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Improved early detection of ovarian cancer using longitudinal multimarker models

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Improved early detection of ovarian cancer using longitudinal multimarker models. / Whitwell, Harry J.; Worthington, Jenny; Blyuss, Oleg; Gentry-Maharaj, Aleksandra; Ryan, Andy; Gunu, Richard; Kalsi, Jatinderpal; Menon, Usha; Jacobs, Ian; Zaikin, Alexey; Timms, John F.

In: British Journal of Cancer, Vol. 122, No. 6, 17.03.2020, p. 847-856.

Research output: Contribution to journalArticlepeer-review

Harvard

Whitwell, HJ, Worthington, J, Blyuss, O, Gentry-Maharaj, A, Ryan, A, Gunu, R, Kalsi, J, Menon, U, Jacobs, I, Zaikin, A & Timms, JF 2020, 'Improved early detection of ovarian cancer using longitudinal multimarker models', British Journal of Cancer, vol. 122, no. 6, pp. 847-856. https://doi.org/10.1038/s41416-019-0718-9

APA

Whitwell, H. J., Worthington, J., Blyuss, O., Gentry-Maharaj, A., Ryan, A., Gunu, R., Kalsi, J., Menon, U., Jacobs, I., Zaikin, A., & Timms, J. F. (2020). Improved early detection of ovarian cancer using longitudinal multimarker models. British Journal of Cancer, 122(6), 847-856. https://doi.org/10.1038/s41416-019-0718-9

Vancouver

Author

Whitwell, Harry J. ; Worthington, Jenny ; Blyuss, Oleg ; Gentry-Maharaj, Aleksandra ; Ryan, Andy ; Gunu, Richard ; Kalsi, Jatinderpal ; Menon, Usha ; Jacobs, Ian ; Zaikin, Alexey ; Timms, John F. / Improved early detection of ovarian cancer using longitudinal multimarker models. In: British Journal of Cancer. 2020 ; Vol. 122, No. 6. pp. 847-856.

Bibtex

@article{4e7534eed4b34b1889be9e6130f1c10b,
title = "Improved early detection of ovarian cancer using longitudinal multimarker models",
abstract = "Background: Ovarian cancer has a poor survival rate due to late diagnosis and improved methods are needed for its early detection. Our primary objective was to identify and incorporate additional biomarkers into longitudinal models to improve on the performance of CA125 as a first-line screening test for ovarian cancer. Methods: This case–control study nested within UKCTOCS used 490 serial serum samples from 49 women later diagnosed with ovarian cancer and 31 control women who were cancer-free. Proteomics-based biomarker discovery was carried out using pooled samples and selected candidates, including those from the literature, assayed in all serial samples. Multimarker longitudinal models were derived and tested against CA125 for early detection of ovarian cancer. Results: The best performing models, incorporating CA125, HE4, CHI3L1, PEBP4 and/or AGR2, provided 85.7% sensitivity at 95.4% specificity up to 1 year before diagnosis, significantly improving on CA125 alone. For Type II cases (mostly high-grade serous), models achieved 95.5% sensitivity at 95.4% specificity. Predictive values were elevated earlier than CA125, showing the potential of models to improve lead time. Conclusions: We have identified candidate biomarkers and tested longitudinal multimarker models that significantly improve on CA125 for early detection of ovarian cancer. These models now warrant independent validation.",
author = "Whitwell, {Harry J.} and Jenny Worthington and Oleg Blyuss and Aleksandra Gentry-Maharaj and Andy Ryan and Richard Gunu and Jatinderpal Kalsi and Usha Menon and Ian Jacobs and Alexey Zaikin and Timms, {John F.}",
note = "{\textcopyright} The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article{\textquoteright}s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article{\textquoteright}s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.",
year = "2020",
month = mar,
day = "17",
doi = "10.1038/s41416-019-0718-9",
language = "English",
volume = "122",
pages = "847--856",
journal = "British Journal of Cancer",
issn = "0007-0920",
publisher = "Nature Publishing Group",
number = "6",

}

RIS

TY - JOUR

T1 - Improved early detection of ovarian cancer using longitudinal multimarker models

AU - Whitwell, Harry J.

AU - Worthington, Jenny

AU - Blyuss, Oleg

AU - Gentry-Maharaj, Aleksandra

AU - Ryan, Andy

AU - Gunu, Richard

AU - Kalsi, Jatinderpal

AU - Menon, Usha

AU - Jacobs, Ian

AU - Zaikin, Alexey

AU - Timms, John F.

N1 - © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

PY - 2020/3/17

Y1 - 2020/3/17

N2 - Background: Ovarian cancer has a poor survival rate due to late diagnosis and improved methods are needed for its early detection. Our primary objective was to identify and incorporate additional biomarkers into longitudinal models to improve on the performance of CA125 as a first-line screening test for ovarian cancer. Methods: This case–control study nested within UKCTOCS used 490 serial serum samples from 49 women later diagnosed with ovarian cancer and 31 control women who were cancer-free. Proteomics-based biomarker discovery was carried out using pooled samples and selected candidates, including those from the literature, assayed in all serial samples. Multimarker longitudinal models were derived and tested against CA125 for early detection of ovarian cancer. Results: The best performing models, incorporating CA125, HE4, CHI3L1, PEBP4 and/or AGR2, provided 85.7% sensitivity at 95.4% specificity up to 1 year before diagnosis, significantly improving on CA125 alone. For Type II cases (mostly high-grade serous), models achieved 95.5% sensitivity at 95.4% specificity. Predictive values were elevated earlier than CA125, showing the potential of models to improve lead time. Conclusions: We have identified candidate biomarkers and tested longitudinal multimarker models that significantly improve on CA125 for early detection of ovarian cancer. These models now warrant independent validation.

AB - Background: Ovarian cancer has a poor survival rate due to late diagnosis and improved methods are needed for its early detection. Our primary objective was to identify and incorporate additional biomarkers into longitudinal models to improve on the performance of CA125 as a first-line screening test for ovarian cancer. Methods: This case–control study nested within UKCTOCS used 490 serial serum samples from 49 women later diagnosed with ovarian cancer and 31 control women who were cancer-free. Proteomics-based biomarker discovery was carried out using pooled samples and selected candidates, including those from the literature, assayed in all serial samples. Multimarker longitudinal models were derived and tested against CA125 for early detection of ovarian cancer. Results: The best performing models, incorporating CA125, HE4, CHI3L1, PEBP4 and/or AGR2, provided 85.7% sensitivity at 95.4% specificity up to 1 year before diagnosis, significantly improving on CA125 alone. For Type II cases (mostly high-grade serous), models achieved 95.5% sensitivity at 95.4% specificity. Predictive values were elevated earlier than CA125, showing the potential of models to improve lead time. Conclusions: We have identified candidate biomarkers and tested longitudinal multimarker models that significantly improve on CA125 for early detection of ovarian cancer. These models now warrant independent validation.

UR - http://www.scopus.com/inward/record.url?scp=85078054742&partnerID=8YFLogxK

U2 - 10.1038/s41416-019-0718-9

DO - 10.1038/s41416-019-0718-9

M3 - Article

C2 - 31937926

AN - SCOPUS:85078054742

VL - 122

SP - 847

EP - 856

JO - British Journal of Cancer

JF - British Journal of Cancer

SN - 0007-0920

IS - 6

ER -