Dataset Information

Sun2018 - Instantaneous mutation rate in cancer initiation and progression

ABSTRACT: Background Cancer is one of the leading causes for the morbidity and mortality worldwide. Although substantial studies have been conducted theoretically and experimentally in recent years, it is still a challenge to explore the mechanisms of cancer initiation and progression. The investigation for these problems is very important for the diagnosis of cancer diseases and development of treatment schemes. Results To accurately describe the process of cancer initiation, we propose a new concept of gene initial mutation rate based on our recently designed mathematical model using the non-constant mutation rate. Unlike the widely-used average gene mutation rate that depends on the number of mutations, the gene initial mutation rate can be used to describe the initiation process of a single patient. In addition, we propose the instantaneous tumour doubling time that is a continuous function of time based on the non-constant mutation rate. Our proposed concepts are supported by the clinic data of seven patients with advanced pancreatic cancer. The regression results suggest that, compared with the average mutation rate, the estimated initial mutation rate has a larger value of correlation coefficient with the patient survival time. We also provide the estimated tumour size of these seven patients over time. Conclusions The proposed concepts can be used to describe the cancer initiation and progression for different patients more accurately. Since a quantitative understanding of cancer progression is important for clinical treatment, our proposed model and calculated results may provide insights into the development of treatment schemes and also have other clinic implications.

SUBMITTER: Szeyi Ng

PROVIDER: BIOMD0000000915 | BioModels | 2020-02-28

REPOSITORIES: BioModels

ACCESS DATA

Publications

Instantaneous mutation rate in cancer initiation and progression.

Sun Shuhao S Klebaner Fima F Zhang Xinan X Tian Tianhai T

BMC systems biology 20181122 Suppl 6

<h4>Background</h4>Cancer is one of the leading causes for the morbidity and mortality worldwide. Although substantial studies have been conducted theoretically and experimentally in recent years, it is still a challenge to explore the mechanisms of cancer initiation and progression. The investigation for these problems is very important for the diagnosis of cancer diseases and development of treatment schemes.<h4>Results</h4>To accurately describe the process of cancer initiation, we propose a ...[more]

PMID: 30463617

Similar Datasets

Project description:KRAS mutation is a negative predictive factor for treatment with anti-epidermal growth factor receptor (EGFR) antibodies in metastatic colorectal cancer (mCRC). Novel predictive markers are required to further improve the selection of patients for this treatment. Here, we assessed the influence of modification of KRAS by gene copy number aberration (CNA) and microRNAs (miRNAs) in correlation to clinical outcome in mCRC patients treated with cetuximab in combination with chemotherapy and bevacizumab. Formalin-fixed paraffin-embedded primary tumour tissue was used from 34 mCRC patients in a phase III trial, who were selected based upon their good (n=17) or poor (n=17) progression-free survival (PFS) upon treatment with cetuximab in combination with capecitabine, oxaliplatin, and bevacizumab. Gene copy number at the KRAS locus was assessed using high resolution genome-wide array CGH and the expression levels of 17 miRNAs targeting KRAS were determined by real-time PCR. Good response was associated with 12p12.1 copy number loss, even in patients with a KRAS mutation, while copy number gain in wild-type KRAS patients was correlated with a poor response. In KRAS mutated tumours increased miR-200b and decreased miR-143 expression were associated with a good response. In wild-type KRAS patients, miRNA expression did not predict response in a multivariate model. Thus, assessment of KRAS CNA and miRNAs targeting KRAS might further optimize the selection of patients eligible for anti-EGFR therapy. Copy number detection was performed using NimbleScan and Nexus software Formalin-fixed paraffin-embedded primary tumour tissue was used from 34 metastisized colorectal cancer patients in a phase III trial (CKTO 2005-02; ClinTrials.gov NCT00208546) of the Dutch Colorectal Cancer Group (DCCG), who were selected based upon their good (n=17) or poor (n=17) progression-free survival (PFS) upon treatment with cetuximab in combination with capecitabine, oxaliplatin, and bevacizumab.

Project description:Murphy2016 - Differences in predictions of ODE models of tumor growth Comparison of 7 ODE models for tumour size. This models have been compared to experimental data. This model is described in the article: Differences in predictions of ODE models of tumor growth: a cautionary example. Murphy H, Jaafari H, Dobrovolny HM. BMC Cancer 2016 Feb; 16: 163 Abstract: While mathematical models are often used to predict progression of cancer and treatment outcomes, there is still uncertainty over how to best model tumor growth. Seven ordinary differential equation (ODE) models of tumor growth (exponential, Mendelsohn, logistic, linear, surface, Gompertz, and Bertalanffy) have been proposed, but there is no clear guidance on how to choose the most appropriate model for a particular cancer.We examined all seven of the previously proposed ODE models in the presence and absence of chemotherapy. We derived equations for the maximum tumor size, doubling time, and the minimum amount of chemotherapy needed to suppress the tumor and used a sample data set to compare how these quantities differ based on choice of growth model.We find that there is a 12-fold difference in predicting doubling times and a 6-fold difference in the predicted amount of chemotherapy needed for suppression depending on which growth model was used.Our results highlight the need for careful consideration of model assumptions when developing mathematical models for use in cancer treatment planning. This model is hosted on BioModels Database and identified by: BIOMD0000000671. To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Dataset Information

Sun2018 - Instantaneous mutation rate in cancer initiation and progression

Publications

Instantaneous mutation rate in cancer initiation and progression.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure