Title: Using Multi-omics approach to identify novel metabolic liabilities associated with cancer
Iqbal Mahmud, PhD (Postdoctoral Associate)
Timothy J Garrett, PhD (Postdoctoral Mentor)
Summary of the awarded project:
Of the 10 leading causes of death, cancer is the deadliest form of public health problem in the United States. In 2018, 1,735,350 new cancer cases and 609,640 cancer deaths are projected to occur in the United States. Currently, there is no efficient and safe treatment option for the management of cancer. Targeted biomarker-based therapy has been a highly prolific and exciting field of cancer research and its management. Biomarkers can improve diagnosis, prognosis, prediction, overall management, and eventually the health outcomes of the cancer patient. Unfortunately, the yield of successful biomarkers with indisputably favorable health impacts has been extremely limited to date. Less than 1% of published cancer biomarkers actually enter clinical practice by which the other 99% do indeed fail. The major drawback for this massive unsuccessfulness of biomarkers are the absence of clinical relevance, inappropriate statistical methods, and lack of systematic multi-omics approach.
Recently, multi-omics approach has widely utilized to integrates multiple types of omics data including genomics (study of genetic alteration), transcriptomics (study of gene expression), proteomics (study of protein), metabolomics, and lipidomics (study of small molecules such as metabolite and lipid), in order to understand the global scenario of complex diseases and consequently biomarker discovery.
Metabolism is an emerging addition of integrated omics. Altered metabolism is recognized hallmark of cancer liability and is critical for cancer cells in their demand to satisfy bioenergetics, biosynthesis, and redox balance in the niche of growth-related stress. Therefore, our rationale is that identification of metabolic biomarker across human cancer will offer new avenues in advances of cancer metabolism and drug discovery research. In our preliminary result, we identified SQLE (gene encoding metabolic enzyme drive production of Squalene, rate limiting metabolite of cholesterol biosynthesis), which is a bona fide oncogene by amplification with clinical relevance in TCGA pan-cancer types. The next level of research should see a more detailed description of the roles of the SQLE in cancer metabolism as well as the use of heightened SQLE expression as a biomarker to suggest therapy or predict an outcome. Future clinical study could see whether inhibition of SQLE functions lower cholesterol and inhibit tumor growth or not? Using multi-omics technology, there are surely other surprises in store for us.