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Affinity-free enrichment and mass spectrometry analysis of the ovarian cancer biomarker CA125 (MUC16) from patient-derived ascites.

ABSTRACT: Developing a mass spectrometry-based assay for the ovarian cancer biomarker CA125 (MUC16) is a desirable goal, because it may enable detection of molecular regions that are not recognized by antibodies and are therefore analytically silent in the current immunoassay. Additionally, the ability to characterize the CA125 proteoforms expressed by individuals may offer clinical insight. Enrichment of CA125 from malignant ascites may provide a high-quality source of this important ovarian cancer biomarker, but a reliable strategy for such enrichment is currently lacking. Beginning with crude ascites isolated from three individual patients with high grade serous ovarian cancer, we enriched for MUC16 using filtration, ion exchange, and size exclusion chromatography and then performed bottom-up proteomics on the isolated proteins. This approach of enrichment and analysis reveals that the peptides detected via mass spectrometry map to the SEA domain and C-loop regions within the tandem repeat domains of CA125 and that peptide abundance correlates with clinical CA125 counts.

SUBMITTER: Schuster-Little N

PROVIDER: S-EPMC7785687 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Affinity-free enrichment and mass spectrometry analysis of the ovarian cancer biomarker CA125 (MUC16) from patient-derived ascites.

Schuster-Little Naviya N Fritz-Klaus Roberta R Etzel Mark M Patankar Niharika N Javeri Saahil S Patankar Manish S MS Whelan Rebecca J RJ

The Analyst 20210101 1

Developing a mass spectrometry-based assay for the ovarian cancer biomarker CA125 (MUC16) is a desirable goal, because it may enable detection of molecular regions that are not recognized by antibodies and are therefore analytically silent in the current immunoassay. Additionally, the ability to characterize the CA125 proteoforms expressed by individuals may offer clinical insight. Enrichment of CA125 from malignant ascites may provide a high-quality source of this important ovarian cancer bioma ...[more]

PMID: 33141132

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Project description:The biomarker CA125, a peptide epitope located in several tandem repeats of the mucin MUC16, is the gold standard for monitoring regression and recurrence of high-grade serous ovarian cancer in response to therapy. However, the CA125 epitope along with several structural features of the MUC16 molecule are ill defined. One central aspect still unresolved is the number of tandem repeats in MUC16 and how many of these repeats contain the CA125 epitope. Studies from the early 2000s assembled short DNA reads to estimate that MUC16 contained 63 repeats.Here, we conduct Nanopore long-read sequencing of MUC16 transcripts from three primary ovarian tumors and established cell lines (OVCAR3, OVCAR5, and Kuramochi) for a more exhaustive and accurate estimation and sequencing of the MUC16 tandem repeats.The consensus sequence derived from these six sources was confirmed by proteomics validation and agrees with recent additions to the NCBI database. We propose a model of MUC16 containing 19-not 63-tandem repeats. In addition, we predict the structure of the tandem repeat domain using the deep learning algorithm, AlphaFold.The predicted structure displays an SEA domain and unstructured linker region rich in proline, serine, and threonine residues in all 19 tandem repeats. These studies now pave the way for a detailed characterization of the CA125 epitope. Sequencing and modeling of the MUC16 tandem repeats along with their glycoproteomic characterization, currently underway in our laboratories, will help identify novel epitopes in the MUC16 molecule that improve on the sensitivity and clinical utility of the current CA125 assay.SignificanceDespite its crucial role in clinical management of ovarian cancer, the exact molecular sequence and structure of the biomarker, CA125, are not defined. Here, we combine long-read sequencing, mass spectrometry, and in silico modeling to provide the foundational dataset for a more complete characterization of the CA125 epitope.

Dataset Information

Affinity-free enrichment and mass spectrometry analysis of the ovarian cancer biomarker CA125 (MUC16) from patient-derived ascites.

Publications

Affinity-free enrichment and mass spectrometry analysis of the ovarian cancer biomarker CA125 (MUC16) from patient-derived ascites.

Similar Datasets

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