SHP Student Interns for Research and Scholarly Activities
Application of Project Proposal Form
Instructions: Please fill out the form and return via email to Alexis Fulks at by March 31, 2017. Please fill each box to the right of each required field. If you are sending attachments, please ensure your contact information is added to all your forms.
Faculty Contact Information:
Date submitted: / 03/31/2017Faculty Name: / Antonina Mitrofanova
Department/Program: / Health Informatics, SHP
Telephone number: / (973) 972-5241
E-mail: /
Project Detail:
Project Title: (56 characters max) / PROJECT TITLEHypothesis: / Pancancer approaches to elucidate mechanisms of chemoresistance.
Description:
(Include design, methodology, data collection, techniques, data
analysis to be employed, evaluation and interpretation methodology for research component) / Background and Significance: Despite continuous discovery of a wide array of novel chemotherapy agents, the heterogeneity of patient response remains a central challenge in clinical oncology. Several groups have investigated diverse response to chemotherapy [1-4]; however, a systematic methodology to uncover molecular markers that govern chemo resistance has not yet been established. Several mechanisms have been shown to be implicated in chemotherapy resistance, including (i) dysfunction of P-glycoproteins [1, 5] (i.e., so called drug pumps) located on cell membrane which allow the chemo delivery from inside to the outside of the cell; (ii) defect of genes and proteins involved in apoptosis pathways [6-8]; (iii) defect in DNA repair proteins, such as XPE-BF and ERCC1, leading to cisplatin [9] and carboplatin [10] resistance in lung cancer; (iv) alteration of enzyme expression (e.g., glutathione, ubiquitin etc.) enrolled in drug metabolism [11, 12], among others. Even though such mechanisms have been widely investigated, managing resistance to various chemotherapy agents remains a central challenge for patient survival.
Objective: Here, we propose to develop a systematic approach to analyze genomic (RNA sequencing) and epigenomic (DNA methylation) profiles of cancer patients to identify molecular markers of chemotherapy response and resistance.
Approach and methodology: We will utilize epigenomic and genomic publically available data across various cancer types and chemotherapy classes. Molecular signatures will be utilized to run pathway enrichment analysis using GSEA [13] and Fisher exact test [14]. Identified markers will be evaluated using cox proportional hazard model [15] and Kaplan-Meier survival analysis [16] for different end-points (relapse, metastasis, death etc.).
Evaluation of Results: To define a metric depicting cancers with conserved and divergent mechanisms of chemoresistance, we will perform correlation, GSEA, and Principle Component Analysis (PCA) [17]. Furthermore, identified pan-cancer molecular determinants will be evaluated for their clinical relevance using univariate and multivariate survival analysis [18].
Anticipated Outcomes: The proposed study will introduce a systematic computational approach to uncover genomic and epigenomic mechanisms of chemotherapy resistance across different cancer types. Such approach will pioneer a method to identify patients at risk of resistance and patients predisposed to favorable treatment response. We anticipate that identified genomic and epigenomic markers will be valuable candidates for experimental validation with potential for near-term translational applications.
References:
1. Luqmani Y. Mechanisms of drug resistance in cancer chemotherapy. Medical Principles and Practice. 2005;14(Suppl. 1):35-48.
2. Hurley LH. DNA and its associated processes as targets for cancer therapy. Nat Rev Cancer. 2002;2(3):188-200. Epub 2002/05/07. doi: 10.1038/nrc749. PubMed PMID: 11990855.
3. Ferguson PJ. The role of gene amplification in clinical resistance to chemotherapy: a review. J Otolaryngol. 1991;20(2):130-6. Epub 1991/04/01. PubMed PMID: 2041063.
4. Turner NC, Reis-Filho JS. Genetic heterogeneity and cancer drug resistance. Lancet Oncol. 2012;13(4):e178-85. Epub 2012/04/04. doi: 10.1016/s1470-2045(11)70335-7. PubMed PMID: 22469128.
5. Gottesman MM. Mechanisms of cancer drug resistance. Annu Rev Med. 2002;53:615-27. Epub 2002/01/31. doi: 10.1146/annurev.med.53.082901.103929. PubMed PMID: 11818492.
6. Johnstone RW, Ruefli AA, Lowe SW. Apoptosis: a link between cancer genetics and chemotherapy. Cell. 2002;108(2):153-64. Epub 2002/02/08. PubMed PMID: 11832206.
7. Dive C, Hickman JA. Drug-target interactions: only the first step in the commitment to a programmed cell death? Br J Cancer. 1991;64(1):192-6. PubMed PMID: 1854622; PubMed Central PMCID: PMCPMC1977304.
8. Gorre ME, Mohammed M, Ellwood K, Hsu N, Paquette R, Rao PN, Sawyers CL. Clinical resistance to STI-571 cancer therapy caused by BCR-ABL gene mutation or amplification. Science. 2001;293(5531):876-80. Epub 2001/06/26. doi: 10.1126/science.1062538. PubMed PMID: 11423618.
9. Rosell R, Taron M, Ariza A, Barnadas A, Mate JL, Reguart N, Margel M, Felip E, Mendez P, Garcia-Campelo R. Molecular predictors of response to chemotherapy in lung cancer. Semin Oncol. 2004;31(1 Suppl 1):20-7. Epub 2004/02/26. PubMed PMID: 14981577.
10. Chu G. Cellular responses to cisplatin. The roles of DNA-binding proteins and DNA repair. J Biol Chem. 1994;269(2):787-90. Epub 1994/01/14. PubMed PMID: 8288625.
11. Akhdar H, Legendre C, Aninat C. Anticancer drug metabolism: chemotherapy resistance and new therapeutic approaches2012.
12. Gamcsik MP, Dubay GR, Cox BR. Increased rate of glutathione synthesis from cystine in drug-resistant MCF-7 cells. Biochem Pharmacol. 2002;63(5):843-51. Epub 2002/03/26. PubMed PMID: 11911835.
13. Subramanian A, Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., Mesirov, J. P. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles2005(102):15545-50.
14. Fisher RA. On the Interpretation of χ<sup>2</sup> from Contingency Tables, and the Calculation of P. Journal of the Royal Statistical Society. 1922;85(1):87-94. doi: 10.2307/2340521.
15. Cox DR. Regression Models and Life-Tables. Journal of the Royal Statistical Society Series B (Methodological). 1972;34(2):187-220.
16. Kaplan EL, Meier P. Nonparametric Estimation from Incomplete Observations. Journal of the American Statistical Association. 1958;53(282):457-81. doi: 10.1080/01621459.1958.10501452.
17. Pearson K. LIII. On lines and planes of closest fit to systems of points in space. Philosophical Magazine Series 6. 1901;2(11):559-72. doi: 10.1080/14786440109462720.
18. Hammermeister KE, DeRouen TA, Dodge HT. Variables predictive of survival in patients with coronary disease. Selection by univariate and multivariate analyses from the clinical, electrocardiographic, exercise, arteriographic, and quantitative angiographic evaluations. Circulation. 1979;59(3):421-30. doi: 10.1161/01.cir.59.3.421.
Specific Student Responsibilities: / Student will be responsible for
§ Literature review necessary to understand the proposed projects
§ Download DNA methylation and RNAseq datasets from publically available sources
§ Computational implementation of the proposed algorithms ( R is preferred; MATLAB, C, C++, Java are also acceptable)
§ Algorithm evaluation using statistical methods
Start / end date of project: / June 16, 2017 – August 31, 2017
Educational:
WHAT OTHER EDUCATIONAL OPPORTUNITIES ARE AVAILABLE TO STUDENTS?(e.g., journal club, seminars, clinic, rounds) / Students will have an opportunity to participate in journal club meetings held in my lab. In addition, students are encouraged to attend seminars held at Rutgers and other institutions in the greater NY area (i.e., Columbia University, NY Genome Center, etc).
WHERE DO YOU PLAN TO PRESENT OR PUBLISH THE FINDINGS WITH THE STUDENT?
(e.g., national or state meetings, newsletter or journal, SHP poster day) / We intend to develop and publish results of this project in peer-review journal, such as Cell Reports, Genes and Development, Bioinformatics, or BMC Bioinformatics. This work would also be presented at SHP poster day.
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CHECK ALL APPROPRIATE BOXES BELOW AND PROVIDE REQUESTED INFORMATION.
This project is: clinical laboratory behavioral survey educational
Other: please specify Computational (Computational Biology)
This project involves the use of human subjects (including chart review, retrospective studies and questionnaires).
Pending Approved IRB Protocol Number ______
IRB approval must be obtained by June 2017
3/31/17
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Signature of Department Chair Date
OR-For internal use
Form: (1)
Reviewed date:______
Date processed on website:______
1
4/4/2017