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

2. 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

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

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

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

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 controlscases

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

9. Discrimination based on specific peptides MAX < MIN controlscases Cancer specific peptides: Control specific peptides: MIN > MAX controlscases

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. AVG correlation: Threshold : (0.12+0.03)/2=0.075 Discrimination based on the whole peptide library 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 (including true 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