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2/19/2010 DISCLAIMER Kevin Breuel, Ph.D., HCLD Department of OB/GYN Quillen College of Medicine East Tennessee State University Ovarian Cancer, Incidence and Mortality NEITHER THE PUBLISHER NOR THE AUTHORS ASSUME ANY LIABILITY FOR ANY INJURY AND OR DAMAGE TO PERSONS OR PROPERTY ARISING FROM THIS WEBSITE AND ITS CONTENT. Ovarian Cancer Incidence Mortality Definition of ovarian cancer: Cancer that forms in tissues of the ovary. There are three types of ovarian cancer: 1. 2. 3. 3 Epithelial (85‐90%) Germ cell tumors (5%) Sex cord Sex cord–stromal stromal tumors (occur in connective tissue inside the ovary) Data from the American Cancer Society ACOG Education Pamphlet, 2010 Estimated new cases and deaths from ovarian cancer in the United States in 2009: New cases: 21,550 Deaths: 14,600 National Cancer Institute, US National Institutes of Health Dr. Eric Feung, Vermillion 4 1 2/19/2010 So the Story Goes ~~~~~~~~~~~ Stage ~% Description 5 Year Survival Rate (%) I 15% The cancer is still contained within the ovary (or ovaries). II 17% The cancer is in one or both ovaries and has involved other organs (such as the uterus, fallopian tubes, bladder, the sigmoid colon, or the rectum) within the pelvis. 66% The cancer involves one or both ovaries, and one or both of the following are present: (1) cancer has spread beyond the pelvis to the lining of the abdomen; (2) cancer has spread to lymph nodes. 34% This is the most advanced stage of ovarian cancer. In this stage the cancer has spread to the inside of the liver, the lungs, or other organs located outside of the peritoneal cavity. 18% III 89% >60% IV Identifying the Question Early in the process, scientists at Vermillion, Inc. understood that the most important ingredient in a recipe is the unmet clinical need. Their initial instinct was to develop a test to screen for ovarian cancer in hopes of identifying the disease earlier. After discussions with leaders working in this field, they came to understand that due to the low prevalence of ovarian cancer, development of a screening test was out of their reach. These colleagues, however, suggested that their was a critical unmet need in the area of ovarian tumor triage. Although diagnostic test development remains challenging, novel technologies, including proteomics, genomics, and microRNA analysis, provide opportunities to identify biomarkers that in principle could accelerate the development of new diagnostic tests. Eric T. Fung, Vermillion, Inc. “Clinical Chemistry, 2010, 56(2):327‐329 Identifying the Question (Cont) Although ovarian tumors are relatively common, only a fraction of them are malignant. 5‐10% of women in the US will undergo a surgical procedure for a suspected ovarian neoplasm during their lifetime (ACOG) 13‐21% of these women will have an ovarian malignancy. They decided that being able to identify the malignant ones preoperatively would permit better preoperative management of women with ovarian tumors. Furthermore, women with a high likelihood of malignancy could benefit from referral to gynecologic oncologists. 2 2/19/2010 Surface‐Enhanced Laser Desorption Ionisation Time‐Of‐Flight Mass Spectrometry (SELDI TOF MS) The analysis of proteins by SELDI TOF MS can be divided up into 5 distinct phases; 1. Purification / enrichment of selective proteins of interest on the array or P ifi ti / i h t f l ti t i f i t t th chip surface. 2. The addition of a u.v. absorbing matrix to the sample on the chip surface and placement into the mass spectrometer. 3. The firing of a laser at the sample / matrix mixture. 4. Determination of the masses of the proteins in that sample. 5. Computer analysis and comparison of individual samples or groups of samples looking for changes in the expression of those proteins e.g. affected patient and control samples. Purification / enrichment of selective proteins of interest on the array or chip surface. Figure 1. Surface‐enhanced laser desorbtion and ionization (SELDI) technology. This type of proteomic analytical tool is a class of MS instrument that is useful for the high‐throughput proteomic fingerprinting of serum. Using a robotic sample dispenser, 1 μL of raw serum is applied to the surface of a protein‐binding chip. Some laboratories pre‐fractionate their samples beforehand, whereas others perform complex analysis for optimizing binding by diluting into a myriad of pH and salt permutations. Regardless of the upfront manipulations, but based on the underlying SELDI chip chemistry and the pH and buffer used, only a subset of the proteins in the sample bind to the surface of the chip. The bound proteins are then treated with a MALDI matrix, washed and dried. The chip, containing multiple patient samples, is inserted into a vacuum chamber where it is irradiated with a laser. The laser desorbs the adherent proteins, causing them to be launched as ions. The time‐of‐flight (TOF) of the ion before detection by an electrode is a measure of the mass to charge (m/z) value of the ion. The ion spectra can be analyzed by computer‐assisted tools that classify a subset of the spectra by their characteristic patterns of relative intensity. Proteomics study design and results Total 503 samples from 4 different medical institutions 153 Invasive epithelial ovarian cancer 42 Borderline ovarian cancer 166 Benign pelvic mass 142 Healthy women Screening and cross validation 33272 m/z 7 12828 m/z / > CA1255 28043 m/z 43 / CA125 Sensitivity Specificity Purification and identification ITIH4 Apo A1 TTR Assay development 41 ovarian cancer 41 Healthy women 20 breast cancer 20 colon cancer 20 prostate cancer Final 7 Biomarker Candidates Identified by SELDI‐TOF: 1. ITIH4 – (inter‐α‐trypsin inhibitor heavy chain 4) 2. 2 Transthyretin (prealbumin) 3. Apolipoprotein A1 4. Hepcidin 5. ß2‐microglobulin 6. Transferrin 7. CTAP3 Total 142 samples from fifth medical institution Zhang et al, Cancer Research 64, 5882–5890, August 15, 2004 3 2/19/2010 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0 P<.0001 P<.0078 Used two samples sets P<.0043 20 Set 1: samples from University of Kentucky Medical Center (UK) 284 subjects 96 malignancies Set 2: samples randomly selected from set collected under the OVA1 clinical study protocol 125 subjects 26% prevalence ovarian malignancy Pre‐processing: Log transformation for relevant markers All normalized based on manufacturers’ suggested reference range 10 0 Benign Borderline Inv CA Benign Borderline Inv EOC Other CA Other CA Transthyretin Apo A1 1.75 10.0 P<.0001 1.50 P<.0001 P<.0001 1.25 P<.0001 Log TIC 7.5 5.0 1.00 0.75 0.50 2.5 0.25 0.00 0.0 Benign Borderline Inv CA Benign Borderline Inv EOC Other CA Other CA Rigs Quest 4 Second Principal Component 2 0 -2 -4 -2 0 2 First Principal Component 0 -2 -4 -6 4 MD Anderson -4 -2 0 2 First Principal Component 4 GOG 4 Second Principal Component 4 2 0 -2 -4 -6 Complementarity with CA125 and age 2 PC1 Second Principal Component 4 -4 -6 I think we have the right combination for the OVA1 algorithm. Beta 2 microglobulin Transferrin Second Principal Component TIC Development of OVA1 algorithm 30 P<.0001 Peak Ratio Peak Ratio Final Biomarker Candidates Plus CA125 -4 -2 0 2 First Principal Component 4 2 0 -2 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 -0.5 -1.0 -1.5 -2.0 -2.5 -3.0 -3.5 -4.0 -4.5 -5.0 -5.5 -6.0 12.5 7.5 5.0 2.5 25 0.0 -2.5 -5.0 -7.5 10 20 30 40 50 60 Age -4 -6 -4 -2 0 2 First Principal Component Benign Borderline Inv EOC Other CA 10.0 70 80 90 100 1 10 100 1000 Log CA125 10000 100000 4 4 2/19/2010 OVA1 Prospective Clinical Trial Performance Cutoffs chosen to emphasize sensitivity and NPV False negative adversely impacts patient care since these would be missed cancers False positive leads to surgery of benign case by gynecologic oncologist, which introduces no risk Output is index score from 0‐10 >=5.0 in pre‐menopausal subject indicates high likelihood of malignancy >=4.4 in post‐menopausal subject indicates high likelihood of malignancy Caveats Not a standalone test, i.e. to be used in conjunction with other findings (e.g. radiologic, clinical examination) Not for screening In conjunction with clinical assessment, both NPV and sensitivity >90% Prevalence = probability of disease in the entire population at any point in time (i.e. 2% the U.S. population has diabetes mellitus) (i e 2% the U S population has diabetes mellitus) Incidence = probability that a patient without disease develops the disease during an interval (the incidence of diabetes mellitus is 0.2% per year, referring only to new cases) Sensitivity = probability of a positive test among patients with disease Specificity = probability of a negative test among patients without disease Negative predictive value = probability of no disease among patients with a negative test Eric T. Fung Eric T Fung Vermillion, Inc. 5 2/19/2010 Human Epididymis Protein 4 (HE4) 6 2/19/2010 Monitoring HE4 is a secreted protein that is over expressed and can be detected in high levels in women with ovarian cancer. It is cleared by the FDA for use to monitor recurrence or progressive disease in patients with epithelial ovarian cancer. Monitor Ovarian Cancer using HE4 HE4 increases by 25% or greater in 60% of patients with progression of disease3 HE4 remains constant in 75% of patients without progression of disease3 7