INFERRING KINASE ACTIVITY PROFILES FROM PHOSPHOPROTEOMIC DATA

Benjamin Jordan and Kristen NaegleUniversity of Virginia

Biomedical Engineering, Center for Public Health Genomics

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.

OVERVIEW

Can we use identified phosphorylation sites in a

patient biopsy as evidence of specific kinases that were active

in that tissue?

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

ESTIMATING ENRICHMENT IN A KINASE NETWORK

Weighted kinase-substrate network Binary network

p-valuehypergeometricprune

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

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

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

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)