engineering a program to digitally analyze genetic tests for hpv stephen trusheim and daniel...
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Engineering a Program to Digitally Analyze Genetic Tests
for HPV
Stephen Trusheim and Daniel Mokhtari, 2007-2008
Human Papillomavirus• Human Papillomavirus (HPV) is the most common cause
of cervical cancer.
• Cervical cancer is the second most common type of cancer in women (Khan et al., 2005).
• HPV has multiple types, ranging from low-risk to high-risk for cervical cancer (Muñoz et al., 2003).
http://upload.wikimedia.org/wikipedia/en/a/ad/EM_of_pap_virus%2C_basal_tissue_grafted_to_mouse.jpg
Diagnosing HPV
• Diagnosing HPV requires determining the type of HPV contained in a patient sample.
• Polyacrylamide gel electrophoresis is used for this purpose.
Gel Electrophoresis
Gel Electrophoresis
Gel Electrophoresis
Gel Electrophoresis
Gel Electrophoresis
Gel Electrophoresis
Diagnosing HPV• Currently, a double-blind human review process is used
to diagnose HPV types in completed gel-electrophoresis tests
• Human review is often qualitative, slow, and expensive
• Computers have the potential to automate interpretation for gel-electrophoresis tests
Goals
1. To engineer a Java-based program to quantitatively interpret gel electrophoresis tests for HPV
2. To diagnose more than 95% of genetic tests for HPV correctly
3. To reduce the time, cost, and effort required in each gel test
Test Interpretation ProgramOriginal Gel Image
Image-Analysis Algorithms
Test-Interpretation Algorithm
Test Result
HPV Type 16 – High Risk
Test Interpretation ProgramOriginal Gel Image
Image-Analysis Algorithms
Test-Interpretation Algorithm
Test Result
HPV Type 16 – High Risk
Test Interpretation ProgramOriginal Gel Image
Image-Analysis Algorithms
Test-Interpretation Algorithm
Test Result
HPV Type 16 – High Risk
Image Analysis Algorithms
1. Orientation
2. Lane Detection
3. Noise Reduction
4. Band Detection
Image Analysis Algorithms
1. Orientation
2. Lane Detection
3. Noise Reduction
4. Band Detection
Image Analysis Algorithms
1. Orientation
2. Lane Detection
3. Noise Reduction
4. Band Detection
0
50
100
150
200
250
0 100 200 300 400 500 600
X Pixel Coordinate
Brightness (Grey Values)
Image Analysis Algorithms
1. Orientation
2. Lane Detection
3. Noise Reduction
4. Band Detection
Image Analysis Algorithms
1. Orientation
2. Lane Detection
3. Noise Reduction
4. Band Detection
0
50
100
150
200
250
0 50 100 150 200 250 300 350 400 450 500
Y Pixel Coordinate
Brightness (Grey-Values)
Test Interpretation ProgramOriginal Gel Image
Image-Analysis Algorithms
Test-Interpretation Algorithm
Accepted Ranges for HPV Type 16
Band Detected
(Base-Pairs)
Expected Band Location (Base-
Pairs)
Percent Correlatio
n
845 N/A Uncorrelated
842 N/A Uncorrelated
457 450 81.3%
322 310 60.7%
242 200-250 N/A
73 72 94.6%
Band Detected
(Base-Pairs)
Expected Band Location (Base-
Pairs)
Percent Correlatio
n
845 N/A Uncorrelated
842 N/A Uncorrelated
457 450 81.33
322 310 60.65
242 200-250 N/A
73 72 94.62Average Correlation of HPV Type 16:78.9%
Percent Correlation of Test Bands to HPV Types
78.9%
68.6%
52.1%
47.2%43.4% 42.8%
32.5%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
90.0%
16 45 72 51 31 68 GA115 30
Type of HPV
Percent Correlation
Percent Correlation of Test Bands to HPV Types
78.9%
68.6%
52.1%
47.2%43.4% 42.8%
32.5%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
90.0%
16 45 72 51 31 68 GA115 30
Type of HPV
Percent Correlation
Percent Correlation of Test Bands to HPV Types
78.9%
68.6%
52.1%47.2%
43.4% 42.8%
32.5%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
90.0%
16 45 72 51 31 68 GA115 30
Type of HPV
Percent Correlation
This patient is diagnosed with: HPV Type 16.
Percent Correlation of Test Bands to HPV Types
78.9%
68.6%
52.1%
47.2%43.4% 42.8%
32.5%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
90.0%
16 45 72 51 31 68 GA115 30
Type of HPV
Percent Correlation
Test Interpretation ProgramOriginal Gel Image
Image-Analysis Algorithms
Test-Interpretation Algorithm
Test Result
HPV Type 16 – High Risk
Results
Algorithm Success Rates In Determining Patient HPV Type when Compared to Human-Reviewed Results
Primary Test 54%
Secondary Test - Top 5 47%
Secondary Test - Top 3 43%
Secondary Test - Most Correlated 19%
Future Studies
• Improve band detection
• Expand test interpretation capabilities for Chlamydia, Gonorrhea, Cystic Fibrosis, and Inherited Thrombophilia
• Improve program success rate with the goal of receiving FDA approval for HPV tests
Acknowledgements
• Mrs. Lois Fruen• Dr. Ronald McGlennen MD• Mr. Robert Schuldt• Mr. Jeremy Raadt• Ms. Bonita Bryant
MT(ASCP), CLSpMB(NCA)• Access Genetics• Breck Team Research
Engineering a Program to Digitally Analyze Genetic Tests
for HPV
Stephen Trusheim and Daniel Mokhtari, 2007-2008