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INFERRING KINASE ACTIVITY PROFILES FROM PHOSPHOPROTEOMIC DATA
Benjamin Jordan and Kristen NaegleUniversity of Virginia
Biomedical Engineering, Center for Public Health Genomics
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OVERVIEW
Phosphoproteomic profiling of human tumors captures thousands of phosphorylation sitesSparsity, due to mass spectrometry methodology, prevents comparison of patients and interpretation of data
1053 pTyr* sites x 107 patientsGray – not detected
Mertins et al. (2016) Nature*phosphotyrosine (pTyr) presented here,
but also pSer/pThr capable.
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OVERVIEW
Can we use identified phosphorylation sites in a
patient biopsy as evidence of specific kinases that were active
in that tissue?
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OVERVIEW
Patient B
iopsy
Phosphoproteomics
Predicted Kinase Activities
Patient Specific Drugs
Kinase-Substrate Network
Inference Kinase
inhibitors
Enabled by: proteomescout.wustl.edu
Matlock et al., NAR, 2015
Supported by NIH Award Number R21CA231853
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ESTIMATING ENRICHMENT IN A KINASE NETWORK
Weighted kinase-substrate network Binary network
p-valuehypergeometricprune
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ESTIMATING ENRICHMENT IN A KINASE NETWORK
Heuristic Pruning Algorithm:• Removal of edges, based on edge weight• Limits the number of kinases a single site gives
evidence for• Maintains enough evidence such that all kinases
have networks
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APPLICATION: BCR-ABLDRIVEN CML TREATMENT
Example kinase activity predictions, where CML treated with 20-minute treatment with dasatinib (EoE) followed by rest for (3 Hr) or (6 Hr), and before treatment (Pre). Phosphoproteomic data from Asmussen et al. (2014) Cancer Discovery
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APPLICATION: BREAST CANCER BIOPSIES
Comparison of HER2-status with predicted HER2-activity for breast cancer patients. Phosphoproteomic data from Mertins et al. (2016) Nature (1053 pTyr sites x 107 patients)
50% of HER2+ tumors are not HER2-active
26% of HER2- tumors are detected as HER2-active
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FUTURE WORK
• Understanding biopsy feasibility: Working with Dr. Eric Haura(Moffitt), Dr. Katherine Fuh and Dr. Cynthia Ma (Washington Univ. in St. Louis).
• More training data: positive controls for kinase-specific networks• Better networks: combine multiple kinase-substrate predictors • Better physiology: use Bayesian approaches to build prior
expectation based on tissue type, tissue mixtures, and tissue handling (stress/hypoxic signaling)