Predictive Value of Drug Sensitivity Testing Tumorspheres From Patients With Metastatic Colorectal Cancer
Ontology highlight
ABSTRACT:
DISEASE(S): Colorectal Neoplasms,Colorectal Cancer Metastatic
PROVIDER: 2251938 | ecrin-mdr-crc |
REPOSITORIES: ECRIN MDR
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2018-03-28 | GSE112335 | GEO
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2017-08-11 | GSE60321 | GEO
Project description:The research objectives is to compare vitro 3D drug sensitivity test results of micro tumor (PTC) with the clinical outcomes of patients, evaluate the consistency between the test results of the technology platform and the clinical prognosis, and explore the decision-making value and guiding significance of this technology in assisting the precise treatment of colorectal cancer. The completion of this study will provide real-world data support for the clinical application of micro tumor (PTC) in vitro 3D drug sensitivity detection technology, and provide more valuable reference basis for realizing the individualization and accuracy of colorectal cancer treatment and improving the clinical benefit rate.
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Project description:Tumor cell lines and drug-resistant counterparts. These data support the publication Gyorffy et al, Oncogene 2005 (July), Prediction of doxorubicin sensitivity in breast tumors based on gene expression profiles of drug-resistant cell lines correlates with patient survival. We contrasted the expression profiles of 13 different human tumor cell lines of gastric (EPG85-257), pancreatic (EPP85-181), colon (HT29) and breast (MCF7 and MDA-MB-231) origin and their counterparts resistant to the topoisomerase inhibitors daunorubicin, doxorubicin or mitoxantrone. We interrogated cDNA arrays with 43 000 cDNA clones ( approximately 30 000 unique genes) to study the expression pattern of these cell lines. A cell type comparison design experiment design type compares cells of different type for example different cell lines. Using regression correlation
2005-07-30 | E-GEOD-3034 | biostudies-arrayexpress
Project description:ImportanceMyelin oligodendrocyte glycoprotein-IgG1-associated disorder (MOGAD) is a distinct central nervous system-demyelinating disease. Positive results on MOG-IgG1 testing by live cell-based assays can confirm a MOGAD diagnosis, but false-positive results may occur.ObjectiveTo determine the positive predictive value (PPV) of MOG-IgG1 testing in a tertiary referral center.Design, setting, and participantsThis diagnostic study was conducted over 2 years, from January 1, 2018, through December 31, 2019. Patients in the Mayo Clinic who were consecutively tested for MOG-IgG1 by live cell-based flow cytometry during their diagnostic workup were included. Patients without research authorization were excluded.Main outcomes and measuresMedical records of patients who were tested were initially reviewed by 2 investigators blinded to MOG-IgG1 serostatus, and pretest probability was classified as high or low (suggestive of MOGAD or not). Testing of MOG-IgG1 used a live-cell fluorescence-activated cell-sorting assay; an IgG binding index value of 2.5 or more with an end titer of 1:20 or more was considered positive. Cases positive for MOG-IgG1 were independently designated by 2 neurologists as true-positive or false-positive results at last follow-up, based on current international recommendations on diagnosis or identification of alternative diagnoses; consensus was reached for cases in which disagreement existed.ResultsA total of 1617 patients were tested, and 357 were excluded. Among 1260 included patients tested over 2 years, the median (range) age at testing was 46 (0-98) years, and 792 patients were female (62.9%). A total of 92 of 1260 (7.3%) were positive for MOG-IgG1. Twenty-six results (28%) were designated as false positive by the 2 raters, with an overall agreement on 91 of 92 cases (99%) for true and false positivity. Alternative diagnoses included multiple sclerosis (n = 11), infarction (n = 3), B12 deficiency (n = 2), neoplasia (n = 2), genetically confirmed adrenomyeloneuropathy (n = 1), and other conditions (n = 7). The overall PPV (number of true-positive results/total positive results) was 72% (95% CI, 62%-80%) and titer dependent (PPVs: 1:1000, 100%; 1:100, 82%; 1:20-40, 51%). The median titer was higher with true-positive results (1:100 [range, 1:20-1:10000]) than false-positive results (1:40 [range, 1:20-1:100]; P < .001). The PPV was higher for children (94% [95% CI, 72%-99%]) vs adults (67% [95% CI, 56%-77%]) and patients with high pretest probability (85% [95% CI, 76%-92%]) vs low pretest probability (12% [95% CI, 3%-34%]). The specificity of MOG-IgG1 testing was 97.8%.Conclusions and relevanceThis study confirms MOG-IgG1 as a highly specific biomarker for MOGAD, but when using a cutoff of 1:20, it has a low PPV of 72%. Caution is advised in the interpretation of low titers among patients with atypical phenotypes, because ordering MOG-IgG1 in low pretest probability situations will increase the proportion of false-positive results.
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Project description:The research objectives is to compare vitro 3D drug sensitivity test results of micro tumor (PTC) with the clinical outcomes of patients, evaluate the consistency between the test results of the technology platform and the clinical prognosis, and explore the decision-making value and guiding significance of this technology in assisting the precise treatment of colorectal cancer. The completion of this study will provide real-world data support for the clinical application of micro tumor (PTC) in vitro 3D drug sensitivity detection technology, and provide more valuable reference basis for realizing the individualization and accuracy of colorectal cancer treatment and improving the clinical benefit rate.
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Project description:BackgroundWhile high-dimensional molecular data such as microarray gene expression data have been used for disease outcome prediction or diagnosis purposes for about ten years in biomedical research, the question of the additional predictive value of such data given that classical predictors are already available has long been under-considered in the bioinformatics literature.ResultsWe suggest an intuitive permutation-based testing procedure for assessing the additional predictive value of high-dimensional molecular data. Our method combines two well-known statistical tools: logistic regression and boosting regression. We give clear advice for the choice of the only method parameter (the number of boosting iterations). In simulations, our novel approach is found to have very good power in different settings, e.g. few strong predictors or many weak predictors. For illustrative purpose, it is applied to the two publicly available cancer data sets.ConclusionsOur simple and computationally efficient approach can be used to globally assess the additional predictive power of a large number of candidate predictors given that a few clinical covariates or a known prognostic index are already available. It is implemented in the R package "globalboosttest" which is publicly available from R-forge and will be sent to the CRAN as soon as possible.
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Project description:Tumor cell lines and drug-resistant counterparts. These data support the publication Gyorffy et al, Oncogene 2005 (July), Prediction of doxorubicin sensitivity in breast tumors based on gene expression profiles of drug-resistant cell lines correlates with patient survival. We contrasted the expression profiles of 13 different human tumor cell lines of gastric (EPG85-257), pancreatic (EPP85-181), colon (HT29) and breast (MCF7 and MDA-MB-231) origin and their counterparts resistant to the topoisomerase inhibitors daunorubicin, doxorubicin or mitoxantrone. We interrogated cDNA arrays with 43 000 cDNA clones ( approximately 30 000 unique genes) to study the expression pattern of these cell lines. A cell type comparison design experiment design type compares cells of different type for example different cell lines. Keywords: cell_type_comparison_design
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