Using random peptide phage display l ibraries for early breast cancer detection
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Using Random Peptide Phage Display L ibraries 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

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Using random peptide phage display l ibraries for early breast cancer detection

Using Random Peptide Phage Display Libraries forearly Breast cancer detection

Ekaterina Nenastyeva


Outline
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


Motivation for early cancer detection
Motivation for early cancer detection

  • Earlier stages 

     Simpler/ more effective treatment

  • Promising earlier stage biomarkers: Antibodies


State of the art
State of the art

The current methods of analysis of antitumor humoralimmune response:

  • SEREX

  • SERPA

  • ELISA

  • Antigen microarrays

  • Random peptide microarrays


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



Data set
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


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:

  • Control specific peptides:

controls

cases

MAX < MIN

controls

cases

MIN > MAX


Peptides specific for breast cancer
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
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


Discrimination based on the whole peptide library peptides

  • 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


Leave one out cross validation for discrimination based on correlation
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


Conclusion
Conclusion correlation

  • 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
Future work correlation

Discrimination methods based on:

  • Correlation and enriching library by cancer specific peptides

  • Principal component analysis 


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