During my postdoctoral work, I was primarily focused on understanding the proteomic and genomic drivers of cancer. This research was conducted as part of the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and has continued in my own laboratory ever since. To gain a deeper understanding of tumor biology and metastasis mechanisms, we have developed and applied various computational tools to evaluate and integrate proteomics and genomics data from tumors.
Among all the different types of data available, I am particularly excited about the potential of phosphoproteomics data in advancing cancer research. Having an unbiased view of phosphoproteomics and signaling in tumors is extremely valuable, especially considering that many current cancer treatments are based on inhibiting kinases and signaling cascades. As part of our investigations, we created a tool called BlackSheep (L. Blumenberg et al. J. Proteome Res. 20, 3767–3773; 2021) which identifies abnormal phosphorylation expression linked to specific disease subtypes of interest.
Our goal is to leverage the power of computational tools and innovative approaches to further our understanding of cancer progression and identify potential therapeutic targets. By integrating proteomic and genomic data, we aim to uncover key drivers of cancer development and spread, ultimately paving the way for more effective and targeted treatments for patients. Our research is focused on advancing cancer discovery and providing insights that can improve patient outcomes and overall survival.
