The Graham Lab is pursuing experimental and computational systems biology approaches to develop quantitative models of the cancer and other human diseases. We draw on biology, statistics and engineering to build data-driven, predictive models of tumor phenotypes using quantitative data generated in-house. Through collaboration with physicians and clinicians, we are applying these approaches to questions that impact clinical care.
Proteomics
The genetic events that drive human disease must be implemented at the protein level. Using mass spectrometry, we are identifying and quantifying proteins and their post-translational modifications in cell lines, mouse models and human patient samples.
Selected publications:
Phospho-proteomics reveals that RSK signaling is required for proliferation of natural killer cells stimulated with IL-2 or IL-15 (2022). Cytokine. Link.
Identification of a Proteomic Signature of Senescence in Primary Human Mammary Epithelial Cells (2021). Journal of Proteome Research. Link.
Improved discrimination of asymmetric and symmetric arginine dimethylation by optimization of the normalized collision energy in LC-MS proteomics (2020). Journal of Proteome Research. Link.
Deep protein methylation profiling by combined chemical and immunoaffinity approaches reveals novel PRMT1 targets (2019). Molecular and Cellular Proteomics. Link.
Metabolomics
Many human diseases including cancer and aging involve metabolic dysfunction. Cancer cells are known to exhibit increased glucose uptake and lactate production, even in the presence of oxygen (aka the Warburg effect). The Graham lab is using mass spectrometry to quantify the abundance of metabolites in cancer cells, and through labeling cells with stable isotope-labeled nutrients, the flux through metabolic pathways.
AKT but not MYC promotes reactive oxygen species-mediated cell death in oxidative culture (2020). Journal of Cell Science. Link.
A synthetic lethal drug combination mimics glucose deprivation-induced cancer cell death in the presence of glucose (2020). Journal of Biological Chemistry. Link.
Inhibition of nucleotide synthesis promotes replicative senescence of human mammary epithelial cells (2019). Journal of Biological Chemistry. Link.
Recurrent aneuploidy patterns enable fitness gains in tumor metabolism (2017). Molecular Systems Biology. Link.
Bioinformatics
Complex biological data sets necessitate bioinformatic methods to generate biological insight. We are developing and applying methods to extract meaning from quantitative, complex data sets. Our data-driven modeling approaches include pathway analysis (gene set enrichment analysis), kinase-substrate predictions via consensus motifs, statistical methods (eg, principal component analysis, partial least squares regression) and machine learning algorithms (eg, self-organizing maps).
Selected publications:
Drug mechanism enrichment analysis interprets rank-ordered drug lists to facilitate biological understanding [pre-print]. Link.
The landscape of metabolic pathway dependencies in cancer cell lines (2021). PLoS Computational Biology. Link.
Differential Gene Set Enrichment Analysis: A statistical approach to quantify the relative enrichment of two gene sets (2020). Bioinformatics. Link.
