1. PU-Caller: Sensitive somatic variant calling using positive-unlabeled learning
Elham Sherafat and Ion Mandoiu
Computer Science & Engineering Department
University of Connecticut

2. Outline
Motivation
Positive-unlabeled learning
Results
Ongoing work

3. Ongoing Ovarian Cancer Immunotherapy Trial
Bulk exome & RNA
sequencing
LC-MS/MS of
eluted peptides
GeNeo suite of Galaxy tools
neo.engr.uconn.edu
Somatic variant validation and clonal analysis by targeted DNA sequencing using AccessArray
Neoantigen prediction
Peptide vaccine

4. Consensus Caller Cross-Platform

5. AccessArray Validation

6. Somatic Mutation Prevalence
Goal: use machine learning to increase sensitivity without much loss of precision (AccessArray capacity is bounded)

7. Previous Work Supervised ML approaches
Need large amounts of training data
Assume matched distributions between training and test data

8. Outline
Motivation
Positive-unlabeled learning
Results
Ongoing work

9. PU Learning
Input:
10s-100s of high confidence SNVs from CCCP/2CP (“positives”)
of SNV candidates that fail 2CP filter (“unlabeled”)
Two-step approach:
Infer “reliable negatives’’ from unlabeled data
Train classifier using positives and reliable negatives, then classify all points
Robust to patient-to-patient variability

10. PU-Caller Workflow Robustness increased by
Informed undersampling to balance reliable negatives with positives
Use of “spy” positives for threshold selection
Bootstrapping

11. Outline
Motivation
Positive-unlabeled learning
Results
Ongoing work

12. AccessArray Validation
PU-Caller yields 7-17% increase in validated SNVs compared to CCCP/2CP

13. SNV Feature Importance

14. Outline
Motivation
Positive-unlabeled learning
Results
Ongoing work

15. Ongoing Work PU learning technique is broadly applicable
Currently using PU learning for improving sensitivity of peptide identification from LC-MS/MS data
MS-GF+ database search engine generates 1000s of confident identifications, but leaves 10,000s of spectra unmatched

16. Acknowledgments
Elham Sherafat
Jordan Force