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Jan IS, Ch'ang HJ. Selection of patients with pancreatic adenocarcinoma who may benefit from radiotherapy. Radiat Oncol 2023; 18:137. [PMID: 37596627 PMCID: PMC10439654 DOI: 10.1186/s13014-023-02328-y] [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: 03/28/2023] [Accepted: 08/03/2023] [Indexed: 08/20/2023] Open
Abstract
Despite combination chemotherapy demonstrating a positive effect on survival, the clinical outcomes of pancreatic adenocarcinoma (PDAC) remain poor. Radiotherapy was previously a component of the curative treatment of PDAC. Advances in imaging and computer sciences have enabled the prescription of higher dosage of radiation focused on tumours with minimal toxicity to normal tissue. However, the role of radiotherapy has not been established in the curative treatment of localized PDAC because of the conflicting results from large prospective trials. Most studies have demonstrated improved locoregional control but no survival benefit from additional chemoradiotherapy (CRT) in addition to chemotherapy for resectable, borderline or locally advanced PDAC. The improved locoregional control enabled by CRT does not cause extended survival because of rapid distant progression in a significant proportion of patients with PDAC. Several single-institute studies of prescribing intensive chemotherapy with modern ablative radiotherapy for locally advanced PDAC have demonstrated extended survival with an acceptable safety profile. In an analysis after long-term follow-up, the PREOPANC study demonstrated a survival benefit from neoadjuvant gemcitabine-based CRT in resected PDAC relative to upfront surgery followed by adjuvant gemcitabine only. These observations indicated that the role of radiotherapy in PDAC should be evaluated in a subgroup of patients without rapid distant progression because systemic therapy for PDAC remains underdeveloped. We reviewed critical imaging, tissue, liquid and clinical biomarkers to differentiate the heterogeneous biologic spectra of patients with PDAC to identify those who may benefit the most from local radiotherapy. Exclusion of patients with localised PDAC who develop distant progression in a short time and undergo extended upfront chemotherapy for over 4 months may enable the identification of a survival benefit of local radiotherapy. Though promising, the effectiveness of biomarkers must be validated in a multi-institutional prospective study of patients with PDAC receiving CRT or not receiving CRT.
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Affiliation(s)
- I-Shiow Jan
- Department of Laboratory Medicine, College of Medicine, National Taiwan University Hospital, National Taiwan University, Taipei, Taiwan
| | - Hui Ju Ch'ang
- National Institute of Cancer Research, National Health Research Institutes, Miaoli, Taiwan.
- Program for Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.
- Department of Radiation Oncology, Taipei Medical University, Taipei, Taiwan.
- Department of Oncology, School of Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
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A Quantitative Paradigm for Decision-Making in Precision Oncology. Trends Cancer 2021; 7:293-300. [PMID: 33637444 DOI: 10.1016/j.trecan.2021.01.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 01/16/2021] [Accepted: 01/20/2021] [Indexed: 11/24/2022]
Abstract
The complexity and variability of cancer progression necessitate a quantitative paradigm for therapeutic decision-making that is dynamic, personalized, and capable of identifying optimal treatment strategies for individual patients under substantial uncertainty. Here, we discuss the core components and challenges of such an approach and highlight the need for comprehensive longitudinal clinical and molecular data integration in its development. We describe the complementary and varied roles of mathematical modeling and machine learning in constructing dynamic optimal cancer treatment strategies and highlight the potential of reinforcement learning approaches in this endeavor.
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Kazerouni AS, Gadde M, Gardner A, Hormuth DA, Jarrett AM, Johnson KE, Lima EAF, Lorenzo G, Phillips C, Brock A, Yankeelov TE. Integrating Quantitative Assays with Biologically Based Mathematical Modeling for Predictive Oncology. iScience 2020; 23:101807. [PMID: 33299976 PMCID: PMC7704401 DOI: 10.1016/j.isci.2020.101807] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
We provide an overview on the use of biological assays to calibrate and initialize mechanism-based models of cancer phenomena. Although artificial intelligence methods currently dominate the landscape in computational oncology, mathematical models that seek to explicitly incorporate biological mechanisms into their formalism are of increasing interest. These models can guide experimental design and provide insights into the underlying mechanisms of cancer progression. Historically, these models have included a myriad of parameters that have been difficult to quantify in biologically relevant systems, limiting their practical insights. Recently, however, there has been much interest calibrating biologically based models with the quantitative measurements available from (for example) RNA sequencing, time-resolved microscopy, and in vivo imaging. In this contribution, we summarize how a variety of experimental methods quantify tumor characteristics from the molecular to tissue scales and describe how such data can be directly integrated with mechanism-based models to improve predictions of tumor growth and treatment response.
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Affiliation(s)
- Anum S. Kazerouni
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
| | - Manasa Gadde
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX 78712, USA
| | - Andrea Gardner
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
| | - David A. Hormuth
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA
| | - Angela M. Jarrett
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA
| | - Kaitlyn E. Johnson
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
| | - Ernesto A.B. F. Lima
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
- Texas Advanced Computing Center, The University of Texas at Austin, Austin, TX 78712, USA
| | - Guillermo Lorenzo
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Caleb Phillips
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Amy Brock
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA
| | - Thomas E. Yankeelov
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Oncology, The University of Texas at Austin, Austin, TX 78712, USA
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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Irurzun-Arana I, Rackauckas C, McDonald TO, Trocóniz IF. Beyond Deterministic Models in Drug Discovery and Development. Trends Pharmacol Sci 2020; 41:882-895. [PMID: 33032836 PMCID: PMC7534664 DOI: 10.1016/j.tips.2020.09.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2020] [Revised: 07/28/2020] [Accepted: 09/10/2020] [Indexed: 02/06/2023]
Abstract
The model-informed drug discovery and development paradigm is now well established among the pharmaceutical industry and regulatory agencies. This success has been mainly due to the ability of pharmacometrics to bring together different modeling strategies, such as population pharmacokinetics/pharmacodynamics (PK/PD) and systems biology/pharmacology. However, there are promising quantitative approaches that are still seldom used by pharmacometricians and that deserve consideration. One such case is the stochastic modeling approach, which can be important when modeling small populations because random events can have a huge impact on these systems. In this review, we aim to raise awareness of stochastic models and how to combine them with existing modeling techniques, with the ultimate goal of making future drug-disease models more versatile and realistic.
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Affiliation(s)
- Itziar Irurzun-Arana
- Pharmacometrics and Systems Pharmacology, Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, Pamplona, 31008, Spain; Navarra Institute for Health Research (IdisNA), University of Navarra, 31080, Pamplona, Spain.
| | - Christopher Rackauckas
- Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Thomas O McDonald
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA 02115, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA; Center for Cancer Evolution, Dana-Farber Cancer Institute, Boston, MA 02115, USA
| | - Iñaki F Trocóniz
- Pharmacometrics and Systems Pharmacology, Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, Pamplona, 31008, Spain; Navarra Institute for Health Research (IdisNA), University of Navarra, 31080, Pamplona, Spain; Institute of Data Science and Artificial Intelligence, DATAI, University of Navarra, Pamplona, 31080, Spain.
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Dogra P, Ramírez JR, Peláez MJ, Wang Z, Cristini V, Parasher G, Rawat M. Mathematical Modeling to Address Challenges in Pancreatic Cancer. Curr Top Med Chem 2020; 20:367-376. [PMID: 31893993 PMCID: PMC7279939 DOI: 10.2174/1568026620666200101095641] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Revised: 09/10/2019] [Accepted: 10/20/2019] [Indexed: 12/30/2022]
Abstract
Pancreatic Ductal Adenocarcinoma (PDAC) is regarded as one of the most lethal cancer types for its challenges associated with early diagnosis and resistance to standard chemotherapeutic agents, thereby leading to a poor five-year survival rate. The complexity of the disease calls for a multidisciplinary approach to better manage the disease and improve the status quo in PDAC diagnosis, prognosis, and treatment. To this end, the application of quantitative tools can help improve the understanding of disease mechanisms, develop biomarkers for early diagnosis, and design patient-specific treatment strategies to improve therapeutic outcomes. However, such approaches have only been minimally applied towards the investigation of PDAC, and we review the current status of mathematical modeling works in this field.
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Affiliation(s)
- Prashant Dogra
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA
| | - Javier Ruiz Ramírez
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA
| | - María J. Peláez
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA
- Applied Physics Graduate Program, Rice University, Houston, TX 77005, USA
| | - Zhihui Wang
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA
| | - Vittorio Cristini
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA
| | - Gulshan Parasher
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM 87131, USA
| | - Manmeet Rawat
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM 87131, USA
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