# Targeted proteomics of MUC16 to enable early detection of ovarian cancer recurrence

> **NIH NIH R21** · UNIVERSITY OF KANSAS LAWRENCE · 2022 · $219,069

## Abstract

High-grade serous ovarian cancer (HGSOC) is a deadly disease, in large part because most cases recur, and
little can be done after that point. New diagnostic tools are urgently needed to improve HGSOC management
and detect recurrence before clinical presentation. MUC16 is overexpressed on ovarian cancer cells and bears
the CA125 epitope that is currently detected in clinical assays. The value of CA125 titers for surveillance is an
active area of debate within the gynecologic oncology community. Some studies report no survival benefit,
while others point to higher quality secondary cytoreductive surgery if action is taken quickly. Successfully
fighting HGSOC requires that new assays are developed for more reliable and sensitive detection of recurrent
disease. Our goal is to develop a new biomarker for HGSOC recurrence by fundamentally rethinking MUC16.
Instead of considering MUC16 as the carrier of the CA125 epitope, we recognize that MUC16 itself is present
as multiple diverse proteoforms. Because of variation in mRNA splicing, post-translational cleavage, and post-
translational modifications, the population of MUC16 molecules present in an individual is heterogeneous.
MUC16 is abundantly glycosylated, and glycan profiles of MUC16 derived from cancer cells is an untapped
wealth of diagnostic information that is now lost using the existing peptide-based CA125 immunoassay. The
goal of this project is to develop a method to enrich MUC16 from the serum of women undergoing treatment for
HGSOC and analyze MUC16 using targeted (glyco)proteomics. We hypothesize that the molecular diversity of
MUC16 revealed using modern bioanalytical and computational tools will enable novel diagnostics to detect
HGSOC resurgence earlier than is currently possible. This hypothesis will be investigated by pursuing three
research aims. In Aim 1 (Develop methods to quantitate MUC16 (glyco)peptide libraries using mass
spectrometry), MUC16 from banked peritoneal fluid of HGSOC patients will be used to develop an optimized
novel targeted mass spectrometry method for identification of MUC16 glycopeptides and peptides before and
after deglycosylation. A parallel reaction monitoring (PRM) inclusion list will be generated by examination of in
silico digest data and experimental MS/MS data. In Aim 2 (Develop a microscale separation method to enrich
MUC16 from serum), we will adapt an immunoaffinity-free protocol that we developed to enrich MUC16 from
peritoneal fluid to isolate the mucin from serum samples. A cartridge-based format will be used to capture
MUC16 based on its negative charge and specific glycosylation. Finally, in Aim 3: (Develop pilot data on
longitudinal variations in MUC16 (glyco)peptide libraries in patients with HGSOC before and during treatment),
longitudinal serum samples from HGSOC patients will be enriched for MUC16 using the microscale protocol.
Samples will be proteolyzed and analyzed by nLC-PRM. Quantitative (glyco)peptide maps for MUC16 from
each patien...

## Key facts

- **NIH application ID:** 10619871
- **Project number:** 7R21CA267532-02
- **Recipient organization:** UNIVERSITY OF KANSAS LAWRENCE
- **Principal Investigator:** Rebecca Jean Whelan
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $219,069
- **Award type:** 7
- **Project period:** 2022-01-01 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10619871

## Citation

> US National Institutes of Health, RePORTER application 10619871, Targeted proteomics of MUC16 to enable early detection of ovarian cancer recurrence (7R21CA267532-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10619871. Licensed CC0.

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