Homo sapiens
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PROVIDER: PRJNA316074 | ENA |
REPOSITORIES: ENA
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Project description:Autoanitbody Biomarkers for the Early Detection of Serous Ovarian Cancer [Discovery Screen]
| PRJNA315998 | ENA
Project description:Introduction: Serous ovarian cancer is the leading cause of gynecological cancers, with a 5-year survival rate below 45% due in part to the nonspecific symptoms and lack of accurate screening for early detection. In comparison, patients diagnosed at an early stage have a five-year survival rate of 92%, demonstrating the urgent need for biomarkers for the early detection of disease. Serum from patients with serous ovarian cancer contain antibodies to tumor antigens that are potential biomarkers for early detection. The purpose of this study is to identify a panel of novel serum autoantibody (AAb) biomarkers for the early diagnosis of serous ovarian cancer. Methods: To detect AAb we probed high-density programmable protein microarrays (NAPPA) containing 10,247 antigens with sera from patients with serous ovarian cancer (n = 30 cases/ 30 healthy controls) and measured bound IgG. We identified 735 promising tumor antigens using cutoff values of 10% sensitivity at 95% specificity and K-value>0.8, as well as visual analysis and evaluated these with an independent set of serous ovarian cancer sera (n = 30 cases/ 30 benign disease controls/ 30 heathy controls). Thirty-nine potential tumor autoantigens were identified with sensitivities ranging from 3 to 39.7% sensitivity at 95% specificity and were retested using an orthogonal programmable ELISA assay. A total of 13 potential tumor antigens were identified for further validation using an independent ovarian cancer sera set (n = 44 cases/ 34 healthy controls). Sensitivities at 95% specificity were calculated and a serous ovarian cancer classifier was constructed. In addition, we evaluated a longitudinal study using blinded serous pre-diagnostic ovarian cancer sera (n = 9 cases/ 90 controls) to examine the value of three (CTAG1, CTAG2, and p53) of these AAb in comparison to CA 125. Results: We identified 11-AAbs (ICAM3, CTAG2, p53, STYXL1, PVR, POMC, NUDT11, TRIM39, UHMK1, KSR1, and NXF3) that distinguished serous ovarian cancer cases from healthy controls with a combined 45% sensitivity at 100% specificity. In our longitudinal analysis, p53- and CTAG-AAb were detected up to 9 months prior to ovarian cancer diagnosis and increased with CA 125 levels. Conclusion: These are potential circulating biomarkers for the early detection of serous ovarian cancer, and warrant confirmation in larger clinical cohorts. In addition, p53- and CTAG1/2-AAb are detected in a subset of women with ovarian cancer up to 9 months prior to clinical diagnosis. Their utility as a biomarker for early detection, beyond CA 125, warrant further investigation.
2017-06-01 | GSE79516 | GEO
Project description:Introduction: Serous ovarian cancer is the leading cause of gynecological cancers, with a 5-year survival rate below 45% due in part to the nonspecific symptoms and lack of accurate screening for early detection. In comparison, patients diagnosed at an early stage have a five-year survival rate of 92%, demonstrating the urgent need for biomarkers for the early detection of disease. Serum from patients with serous ovarian cancer contain antibodies to tumor antigens that are potential biomarkers for early detection. The purpose of this study is to identify a panel of novel serum autoantibody (AAb) biomarkers for the early diagnosis of serous ovarian cancer. Methods: To detect AAb we probed high-density programmable protein microarrays (NAPPA) containing 10,247 antigens with sera from patients with serous ovarian cancer (n = 30 cases/ 30 healthy controls) and measured bound IgG. We identified 735 promising tumor antigens using cutoff values of 10% sensitivity at 95% specificity and K-value>0.8, as well as visual analysis and evaluated these with an independent set of serous ovarian cancer sera (n = 30 cases/ 30 benign disease controls/ 30 heathy controls). Thirty-nine potential tumor autoantigens were identified with sensitivities ranging from 3 to 39.7% sensitivity at 95% specificity and were retested using an orthogonal programmable ELISA assay. A total of 13 potential tumor antigens were identified for further validation using an independent ovarian cancer sera set (n = 44 cases/ 34 healthy controls). Sensitivities at 95% specificity were calculated and a serous ovarian cancer classifier was constructed. In addition, we evaluated a longitudinal study using blinded serous pre-diagnostic ovarian cancer sera (n = 9 cases/ 90 controls) to examine the value of three (CTAG1, CTAG2, and p53) of these AAb in comparison to CA 125. Results: We identified 11-AAbs (ICAM3, CTAG2, p53, STYXL1, PVR, POMC, NUDT11, TRIM39, UHMK1, KSR1, and NXF3) that distinguished serous ovarian cancer cases from healthy controls with a combined 45% sensitivity at 100% specificity. In our longitudinal analysis, p53- and CTAG-AAb were detected up to 9 months prior to ovarian cancer diagnosis and increased with CA 125 levels. Conclusion: These are potential circulating biomarkers for the early detection of serous ovarian cancer, and warrant confirmation in larger clinical cohorts. In addition, p53- and CTAG1/2-AAb are detected in a subset of women with ovarian cancer up to 9 months prior to clinical diagnosis. Their utility as a biomarker for early detection, beyond CA 125, warrant further investigation.
2017-06-01 | GSE79498 | GEO
Project description:Introduction: Serous ovarian cancer is the leading cause of gynecological cancers, with a 5-year survival rate below 45% due in part to the nonspecific symptoms and lack of accurate screening for early detection. In comparison, patients diagnosed at an early stage have a five-year survival rate of 92%, demonstrating the urgent need for biomarkers for the early detection of disease. Serum from patients with serous ovarian cancer contain antibodies to tumor antigens that are potential biomarkers for early detection. The purpose of this study is to identify a panel of novel serum autoantibody (AAb) biomarkers for the early diagnosis of serous ovarian cancer. Methods: To detect AAb we probed high-density programmable protein microarrays (NAPPA) containing 10,247 antigens with sera from patients with serous ovarian cancer (n = 30 cases/ 30 healthy controls) and measured bound IgG. We identified 735 promising tumor antigens using cutoff values of 10% sensitivity at 95% specificity and K-value>0.8, as well as visual analysis and evaluated these with an independent set of serous ovarian cancer sera (n = 30 cases/ 30 benign disease controls/ 30 heathy controls). Thirty-nine potential tumor autoantigens were identified with sensitivities ranging from 3 to 39.7% sensitivity at 95% specificity and were retested using an orthogonal programmable ELISA assay. A total of 13 potential tumor antigens were identified for further validation using an independent ovarian cancer sera set (n = 44 cases/ 34 healthy controls). Sensitivities at 95% specificity were calculated and a serous ovarian cancer classifier was constructed. In addition, we evaluated a longitudinal study using blinded serous pre-diagnostic ovarian cancer sera (n = 9 cases/ 90 controls) to examine the value of three (CTAG1, CTAG2, and p53) of these AAb in comparison to CA 125. Results: We identified 11-AAbs (ICAM3, CTAG2, p53, STYXL1, PVR, POMC, NUDT11, TRIM39, UHMK1, KSR1, and NXF3) that distinguished serous ovarian cancer cases from healthy controls with a combined 45% sensitivity at 100% specificity. In our longitudinal analysis, p53- and CTAG-AAb were detected up to 9 months prior to ovarian cancer diagnosis and increased with CA 125 levels. Conclusion: These are potential circulating biomarkers for the early detection of serous ovarian cancer, and warrant confirmation in larger clinical cohorts. In addition, p53- and CTAG1/2-AAb are detected in a subset of women with ovarian cancer up to 9 months prior to clinical diagnosis. Their utility as a biomarker for early detection, beyond CA 125, warrant further investigation.
2017-06-01 | GSE79517 | GEO
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.
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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.
2024-09-02 | BIOMD0000000741 | BioModels
Project description:Autoanitbody Biomarkers for the Early Detection of Serous Ovarian Cancer
| PRJNA316072 | ENA
Project description:We aimed to discover and validate a panel of serum biomarkers for high grade serous ovarian cancer (HGSOC) using our lectin-magnetic bead array-coupled proteomics platform. Serum from age-matched women with HGSOC, benign tumours or healthy controls were analysed in discovery (UKCTOCS, n=30 and UKOPS, n=30) and validation (Australian Ovarian Cancer Study, n=95) cohorts using shotgun and targeted proteomics, respectively. A 7-lectin discovery screen shortlisted 60 candidate proteins and 3 lectins for validation, which revealed elevated levels of AAL, SNA or STL-binding FIBB, CO9, ITIH3, HPT, A1AT, AACT in HGSOC, while IBP3, A2MG, PON1, CHLE and ALS were reduced. Multimarker panels were developed using generalized regression with lasso estimation and leave-one-out cross-validation. The best performing panel comprised of 13 peptides from Solanum Tuberosum lectin (STL)-binding proteins with 96.3% area under the receiver operating curve, 97.7% specificity and 78.6% sensitivity for distinguishing HGSOC from benign and healthy groups. The peptides robust in cross-validations were from IBP3, KNG1, CO9, THRB, HPTR, HPT, FINC, FA10, GELS. The validated serum biomarkers show promise for early detection of HGSOC and should be further evaluated.
2023-10-24 | PXD032299 | Pride
Project description:Identification and validation of potential prognostic biomarkers in older ovarian cancer patients with high-grade serous adenocarcinoma (HGSC)
2020-03-01 | GSE126308 | GEO
Project description:Ovarian cancer is the most lethal gynecologic malignancy in women, and high-grade serous ovarian cancer (HGSOC) is the most common subtype. Currently, no clinical test has been approved by the FDA to screen the general population for ovarian cancer. This underscores the critical need for the development of a robust methodology combined with novel technology to detect diagnostic biomarkers for HGSOC in the sera of women. Targeted mass spectrometry (MS) can be used to identify and quantify specific peptides/proteins in complex biological samples with high accuracy, sensitivity, and reproducibility. In this study, we sought to develop and conduct analytical validation of a multiplexed Tier 2 targeted MS parallel reaction monitoring (PRM) assay for the relative quantification of 23 putative ovarian cancer protein biomarkers in sera.
2024-01-26 | PXD047188 | Pride
Project description:We aimed to discover and validate a panel of serum biomarkers for high grade serous ovarian cancer (HGSOC) using our lectin-magnetic bead array-coupled proteomics platform. Serum from age-matched women with HGSOC, benign tumours or healthy controls were analysed in discovery (UKCTOCS, n=30 and UKOPS, n=30) and validation (Australian Ovarian Cancer Study, n=95) cohorts using shotgun and targeted proteomics, respectively. A 7-lectin discovery screen shortlisted 60 candidate proteins and 3 lectins for validation, which revealed elevated levels of AAL, SNA or STL-binding FIBB, CO9, ITIH3, HPT, A1AT, AACT in HGSOC, while IBP3, A2MG, PON1, CHLE and ALS were reduced. Multimarker panels were developed using generalized regression with lasso estimation and leave-one-out cross-validation. The best performing panel comprised of 13 peptides from Solanum Tuberosum lectin (STL)-binding proteins with 96.3% area under the receiver operating curve, 97.7% specificity and 78.6% sensitivity for distinguishing HGSOC from benign and healthy groups. The peptides robust in cross-validations were from IBP3, KNG1, CO9, THRB, HPTR, HPT, FINC, FA10, GELS. The validated serum biomarkers show promise for early detection of HGSOC and should be further evaluated.
2024-08-06 | PXD033108 | panorama