Investigators: Elizabeth Winzeler, Sumit Chanda, Trey Ideker, Hannah Carter, Jason Kreisberg
Over time, many chemotherapeutic agents lose their efficacy as their targets – whether an infectious agent or a cancer – become resistant. The CDC estimated in 2013 that 23,000 Americans die every year due to antibiotic resistant pathogens. Millions of lives were lost in the 1990s because malaria parasites acquired resistance to the most commonly used antimalarial therapy. Drug resistance is a major reason why metastatic disease is nearly always incurable. While one strategy for combating drug resistance is to create new classes of chemotherapies, an alternative approach is to tailor therapies for metastatic or recurrent disease based on whether known resistance alleles (both copy number and single nucleotide variants) are present. Prior knowledge of likely resistance mechanisms can be used to suppress the development of resistant clones by inhibiting multiple pathways simultaneously – a method that has been used to treat HIV infection and cure tuberculosis and HCV. Before this vision of personalized medicine can be realized though, a more complete understanding of how cells respond to environmental stresses is needed.
In this project, we will study the relationship between genes and chemicals in a variety of experimentally tractable systems in order to better define the resistome – the collection of resistance genes, their precursors and their interactions. Part of this project will focus on the model organism Saccharomyces cerevisiae as it is a highly genetically amendable organism for the interrogation of biological systems. Both the Winzeler lab and the Ideker lab have used yeast extensively to study gene function and genetic variation (Winzeler et al., Science 1998 and Winzeler et al., Science 1999) and to screen gene-gene, gene-environment, or protein-protein interactions (Guenole et al., Mol Cell 2013). As many molecular pathways are well preserved between humans and yeast, quantitative phenotypic data from screens in yeast can be used to infer highly informative network maps of molecular interactions in human cells. To maximize the amount of biological information from a given screen, it is desirable to maximize the test space (number of genes/gene pairs/conditions) and to record multiple orthogonal/conditional phenotypes (Califano et al., Nat Genet 2012 and Dutkowski et al., Nat Biotechnol 2013). This project will use in vitro evolution and sequencing of evolved strains for a diverse set of small molecule inhibitors with a relationship to drug resistance and human cancer to create inter and intramolecular interaction maps. In addition, we will create multidimensional, orthogonal phenotype maps of resistome-drug interactions in yeast using our previously described ultra-high-density screening approach (Bean et al., PLoS One 2014) combined with our new massively parallel time-lapse imaging system to rapidly and automatically generate growth profiles. The major difference between these approaches is that the former identifies specific base changes within the map that are critical for resistance, while the latter considers gene dosage and is relevant to understanding structural rearrangements that develop after cells are exposed to small molecules.
It is now becoming clear that when it exists, knowledge of genetic interactions can be used to predict response to chemotherapy. One recent example found that mutation in RAD50 conferred dramatic sensitivity to combined therapy with topoisomerase and checkpoint inhibitors. Expanded knowledge of similar synthetic lethal and other genetic interactions, discovered in high-throughput yeast screening and validated in human studies, could be used to identify patients likely (or unlikely) to respond to currently-available therapies. In this way, a comprehensive model of drug-gene interaction built upon maps of genetic interactions derived from systematic experimentation, could transform the measured copy number and somatic mutation profile of a tumor into a biomarker of drug response. Therefore, we are also seeking to translate the chemical-genetic maps generated above into a framework for general prediction of such interactions. Such knowledge could have a dramatic clinical impact, as currently there is a notable lack of predictive biomarkers in clinical oncology, a problem that will continue to grow as more targeted therapeutic options become available. In particular it has been proposed that the ability to reliably predict drug resistance before it occurs could inform rational combination therapies designed to prevent the emergence drug resistance.
Recent SDCSB Publications by these Investigators:
- Cowell, AN, Valdivia, HO, Bishop, DK, Winzeler, EA. Exploration of Plasmodium vivax transmission dynamics and recurrent infections in the Peruvian Amazon using whole genome sequencing. Genome Med. 2018;10 (1):52. doi: 10.1186/s13073-018-0563-0. PubMed PMID:29973248 PubMed Central PMC6032790.
- Zhang, W, Ma, J, Ideker, T. Classifying tumors by supervised network propagation. Bioinformatics. 2018;34 (13):i484-i493. doi: 10.1093/bioinformatics/bty247. PubMed PMID:29949979 PubMed Central PMC6022559.
- Ozturk, K, Dow, M, Carlin, DE, Bejar, R, Carter, H. The Emerging Potential for Network Analysis to Inform Precision Cancer Medicine. J. Mol. Biol. 2018;430 (18 Pt A):2875-2899. doi: 10.1016/j.jmb.2018.06.016. PubMed PMID:29908887 PubMed Central PMC6097914.
- Bui, N, Huang, JK, Bojorquez-Gomez, A, Licon, K, Sanchez, KS, Tang, SN et al.. Disruption of NSD1 in Head and Neck Cancer Promotes Favorable Chemotherapeutic Responses Linked to Hypomethylation. Mol. Cancer Ther. 2018;17 (7):1585-1594. doi: 10.1158/1535-7163.MCT-17-0937. PubMed PMID:29636367 PubMed Central PMC6030464.
- Zhang, W, Bojorquez-Gomez, A, Velez, DO, Xu, G, Sanchez, KS, Shen, JP et al.. A global transcriptional network connecting noncoding mutations to changes in tumor gene expression. Nat. Genet. 2018;50 (4):613-620. doi: 10.1038/s41588-018-0091-2. PubMed PMID:29610481 PubMed Central PMC5893414.
- Huang, JK, Carlin, DE, Yu, MK, Zhang, W, Kreisberg, JF, Tamayo, P et al.. Systematic Evaluation of Molecular Networks for Discovery of Disease Genes. Cell Syst. 2018;6 (4):484-495.e5. doi: 10.1016/j.cels.2018.03.001. PubMed PMID:29605183 PubMed Central PMC5920724.
- Cowell, AN, Istvan, ES, Lukens, AK, Gomez-Lorenzo, MG, Vanaerschot, M, Sakata-Kato, T et al.. Mapping the malaria parasite druggable genome by using in vitro evolution and chemogenomics. Science. 2018;359 (6372):191-199. doi: 10.1126/science.aan4472. PubMed PMID:29326268 PubMed Central PMC5925756.
- Wang, T, Tsui, B, Kreisberg, JF, Robertson, NA, Gross, AM, Yu, MK et al.. Epigenetic aging signatures in mice livers are slowed by dwarfism, calorie restriction and rapamycin treatment. Genome Biol. 2017;18 (1):57. doi: 10.1186/s13059-017-1186-2. PubMed PMID:28351423 PubMed Central PMC5371228.
- Cole, JJ, Robertson, NA, Rather, MI, Thomson, JP, McBryan, T, Sproul, D et al.. Diverse interventions that extend mouse lifespan suppress shared age-associated epigenetic changes at critical gene regulatory regions. Genome Biol. 2017;18 (1):58. doi: 10.1186/s13059-017-1185-3. PubMed PMID:28351383 PubMed Central PMC5370462.
- Shen, JP, Zhao, D, Sasik, R, Luebeck, J, Birmingham, A, Bojorquez-Gomez, A et al.. Combinatorial CRISPR-Cas9 screens for de novo mapping of genetic interactions. Nat. Methods. 2017;14 (6):573-576. doi: 10.1038/nmeth.4225. PubMed PMID:28319113 PubMed Central PMC5449203.