BA15 - Discovery and Confirmation of Cancer-specific Serum Protein Markers for Ovarian Cancer Early Detection

This page provides study documentation for BA15. For description of the specimen results, see Specimen Results Description (open to public).  When available, dData sets of the specimen results are included in the existing WHI datasets located on the WHI Data on this site (sign in and a completed Data Distribution Agreement are required; see details on the Data site).

Investigator Names and Contact Information

Marty McIntosh, PhD,


Ovarian Cancer (OC) is the fourth leading cause of cancer death of women in the United States.  Our goal is to develop strategies to detect it in an earlier more treatable stage. We propose to use state-of-the-art proteomics technologies and novel computational strategies to identify plasma proteins that can detect the majority of women with Serous Ovarian Cancer (SOC), the most common and most deadly subtype of OC, three years or more before it presents clinically. The rationale for our goal follows from several factors, including the evidence that some proteins in the blood of SOC patients elevate three or more years before clinical diagnosis, and that our proteomic technologies are capable of identifying and measuring blood protein changes following OC development. Together these factors suggest that blood-based discovery of biomarkers for OC is feasible with the technologies and methodologies at our disposal.

We propose a nested case-control study to compare plasma proteins of SOC cases and matched controls using the 200 or more SOC plasma samples that were collected between six months and three years prior to diagnosis. Our experimental design includes two phases. Phase 1 is exclusively discovery-based and uses our intact protein analysis method and tandem mass spectrometry (MS) to interrogate the half of the SOC plasma samples collected closest to diagnosis. Phase 2 uses our discovery-based platform as well as a complementary targeted MS approach to ensure that we also measure our top candidates in those SOC plasma samples collected furthest from diagnosis, and thus allows us to estimate lead-times of up to three years. These proteomic data will be mined using state-of-the art computational approaches and then integrated with several existing proteomic and genomic data sets available to us, including plasma profiles of cancer-free women from the WHI, and proteomic and genomic analysis of SOC cells and tissues. Our integrated data will permit us to evaluate thousands of proteins and identify differential proteins that are potentially tumor associated.
It is our hypothesis that the best performing biomarkers will be those that measure the consequence of molecular alterations distinguishing ovarian cancer cells from normal cells and that contain novel peptide sequences specific to SOC. To identify these proteins we employ genomic approaches to interpret our proteomic data. Specifically, we augment our protein sequence databases with sequences predicted from public genomic databases and also from our own genomic sequence data measuring SOC tumor cells, and then we analyze our plasma proteome data using these sequences.
Aim 1:     Use comprehensive intact protein analysis with tandem mass spectrometry and targeted mass spectrometry to interrogate 10 pools containing 20 isotopically labeled plasma from SOC cases and 20 isotopically labeled matched controls.
Aim 2:     Use novel data mining and integration strategies to identify dozens of tumor associated plasma proteins, including many containing novel peptide sequences, that differentiate SOC cases from controls three years or more before clinical diagnosis.