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Plaszczynski S, Grammaticos B, Pallud J, Campagne JE, Badoual M. Predicting regrowth of low-grade gliomas after radiotherapy. PLoS Comput Biol 2023; 19:e1011002. [PMID: 37000852 PMCID: PMC10128962 DOI: 10.1371/journal.pcbi.1011002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 04/25/2023] [Accepted: 03/04/2023] [Indexed: 04/03/2023] Open
Abstract
Diffuse low grade gliomas are invasive and incurable brain tumors that inevitably transform into higher grade ones. A classical treatment to delay this transition is radiotherapy (RT). Following RT, the tumor gradually shrinks during a period of typically 6 months to 4 years before regrowing. To improve the patient’s health-related quality of life and help clinicians build personalized follow-ups, one would benefit from predictions of the time during which the tumor is expected to decrease. The challenge is to provide a reliable estimate of this regrowth time shortly after RT (i.e. with few data), although patients react differently to the treatment. To this end, we analyze the tumor size dynamics from a batch of 20 high-quality longitudinal data, and propose a simple and robust analytical model, with just 4 parameters. From the study of their correlations, we build a statistical constraint that helps determine the regrowth time even for patients for which we have only a few measurements of the tumor size. We validate the procedure on the data and predict the regrowth time at the moment of the first MRI after RT, with precision of, typically, 6 months. Using virtual patients, we study whether some forecast is still possible just three months after RT. We obtain some reliable estimates of the regrowth time in 75% of the cases, in particular for all “fast-responders”. The remaining 25% represent cases where the actual regrowth time is large and can be safely estimated with another measurement a year later. These results show the feasibility of making personalized predictions of the tumor regrowth time shortly after RT.
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Affiliation(s)
- Stéphane Plaszczynski
- Université Paris-Saclay, CNRS/IN2P3, IJCLab, Orsay, France
- Université Paris-Cité, IJCLab, Orsay, France
- * E-mail:
| | - Basile Grammaticos
- Université Paris-Saclay, CNRS/IN2P3, IJCLab, Orsay, France
- Université Paris-Cité, IJCLab, Orsay, France
| | - Johan Pallud
- Department of Neurosurgery, GHU Paris Sainte-Anne Hospital, Paris, France
- Université de Paris, Sorbonne Paris Cité, Paris, France
- Inserm, U1266, IMA-Brain, Institut de Psychiatrie et Neurosciences de Paris, Paris, France
| | - Jean-Eric Campagne
- Université Paris-Saclay, CNRS/IN2P3, IJCLab, Orsay, France
- Université Paris-Cité, IJCLab, Orsay, France
| | - Mathilde Badoual
- Université Paris-Saclay, CNRS/IN2P3, IJCLab, Orsay, France
- Université Paris-Cité, IJCLab, Orsay, France
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Li L, Wei Y, Shi G, Yang H, Li Z, Fang R, Cao H, Cui Y. Multi-omics data integration for subtype identification of Chinese lower-grade gliomas: a joint similarity network fusion approach. Comput Struct Biotechnol J 2022; 20:3482-3492. [PMID: 35860412 PMCID: PMC9284445 DOI: 10.1016/j.csbj.2022.06.065] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 06/30/2022] [Accepted: 06/30/2022] [Indexed: 12/28/2022] Open
Abstract
Lower-grade gliomas (LGG), characterized by heterogeneity and invasiveness, originate from the central nervous system. Although studies focusing on molecular subtyping and molecular characteristics have provided novel insights into improving the diagnosis and therapy of LGG, there is an urgent need to identify new molecular subtypes and biomarkers that are promising to improve patient survival outcomes. Here, we proposed a joint similarity network fusion (Joint-SNF) method to integrate different omics data types to construct a fused network using the Joint and Individual Variation Explained (JIVE) technique under the SNF framework. Focusing on the joint network structure, a spectral clustering method was employed to obtain subtypes of patients. Simulation studies show that the proposed Joint-SNF method outperforms the original SNF approach under various simulation scenarios. We further applied the method to a Chinese LGG data set including mRNA expression, DNA methylation and microRNA (miRNA). Three molecular subtypes were identified and showed statistically significant differences in patient survival outcomes. The five-year mortality rates of the three subtypes are 80.8%, 32.1%, and 34.4%, respectively. After adjusting for clinically relevant covariates, the death risk of patients in Cluster 1 was 5.06 times higher than patients in other clusters. The fused network attained by the proposed Joint-SNF method enhances strong similarities, thus greatly improves subtyping performance compared to the original SNF method. The findings in the real application may provide important clues for improving patient survival outcomes and for precision treatment for Chinese LGG patients. An R package to implement the method can be accessed in Github at https://github.com/Sameerer/Joint-SNF.
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Affiliation(s)
- Lingmei Li
- Division of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi 030001, PR China
| | - Yifang Wei
- Division of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi 030001, PR China
| | - Guojing Shi
- Division of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi 030001, PR China
| | - Haitao Yang
- Division of Health Statistics, School of Public Health, Hebei Medical University, Shijiazhuang, Hebei 050017, PR China
| | - Zhi Li
- Department of Hematology, Taiyuan Central Hospital of Shanxi Medical University, Taiyuan, Shanxi 030001, PR China
| | - Ruiling Fang
- Division of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi 030001, PR China
| | - Hongyan Cao
- Division of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi 030001, PR China
- Shanxi Medical University-Yidu Cloud Institute of Medical Data Science, Taiyuan, Shanxi 030001, PR China
- Corresponding authors at: Division of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, PR China.
| | - Yuehua Cui
- Department of Statistics and Probability, Michigan State University, East Lansing, MI 48824, USA
- Corresponding authors at: Division of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, PR China.
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Optimal Combinations of Chemotherapy and Radiotherapy in Low-Grade Gliomas: A Mathematical Approach. J Pers Med 2021; 11:jpm11101036. [PMID: 34683177 PMCID: PMC8537400 DOI: 10.3390/jpm11101036] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 09/30/2021] [Accepted: 10/11/2021] [Indexed: 12/16/2022] Open
Abstract
Low-grade gliomas (LGGs) are brain tumors characterized by their slow growth and infiltrative nature. Treatment options for these tumors are surgery, radiation therapy and chemotherapy. The optimal use of radiation therapy and chemotherapy is still under study. In this paper, we construct a mathematical model of LGG response to combinations of chemotherapy, specifically to the alkylating agent temozolomide and radiation therapy. Patient-specific parameters were obtained from longitudinal imaging data of the response of real LGG patients. Computer simulations showed that concurrent cycles of radiation therapy and temozolomide could provide the best therapeutic efficacy in-silico for the patients included in the study. The patient cohort was extended computationally to a set of 3000 virtual patients. This virtual cohort was subject to an in-silico trial in which matching the doses of radiotherapy to those of temozolomide in the first five days of each cycle improved overall survival over concomitant radio-chemotherapy according to RTOG 0424. Thus, the proposed treatment schedule could be investigated in a clinical setting to improve combination treatments in LGGs with substantial survival benefits.
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