Using Random Peptide Phage Display L ibraries for early Breast cancer detection

1. Using Random Peptide Phage Display Libraries forearly Breast cancer detection Ekaterina Nenastyeva

2. OUTLINE • Introduction • Motivationfor 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

3. Motivation for early cancer detection • Earlier stages   Simpler/ more effective treatment • Promising earlier stage biomarkers: Antibodies

4. State of the art The current methods of analysis of antitumor humoralimmune response: • SEREX • SERPA • ELISA • Antigen microarrays • Random peptide microarrays

5. Any antigen can be substituted by a library of random peptides c N E F E P C K V A Q D D L R A Y F W R P Peptide A peptide sequence can mimic the epitope recognized by an antibody Phage envelop Peptide coding sequence Phage DNA

6. Detailed assay

7. 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 • Total number of peptides in a replica:  normalization  Total number of distinct 7-mer peptides in all replicas controls cases

8. Approaches for early Breast cancer detection • Identification of peptides specific for Breast cancer • Discrimination based on the whole list of peptides

9. Discrimination based on specific peptides • Cancer specific peptides: • Control specific peptides: controls cases MAX < MIN controls cases MIN > MAX

10. Peptides specific for Breast cancer 7-mers: 1; 6-mers: 9; 5-mers: 44 (There are no control specific peptides!)

11. 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 6-mer and forty four 5-mer specific peptides” • Permutation test: • permutations • P-value = 0.028

12. Discrimination based on the whole peptide library • AVG correlation: • Threshold : • (0.12+0.03)/2=0.075 • Correlation between peptides assigned to cases is higher than between controls IF AVG correlation:  case OTHERWISE  control

13. 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 • permutations • 5 permutations have accuracy 0.9 (includingtrue statuses arrangement) • P-value = 0.02 controls A,B,C,E,H cases D,F,G,I,J

14. 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

15. Future work Discrimination methods based on: • Correlation and enriching library by cancer specific peptides • Principal component analysis