During my postdoctoral research, my primary focus was on uncovering the proteomic and genomic drivers of cancer. This work was carried out as part of the Clinical Proteomic Tumor Analysis Consortium (CPTAC), and has since been continued in my own laboratory. To gain a deeper understanding of tumor biology and metastasis, we developed and utilized various computational tools to analyze and integrate tumor proteomics and genomics data.
One type of data that I am particularly enthusiastic about is phosphoproteomics data, as it has the potential to advance cancer research significantly. Obtaining an unbiased view of phosphoproteomics and signaling within a tumor is highly valuable, especially as many current cancer treatments are based on inhibiting kinases and signaling cascades. As part of our research efforts, we created a tool called BlackSheep (L. Blumenberg et al., J. Proteome Res., 2021) for outlier analysis, which helps identify abnormal phosphorylation patterns linked to specific disease subtypes.
Analyzing these aberrant phosphorylation expressions associated with disease subtypes provides valuable insights for understanding cancer progression and identifying potential therapeutic targets. By leveraging advanced computational tools and integrating diverse types of data, we aim to further advance cancer discovery and improve treatment strategies for better patient outcomes.
