Translational phosphoproteomics methods to identify biomarkers
and novel therapeutic targets
Name
kohale-ikohale-phd-be-2021-thesis.pdf
Description
Thesis PDF
Size
10.37 MB
Format
Adobe PDF
Checksum (MD5)
446596e051d3ae0437d0c6e087ad4fff
Author(s)
Kohale, Ishwar N.
Advisor(s)
White, Forest M.
Date Issued
September 2021
Publisher
Massachusetts Institute of Technology
Abstract
Phosphorylation plays a fundamental role in cellular processes, and it is commonly dysregulated in cancer. Characterization of phosphorylation mediated signaling networks in tumors can inform therapeutic interventions. Additionally, analysis of phosphoproteome in response to drug candidates can help identify biomarkers for therapeutic response as well as lend direct insights into potential adaptive resistance mechanisms. However, quantification of phosphoproteome, especially the translationally relevant low abundant signals including tyrosine and pathway specific phosphorylation, is limited in the clinic. Here, we describe mass spectrometry based methods and their applications for quantitative analysis of low level phosphoproteome in preclinical models and patient tumors.
In the first part, we describe a method for highly sensitive and quantitative analysis of tyrosine phosphorylation from 1 to 2 10-µm sections of formalin fixed paraffin embedded (FFPE) clinical tissue specimens, opening the doors for direct translational insights from FFPE tumor tissue banks in hospitals. In the second part, we present an integrative platform using mass spectrometry imaging, phosphoproteomics and multiplexed tissue imaging for mapping drug distribution, target engagement, and adaptive response to gain insights into heterogeneous response to therapy. In the last part of thesis, we demonstrate the application of quantitative tyrosine phosphorylation in identifying novel therapeutic targets in chemotherapy resistant triple negative breast cancer tumors. Together these approaches highlight the importance of low level phosphorylation signals to serve as biomarkers and inform novel therapeutic strategies.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
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