Project description:The paper describes a model on the detection of cancer based on cancer and immune biomarkers. Created by COPASI 4.25 (Build 207) This model is described in the article: Improving cancer detection through combinations of cancer and immune biomarkers: a modelling approach Raluca Eftimie and and Esraa Hassanein J Transl Med (2018) 16:73 Abstract: Background: Early cancer diagnosis is one of the most important challenges of cancer research, since in many can- cers it can lead to cure for patients with early stage diseases. For epithelial ovarian cancer (which is the leading cause of death among gynaecologic malignancies) the classical detection approach is based on measurements of CA-125 biomarker. However, the poor sensitivity and specificity of this biomarker impacts the detection of early-stage cancers. Methods: Here we use a computational approach to investigate the effect of combining multiple biomarkers for ovarian cancer (e.g., CA-125 and IL-7), to improve early cancer detection. Results: We show that this combined biomarkers approach could lead indeed to earlier cancer detection. However, the immune response (which influences the level of secreted IL-7 biomarker) plays an important role in improving and/or delaying cancer detection. Moreover, the detection level of IL-7 immune biomarker could be in a range that would not allow to distinguish between a healthy state and a cancerous state. In this case, the construction of solu- tion diagrams in the space generated by the IL-7 and CA-125 biomarkers could allow us predict the long-term evolu- tion of cancer biomarkers, thus allowing us to make predictions on cancer detection times. Conclusions: Combining cancer and immune biomarkers could improve cancer detection times, and any predic- tions that could be made (at least through the use of CA-125/IL-7 biomarkers) are patient specific. Keywords: Ovarian cancer, Mathematical model, CA-125 biomarker, IL-7 biomarker, Cancer detection times This model is hosted on BioModels Database and identified by: MODEL1907050002. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. 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.

Project description:Lung cancer has been the leading cause of cancer death in the United States and worldwide for many decades. Low dose spiral computerized tomography (LDCT) is likely to become the first approved screening and early detection test in the upcoming year, but it is plagued by a high false-positive rate. There is a need to develop complementary screening and early detection tools. A blood-based lung cancer signature is an attractive solution. Given that our knowledge of the molecular biology of smoking-induced lung cancer has dramatically increased over the past few years, this approach is plausible. To date, this effort has been focused on the identification of genomic and proteomic signatures with limited success. A broader strategy that incorporates additional cancer traits is needed. It is well recognized that wide coverage of cellular metabolism in cancer could help provide valuable diagnostic biomarkers and potentially identify molecular drivers of tumorigenesis. Recent advances in mass spectrometry have enabled comprehensive metabolomic analyses of lipids, carbohydrates, amino acids, and nucleotides within a variety of biologic matrices. Early evidence from metabolomic investigation of cancer has identified many altered biochemical profiles. However, to date, there have been few investigations of lung cancer, and most studies have looked at blood plasma or were limited by small sample sizes with mixed histologies. In the current investigation, gas chromatography time-offlight mass spectrometry (GC-TOF) was used to measure 462 lipid, carbohydrate, amino acid, organic acid, and nucleotide metabolites in 39 malignant and nonmalignant lung tissue pairs from current or former smokers with early stage adenocarcinoma. This study cohort represents patient characteristics and tumor histology most likely to be detected with LDCT screening. We hypothesize that identification of cancer-induced cellular and tissue level biochemical changes can offer a robust method for identification of candidate circulating biomarkers and improve our understanding of biochemical changes involved in adenocarcinoma tumorigenesis.

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Systematic analyses of early-stage lung cancer by scRNA-seq and lipidomic reveals aberrant lipid metabolism as detection biomarkers

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