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      Characterization of Cell-Bound CA125 on Immune Cell Subtypes of Ovarian Cancer Patients Using a Novel Imaging Platform

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          Abstract

          Simple Summary

          High-grade serous ovarian cancer is a fatal disease typically detected at an advanced stage when options for effective treatment are significantly limited. The lack of a screening modality to identify ovarian cancer in its early stage continues to be a major impediment in the management of this malignancy. The serum biomarker CA125, a repeating peptide epitope present in the sialomucin, MUC16, is unsuitable as a screening test. We have demonstrated that immune cells of ovarian cancer patients capture miniscule amounts of CA125 on their cell surface. Here, we report an automated, sensitive, alignment-free microscopy platform to qualitatively and quantitatively assess the low-abundance binding of CA125 to circulating leucocyte subsets. Through a comparison of the CA125 levels on immune cell subsets of ovarian cancer patients versus healthy donors, we demonstrate that our new technique can serve as a novel diagnostic platform for detection and monitoring of ovarian cancer.

          Abstract

          MUC16, a sialomucin that contains the ovarian cancer biomarker CA125, binds at low abundance to leucocytes via the immune receptor, Siglec-9. Conventional fluorescence-based imaging techniques lack the sensitivity to assess this low-abundance event, prompting us to develop a novel “digital” optical cytometry technique for qualitative and quantitative assessment of CA125 binding to peripheral blood mononuclear cells (PBMC). Plasmonic nanoparticle labeled detection antibody allows assessment of CA125 at the near-single molecule level when bound to specific immune cell lineages that are simultaneously identified using multiparameter fluorescence imaging. Image analysis and deep learning were used to quantify CA125 per each cell lineage. PBMC from treatment naïve ovarian cancer patients (N = 14) showed higher cell surface abundance of CA125 on the aggregate PBMC population as well as on NK ( p = 0.013), T ( p < 0.001) and B cells ( p = 0.024) compared to circulating lymphocytes of healthy donors (N = 7). Differences in CA125 binding to monocytes or NK-T cells between the two cohorts were not significant. There was no correlation between the PBMC-bound and serum levels of CA125, suggesting that these two compartments are not in stoichiometric equilibrium. Understanding where and how subset-specific cell-bound surface CA125 takes place may provide guidance towards a new diagnostic biomarker in ovarian cancer.

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          U-Net: deep learning for cell counting, detection, and morphometry

          U-Net is a generic deep-learning solution for frequently occurring quantification tasks such as cell detection and shape measurements in biomedical image data. We present an ImageJ plugin that enables non-machine-learning experts to analyze their data with U-Net on either a local computer or a remote server/cloud service. The plugin comes with pretrained models for single-cell segmentation and allows for U-Net to be adapted to new tasks on the basis of a few annotated samples.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Cancers (Basel)
                Cancers (Basel)
                cancers
                Cancers
                MDPI
                2072-6694
                25 April 2021
                May 2021
                : 13
                : 9
                : 2072
                Affiliations
                [1 ]PNP Research Corporation, Drury, MA 01343, USA; petra@ 123456pnpresearch.com (P.K.); peter@ 123456pnpresearch.com (W.P.H.)
                [2 ]Brigham and Women’s Hospital, Department of Obstetrics, Gynecology and Reproductive Biology, Boston, MA 02115, USA; klakatos@ 123456bwh.harvard.edu (K.L.); dcramer@ 123456bwh.harvard.edu (D.W.C.)
                [3 ]Thorlabs Imaging Systems, Sterling, VA 20166, USA; jhoballah@ 123456thorlabs.com (J.H.); jbrooker@ 123456thorlabs.com (J.B.)
                [4 ]Department of Obstetrics and Gynecology, University of Wisconsin Madison, Madison, WI 53706, USA; rfritzklaus@ 123456wisc.edu (R.F.-K.); laljohani@ 123456wisc.edu (L.A.-J.)
                [5 ]Massachusetts General Hospital, Wellman Center for Photomedicine, Boston, MA 02114, USA; sjeong4@ 123456mgh.harvard.edu (S.J.); evans.conor@ 123456mgh.harvard.edu (C.L.E.)
                [6 ]Private Researcher, Philadelphia, PA 98516, USA; hoffman.ra@ 123456gmail.com
                Author notes
                Author information
                https://orcid.org/0000-0002-8205-6432
                Article
                cancers-13-02072
                10.3390/cancers13092072
                8123299
                33922973
                996d060e-2e66-4916-b059-fb23dc72cb1e
                © 2021 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 17 February 2021
                : 22 April 2021
                Categories
                Article

                ovarian cancer,ca125,muc16,siglec-9,surface plasmon resonance,multiparameter imaging,deep learning,lymphocyte

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