Dr. Jeanne Kowalski
Associate Professor of Oncology
Ph.D., Statistics, University of Pittsburgh, Pittsburgh, PA
A great deal of genomic analysis may be well addressed within a hypothesis-based framework, though not without effort. For this purpose, a paradigm that integrates formal statistical inference principles within a bioinformatics setting is required. It is the accomplishment of this task, i.e., the development of a new field, inferential bioinformatics, that is a long-term goal of Dr. Kowalski’s. A benefit from this approach to the analysis of high-dimensional data is that it posits the problem in a setting that is familiar to an investigator for interpreting conclusions drawn from such important research into practice. Dr. Kowalski’s career plan is to establish an independent research program that fosters the development and application of statistical methods in combination with molecular technological advancements to fully and formally address molecular analyses of cancer, as well as HIV/AIDS, for advancing the science of these conditions. A short-term research goal of Dr. Kowalski is to combine molecular technological advancement with biologic mechanisms of diseases to aid in the development of genetic-and genomic-based strategies for improved detection, diagnosis, and treatment of patients. She also plans to extend methods developed for analysis of genetic heterogeneity associated with phenotype to accommodate multiple gene regions and covariates and to examine the use of biotechnologies for comparative phenotype characterization, such as in distinguishing benign from malignant tumors.
Her general research area is in the development of statistical methods to address the analysis of high-dimensional data. Within this field, a specific focus of hers is on the statistical analysis of genomic data, such as that obtained from high-throughput technologies, for advancement of the molecular science in cancer research. The study of gene expression through microarrays is perhaps the premier example of this type of genomic investigation. The proliferation of high-throughput techniques for molecular biology and biochemistry is creating a need for statistical methods that address the scientific questions of interest and anticipate the structure of such data. Conventional statistical methods are wholly inadequate for the analysis of data from microarray experiments whose output is of very high-dimension relative to the number of samples. Although existing methods from statistical and computer science do provide a powerful set of tools, there is still considerable need for innovation in this area in terms of the development of formal analysis frameworks that address timely research objectives, such as in characterizing hypothesized pathways associated with cancerous stages. By combining molecular technological advances with the clinical course of disease, Dr. Kowalski’s research efforts focus on the greater goal of the development of statistical methods to examine molecular-based strategies, leading ultimately to improved patient management.
Goodwin, A.C., Jadallah, S., Toubaji, A., Lecksell, K., Hicks, J.L., Kowalski, J., Bova, G.S., De Marzo, A.M., Netto, G.J., and Casero, R.A., Jr. (2008). Increased spermine oxidase expression in human prostate cancer and prostatic intraepithelial neoplasia tissues. Prostate 68, 766-772.
Hillion, J., Dhara, S., Sumter, T.F., Mukherjee, M., Di Cello, F., Belton, A., Turkson, J., Jaganathan, S., Cheng, L., Ye, Z., Jove, R., Aplan, P., Lin, Y.W., Wertzler, K., Reeves, R., Elbahlouh, O., Kowalski, J., Bhattacharya, R., and Resar, L.M. (2008). The high-mobility group A1a/signal transducer and activator of transcription-3 axis: an achilles heel for hematopoietic malignancies? Cancer Res 68, 10121-10127.
Kowalski, J., Morsberger, L.A., Blackford , A., Hawkins A., Yeo, C.J., Hruban, R.H., and Griffin, C.A. 2008. Chromosomal abnormalities of adenocarcinoma of the pancreas: Identifying early and late changes. Cancer Genet Cytogenet 178:26-35.
Paz-Priel, I., Cai, D. H., Wang, D., Kowalski, J., Blackford, A., Liu, H., Heckman, C. A., Gombart, A. F., Koeffler, H. P., Boxer, L. M. & Friedman, A. D. (2005). CCAAT/enhancer binding protein alpha (C/EBPalpha) and C/EBPalpha myeloid oncoproteins induce bcl-2 via interaction of their basic regions with nuclear factor-kappaB p50. Mol Cancer Res 3, 585-96.
Paz-Priel, I., Ghosal, A.K., Kowalski, J., and Friedman, A.D. (2008). C/EBPalpha or C/EBPalpha oncoproteins regulate the intrinsic and extrinsic apoptotic pathways by direct interaction with NF-kappaB p50 bound to the bcl-2 and FLIP gene promoters. Leukemia. 23, 365-374.
Vali, M., E. Liapi, J. Kowalski, K. Hong, A. Khwaja, M. S. Torbenson, C. Georgiades, and J. F. Geschwind. 2007. Intraarterial therapy with a new potent inhibitor of tumor metabolism (3-bromopyruvate): identification of therapeutic dose and method of injection in an animal model of liver cancer. J Vasc Interv Radiol 18:95-101.
Wang, Y., Kowalski, J., Tsai, H.L., Marik, R., Prasad, N., Somervell, H., Lo, P.K., Sangenario, L.E., Dyrskjot, L., Orntoft, T.F., Westra, W.H., Meeker, A.K., Eshleman, J.R., Umbricht, C.B., and Zeiger, M.A. (2008). Differentiating alternative splice variant patterns of human telomerase reverse transcriptase in thyroid neoplasms. Thyroid 18, 1055-1063.
Zarek, P.E., Huang, C.T., Lutz, E.R., Kowalski, J., Horton, M.R., Linden, J., Drake, C.G., and Powell, J.D. (2008). A2A receptor signaling promotes peripheral tolerance by inducing T-cell anergy and the generation of adaptive regulatory T cells. Blood 111, 251-259.
Georgantas, R. W., Tanavde, V., Malehorn, M., Heimfeld, S., Chen, C., Carr, L., et al. 2004. Microarray and serial analysis of gene expression analyses identify known and novel transcripts over-expressed in hematopoietic stem cells. Cancer Res. 64:4434-4441.
Huang, C. T., Drake, C. G., Marson, A. L., Zhou, G., Hipkiss, E. L., Ravi, S., et al. 2004. Role of Lag-3 in regulatory T cells. Immunity. 21:503-513.
Kowalski, J., Drake, C., Schwartz, R. H., & Powell, J. 2004. Non-parametric, hypothesis-based analysis of microarrays for comparison of several phenotypes. Bioinformatics. 20:364-373.
Kowalski, J., & Powell, J. 2004. Nonparametric inference for stochastic linear hypotheses: application to high dimensional data. Biometrika. 91:393-408.
Mamelak, A. J., Kowalski, J., Murphy, K., Yadava, N., Zahurak, M., Kouba, D. J., et al. 2004. Downregulation of NDUFA1 and other exidative phosphorylation-related genes is a consistent feature of basal cell carcinoma. Exp. Dermatol. 14:336-348.
Yegnasubramanian, S., Kowalski, J., Gonzalgo, M. L., Zahurak, M., Piantadosi, S., Walsh, P. C., et al. 2004. Hypermethylation of CpG islands in primary and metastatic human prostate cancer. Cancer Res. 64:1975-1986.