Ovarian cancer is often a silent disease, showing no obvious signs until late in its development. The 5-year survival rate is only 15-20%, with most tumors ultimately becoming resistant to treatment due to the late stage at diagnosis. However, if diagnosed and treated early, the survival rate is 90%. Therefore, prevention and early detection represent our best hope to overcome this disease. Under the aegis of the original award R01CA108990 """"""""Multianalyte assay for early diagnosis of ovarian cancer"""""""", we have identified and validated a 4-biomarker panel that recognizes early stages (I-IIB) ovarian cancer with high sensitivity of 86% at 98% specificity. We have further observed that same biomarkers measured in urine confer additional sensitivity to the test while maintaining the required 98% specificity. Recently published data indicated that performance biomarker combinations with added classification power for ovarian cancer as compared with CA 125 alone that were identified in the sets of samples from patients with clinically diagnosed cancer was similar to CA 125 alone in pre-diagnostic samples.. We hypothesize that to develop and effective screening algorithm for identification of preclinical (pre-diagnostic) stages of ovarian cancer, algorithm training should be performed on samples collected prior to diagnosis. We also hypothesize that measuring biomarkers in urine instead of serum could substaitally improve classification power of screening and diagnostic assays. Our primary objectives are to develop and validate reliable and highly sensitive multimarker assays for early (preclinical) detection and diagnosis of ovarian cancer. To achieve these Objectives, the following Specific Aims are proposed:
Aim 1. Identify and validate serum panel(s) for classification of preclinical ovarian cancer.
Aim 2. Identify and validate urine and serum biomarkers in a multimarker panel to detect early stage ovarian cancer.
Aim 3. Evaluate the performance of urine or urine/serum multimarker panel for triage of ovarian cancer patients to specially trained surgeons. At the conclusion of this study, we expect to develop strategies for the early detection of ovarian cancer that are practical and efficient and can be projected to offer the best opportunity to reduce the mortality from this disease.

Public Health Relevance

Ovarian cancer is often a silent disease, showing no specific signs or symptoms until late in its progression. The 5-year survival rate is only 15-20%, with most tumors ultimately becoming resistant to treatment due to the late stage at diagnosis. However, if diagnosed and treated early, the survival rate is 90%. Therefore, prevention and early detection represent our best hope to overcome this disease. Under the aegis of the original award R01CA108990 Multianalyte assay for early diagnosis of ovarian cancer, we have identified and validated a 4- biomarker panel that recognizes early stage (I-IIB) ovarian cancer with high sensitivity of 86% at 98% specificity. We have further observed that same biomarkers measured in urine confer additional sensitivity to the test while maintaining the required 98% specificity. Recently, published data indicated that performance biomarker combinations with added classification power for ovarian cancer as compared with CA 125 alone that were identified in the sets of samples from patients with clinically diagnosed cancer was similar to CA 125 alone in pre-diagnostic samples.. We hypothesize that to develop an effective screening algorithm for identification of preclinical (pre-diagnostic) ovarian cancer, algorithm training should be performed on samples collected prior to diagnosis. We also hypothesize that measuring biomarkers in urine instead of serum could substantially improve classification power of screening and diagnostic assays. Our primary objectives are to develop and validate reliable and highly sensitive multimarker assays for early (preclinical) detection and diagnosis of ovarian cancer. To achieve these Objectives, the following Specific Aims are proposed: Aim 1. Identify and validate serum panel(s) for classification of preclinical ovarian cancer. (a) Identify serum multimarker panel for ovarian cancer that can reliably recognize preclinical disease. (b) Validate performance of serum multimarker panel with high classification power for preclinical ovarian cancer in PLCO and UKCTOCS sets. (c) Validate performance of a multimarker serum panel in a high-risk cohort. Aim 2. Identify and validate urine and serum biomarkers in a multimarker panel to detect early stage ovarian cancer. (a) Acquire an adequate number of urine and serum samples. (b) Identify an optimal urine and serum biomarker panel with high classification power for early stage ovarian cancer (c) Validate the performance of the selected urine/serum multi-biomarker panel(s) in an independent blinded set of samples from women with or without early and late stages ovarian cancer. (d) Validate the predictive power of a selected multimarker panel in women with other benign and malignant conditions (e) Identify and validate performance of urine-based multimarker panel in a high-risk cohort. (f) Determine the nature of urine biomarkers. Aim 3. Evaluate the performance of urine or urine/serum multimarker panel for triage of ovarian cancer patients to specially trained surgeons. (a) Acquire urine and serum samples from patients with ovarian cancer and benign pelvic mass (b) Develop an algorithm that uses both urine and serum biomarkers to distinguish malignant from benign pelvic masses. (c) Test the performance of the algorithm in a blinded validation set. (d) Test the performance of the algorithm in a prospective study. At the conclusion of this study, we expect to develop strategies for the early detection of ovarian cancer that are practical and efficient and can be projected to offer the best opportunity to reduce the mortality from this disease.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA108990-08
Application #
8214635
Study Section
Cancer Biomarkers Study Section (CBSS)
Program Officer
Patriotis, Christos F
Project Start
2004-07-01
Project End
2015-01-31
Budget Start
2012-02-01
Budget End
2013-01-31
Support Year
8
Fiscal Year
2012
Total Cost
$551,606
Indirect Cost
$104,762
Name
University of Pittsburgh
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
004514360
City
Pittsburgh
State
PA
Country
United States
Zip Code
15213
Nolen, Brian M; Lomakin, Aleksey; Marrangoni, Adele et al. (2015) Urinary protein biomarkers in the early detection of lung cancer. Cancer Prev Res (Phila) 8:111-9
Goufman, Eugene I; Iakovlev, Vasily N; Tikhonova, Natalia B et al. (2015) Quantification of autoantibodies to plasminogen in plasma of patients with cancer. Cancer Biomark 15:281-7
Mirus, Justin E; Zhang, Yuzheng; Li, Christopher I et al. (2015) Cross-species antibody microarray interrogation identifies a 3-protein panel of plasma biomarkers for early diagnosis of pancreas cancer. Clin Cancer Res 21:1764-71
Nolen, Brian M; Brand, Randall E; Prosser, Denise et al. (2014) Prediagnostic serum biomarkers as early detection tools for pancreatic cancer in a large prospective cohort study. PLoS One 9:e94928
Belfer, Inna; Greco, Carol M; Lokshin, Anna et al. (2014) The design and methods of genetic studies on acute and chronic postoperative pain in patients after total knee replacement. Pain Med 15:1590-602
Nolen, Brian M; Lokshin, Anna E (2013) Biomarker testing for ovarian cancer: clinical utility of multiplex assays. Mol Diagn Ther 17:139-46
Nolen, Brian M; Orlichenko, Lidiya S; Marrangoni, Adele et al. (2013) An extensive targeted proteomic analysis of disease-related protein biomarkers in urine from healthy donors. PLoS One 8:e63368
Nolen, Brian M; Lokshin, Anna E (2012) EODG review, US spelling, NIH funding grant no RO1 CA108990-01 Multianalyte assay systems in the differential diagnosis of ovarian cancer. Expert Opin Med Diagn 6:131-138
Nolen, Brian M; Lokshin, Anna E (2012) Protein biomarkers of ovarian cancer: the forest and the trees. Future Oncol 8:55-71
Nolen, Brian M; Langmead, Christopher J; Choi, Sunguk et al. (2011) Serum biomarker profiles as diagnostic tools in lung cancer. Cancer Biomark 10:3-12

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