshan Chancheng Hospital Foshan Guangdong China
| | - Jie Zhang
- Department of Radiology University of Kentucky Lexington Kentucky USA
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23
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Liu A, Vicenzi P, Sharma I, Orr K, Teller C, Koentz M, Trinkman H, Vallance K, Ray A. Molecular Tumor Boards: The Next Step towards Precision Therapy in Cancer Care. Hematol Rep 2023; 15:244-255. [PMID: 37092519 PMCID: PMC10123678 DOI: 10.3390/hematolrep15020025] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 03/05/2023] [Accepted: 03/30/2023] [Indexed: 04/08/2023] Open
Abstract
The application of molecular tumor profiles in clinical decision making remains a challenge. To aid in the interpretation of complex biomarkers, molecular tumor boards (MTBs) have been established worldwide. In the present study, we show that a multidisciplinary approach is essential to the success of MTBs. Our MTB, consisting of pediatric oncologists, pathologists, and pharmacists, evaluated 115 cases diagnosed between March 2016 and September 2021. If targetable mutations were identified, pharmacists aided in the evaluation of treatment options based on drug accessibility. Treatable genetic alterations detected through molecular testing most frequently involved the cell cycle. For 85% of the cases evaluated, our MTB provided treatment recommendations based on the patient’s history and results of molecular tumor testing. Only three patients, however, received MTB-recommended targeted therapy, and only one of these patients demonstrated an improved clinical outcome. For the remaining patients, MTB-recommended treatment often was not administered because molecular tumor profiling was not performed until late in the disease course. For the three patients who did receive MTB-recommended therapy, such treatment was not administered until months after diagnosis due to physician preference. Thus, the education of healthcare providers regarding the benefits of targeted therapy may increase acceptance of these novel agents and subsequently improve patient survival.
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Affiliation(s)
- Angela Liu
- Texas College of Osteopathic Medicine, University of North Texas Health Science Center, Fort Worth, TX 76107, USA
| | - Paige Vicenzi
- Department of Pediatrics, Dell Children’s Medical Center, Austin, TX 78723, USA
| | - Ishna Sharma
- Texas College of Osteopathic Medicine, University of North Texas Health Science Center, Fort Worth, TX 76107, USA
| | - Kaci Orr
- Texas A&M Health Science Center School of Medicine, Bryan, TX 77807, USA
| | - Christa Teller
- Department of Pediatric Hematology/Oncology, Cook Children’s Medical Center, Fort Worth, TX 76104, USA
| | - Micha Koentz
- Department of Pharmacy, Cook Children’s Medical Center, Fort Worth, TX 76104, USA
| | - Heidi Trinkman
- Department of Pharmacy, Cook Children’s Medical Center, Fort Worth, TX 76104, USA
| | - Kelly Vallance
- Department of Pediatric Hematology/Oncology, Cook Children’s Medical Center, Fort Worth, TX 76104, USA
| | - Anish Ray
- Department of Pediatric Hematology/Oncology, Cook Children’s Medical Center, Fort Worth, TX 76104, USA
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24
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Harada G, Yang SR, Cocco E, Drilon A. Rare molecular subtypes of lung cancer. Nat Rev Clin Oncol 2023; 20:229-249. [PMID: 36806787 PMCID: PMC10413877 DOI: 10.1038/s41571-023-00733-6] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/17/2023] [Indexed: 02/22/2023]
Abstract
Oncogenes that occur in ≤5% of non-small-cell lung cancers have been defined as 'rare'; nonetheless, this frequency can correspond to a substantial number of patients diagnosed annually. Within rare oncogenes, less commonly identified alterations (such as HRAS, NRAS, RIT1, ARAF, RAF1 and MAP2K1 mutations, or ERBB family, LTK and RASGRF1 fusions) can share certain structural or oncogenic features with more commonly recognized alterations (such as KRAS, BRAF, MET and ERBB family mutations, or ALK, RET and ROS1 fusions). Over the past 5 years, a surge in the identification of rare-oncogene-driven lung cancers has challenged the boundaries of traditional clinical grade diagnostic assays and profiling algorithms. In tandem, the number of approved targeted therapies for patients with rare molecular subtypes of lung cancer has risen dramatically. Rational drug design has iteratively improved the quality of small-molecule therapeutic agents and introduced a wave of antibody-based therapeutics, expanding the list of actionable de novo and resistance alterations in lung cancer. Getting additional molecularly tailored therapeutics approved for rare-oncogene-driven lung cancers in a larger range of countries will require ongoing stakeholder cooperation. Patient advocates, health-care agencies, investigators and companies with an interest in diagnostics, therapeutics and real-world evidence have already taken steps to surmount the challenges associated with research into low-frequency drivers.
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Affiliation(s)
- Guilherme Harada
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Soo-Ryum Yang
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Emiliano Cocco
- Department of Biochemistry and Molecular Biology/Sylvester Comprehensive Cancer Center, University of Miami/Miller School of Medicine, Miami, FL, USA.
| | - Alexander Drilon
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- Department of Medicine, Weill Cornell Medical College, New York, NY, USA.
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25
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Identifying polymorphic cis-regulatory variants as risk markers for lung carcinogenesis and chemotherapy responses in tobacco smokers from eastern India. Sci Rep 2023; 13:4019. [PMID: 36899086 PMCID: PMC10006236 DOI: 10.1038/s41598-023-30962-9] [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: 07/09/2022] [Accepted: 03/03/2023] [Indexed: 03/12/2023] Open
Abstract
Aberrant expression of xenobiotic metabolism and DNA repair genes is critical to lung cancer pathogenesis. This study aims to identify the cis-regulatory variants of the genes modulating lung cancer risk among tobacco smokers and altering their chemotherapy responses. From a list of 2984 SNVs, prioritization and functional annotation revealed 22 cis-eQTLs of 14 genes within the gene expression-correlated DNase I hypersensitive sites using lung tissue-specific ENCODE, GTEx, Roadmap Epigenomics, and TCGA datasets. The 22 cis-regulatory variants predictably alter the binding of 44 transcription factors (TFs) expressed in lung tissue. Interestingly, 6 reported lung cancer-associated variants were found in linkage disequilibrium (LD) with 5 prioritized cis-eQTLs from our study. A case-control study with 3 promoter cis-eQTLs (p < 0.01) on 101 lung cancer patients and 401 healthy controls from eastern India with confirmed smoking history revealed an association of rs3764821 (ALDH3B1) (OR = 2.53, 95% CI = 1.57-4.07, p = 0.00014) and rs3748523 (RAD52) (OR = 1.69, 95% CI = 1.17-2.47, p = 0.006) with lung cancer risk. The effect of different chemotherapy regimens on the overall survival of lung cancer patients to the associated variants showed that the risk alleles of both variants significantly decreased (p < 0.05) patient survival.
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Ruberg SJ, Beckers F, Hemmings R, Honig P, Irony T, LaVange L, Lieberman G, Mayne J, Moscicki R. Application of Bayesian approaches in drug development: starting a virtuous cycle. Nat Rev Drug Discov 2023; 22:235-250. [PMID: 36792750 PMCID: PMC9931171 DOI: 10.1038/s41573-023-00638-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/06/2023] [Indexed: 02/17/2023]
Abstract
The pharmaceutical industry and its global regulators have routinely used frequentist statistical methods, such as null hypothesis significance testing and p values, for evaluation and approval of new treatments. The clinical drug development process, however, with its accumulation of data over time, can be well suited for the use of Bayesian statistical approaches that explicitly incorporate existing data into clinical trial design, analysis and decision-making. Such approaches, if used appropriately, have the potential to substantially reduce the time and cost of bringing innovative medicines to patients, as well as to reduce the exposure of patients in clinical trials to ineffective or unsafe treatment regimens. Nevertheless, despite advances in Bayesian methodology, the availability of the necessary computational power and growing amounts of relevant existing data that could be used, Bayesian methods remain underused in the clinical development and regulatory review of new therapies. Here, we highlight the value of Bayesian methods in drug development, discuss barriers to their application and recommend approaches to address them. Our aim is to engage stakeholders in the process of considering when the use of existing data is appropriate and how Bayesian methods can be implemented more routinely as an effective tool for doing so.
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Affiliation(s)
| | | | | | | | - Telba Irony
- Janssen Pharmaceutical Companies of J & J, Titusville, NJ, USA
| | - Lisa LaVange
- University of North Carolina, Chapel Hill, NC, USA
| | | | - James Mayne
- Pharmaceutical Research and Manufacturers of America, Washington, DC, USA
| | - Richard Moscicki
- Pharmaceutical Research and Manufacturers of America, Washington, DC, USA
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Avalos-Pacheco A, Ventz S, Arfè A, Alexander BM, Rahman R, Wen PY, Trippa L. Validation of Predictive Analyses for Interim Decisions in Clinical Trials. JCO Precis Oncol 2023; 7:e2200606. [PMID: 36848613 PMCID: PMC10166373 DOI: 10.1200/po.22.00606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 12/20/2022] [Accepted: 01/12/2023] [Indexed: 03/01/2023] Open
Abstract
PURPOSE Adaptive clinical trials use algorithms to predict, during the study, patient outcomes and final study results. These predictions trigger interim decisions, such as early discontinuation of the trial, and can change the course of the study. Poor selection of the Prediction Analyses and Interim Decisions (PAID) plan in an adaptive clinical trial can have negative consequences, including the risk of exposing patients to ineffective or toxic treatments. METHODS We present an approach that leverages data sets from completed trials to evaluate and compare candidate PAIDs using interpretable validation metrics. The goal is to determine whether and how to incorporate predictions into major interim decisions in a clinical trial. Candidate PAIDs can differ in several aspects, such as the prediction models used, timing of interim analyses, and potential use of external data sets. To illustrate our approach, we considered a randomized clinical trial in glioblastoma. The study design includes interim futility analyses on the basis of the predictive probability that the final analysis, at the completion of the study, will provide significant evidence of treatment effects. We examined various PAIDs with different levels of complexity to investigate if the use of biomarkers, external data, or novel algorithms improved interim decisions in the glioblastoma clinical trial. RESULTS Validation analyses on the basis of completed trials and electronic health records support the selection of algorithms, predictive models, and other aspects of PAIDs for use in adaptive clinical trials. By contrast, PAID evaluations on the basis of arbitrarily defined ad hoc simulation scenarios, which are not tailored to previous clinical data and experience, tend to overvalue complex prediction procedures and produce poor estimates of trial operating characteristics such as power and the number of enrolled patients. CONCLUSION Validation analyses on the basis of completed trials and real world data support the selection of predictive models, interim analysis rules, and other aspects of PAIDs in future clinical trials.
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Affiliation(s)
- Alejandra Avalos-Pacheco
- Applied Statistics Research Unit, Faculty of Mathematics and Geoinformation, TU Wien, Vienna, Austria
- Harvard-MIT Center for Regulatory Science, Harvard Medical School, Boston, MA
| | - Steffen Ventz
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN
| | - Andrea Arfè
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Brian M. Alexander
- Dana-Farber Cancer Institute, Boston, MA
- Foundation Medicine, Cambridge, MA
| | - Rifaquat Rahman
- Dana-Farber Cancer Institute, Boston, MA
- Harvard Medical School, Boston, MA
| | - Patrick Y. Wen
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Boston, MA
| | - Lorenzo Trippa
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA
- Harvard T.H. Chan School of Public Health, Boston, MA
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Ouma LO, Wason JMS, Zheng H, Wilson N, Grayling M. Design and analysis of umbrella trials: Where do we stand? Front Med (Lausanne) 2022; 9:1037439. [PMID: 36313987 PMCID: PMC9596938 DOI: 10.3389/fmed.2022.1037439] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 09/26/2022] [Indexed: 11/13/2022] Open
Abstract
Background The efficiencies that master protocol designs can bring to modern drug development have seen their increased utilization in oncology. Growing interest has also resulted in their consideration in non-oncology settings. Umbrella trials are one class of master protocol design that evaluates multiple targeted therapies in a single disease setting. Despite the existence of several reviews of master protocols, the statistical considerations of umbrella trials have received more limited attention. Methods We conduct a systematic review of the literature on umbrella trials, examining both the statistical methods that are available for their design and analysis, and also their use in practice. We pay particular attention to considerations for umbrella designs applied outside of oncology. Findings We identified 38 umbrella trials. To date, most umbrella trials have been conducted in early phase settings (73.7%, 28/38) and in oncology (92.1%, 35/38). The quality of statistical information available about conducted umbrella trials to date is poor; for example, it was impossible to ascertain how sample size was determined in the majority of trials (55.3%, 21/38). The literature on statistical methods for umbrella trials is currently sparse. Conclusions Umbrella trials have potentially great utility to expedite drug development, including outside of oncology. However, to enable lessons to be effectively learned from early use of such designs, there is a need for higher-quality reporting of umbrella trials. Furthermore, if the potential of umbrella trials is to be realized, further methodological research is required.
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Affiliation(s)
- Luke O. Ouma
- Biostatistics Research Group, Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - James M. S. Wason
- Biostatistics Research Group, Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Haiyan Zheng
- Medical Research Council (MRC) Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
| | - Nina Wilson
- Biostatistics Research Group, Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Michael Grayling
- Biostatistics Research Group, Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
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Gao G, Gajewski BJ, Wick J, Beall J, Saver JL, Meinzer C. Optimizing a Bayesian hierarchical adaptive platform trial design for stroke patients. Trials 2022; 23:754. [PMID: 36068547 PMCID: PMC9446515 DOI: 10.1186/s13063-022-06664-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 08/16/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Platform trials are well-known for their ability to investigate multiple arms on heterogeneous patient populations and their flexibility to add/drop treatment arms due to efficacy/lack of efficacy. Because of their complexity, it is important to develop highly optimized, transparent, and rigorous designs that are cost-efficient, offer high statistical power, maximize patient benefit, and are robust to changes over time. METHODS To address these needs, we present a Bayesian platform trial design based on a beta-binomial model for binary outcomes that uses three key strategies: (1) hierarchical modeling of subgroups within treatment arms that allows for borrowing of information across subgroups, (2) utilization of response-adaptive randomization (RAR) schemes that seek a tradeoff between statistical power and patient benefit, and (3) adjustment for potential drift over time. Motivated by a proposed clinical trial that aims to find the appropriate treatment for different subgroup populations of ischemic stroke patients, extensive simulation studies were performed to validate the approach, compare different allocation rules, and study the model operating characteristics. RESULTS AND CONCLUSIONS Our proposed approach achieved high statistical power and good patient benefit and was also robust against population drift over time. Our design provided a good balance between the strengths of both the traditional RAR scheme and fixed 1:1 allocation and may be a promising choice for dichotomous outcomes trials investigating multiple subgroups.
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Affiliation(s)
- Guangyi Gao
- Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, KS, 66160, USA.
| | - Byron J Gajewski
- Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, KS, 66160, USA
| | - Jo Wick
- Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, KS, 66160, USA
| | - Jonathan Beall
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, 29425, USA
| | - Jeffrey L Saver
- Department of Neurology and Comprehensive Stroke Center, University of California, Los Angeles, CA, 90095, USA
| | - Caitlyn Meinzer
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, 29425, USA
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Fountzilas E, Tsimberidou AM, Vo HH, Kurzrock R. Clinical trial design in the era of precision medicine. Genome Med 2022; 14:101. [PMID: 36045401 PMCID: PMC9428375 DOI: 10.1186/s13073-022-01102-1] [Citation(s) in RCA: 63] [Impact Index Per Article: 31.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 08/09/2022] [Indexed: 12/24/2022] Open
Abstract
Recent rapid biotechnological breakthroughs have led to the identification of complex and unique molecular features that drive malignancies. Precision medicine has exploited next-generation sequencing and matched targeted therapy/immunotherapy deployment to successfully transform the outlook for several fatal cancers. Tumor and liquid biopsy genomic profiling and transcriptomic, immunomic, and proteomic interrogation can now all be leveraged to optimize therapy. Multiple new trial designs, including basket and umbrella trials, master platform trials, and N-of-1 patient-centric studies, are beginning to supplant standard phase I, II, and III protocols, allowing for accelerated drug evaluation and approval and molecular-based individualized treatment. Furthermore, real-world data, as well as exploitation of digital apps and structured observational registries, and the utilization of machine learning and/or artificial intelligence, may further accelerate knowledge acquisition. Overall, clinical trials have evolved, shifting from tumor type-centered to gene-directed and histology-agnostic trials, with innovative adaptive designs and personalized combination treatment strategies tailored to individual biomarker profiles. Some, but not all, novel trials now demonstrate that matched therapy correlates with superior outcomes compared to non-matched therapy across tumor types and in specific cancers. To further improve the precision medicine paradigm, the strategy of matching drugs to patients based on molecular features should be implemented earlier in the disease course, and cancers should have comprehensive multi-omic (genomics, transcriptomics, proteomics, immunomic) tumor profiling. To overcome cancer complexity, moving from drug-centric to patient-centric individualized combination therapy is critical. This review focuses on the design, advantages, limitations, and challenges of a spectrum of clinical trial designs in the era of precision oncology.
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Affiliation(s)
- Elena Fountzilas
- Department of Medical Oncology, St. Lukes's Hospital, Thessaloniki, Greece.,European University Cyprus, Limassol, Cyprus
| | - Apostolia M Tsimberidou
- Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Henry Hiep Vo
- Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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Strzebonska K, Blukacz M, Wasylewski MT, Polak M, Gyawali B, Waligora M. Risk and benefit for umbrella trials in oncology: a systematic review and meta-analysis. BMC Med 2022; 20:219. [PMID: 35799149 PMCID: PMC9264503 DOI: 10.1186/s12916-022-02420-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 05/30/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Umbrella clinical trials in precision oncology are designed to tailor therapies to the specific genetic changes within a tumor. Little is known about the risk/benefit ratio for umbrella clinical trials. The aim of our systematic review with meta-analysis was to evaluate the efficacy and safety profiles in cancer umbrella trials testing targeted drugs or a combination of targeted therapy with chemotherapy. METHODS Our study was prospectively registered in PROSPERO (CRD42020171494). We searched Embase and PubMed for cancer umbrella trials testing targeted agents or a combination of targeted therapies with chemotherapy. We included solid tumor studies published between 1 January 2006 and 7 October 2019. We measured the risk using drug-related grade 3 or higher adverse events (AEs), and the benefit by objective response rate (ORR), progression-free survival (PFS), and overall survival (OS). When possible, data were meta-analyzed. RESULTS Of the 6207 records identified, we included 31 sub-trials or arms of nine umbrella trials (N = 1637). The pooled overall ORR was 17.7% (95% confidence interval [CI] 9.5-25.9). The ORR for targeted therapies in the experimental arms was significantly lower than the ORR for a combination of targeted therapy drugs with chemotherapy: 13.3% vs 39.0%; p = 0.005. The median PFS was 2.4 months (95% CI 1.9-2.9), and the median OS was 7.1 months (95% CI 6.1-8.4). The overall drug-related death rate (drug-related grade 5 AEs rate) was 0.8% (95% CI 0.3-1.4), and the average drug-related grade 3/4 AE rate per person was 0.45 (95% CI 0.40-0.50). CONCLUSIONS Our findings suggest that, on average, one in five cancer patients in umbrella trials published between 1 January 2006 and 7 October 2019 responded to a given therapy, while one in 125 died due to drug toxicity. Our findings do not support the expectation of increased patient benefit in cancer umbrella trials. Further studies should investigate whether umbrella trial design and the precision oncology approach improve patient outcomes.
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Affiliation(s)
- Karolina Strzebonska
- Research Ethics in Medicine Study Group (REMEDY), Faculty of Health Sciences, Jagiellonian University Medical College, Kraków, Poland
| | - Mateusz Blukacz
- Institute of Psychology, University of Silesia, Katowice, Poland
- Institute of Public Health, Faculty of Health Sciences, Jagiellonian University Medical College, Kraków, Poland
| | - Mateusz T. Wasylewski
- Research Ethics in Medicine Study Group (REMEDY), Faculty of Health Sciences, Jagiellonian University Medical College, Kraków, Poland
| | - Maciej Polak
- Research Ethics in Medicine Study Group (REMEDY), Faculty of Health Sciences, Jagiellonian University Medical College, Kraków, Poland
- Department of Epidemiology and Population Studies, Institute of Public Health, Faculty of Health Sciences, Jagiellonian University Medical College, Kraków, Poland
| | - Bishal Gyawali
- Department of Oncology and the Department of Public Health Sciences, Queen’s University, Kingston, Ontario Canada
| | - Marcin Waligora
- Research Ethics in Medicine Study Group (REMEDY), Faculty of Health Sciences, Jagiellonian University Medical College, Kraków, Poland
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MRI Radiogenomics in Precision Oncology: New Diagnosis and Treatment Method. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:2703350. [PMID: 35845886 PMCID: PMC9282990 DOI: 10.1155/2022/2703350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 05/04/2022] [Accepted: 05/25/2022] [Indexed: 11/21/2022]
Abstract
Precision medicine for cancer affords a new way for the most accurate and effective treatment to each individual cancer. Given the high time-evolving intertumor and intratumor heterogeneity features of personal medicine, there are still several obstacles hindering its diagnosis and treatment in clinical practice regardless of extensive exploration on it over the past years. This paper is to investigate radiogenomics methods in the literature for precision medicine for cancer focusing on the heterogeneity analysis of tumors. Based on integrative analysis of multimodal (parametric) imaging and molecular data in bulk tumors, a comprehensive analysis and discussion involving the characterization of tumor heterogeneity in imaging and molecular expression are conducted. These investigations are intended to (i) fully excavate the multidimensional spatial, temporal, and semantic related information regarding high-dimensional breast magnetic resonance imaging data, with integration of the highly specific structured data of genomics and combination of the diagnosis and cognitive process of doctors, and (ii) establish a radiogenomics data representation model based on multidimensional consistency analysis with multilevel spatial-temporal correlations.
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Racine-Poon A, D’Amelio A, Sverdlov O, Haas T. OPTIM-ARTS—An Adaptive Phase II Open Platform Trial Design With Application to a Metastatic Melanoma Study. Stat Biopharm Res 2022. [DOI: 10.1080/19466315.2020.1749722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Shen F, Liang N, Fan Z, Zhao M, Kang J, Wang X, Hu Q, Mu Y, Wang K, Yuan M, Chen R, Guo W, Dong G, Zhao J, Bai J. Genomic Alterations Identification and Resistance Mechanisms Exploration of NSCLC With Central Nervous System Metastases Using Liquid Biopsy of Cerebrospinal Fluid: A Real-World Study. Front Oncol 2022; 12:889591. [PMID: 35814426 PMCID: PMC9259993 DOI: 10.3389/fonc.2022.889591] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 04/19/2022] [Indexed: 12/24/2022] Open
Abstract
Background Genomic profiling of cerebrospinal fluid (CSF) can be used to detect actionable mutations and guide clinical treatment of non-small cell lung cancer (NSCLC) patients with central nervous system (CNS) metastases. Examining the performance of CSF samples in real-world settings can confirm the potential of CSF genotyping for guiding therapy in clinical practice. Patients and Methods We included 1,396 samples from 970 NSCLC patients with CNS metastases in real-world settings. All samples underwent targeted next-generation sequencing of 1,021 cancer-relevant genes. In total, 100 CSF samples from 77 patients who had previously received targeted treatment were retrospectively analyzed to explore the mechanisms of TKI-resistance. Results For NSCLC patients with CNS metastases, CSF samples were slightly more often used for genomic sequencing in treated patients with only distant CNS metastases compared to other patients (10.96% vs. 0.81–9.61%). Alteration rates in CSF samples were significantly higher than those in plasma, especially for copy number variants (CNV). The MSAFs of CSF samples were significantly higher than those of plasma and tumor tissues (all p <0.001). Remarkably, detection rates of all actionable mutations and EGFR in CSF were higher than those in plasma samples of treated patients (all p <0.0001). For concordance between paired CSF and plasma samples that were simultaneously tested, the MSAF of the CSF was significantly higher than that of matched plasma cfDNA (p <0.001). From multiple comparisons, it can be seen that CSF better detects alterations compared to plasma, especially CNV and structural variant (SV) alterations. CSF cfDNA in identifying mutations can confer the reason for the limited efficacy of EGFR-TKIs for 56 patients (78.87%, 56/71). Conclusions This real-world large cohort study confirmed that CSF had higher sensitivity than plasma in identifying actionable mutations and showed high potential in exploring underlying resistance mechanisms. CSF can be used in genomics profiling to facilitate the broad exploration of potential resistance mechanisms for NSCLC patients with CNS metastases.
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Affiliation(s)
- Fangfang Shen
- Department of Respiratory Medicine, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, China
| | - Naixin Liang
- Department of Thoracic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Zaiwen Fan
- Department of Medical Oncology, Air Force Medical Center, Chinese People's Liberation Army (PLA), Beijing, China
| | - Min Zhao
- Department of Oncology, Hebei Chest Hospital, Research Center of Hebei Lung Cancer Prevention and Treatment, Shijiazhuang, China
| | - Jing Kang
- Department of Oncology, Honghui Hospital, Xi’an Jiaotong University, Xi’an, China
| | - Xifang Wang
- Department of Medical Oncology, Shaanxi Provincial People’s Hospital, Xi’an, China
| | - Qun Hu
- Department of Oncology, The Affiliated Hospital of Inner Mongolia Medical University, Hohhot, China
| | - Yongping Mu
- Department of Clinical Laboratory Center, The Affiliated People’s Hospital of Inner Mongolia Medical University, Inner Mongolia Autonomous Region Cancer Hospital, Hohhot, China
| | - Kai Wang
- Medical Center, Geneplus-Beijing, Beijing, China
| | | | | | - Wei Guo
- Department of Respiratory Medicine, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, China
- *Correspondence: Jun Bai, ; Jun Zhao, ; Guilan Dong, ; Wei Guo,
| | - Guilan Dong
- Department of Medical Oncology, Tangshan People’s Hospital, Tangshan, China
- *Correspondence: Jun Bai, ; Jun Zhao, ; Guilan Dong, ; Wei Guo,
| | - Jun Zhao
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department I of Thoracic Oncology, Peking University Cancer Hospital and Institute, Beijing, China
- *Correspondence: Jun Bai, ; Jun Zhao, ; Guilan Dong, ; Wei Guo,
| | - Jun Bai
- Department of Medical Oncology, Shaanxi Provincial People’s Hospital, Xi’an, China
- *Correspondence: Jun Bai, ; Jun Zhao, ; Guilan Dong, ; Wei Guo,
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Quazi S. Artificial intelligence and machine learning in precision and genomic medicine. Med Oncol 2022; 39:120. [PMID: 35704152 PMCID: PMC9198206 DOI: 10.1007/s12032-022-01711-1] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 03/14/2022] [Indexed: 10/28/2022]
Abstract
The advancement of precision medicine in medical care has led behind the conventional symptom-driven treatment process by allowing early risk prediction of disease through improved diagnostics and customization of more effective treatments. It is necessary to scrutinize overall patient data alongside broad factors to observe and differentiate between ill and relatively healthy people to take the most appropriate path toward precision medicine, resulting in an improved vision of biological indicators that can signal health changes. Precision and genomic medicine combined with artificial intelligence have the potential to improve patient healthcare. Patients with less common therapeutic responses or unique healthcare demands are using genomic medicine technologies. AI provides insights through advanced computation and inference, enabling the system to reason and learn while enhancing physician decision making. Many cell characteristics, including gene up-regulation, proteins binding to nucleic acids, and splicing, can be measured at high throughput and used as training objectives for predictive models. Researchers can create a new era of effective genomic medicine with the improved availability of a broad range of datasets and modern computer techniques such as machine learning. This review article has elucidated the contributions of ML algorithms in precision and genome medicine.
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Affiliation(s)
- Sameer Quazi
- GenLab Biosolutions Private Limited, Bangalore, Karnataka, 560043, India.
- Department of Biomedical Sciences, School of Life Sciences, Anglia Ruskin University, Cambridge, UK.
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Abstract
The advancement of precision medicine in medical care has led behind the conventional symptom-driven treatment process by allowing early risk prediction of disease through improved diagnostics and customization of more effective treatments. It is necessary to scrutinize overall patient data alongside broad factors to observe and differentiate between ill and relatively healthy people to take the most appropriate path toward precision medicine, resulting in an improved vision of biological indicators that can signal health changes. Precision and genomic medicine combined with artificial intelligence have the potential to improve patient healthcare. Patients with less common therapeutic responses or unique healthcare demands are using genomic medicine technologies. AI provides insights through advanced computation and inference, enabling the system to reason and learn while enhancing physician decision making. Many cell characteristics, including gene up-regulation, proteins binding to nucleic acids, and splicing, can be measured at high throughput and used as training objectives for predictive models. Researchers can create a new era of effective genomic medicine with the improved availability of a broad range of datasets and modern computer techniques such as machine learning. This review article has elucidated the contributions of ML algorithms in precision and genome medicine.
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Affiliation(s)
- Sameer Quazi
- GenLab Biosolutions Private Limited, Bangalore, Karnataka, 560043, India.
- Department of Biomedical Sciences, School of Life Sciences, Anglia Ruskin University, Cambridge, UK.
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Wang XS, Lee S, Zhang H, Tang G, Wang Y. An integral genomic signature approach for tailored cancer therapy using genome-wide sequencing data. Nat Commun 2022; 13:2936. [PMID: 35618721 PMCID: PMC9135729 DOI: 10.1038/s41467-022-30449-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 04/29/2022] [Indexed: 11/19/2022] Open
Abstract
Low-cost multi-omics sequencing is expected to become clinical routine and transform precision oncology. Viable computational methods that can facilitate tailored intervention while tolerating sequencing biases are in high demand. Here we propose a class of transparent and interpretable computational methods called integral genomic signature (iGenSig) analyses, that address the challenges of cross-dataset modeling through leveraging information redundancies within high-dimensional genomic features, averaging feature weights to prevent overweighing, and extracting unbiased genomic information from large tumor cohorts. Using genomic dataset of chemical perturbations, we develop a battery of iGenSig models for predicting cancer drug responses, and validate the models using independent cell-line and clinical datasets. The iGenSig models for five drugs demonstrate predictive values in six clinical studies, among which the Erlotinib and 5-FU models significantly predict therapeutic responses in three studies, offering clinically relevant insights into their inverse predictive signature pathways. Together, iGenSig provides a computational framework to facilitate tailored cancer therapy based on multi-omics data.
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Affiliation(s)
- Xiao-Song Wang
- UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, 15213, USA.
- Department of Pathology, University of Pittsburgh, Pittsburgh, PA, 15213, USA.
| | - Sanghoon Lee
- UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, 15213, USA
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, 15206, USA
| | - Han Zhang
- UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, 15213, USA
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, 15206, USA
| | - Gong Tang
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | - Yue Wang
- UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, 15213, USA
- Department of Pathology, University of Pittsburgh, Pittsburgh, PA, 15213, USA
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Luo MC, Nikolopoulou E, Gevertz JL. From Fitting the Average to Fitting the Individual: A Cautionary Tale for Mathematical Modelers. Front Oncol 2022; 12:793908. [PMID: 35574407 PMCID: PMC9097280 DOI: 10.3389/fonc.2022.793908] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 03/09/2022] [Indexed: 11/13/2022] Open
Abstract
An outstanding challenge in the clinical care of cancer is moving from a one-size-fits-all approach that relies on population-level statistics towards personalized therapeutic design. Mathematical modeling is a powerful tool in treatment personalization, as it allows for the incorporation of patient-specific data so that treatment can be tailor-designed to the individual. Herein, we work with a mathematical model of murine cancer immunotherapy that has been previously-validated against the average of an experimental dataset. We ask the question: what happens if we try to use this same model to perform personalized fits, and therefore make individualized treatment recommendations? Typically, this would be done by choosing a single fitting methodology, and a single cost function, identifying the individualized best-fit parameters, and extrapolating from there to make personalized treatment recommendations. Our analyses show the potentially problematic nature of this approach, as predicted personalized treatment response proved to be sensitive to the fitting methodology utilized. We also demonstrate how a small amount of the right additional experimental measurements could go a long way to improve consistency in personalized fits. Finally, we show how quantifying the robustness of the average response could also help improve confidence in personalized treatment recommendations.
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Affiliation(s)
- Michael C Luo
- Department of Mathematics and Statistics, The College of New Jersey, Ewing, NJ, United States
| | - Elpiniki Nikolopoulou
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, United States
| | - Jana L Gevertz
- Department of Mathematics and Statistics, The College of New Jersey, Ewing, NJ, United States
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Zhao Y, Tang RS, Du Y, Yuan Y. A bayesian platform trial design to simultaneously evaluate multiple drugs in multiple indications with mixed endpoints. Biometrics 2022. [PMID: 35546501 DOI: 10.1111/biom.13694] [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: 04/24/2021] [Accepted: 04/22/2022] [Indexed: 11/29/2022]
Abstract
In the era of targeted therapies and immunotherapies, the traditional drug development paradigm of testing one drug at a time in one indication has become increasingly inefficient. Motivated by a real-world application, we propose a master-protocol-based Bayesian platform trial design with mixed endpoints (PDME) to simultaneously evaluate multiple drugs in multiple indications, where different subsets of efficacy measures (e.g., objective response and landmark progression-free survival) may be used by different indications as single or multiple endpoints. We propose a Bayesian hierarchical model to accommodate mixed endpoints and reflect the trial structure of indications that are nested within treatments. We develop a two-stage approach that first clusters the indications into homogeneous subgroups and then applies the Bayesian hierarchical model to each subgroup to achieve precision information borrowing. Patients are enrolled in a group-sequential way and adaptively assigned to treatments according to their efficacy estimates. At each interim analysis, the posterior probabilities that the treatment effect exceeds prespecified clinically relevant thresholds are used to drop ineffective treatments and "graduate" effective treatments. Simulations show that the PDME design has desirable operating characteristics compared to existing method. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Yujie Zhao
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, United States
| | - Rui Sammi Tang
- Servier Pharmaceuticals, Boston, MA, 02210, United States
| | - Yeting Du
- Servier Pharmaceuticals, Boston, MA, 02210, United States
| | - Ying Yuan
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, United States
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Li JX, Li RZ, Ma LR, Wang P, Xu DH, Huang J, Li LQ, Tang L, Xie Y, Leung ELH, Yan PY. Targeting Mutant Kirsten Rat Sarcoma Viral Oncogene Homolog in Non-Small Cell Lung Cancer: Current Difficulties, Integrative Treatments and Future Perspectives. Front Pharmacol 2022; 13:875330. [PMID: 35517800 PMCID: PMC9065471 DOI: 10.3389/fphar.2022.875330] [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: 02/14/2022] [Accepted: 04/04/2022] [Indexed: 11/15/2022] Open
Abstract
In the past few decades, several gene mutations, including the anaplastic lymphoma kinase, epidermal growth factor receptor, ROS proto-oncogene 1 and rat sarcoma viral oncogene homolog (RAS), have been discovered in non-small cell lung cancer (NSCLC). Kirsten rat sarcoma viral oncogene homolog (KRAS) is the isoform most frequently altered in RAS-mutated NSCLC cases. Due to the structural and biochemical characteristics of the KRAS protein, effective approaches to treating KRAS-mutant NSCLC still remain elusive. Extensive recent research on KRAS-mutant inhibitors has made a breakthrough in identifying the covalent KRASG12C inhibitor as an effective agent for the treatment of NSCLC. This review mainly concentrated on introducing new covalent KRASG12C inhibitors like sotorasib (AMG 510) and adagrasib (MRTX 849); summarizing inhibitors targeting the KRAS-related upstream and downstream effectors in RAF/MEK/ERK pathway and PI3K/AKT/mTOR pathway; exploring the efficacy of immunotherapy and certain emerging immune-related therapeutics such as adoptive cell therapy and cancer vaccines. These inhibitors are being investigated in clinical trials and have exhibited promising effects. On the other hand, naturally extracted compounds, which have exhibited safe and effective properties in treating KRAS-mutant NSCLC through suppressing the MAPK and PI3K/AKT/mTOR signaling pathways, as well as through decreasing PD-L1 expression in preclinical studies, could be expected to enter into clinical studies. Finally, in order to confront the matter of drug resistance, the ongoing clinical trials in combination treatment strategies were summarized herein.
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Affiliation(s)
- Jia-Xin Li
- State Key Laboratory of Quality Research in Chinese Medicines, Faculty of Chinese Medicine, Macau University of Science and Technology, Macao, China
| | - Run-Ze Li
- State Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine (Guangdong Provincial Hospital of Chinese Medicine), Guangdong Provincial Academy of Chinese Medical Sciences, Guangzhou, China
| | - Lin-Rui Ma
- State Key Laboratory of Quality Research in Chinese Medicines, Faculty of Chinese Medicine, Macau University of Science and Technology, Macao, China
| | - Peng Wang
- State Key Laboratory of Quality Research in Chinese Medicines, Faculty of Chinese Medicine, Macau University of Science and Technology, Macao, China
| | - Dong-Han Xu
- State Key Laboratory of Quality Research in Chinese Medicines, Faculty of Chinese Medicine, Macau University of Science and Technology, Macao, China
| | - Jie Huang
- State Key Laboratory of Quality Research in Chinese Medicines, Faculty of Chinese Medicine, Macau University of Science and Technology, Macao, China
| | - Li-Qi Li
- State Key Laboratory of Quality Research in Chinese Medicines, Faculty of Chinese Medicine, Macau University of Science and Technology, Macao, China
| | - Ling Tang
- School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Chinese Medicine Pharmaceutics, Guangzhou, China
- Guangdong Provincial Engineering Laboratory of Chinese Medicine Preparation Technology, Guangzhou, China
| | - Ying Xie
- State Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine (Guangdong Provincial Hospital of Chinese Medicine), Guangdong Provincial Academy of Chinese Medical Sciences, Guangzhou, China
| | - Elaine Lai-Han Leung
- State Key Laboratory of Quality Research in Chinese Medicines, Faculty of Chinese Medicine, Macau University of Science and Technology, Macao, China
- Zhuhai Hospital of Integrated Traditional Chinese and Western Medicine, Zhuhai, China
- Dr. Neher’s Biophysics Laboratory for Innovative Drug Discovery, Macau University of Science and Technology, Macao, China
| | - Pei-Yu Yan
- State Key Laboratory of Quality Research in Chinese Medicines, Faculty of Chinese Medicine, Macau University of Science and Technology, Macao, China
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An In Vivo Inflammatory Loop Potentiates KRAS Blockade. Biomedicines 2022; 10:biomedicines10030592. [PMID: 35327394 PMCID: PMC8945202 DOI: 10.3390/biomedicines10030592] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 02/24/2022] [Accepted: 02/25/2022] [Indexed: 12/27/2022] Open
Abstract
KRAS (KRAS proto-oncogene, GTPase) inhibitors perform less well than other targeted drugs in vitro and fail clinical trials. To investigate a possible reason for this, we treated human and murine tumor cells with KRAS inhibitors deltarasin (targeting phosphodiesterase-δ), cysmethynil (targeting isoprenylcysteine carboxylmethyltransferase), and AA12 (targeting KRASG12C), and silenced/overexpressed mutant KRAS using custom-designed vectors. We showed that KRAS-mutant tumor cells exclusively respond to KRAS blockade in vivo, because the oncogene co-opts host myeloid cells via a C-C-motif chemokine ligand 2 (CCL2)/interleukin-1 beta (IL-1β)-mediated signaling loop for sustained tumorigenicity. Indeed, KRAS-mutant tumors did not respond to deltarasin in C-C motif chemokine receptor 2 (Ccr2) and Il1b gene-deficient mice, but were deltarasin-sensitive in wild-type and Ccr2-deficient mice adoptively transplanted with wild-type murine bone marrow. A KRAS-dependent pro-inflammatory transcriptome was prominent in human cancers with high KRAS mutation prevalence and poor predicted survival. Our findings support that in vitro cellular systems are suboptimal for anti-KRAS drug screens, as these drugs function to suppress interleukin-1 receptor 1 (IL1R1) expression and myeloid IL-1β-delivered pro-growth effects in vivo. Moreover, the findings support that IL-1β blockade might be suitable for therapy for KRAS-mutant cancers.
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42
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Tan AC, Tan DSW. Targeted Therapies for Lung Cancer Patients With Oncogenic Driver Molecular Alterations. J Clin Oncol 2022; 40:611-625. [PMID: 34985916 DOI: 10.1200/jco.21.01626] [Citation(s) in RCA: 203] [Impact Index Per Article: 101.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Lung cancer has traditionally been classified by histology. However, a greater understanding of disease biology and the identification of oncogenic driver alterations has dramatically altered the therapeutic landscape. Consequently, the new classification paradigm of non-small-cell lung cancer is further characterized by molecularly defined subsets actionable with targeted therapies and the treatment landscape is becoming increasingly complex. This review encompasses the current standards of care for targeted therapies in lung cancer with driver molecular alterations. Targeted therapies for EGFR exon 19 deletion and L858R mutations, and ALK and ROS1 rearrangements are well established. However, there is an expanding list of approved targeted therapies including for BRAF V600E, EGFR exon 20 insertion, and KRAS G12C mutations, MET exon 14 alterations, and NTRK and RET rearrangements. In addition, there are numerous other oncogenic drivers, such as HER2 exon 20 insertion mutations, for which there are emerging efficacy data for targeted therapies. The importance of diagnostic molecular testing, intracranial efficacy of novel therapies, the optimal sequencing of therapies, role for targeted therapies in early-stage disease, and future directions for precision oncology approaches to understand tumor evolution and therapeutic resistance are also discussed.
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Affiliation(s)
- Aaron C Tan
- Division of Medical Oncology, National Cancer Centre Singapore, Singapore.,Duke-NUS Medical School, National University of Singapore, Singapore
| | - Daniel S W Tan
- Division of Medical Oncology, National Cancer Centre Singapore, Singapore.,Duke-NUS Medical School, National University of Singapore, Singapore.,Genome Institute of Singapore, Singapore
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Wang Y, Carter BZ, Li Z, Huang X. Application of machine learning methods in clinical trials for precision medicine. JAMIA Open 2022; 5:ooab107. [PMID: 35178503 PMCID: PMC8846336 DOI: 10.1093/jamiaopen/ooab107] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 11/15/2021] [Accepted: 12/01/2021] [Indexed: 01/18/2023] Open
Abstract
OBJECTIVE A key component for precision medicine is a good prediction algorithm for patients' response to treatments. We aim to implement machine learning (ML) algorithms into the response-adaptive randomization (RAR) design and improve the treatment outcomes. MATERIALS AND METHODS We incorporated 9 ML algorithms to model the relationship of patient responses and biomarkers in clinical trial design. Such a model predicted the response rate of each treatment for each new patient and provide guidance for treatment assignment. Realizing that no single method may fit all trials well, we also built an ensemble of these 9 methods. We evaluated their performance through quantifying the benefits for trial participants, such as the overall response rate and the percentage of patients who receive their optimal treatments. RESULTS Simulation studies showed that the adoption of ML methods resulted in more personalized optimal treatment assignments and higher overall response rates among trial participants. Compared with each individual ML method, the ensemble approach achieved the highest response rate and assigned the largest percentage of patients to their optimal treatments. For the real-world study, we successfully showed the potential improvements if the proposed design had been implemented in the study. CONCLUSION In summary, the ML-based RAR design is a promising approach for assigning more patients to their personalized effective treatments, which makes the clinical trial more ethical and appealing. These features are especially desirable for late-stage cancer patients who have failed all the Food and Drug Administration (FDA)-approved treatment options and only can get new treatments through clinical trials.
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Affiliation(s)
- Yizhuo Wang
- Department of Biostatistics, The University of Texas
MD Anderson Cancer Center, Houston, Texas, USA
| | - Bing Z Carter
- Section of Molecular Hematology and Therapy,
Department of Leukemia, The University of Texas MD Anderson Cancer
Center, Houston, Texas, USA
| | - Ziyi Li
- Department of Biostatistics, The University of Texas
MD Anderson Cancer Center, Houston, Texas, USA,Corresponding Author: Xuelin Huang, Department of
Biostatistics, The University of Texas MD Anderson Cancer, Center, Houston, TX
77030, USA; ; Ziyi Li, Department of
Biostatistics, The University of Texas MD Anderson Cancer, Center, Houston, TX
77030, USA;
| | - Xuelin Huang
- Department of Biostatistics, The University of Texas
MD Anderson Cancer Center, Houston, Texas, USA,Corresponding Author: Xuelin Huang, Department of
Biostatistics, The University of Texas MD Anderson Cancer, Center, Houston, TX
77030, USA; ; Ziyi Li, Department of
Biostatistics, The University of Texas MD Anderson Cancer, Center, Houston, TX
77030, USA;
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44
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Gao D, Liu Y, Zeng D. Non-asymptotic Properties of Individualized Treatment Rules from Sequentially Rule-Adaptive Trials. JOURNAL OF MACHINE LEARNING RESEARCH : JMLR 2022; 23:https://www.jmlr.org/papers/v23/21-0354.html. [PMID: 37576335 PMCID: PMC10419117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Figures] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Learning optimal individualized treatment rules (ITRs) has become increasingly important in the modern era of precision medicine. Many statistical and machine learning methods for learning optimal ITRs have been developed in the literature. However, most existing methods are based on data collected from traditional randomized controlled trials and thus cannot take advantage of the accumulative evidence when patients enter the trials sequentially. It is also ethically important that future patients should have a high probability to be treated optimally based on the updated knowledge so far. In this work, we propose a new design called sequentially rule-adaptive trials to learn optimal ITRs based on the contextual bandit framework, in contrast to the response-adaptive design in traditional adaptive trials. In our design, each entering patient will be allocated with a high probability to the current best treatment for this patient, which is estimated using the past data based on some machine learning algorithm (for example, outcome weighted learning in our implementation). We explore the tradeoff between training and test values of the estimated ITR in single-stage problems by proving theoretically that for a higher probability of following the estimated ITR, the training value converges to the optimal value at a faster rate, while the test value converges at a slower rate. This problem is different from traditional decision problems in the sense that the training data are generated sequentially and are dependent. We also develop a tool that combines martingale with empirical process to tackle the problem that cannot be solved by previous techniques for i.i.d. data. We show by numerical examples that without much loss of the test value, our proposed algorithm can improve the training value significantly as compared to existing methods. Finally, we use a real data study to illustrate the performance of the proposed method.
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Affiliation(s)
- Daiqi Gao
- Department of Statistics and Operations Research, The University of North Carolina at Chapel Hill Chapel Hill, NC 27599, USA
| | - Yufeng Liu
- Department of Statistics and Operations Research, Department of Genetics, Department of Biostatistics, The University of North Carolina at Chapel Hill Chapel Hill, NC 27599, USA
| | - Donglin Zeng
- Department of Biostatistics, The University of North Carolina at Chapel Hill Chapel Hill, NC 27599, USA
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Abstract
Image-guided lung needle biopsy allows for minimally invasive diagnosis of lung pathology. In the setting of suspected malignancy, the biopsy not only confirms the diagnosis but also allows for molecular profiling, a requisite for tailored systemic therapy. Needle biopsy can also characterize non-neoplastic entities such as infections not responding to treatment and other inflammatory processes. A successful and safe lung needle biopsy starts with lesion and patient selection and careful pre-procedural evaluation. Here we review the indications and contraindications, diagnostic alternatives, approach planning and sequential procedural steps with the goal of maximizing both yield and patient safety. We discuss technical tips for preventing complications such as pleural anesthesia, the saline seal, the blood patch, the banana bend, hydro dissection, and the rapid needle out/patient rollover maneuver. We also review how to manage complications, avoid non-diagnostic biopsies, and provide recommendations for post-procedural observation and imaging follow-up.
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46
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Deshpande Y, Javanmard A, Mehrabi M. Online Debiasing for Adaptively Collected High-Dimensional Data With Applications to Time Series Analysis. J Am Stat Assoc 2021. [DOI: 10.1080/01621459.2021.1979011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Yash Deshpande
- Institute for Data, Systems and Society, Massachusetts Institute of Technology, Cambridge, MA
| | - Adel Javanmard
- Data Sciences and Operations Department, University of Southern California, Los Angeles, CA
| | - Mohammad Mehrabi
- Data Sciences and Operations Department, University of Southern California, Los Angeles, CA
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47
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Stewart CA, Gay CM, Ramkumar K, Cargill KR, Cardnell RJ, Nilsson MB, Heeke S, Park EM, Kundu ST, Diao L, Wang Q, Shen L, Xi Y, Zhang B, Della Corte CM, Fan Y, Kundu K, Gao B, Avila K, Pickering CR, Johnson FM, Zhang J, Kadara H, Minna JD, Gibbons DL, Wang J, Heymach JV, Byers LA. Lung Cancer Models Reveal Severe Acute Respiratory Syndrome Coronavirus 2-Induced Epithelial-to-Mesenchymal Transition Contributes to Coronavirus Disease 2019 Pathophysiology. J Thorac Oncol 2021; 16:1821-1839. [PMID: 34274504 PMCID: PMC8282443 DOI: 10.1016/j.jtho.2021.07.002] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 06/02/2021] [Accepted: 07/02/2021] [Indexed: 01/08/2023]
Abstract
INTRODUCTION Coronavirus disease 2019 is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which enters host cells through the cell surface proteins ACE2 and TMPRSS2. METHODS Using a variety of normal and malignant models and tissues from the aerodigestive and respiratory tracts, we investigated the expression and regulation of ACE2 and TMPRSS2. RESULTS We find that ACE2 expression is restricted to a select population of epithelial cells. Notably, infection with SARS-CoV-2 in cancer cell lines, bronchial organoids, and patient nasal epithelium induces metabolic and transcriptional changes consistent with epithelial-to-mesenchymal transition (EMT), including up-regulation of ZEB1 and AXL, resulting in an increased EMT score. In addition, a transcriptional loss of genes associated with tight junction function occurs with SARS-CoV-2 infection. The SARS-CoV-2 receptor, ACE2, is repressed by EMT through the transforming growth factor-β, ZEB1 overexpression, and onset of EGFR tyrosine kinase inhibitor resistance. This suggests a novel model of SARS-CoV-2 pathogenesis in which infected cells shift toward an increasingly mesenchymal state, associated with a loss of tight junction components with acute respiratory distress syndrome-protective effects. AXL inhibition and ZEB1 reduction, as with bemcentinib, offer a potential strategy to reverse this effect. CONCLUSIONS These observations highlight the use of aerodigestive and, especially, lung cancer model systems in exploring the pathogenesis of SARS-CoV-2 and other respiratory viruses and offer important insights into the potential mechanisms underlying the morbidity and mortality of coronavirus disease 2019 in healthy patients and patients with cancer alike.
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Affiliation(s)
- C Allison Stewart
- Department of Thoracic/Head & Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Carl M Gay
- Department of Thoracic/Head & Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Kavya Ramkumar
- Department of Thoracic/Head & Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Kasey R Cargill
- Department of Thoracic/Head & Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Robert J Cardnell
- Department of Thoracic/Head & Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Monique B Nilsson
- Department of Thoracic/Head & Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Simon Heeke
- Department of Thoracic/Head & Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Elizabeth M Park
- Department of Thoracic/Head & Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Samrat T Kundu
- Department of Thoracic/Head & Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Lixia Diao
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Qi Wang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Li Shen
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Yuanxin Xi
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Bingnan Zhang
- Department of Thoracic/Head & Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Carminia Maria Della Corte
- Oncology Division, Department of Precision Medicine, University of Campania "Luigi Vanvitelli," Naples, Italy
| | - Youhong Fan
- Department of Thoracic/Head & Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Kiran Kundu
- Department of Thoracic/Head & Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Boning Gao
- Department of Internal Medicine and Pharmacology, Hamon Center for Therapeutic Oncology Research, The University of Texas Southwestern Medical Center, Dallas, Texas
| | - Kimberley Avila
- Department of Internal Medicine and Pharmacology, Hamon Center for Therapeutic Oncology Research, The University of Texas Southwestern Medical Center, Dallas, Texas
| | - Curtis R Pickering
- Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Faye M Johnson
- Department of Thoracic/Head & Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Jianjun Zhang
- Department of Thoracic/Head & Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Humam Kadara
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - John D Minna
- Department of Internal Medicine and Pharmacology, Hamon Center for Therapeutic Oncology Research, The University of Texas Southwestern Medical Center, Dallas, Texas
| | - Don L Gibbons
- Department of Thoracic/Head & Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Jing Wang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - John V Heymach
- Department of Thoracic/Head & Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Lauren Averett Byers
- Department of Thoracic/Head & Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
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Bautista W, Adelson PD, Bicher N, Themistocleous M, Tsivgoulis G, Chang JJ. Secondary mechanisms of injury and viable pathophysiological targets in intracerebral hemorrhage. Ther Adv Neurol Disord 2021; 14:17562864211049208. [PMID: 34671423 PMCID: PMC8521409 DOI: 10.1177/17562864211049208] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Accepted: 09/09/2021] [Indexed: 01/18/2023] Open
Abstract
Intracerebral hemorrhage (ICH) can be divided into a primary and secondary phase. In the primary phase, hematoma volume is evaluated and therapies are focused on reducing hematoma expansion. In the secondary, neuroprotective phase, complex systemic inflammatory cascades, direct cellular toxicity, and blood-brain barrier disruption can result in worsening perihematomal edema that can adversely affect functional outcome. To date, all major randomized phase 3 trials for ICH have targeted primary phase hematoma volume and incorporated clot evacuation, intensive blood pressure control, and hemostasis. Reasons for this lack of clinical efficacy in the major ICH trials may be due to the lack of therapeutics involving mitigation of secondary injury and inflexible trial design that favors unilateral mechanisms in a complex pathophysiology. Potential pathophysiological targets for attenuating secondary injury are highlighted in this review and include therapies increasing calcium, antagonizing microglial activation, maintaining macrophage M1 versus M2 balance by decreasing M1 signaling, aquaporin inhibition, NKCCl inhibition, endothelin receptor inhibition, Sur1-TRPM4 inhibition, matrix metalloproteinase inhibition, and sphingosine-1-phosphate receptor modulation. Future clinical trials in ICH focusing on secondary phase injury and, potentially implementing adaptive trial design approaches with multifocal targets, may improve insight into these mechanisms and provide potential therapies that may improve survival and functional outcome.
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Affiliation(s)
- Wendy Bautista
- Center for Advanced Preclinical Research (CAPR), National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - P David Adelson
- Division of Pediatric Neurosurgery, Department of Child Health, Barrow Neurological Institute at Phoenix Children's Hospital, Phoenix, AZ, USA
| | - Nathan Bicher
- Department of Neurology, Georgetown University Medical Center, Washington, DC, USA
| | - Marios Themistocleous
- Department of Neurosurgery, Pediatric Hospital of Athens, Agia Sophia, Athens, Greece
| | - Georgios Tsivgoulis
- Department of Neurology, The University of Tennessee Health Science Center, Memphis, TN, USA
| | - Jason J Chang
- Department of Critical Care Medicine, MedStar Washington Hospital Center, 110 Irving Street, NW, Rm 4B42, Washington, DC 20010, USA
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Wang Y, Jiang F, Xia R, Li M, Yao C, Li Y, Li H, Zhao Q, Shi M, Yu Y, Shao YW, Zhou G, Xia H, Miao L, Cai H. Unique Genomic Alterations of Cerebrospinal Fluid Cell-Free DNA Are Critical for Targeted Therapy of Non-Small Cell Lung Cancer With Leptomeningeal Metastasis. Front Oncol 2021; 11:701171. [PMID: 34671549 PMCID: PMC8522975 DOI: 10.3389/fonc.2021.701171] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 08/20/2021] [Indexed: 12/30/2022] Open
Abstract
We reported unique molecular features of cerebrospinal fluid (CSF) of nonsmall cell lung cancer (NSCLC) patients with leptomeningeal metastasis (LM), suggesting establishing CSF as a better liquid biopsy in clinical practices. We performed next-generation panel sequencing of primary tumor tissue, plasma, and CSF from 131 NSCLC patients with LM and observed high somatic copy number variations (CNV) in CSF of NSCLC patients with LM. The status of EGFR-activating mutations was highly concordant between CSF, plasma, and primary tumors. ALK translocation was detected in 8.3% of tumor tissues but only 2.4% in CSF and 2.7% in plasma. Others such as ROS1 rearrangement, RET fusion, HER2 mutation, NTRK1 fusion, and BRAF V600E mutation were detected in 7.9% of CSF and 11.1% of tumor tissues but only 4% in plasma. Our study has shed light on the unique genomic variations of CSF and demonstrated that CSF might represent better liquid biopsy for NSCLC patients with LM.
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Affiliation(s)
- Yongsheng Wang
- Department of Respiratory Medicine & Radiology & Cardiothoracic Surgery, Nanjing Drum Tower Hospital Affiliated to Medical School of Nanjing University, Nanjing, China
| | - Feng Jiang
- The First Affiliated Hospital/Yijishan Hospital of Wannan Medical College, Wuhu, China
| | - Ruixue Xia
- Department of Respiratory and Critical Care Medicine, Henan University Huaihe Hospital, Kaifeng, China
| | - Ming Li
- Department of Respiratory Medicine & Radiology & Cardiothoracic Surgery, Nanjing Drum Tower Hospital Affiliated to Medical School of Nanjing University, Nanjing, China
| | - Chengyun Yao
- Jiangsu Cancer Hospital, The Affiliated Cancer Hospital of Nanjing Medical University, Jiangsu Institute of Cancer Research, Nanjing, China
| | - Yan Li
- Department of Respiratory Medicine & Radiology & Cardiothoracic Surgery, Nanjing Drum Tower Hospital Affiliated to Medical School of Nanjing University, Nanjing, China
| | - Hui Li
- Department of Respiratory Medicine & Radiology & Cardiothoracic Surgery, Nanjing Drum Tower Hospital Affiliated to Medical School of Nanjing University, Nanjing, China
| | - Qi Zhao
- Department of Respiratory Medicine & Radiology & Cardiothoracic Surgery, Nanjing Drum Tower Hospital Affiliated to Medical School of Nanjing University, Nanjing, China
| | - Mingke Shi
- Department of Respiratory Medicine & Radiology & Cardiothoracic Surgery, Nanjing Drum Tower Hospital Affiliated to Medical School of Nanjing University, Nanjing, China
| | - Yanzhe Yu
- Department of Respiratory Medicine & Radiology & Cardiothoracic Surgery, Nanjing Drum Tower Hospital Affiliated to Medical School of Nanjing University, Nanjing, China
| | - Yang W Shao
- Nanjing Geneseeq Technology Inc., Nanjing, China
| | - Guoren Zhou
- Jiangsu Cancer Hospital, The Affiliated Cancer Hospital of Nanjing Medical University, Jiangsu Institute of Cancer Research, Nanjing, China
| | - Hongping Xia
- Department of Pathology, School of Basic Medical Sciences & Sir Run Run Hospital & Key Laboratory of Antibody Technique of National Health Commission, Nanjing Medical University, Nanjing, China
| | - Liyun Miao
- Department of Respiratory Medicine & Radiology & Cardiothoracic Surgery, Nanjing Drum Tower Hospital Affiliated to Medical School of Nanjing University, Nanjing, China
| | - Hourong Cai
- Department of Respiratory Medicine & Radiology & Cardiothoracic Surgery, Nanjing Drum Tower Hospital Affiliated to Medical School of Nanjing University, Nanjing, China
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He B, Dong D, She Y, Zhou C, Fang M, Zhu Y, Zhang H, Huang Z, Jiang T, Tian J, Chen C. Predicting response to immunotherapy in advanced non-small-cell lung cancer using tumor mutational burden radiomic biomarker. J Immunother Cancer 2021; 8:jitc-2020-000550. [PMID: 32636239 PMCID: PMC7342823 DOI: 10.1136/jitc-2020-000550] [Citation(s) in RCA: 71] [Impact Index Per Article: 23.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/29/2020] [Indexed: 12/20/2022] Open
Abstract
Background Tumor mutational burden (TMB) is a significant predictor of immune checkpoint inhibitors (ICIs) efficacy. This study investigated the correlation between deep learning radiomic biomarker and TMB, including its predictive value for ICIs treatment response in patients with advanced non-small-cell lung cancer (NSCLC). Methods CT images from 327 patients with TMB data (TMB median=6.067 mutations per megabase (range: 0 to 42.151)) were retrospectively collected and randomly divided into a training (n=236), validation (n=26), and test cohort (n=65). We used 3D-densenet to estimate the target tumor area, which used 1020 deep learning features to distinguish High-TMB from Low-TMB patients and establish the TMB radiomic biomarker (TMBRB). The TMBRB was developed in the training cohort combined with validation cohort and evaluated in the test cohort. The predictive value of TMBRB was assessed in a cohort of 123 NSCLC patients who had received ICIs (survival median=462 days (range: 16 to 1128)). Results TMBRB discriminated between High-TMB and Low-TMB patients in the training cohort (area under the curve (AUC): 0.85, 95% CI: 0.84 to 0.87))and test cohort (AUC: 0.81, 95% CI: 0.77 to 0.85). In this study, the predictive value of TMBRB was better than that of a histological subtype (AUC of training cohort: 0.75, 95% CI: 0.72 to 0.77; AUC of test cohort: 0.71, 95% CI: 0.66 to 0.76) or Radiomic model (AUC of training cohort: 0.75, 95% CI: 0.72 to 0.77; AUC of test cohort: 0.74, 95% CI: 0.69 to 0.79). When predicting immunotherapy efficacy, TMBRB divided patients into a high- and low-risk group with distinctly different overall survival (OS; HR: 0.54, 95% CI: 0.31 to 0.95; p=0.030) and progression-free survival (PFS; HR: 1.78, 95% CI: 1.07 to 2.95; p=0.023). Moreover, TMBRB had a better predictive ability when combined with the Eastern Cooperative Oncology Group performance status (OS: p=0.007; PFS: p=0.003). Visual analysis revealed that tumor microenvironment was important for predicting TMB. Conclusion By combining deep learning technology and CT images, we developed an individual non-invasive biomarker that could distinguish High-TMB from Low-TMB, which might inform decisions on the use of ICIs in patients with advanced NSCLC.
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Affiliation(s)
- Bingxi He
- School of Electronic Electrical and Communication Engineering, University of the Chinese Academy of Sciences, Beijing, China.,CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Di Dong
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,School of Artificial Intelligence, University of the Chinese Academy of Sciences, Beijing, China
| | - Yunlang She
- Department of Thoracic Surgery, Tongji University Affiliated Shanghai Pulmonary Hospital, Shanghai, China
| | - Caicun Zhou
- Department of Medical Oncology, Tongji University Affiliated Shanghai Pulmonary Hospital, Shang hai, China
| | - Mengjie Fang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Yongbei Zhu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, China
| | - Henghui Zhang
- Department of Medicine, Beijing Genecast Biotechnology Co, Beijing, China
| | - Zhipei Huang
- School of Electronic Electrical and Communication Engineering, University of the Chinese Academy of Sciences, Beijing, China
| | - Tao Jiang
- Department of Medical Oncology, Tongji University Affiliated Shanghai Pulmonary Hospital, Shang hai, China
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China .,Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, China.,Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China.,Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology, Beijing, China
| | - Chang Chen
- Department of Thoracic Surgery, Tongji University Affiliated Shanghai Pulmonary Hospital, Shanghai, China
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