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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
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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.
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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
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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
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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.
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Human Epididymis Protein 4 (HE4)
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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
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