Using birth-death processes to infer tumor subpopulation structure from live-cell imaging drug screening data.
| العنوان: | Using birth-death processes to infer tumor subpopulation structure from live-cell imaging drug screening data. |
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| المؤلفون: | Wu C; Department of Industrial and Systems Engineering, University of Minnesota, Twin Cities, MN 55455, USA., Gunnarsson EB; School of Mathematics, University of Minnesota, Twin Cities, MN 55455, USA., Myklebust EM; Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, 0372 Oslo, Norway., Köhn-Luque A; Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, 0372 Oslo, Norway.; Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway., Tadele DS; Department of Medical Genetics, Oslo University Hospital, 0424 Oslo, Norway.; Translational Hematology and Oncology Research, Cleveland Clinic, Cleveland, OH 44131, USA., Enserink JM; Department of Molecular Cell Biology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway.; Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Norway.; Section for Biochemistry and Molecular Biology, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway., Frigessi A; Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, 0372 Oslo, Norway.; Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway., Foo J; School of Mathematics, University of Minnesota, Twin Cities, MN 55455, USA., Leder K; Department of Industrial and Systems Engineering, University of Minnesota, Twin Cities, MN 55455, USA. |
| المصدر: | ArXiv [ArXiv] 2023 Jun 13. Date of Electronic Publication: 2023 Jun 13. |
| نوع المنشور: | Preprint |
| اللغة: | English |
| بيانات الدورية: | Country of Publication: United States NLM ID: 101759493 Publication Model: Electronic Cited Medium: Internet ISSN: 2331-8422 (Electronic) Linking ISSN: 23318422 NLM ISO Abbreviation: ArXiv Subsets: PubMed not MEDLINE |
| مستخلص: | Tumor heterogeneity is a complex and widely recognized trait that poses significant challenges in developing effective cancer therapies. In particular, many tumors harbor a variety of subpopulations with distinct therapeutic response characteristics. Characterizing this heterogeneity by determining the subpopulation structure within a tumor enables more precise and successful treatment strategies. In our prior work, we developed PhenoPop, a computational framework for unravelling the drug-response subpopulation structure within a tumor from bulk high-throughput drug screening data. However, the deterministic nature of the underlying models driving PhenoPop restricts the model fit and the information it can extract from the data. As an advancement, we propose a stochastic model based on the linear birth-death process to address this limitation. Our model can formulate a dynamic variance along the horizon of the experiment so that the model uses more information from the data to provide a more robust estimation. In addition, the newly proposed model can be readily adapted to situations where the experimental data exhibits a positive time correlation. We test our model on simulated data ( in silico ) and experimental data ( in vitro ), which supports our argument about its advantages. |
| التعليقات: | Update in: PLoS Comput Biol. 2024 Mar 6;20(3):e1011888. (PMID: 38446830) |
| معلومات مُعتمدة: | R01 CA241137 United States CA NCI NIH HHS |
| تواريخ الأحداث: | Date Created: 20230703 Latest Revision: 20240318 |
| رمز التحديث: | 20240318 |
| مُعرف محوري في PubMed: | PMC10312799 |
| PMID: | 37396610 |
| قاعدة البيانات: | MEDLINE |
| تدمد: | 2331-8422 |
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