Collaborative, cutting-edge research projects at the forefront of cancer and data science are central to the ICADS Program. Below are a few examples of potential projects.
Project #1 (details below): Understanding metabolic vulnerabilities in lung adenocarcinoma with specific driver mutations through integrated analysis of genomic, proteomic, metabolomic and imaging data. Project Leaders – Dr. Brooke Fridley (Data Science) and Dr. Doug Cress (Cancer Biology)
Project #2 (details below): Integrating mass spectrometry-based proteomics and metabolomics to identify critical metabolic circuits in lung cancer. Project Leaders – Dr. Eric Haura (Cancer Biology) and Dr. Steven Eschrich (Data Science)
Project #1: Understanding metabolic vulnerabilities in lung adenocarcinoma with specific driver mutations through integrated analysis of genomic, proteomic, metabolomic and imaging data.
The specific metabolic changes that occur in cancer cells may represent vulnerabilities that can be exploited therapeutically. However, technology has only recently advanced to the point that we can measure the changes in thousands of metabolites in the same sample as the same time. In this project, we seek to understand the specific metabolic changes that occur in lung adenocarcinoma as a function of the most common key driver mutations (EGFR, STK11, KEAP1, KRAS and TP53). Toward this end, the Lung Cancer Center of Excellence has developed a cohort of early-stage lung adenocarcinoma patients. We have subjected these patients to comprehensive clinical chart review (including 125 data elements). Fresh-frozen tumors from these patients have been subjected to extensive molecular (or “omic”) characterization; including: 1) a genome-wide microarray-based gene expression analysis, 2) targeted exome sequencing for the principle driver mutation in lung adenocarcinoma (TP53, KRAS, STK11 and EGFR), 3) an untargeted LC-MS/MS-based proteomic panel representing 5,833 individual proteins, 4) an untargeted LC/MS-based metabolomic panel representing 6,286 individual compounds defined by mass/charge ratio, and 5) a targeted LC/MS-based metabolomic panel representing 259 annotated compounds. We have used the paraffin blocks from these patients to build a tissue microarray that has already been stained with 22 antibodies primarily related to immune biology via single stain immunohistochemistry (IHC), dual stained IHC (CD4/CD8) and multi-channel immunofluorescence to stain for immune markers (PD-1, PD L1, CD3, CD8, Fox-P3). In addition to these data from human patients we have also developed a battery of lung cancer cell lines and genetically modified mouse models that will allow us to explore any patterns defined in patient in systems that can manipulated genetically. We would like to use these data and extensive resources and in two ways, each individually involving a cancer biology fellow and a data science fellow.
Data Science: The key role of the data science fellow will be to completed integrative analysis of the data generated along with the development of new analytical methods and approaches to provide a comprehensive understanding of the biology related to the key driver mutations for lung cancer. For example, from the untargeted metabolomic analysis we only known the compound for a subset of the measured compounds. Is there a way to computationally determine the nature of these unknown individual compounds or the metabolic pathways in which they contribute? Can changes in gene expression or protein expression be used to predict with metabolites are altered? Are there sets of features (modules) or pathways that are associated with clinical response?
Cancer Biology: The key role of the cancer biology fellow will be to develop pre-clinical models (and ultimately proof-of-principle phase I/II clinical trials) to experimentally test hypotheses that emerge from the data analysis of human patients. Data from the preclinical models with then feed back to the data science fellow providing new data for new analysis. For example, initial results suggest that γ-aminobutyric acid (GABA) is dramatically upregulated in patient tumors with STK11 mutations. What is does this mean? Here, the cancer biology fellow, with their knowledge of biochemical pathways, would generate predictions/hypotheses of other molecule and protein that should also be changing and then work with the data scientist, with their knowledge of analytical methods to test the prediction.
Project #2: Integrating mass spectrometry-based proteomics and metabolomics to identify critical metabolic circuits in lung cancer.
This collaborative team employs mass spectrometry-based proteomics to characterize lung cancers with the ultimate goal to develop new therapeutic and diagnostic approaches. Our approach is to perform deep integrated proteomic studies on cancer to produce more complete views of the tumor architecture allowing contextual understanding of major signaling proteins and drug targets. Alterations in the genomes of cancers ultimately get integrated and produce a cancer proteome that can be analyzed using modern state of the art mass spectrometry proteomic tools. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is increasingly used to study cancer proteomes. This includes examining the ‘expressed proteome’ through shotgun proteomics, global signaling by annotating key post-translational events (phosphorylation, acetylation, ubiquitination) events in cancers or assembling protein-protein interaction data that yield network views of cancer. This allows unbiased and global views of signaling events in cancer thus offering complementary views of cancer biology that are not considered by sequencing of genes or gene expression. By integrating DNA-RNA-proteome-network type data, the co-existing driver processes instilled by the genome that either surround or act in parallel to drug targets can be mapped directly onto cancer molecular machines that drive cancer progression and response to therapy. We recently described a proteogenomic landscape of squamous cell lung cancer by integrating DNA copy number, somatic mutations, RNA-sequencing, and expression proteomics (PMID: 31395880). This identified major subgroups as well as potential therapeutic opportunities. Our next goal is to further study these tumors by profiling the ATP-binding proteome and integrate these data with untargeted metabolomics data from the same tumors. Ongoing work in small cell lung cancer has demonstrated the ATP binding proteome can characterize metabolic circuits and when integrated with untargeted metabolomics can highlight new therapeutic opportunities.
Data Science: The data science fellow will receive training and gain experience in the primary analysis of proteomic, metabolomic, gene expression and genomic data. Under the guidance of Dr. Steven Eschrich, the fellow will develop of new analytical methods and approaches to provide a comprehensive understanding of lung cancer biology with the goal to identify new therapeutic opportunities
Cancer Biology: The cancer biology fellow will be trained in cell biology and chemical proteomics under the guidance of Dr. Eric Haura. Dr. Haura is a physician/scientist and leads Moffitt’s Lung Cancer Center of Excellence.