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Beinse G, Tellier V, Charvet V, Deutsch E, Borget I, Massard C, Hollebecque A, Verlingue L. Prediction of Drug Approval After Phase I Clinical Trials in Oncology: RESOLVED2. JCO Clin Cancer Inform 2019; 3:1-10. [DOI: 10.1200/cci.19.00023] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
PURPOSE Drug development in oncology currently is facing a conjunction of an increasing number of antineoplastic agents (ANAs) candidate for phase I clinical trials (P1CTs) and an important attrition rate for final approval. We aimed to develop a machine learning algorithm (RESOLVED2) to predict drug development outcome, which could support early go/no-go decisions after P1CTs by better selection of drugs suitable for further development. METHODS PubMed abstracts of P1CTs reporting on ANAs were used together with pharmacologic data from the DrugBank5.0 database to model time to US Food and Drug Administration (FDA) approval (FDA approval-free survival) since the first P1CT publication. The RESOLVED2 model was trained with machine learning methods. Its performance was evaluated on an independent test set with weighted concordance index (IPCW). RESULTS We identified 462 ANAs from PubMed that matched with DrugBank5.0 (P1CT publication dates 1972 to 2017). Among 1,411 variables, 28 were used by RESOLVED2 to model the FDA approval-free survival, with an IPCW of 0.89 on the independent test set. RESOLVED2 outperformed a model that was based on efficacy/toxicity (IPCW, 0.69). In the test set at 6 years of follow-up, 73% (95% CI, 49% to 86%) of drugs predicted to be approved were approved, whereas 92% (95% CI, 87% to 98%) of drugs predicted to be nonapproved were still not approved (log-rank P < .001). A predicted approved drug was 16 times more likely to be approved than a predicted nonapproved drug (hazard ratio, 16.4; 95% CI, 8.40 to 32.2). CONCLUSION As soon as P1CT completion, RESOLVED2 can predict accurately the time to FDA approval. We provide the proof of concept that drug development outcome can be predicted by machine learning strategies.
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Affiliation(s)
| | | | | | - Eric Deutsch
- Gustave Roussy Cancer Campus, Villejuif, France
- Université Paris-Saclay, Le Kremlin-Bicêtre, France
- Université Paris-Saclay, Villejuif, France
| | - Isabelle Borget
- Gustave Roussy Cancer Campus, Villejuif, France
- Université Versailles Saint-Quentin-en-Yvelines, Villejuif, France
- Université Paris-Sud, Paris, France
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Wermuth PJ, Piera-Velazquez S, Rosenbloom J, Jimenez SA. Existing and novel biomarkers for precision medicine in systemic sclerosis. Nat Rev Rheumatol 2019; 14:421-432. [PMID: 29789665 DOI: 10.1038/s41584-018-0021-9] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The discovery and validation of biomarkers resulting from technological advances in the analysis of genomic, transcriptomic, lipidomic and metabolomic pathways involved in the pathogenesis of complex human diseases have led to the development of personalized and rationally designed approaches for the clinical management of such disorders. Although some of these approaches have been applied to systemic sclerosis (SSc), an unmet need remains for validated, non-invasive biomarkers to aid in the diagnosis of SSc, as well as in the assessment of disease progression and response to therapeutic interventions. Advances in global transcriptomic technology over the past 15 years have enabled the assessment of microRNAs that circulate in the blood of patients and the analysis of the macromolecular content of a diverse group of lipid bilayer membrane-enclosed extracellular vesicles, such as exosomes and other microvesicles, which are released by all cells into the extracellular space and circulation. Such advances have provided new opportunities for the discovery of biomarkers in SSc that could potentially be used to improve the design and evaluation of clinical trials and that will undoubtedly enable the development of personalized and individualized medicine for patients with SSc.
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Affiliation(s)
- Peter J Wermuth
- Jefferson Institute of Molecular Medicine, Thomas Jefferson University, Philadelphia, PA, USA.,The Joan and Joel Rosenbloom Center for Fibrosis Research, Thomas Jefferson University, Philadelphia, PA, USA
| | - Sonsoles Piera-Velazquez
- Jefferson Institute of Molecular Medicine, Thomas Jefferson University, Philadelphia, PA, USA.,The Joan and Joel Rosenbloom Center for Fibrosis Research, Thomas Jefferson University, Philadelphia, PA, USA
| | - Joel Rosenbloom
- Jefferson Institute of Molecular Medicine, Thomas Jefferson University, Philadelphia, PA, USA.,The Joan and Joel Rosenbloom Center for Fibrosis Research, Thomas Jefferson University, Philadelphia, PA, USA
| | - Sergio A Jimenez
- Jefferson Institute of Molecular Medicine, Thomas Jefferson University, Philadelphia, PA, USA. .,The Joan and Joel Rosenbloom Center for Fibrosis Research, Thomas Jefferson University, Philadelphia, PA, USA. .,The Scleroderma Center, Thomas Jefferson University, Philadelphia, PA, USA.
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Yap TA, Rodon J. Development of Molecularly Driven Targeted Combination Strategies. Oncologist 2017; 22:1421-1423. [PMID: 29038233 PMCID: PMC5728037 DOI: 10.1634/theoncologist.2017-0402] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2017] [Accepted: 09/08/2017] [Indexed: 11/17/2022] Open
Abstract
Targeted therapies are often inadequate when used in isolation as single agents. Combining diverse targeted therapies could optimally impact patient care by overcoming cancer resistance mechanisms. This commentary highlights such combinatorial strategies, including discussion on a recent trial.
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Affiliation(s)
- Timothy A Yap
- Department of Investigational Cancer Therapeutics (Phase I Clinical Trials Program) and Khalifa Institute for Personalized Cancer Therapy, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jordi Rodon
- Department of Investigational Cancer Therapeutics (Phase I Clinical Trials Program) and Khalifa Institute for Personalized Cancer Therapy, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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Wildman DE. IFPA award in placentology lecture: Phylogenomic origins and evolution of the mammalian placenta. Placenta 2016; 48 Suppl 1:S31-S39. [DOI: 10.1016/j.placenta.2016.04.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2016] [Revised: 04/01/2016] [Accepted: 04/05/2016] [Indexed: 01/11/2023]
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Zugazagoitia J, Guedes C, Ponce S, Ferrer I, Molina-Pinelo S, Paz-Ares L. Current Challenges in Cancer Treatment. Clin Ther 2016; 38:1551-66. [PMID: 27158009 DOI: 10.1016/j.clinthera.2016.03.026] [Citation(s) in RCA: 426] [Impact Index Per Article: 53.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2015] [Revised: 03/15/2016] [Accepted: 03/17/2016] [Indexed: 02/07/2023]
Abstract
PURPOSE In this review, we highlight the current concepts and discuss some of the current challenges and future prospects in cancer therapy. We frequently use the example of lung cancer. METHODS We conducted a nonsystematic PubMed search, selecting the most comprehensive and relevant research articles, clinical trials, translational papers, and review articles on precision oncology and immuno-oncology. Papers were prioritized and selected based on their originality and potential clinical applicability. FINDINGS Two major revolutions have changed cancer treatment paradigms in the past few years: targeting actionable alterations in oncogene-driven cancers and immuno-oncology. Important challenges are still ongoing in both fields of cancer therapy. On the one hand, druggable genomic alterations are diverse and represent only small subsets of patients in certain tumor types, which limits testing their clinical impact in biomarker-driven clinical trials. Next-generation sequencing technologies are increasingly being implemented for molecular prescreening in clinical research, but issues regarding clinical interpretation of large genomic data make their wide clinical use difficult. Further, dealing with tumor heterogeneity and acquired resistance is probably the main limitation for the success of precision oncology. On the other hand, long-term survival benefits with immune checkpoint inhibitors (anti-programmed death cell protein-1/programmed death cell ligand-1[PD-1/L1] and anti-cytotoxic T lymphocyte antigen-4 monoclonal antibodies) are restricted to a minority of patients, and no predictive markers are yet robustly validated that could help us recognize these subsets and optimize treatment delivery and selection. To achieve long-term survival benefits, drug combinations targeting several molecular alterations or cancer hallmarks might be needed. This will probably be one of the most challenging but promising precision cancer treatment strategies in the future. IMPLICATIONS Targeting single molecular abnormalities or cancer pathways has achieved good clinical responses that have modestly affected survival in some cancers. However, this approach to cancer treatment is still reductionist, and many challenges need to be met to improve treatment outcomes with our patients.
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Affiliation(s)
- Jon Zugazagoitia
- Medical Oncology Department, Hospital Universitario 12 de Octubre, Madrid, Spain; Instituto de Investigación I+12. Lung Cancer Clinical Research Unit CNIO, I+12, Madrid, Spain
| | - Cristiano Guedes
- Medical Oncology Department, Hospital Universitario 12 de Octubre, Madrid, Spain
| | - Santiago Ponce
- Medical Oncology Department, Hospital Universitario 12 de Octubre, Madrid, Spain; Instituto de Investigación I+12. Lung Cancer Clinical Research Unit CNIO, I+12, Madrid, Spain
| | - Irene Ferrer
- Instituto de Investigación I+12. Lung Cancer Clinical Research Unit CNIO, I+12, Madrid, Spain
| | - Sonia Molina-Pinelo
- Instituto de Investigación I+12. Lung Cancer Clinical Research Unit CNIO, I+12, Madrid, Spain
| | - Luis Paz-Ares
- Medical Oncology Department, Hospital Universitario 12 de Octubre, Madrid, Spain; Instituto de Investigación I+12. Lung Cancer Clinical Research Unit CNIO, I+12, Madrid, Spain.
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Rouanne M, Loriot Y, Lebret T, Soria JC. Novel therapeutic targets in advanced urothelial carcinoma. Crit Rev Oncol Hematol 2016; 98:106-15. [DOI: 10.1016/j.critrevonc.2015.10.021] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2015] [Revised: 09/17/2015] [Accepted: 10/29/2015] [Indexed: 01/05/2023] Open
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Ermann J, Rao DA, Teslovich NC, Brenner MB, Raychaudhuri S. Immune cell profiling to guide therapeutic decisions in rheumatic diseases. Nat Rev Rheumatol 2015; 11:541-51. [PMID: 26034835 PMCID: PMC4898649 DOI: 10.1038/nrrheum.2015.71] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Biomarkers are needed to guide treatment decisions for patients with rheumatic diseases. Although the phenotypic and functional analysis of immune cells is an appealing strategy for understanding immune-mediated disease processes, immune cell profiling currently has no role in clinical rheumatology. New technologies, including mass cytometry, gene expression profiling by RNA sequencing (RNA-seq) and multiplexed functional assays, enable the analysis of immune cell function with unprecedented detail and promise not only a deeper understanding of pathogenesis, but also the discovery of novel biomarkers. The large and complex data sets generated by these technologies--big data--require specialized approaches for analysis and visualization of results. Standardization of assays and definition of the range of normal values are additional challenges when translating these novel approaches into clinical practice. In this Review, we discuss technological advances in the high-dimensional analysis of immune cells and consider how these developments might support the discovery of predictive biomarkers to benefit the practice of rheumatology and improve patient care.
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Affiliation(s)
- Joerg Ermann
- Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital, Smith Building, 1 Jimmy Fund Way, Boston, MA 02115, USA
| | - Deepak A Rao
- Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital, Smith Building, 1 Jimmy Fund Way, Boston, MA 02115, USA
| | - Nikola C Teslovich
- 'Division of Genetics, Brigham and Women's Hospital, New Research Building NRB, 77 Avenue Louis Pasteur, Boston, MA 02115, USA
| | - Michael B Brenner
- Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital, Smith Building, 1 Jimmy Fund Way, Boston, MA 02115, USA
| | - Soumya Raychaudhuri
- Division of Rheumatology, Immunology and Allergy, Brigham and Women's Hospital, New Research Building (NRB), 77 Avenue Louis Pasteur, Boston, MA 02115, USA
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Ferlini A, Flanigan KM, Lochmuller H, Muntoni F, 't Hoen PAC, McNally E. 204th ENMC International Workshop on Biomarkers in Duchenne Muscular Dystrophy 24-26 January 2014, Naarden, The Netherlands. Neuromuscul Disord 2015; 25:184-98. [PMID: 25529833 PMCID: PMC4534085 DOI: 10.1016/j.nmd.2014.09.004] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2014] [Accepted: 09/01/2014] [Indexed: 12/23/2022]
Affiliation(s)
- Alessandra Ferlini
- Section of Medical Genetics, Department of Medical Sciences, University of Ferrara, Ferrara, Italy.
| | - Kevin M Flanigan
- Center for Gene Therapy, Nationwide Children's Hospital, Departments of Pediatrics and Neurology, Ohio State University, Columbus, OH, USA
| | - Hanns Lochmuller
- Institute of Genetic Medicine, Newcastle University, Newcastle upon Tyne, UK
| | - Francesco Muntoni
- UCL Institute of Child Health & Great Ormond Street Hospital for Children, London, UK
| | - Peter A C 't Hoen
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Elizabeth McNally
- Department of Medicine, Department of Human Genetics, The University of Chicago, USA
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Hollebecque A, Malka D, Ferté C, Ducreux M, Boige V. Systemic treatment of advanced hepatocellular carcinoma: from disillusions to new horizons. Eur J Cancer 2015; 51:327-39. [PMID: 25559615 DOI: 10.1016/j.ejca.2014.12.005] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2014] [Accepted: 12/11/2014] [Indexed: 02/08/2023]
Abstract
Hepatocellular carcinoma (HCC) is an aggressive malignancy, which accounts for a third of all cancer deaths globally each year. The management of patients with HCC is complex, as both the tumour stage and any underlying liver disease must be considered conjointly. Since the approval of sorafenib in advanced HCC, several phase III clinical trials have failed to demonstrate any superiority over sorafenib in the frontline setting, and no agent has been shown to impact outcomes after sorafenib failure. This review will focus on the range of experimental therapeutics for patients with advanced HCC and highlight the successes and failures of these treatments as well as areas for future development. Specifics such as dose limiting toxicity and safety profile in patients with liver dysfunction related to the underlying chronic liver disease should be considered when developing therapies in HCC. Finally, robust validated and reproducible surrogate end-points as well as predictive biomarkers should be defined in future randomised trials.
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Affiliation(s)
- Antoine Hollebecque
- Department of Cancer Medicine, Gustave Roussy, University of Paris Sud, Villejuif, France; Drug Development Department (DITEP), Gustave Roussy, University of Paris Sud, Villejuif, France
| | - David Malka
- Department of Cancer Medicine, Gustave Roussy, University of Paris Sud, Villejuif, France
| | - Charles Ferté
- Department of Cancer Medicine, Gustave Roussy, University of Paris Sud, Villejuif, France
| | - Michel Ducreux
- Department of Cancer Medicine, Gustave Roussy, University of Paris Sud, Villejuif, France
| | - Valérie Boige
- Department of Cancer Medicine, Gustave Roussy, University of Paris Sud, Villejuif, France.
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Heinzel A, Perco P, Mayer G, Oberbauer R, Lukas A, Mayer B. From molecular signatures to predictive biomarkers: modeling disease pathophysiology and drug mechanism of action. Front Cell Dev Biol 2014; 2:37. [PMID: 25364744 PMCID: PMC4207010 DOI: 10.3389/fcell.2014.00037] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2014] [Accepted: 07/29/2014] [Indexed: 12/31/2022] Open
Abstract
Omics profiling significantly expanded the molecular landscape describing clinical phenotypes. Association analysis resulted in first diagnostic and prognostic biomarker signatures entering clinical utility. However, utilizing Omics for deepening our understanding of disease pathophysiology, and further including specific interference with drug mechanism of action on a molecular process level still sees limited added value in the clinical setting. We exemplify a computational workflow for expanding from statistics-based association analysis toward deriving molecular pathway and process models for characterizing phenotypes and drug mechanism of action. Interference analysis on the molecular model level allows identification of predictive biomarker candidates for testing drug response. We discuss this strategy on diabetic nephropathy (DN), a complex clinical phenotype triggered by diabetes and presenting with renal as well as cardiovascular endpoints. A molecular pathway map indicates involvement of multiple molecular mechanisms, and selected biomarker candidates reported as associated with disease progression are identified for specific molecular processes. Selective interference of drug mechanism of action and disease-associated processes is identified for drug classes in clinical use, in turn providing precision medicine hypotheses utilizing predictive biomarkers.
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Affiliation(s)
| | - Paul Perco
- emergentec biodevelopment GmbHVienna, Austria
| | - Gert Mayer
- Department of Internal Medicine IV, Medical University of InnsbruckInnsbruck, Austria
| | - Rainer Oberbauer
- Department of Internal Medicine III, KH Elisabethinen Linz and Medical University of ViennaVienna, Austria
| | - Arno Lukas
- emergentec biodevelopment GmbHVienna, Austria
| | - Bernd Mayer
- emergentec biodevelopment GmbHVienna, Austria
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