Download Using random peptide phage display libraries for early breast

Using Random Peptide Phage
Display Libraries for
early Breast cancer detection
Ekaterina Nenastyeva
OUTLINE
• Introduction
– Motivation for early cancer detection
– State of the art
– Proposed assay based on Random Peptide Phage Display Libraries and Next Generation
sequencing
• Data Set
– Data preprocessing
• Approaches for early Breast cancer detection
– Identification of peptides specific for Breast cancer
– Discrimination based on the whole peptide library
• Results and evaluation
– LOO cross-validation
– Permutation test
•
Future work
–
–
Enriching library by cancer specific peptides
PCA
Motivation for early cancer detection
• Earlier stages 
 Simpler/ more effective treatment
• Promising earlier stage biomarkers: Antibodies
State of the art
The current methods of analysis of antitumor humoral
immune response:
– SEREX
– SERPA
– ELISA
– Antigen microarrays
– Random peptide microarrays
Any antigen can be substituted
by a library of random peptides
Peptide
Phage
envelop
Peptide
coding
sequence
Phage
DNA
c
E
K
D
R
F
P
N
P
V
Q
D
A
R
E
F
C
A
L
Y
W
A peptide sequence
can mimic the
epitope recognized
by an antibody
Detailed assay
Data Set
10 samples:
– 5 cases = stage 0 breast cancer patients
– 5 controls = cancer-free women
Each sample = 2 replicas
Each replica has
– Number of distinct 7-mer peptides  3 * 106
– Total number of peptides in a replica:
[1.4..6.4] *107  normalization  108
Total number of distinct 7-mer peptides in all
replicas  5 *107
controls
cases
Approaches for early Breast cancer
detection
• Identification of peptides specific for Breast cancer
• Discrimination based on the whole list of peptides
Discrimination based on specific
peptides
• Cancer specific peptides:
controls
MAX
cases
<
MIN
• Control specific peptides:
controls
MIN
cases
>
MAX
Peptides specific for Breast cancer
7-mers: 1; 6-mers: 9; 5-mers: 44 (There are no control specific peptides!)
Permutation test for discrimination
based on specific peptides
Hypothesis: “Controls do not have any peptide distinguishing
them from cases, and cases have no less than one 7-mer, nine 6mer and forty four 5-mer specific peptides”
Permutation test:
• С105  252 permutations
• P-value = 0.028
Discrimination based on the whole
peptide library
AVG correlation:
case  case  0.12
case  control  0.03
Correlation between peptides
assigned to cases is higher than
between controls
Threshold :
(0.12+0.03)/2=0.075
IF AVG correlation:
case  unknown  Threshold  case
OTHERWISE
 control
Leave-one-out cross-validation for
discrimination based on correlation
• Sensitivity =0.8 (4/5 correct predicted cases)
• Specificity =1 (5/5 correct predicted controls)
• Accuracy = 0.9
Permutation test for leave-one-out
5
• С10
 252 permutations
controls
• 5 permutations have accuracy 0.9 A,B,C,E,H
(including true statuses arrangement)
• P-value = 0.02
cases
D,F,G,I,J
Conclusion
• Discrimination method based on whole peptide
library and correlation showed statistically significant
results
• Found Breast cancer specific peptides were not
statistically significant although the hypothesis that
there were no peptides specific for controls was
statistically significant
Future work
Discrimination methods based on:
• Correlation and enriching library by cancer
specific peptides
• Principal component analysis