Assistant Professor
Ph.D., Tel Aviv University, 1990

Genomics of cancer; machine learning for biology; inference from noisy biological data; large-scale numerical computing

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Cancer is a highly complex genetic disease. A single tumor is an evolving biological system which often contains heterogeneous cell population. Clinical practice only provides us with partial snapshots of this evolution, never its full time course or its full picture at any given time.  It is our goal to identify key genetic elements involved in the disease using this incomplete information.

Array-CGH, and, more recently, sequencing experiments reveal pattern of frequent and widespread aberration in cancer genomes. It is plausible that recurrently aberrant loci, or epicenters, are under selection and are therefore enriched in important cancer genes. With this in mind, a novel comprehensive methodology for epicenter analysis was developed and used to analyze multiple-genome data sets for breast, lung, colon and liver cancer. Results of this analysis were adopted by cancer biology laboratories at CSHL to assist functional studies using mouse models and RNAi. We also expect a rich list of targets to emerge from our ongoing epicenter analysis of publicly available datasets of the Cancer Genome Atlas.

These statistical methods hold even larger promise of discovery if applied to the latest generation of experimental data, derived from multiple assays of small, down to a single-cell level, sections of tumors. Using this more detailed input, we hope to learn how cancer evolves and how cancer cells migrate. Including DNA profiles from both primary and metastatic tumors in the study, we will begin to reconstruct the evolutionary tree formed by cells of these closely related tumors.

Hicks, J., Krasnitz,A., Lakshmi, B., Navin, N., Riggs, M., Leibu, E., Esposito, D., Alexander, J., Troge, J.,  Grubor,V., Yoon, S., Wigler, M., Ye, K., Børresen-Dale, A-L.,  Naume, B., Schlicting, E., Norton, L., Hagerstrom, T., Skoog, L., Auer G., Maner, S.,  Lundin, P., and Zetterberg, A., 2006.  Genome Novel Patterns of Genomic rearrangement and their association with survival in breast cancer. Genome Research 16:1465­-1479.

Xue, W., Krasnitz, A., Lucito, R., Sordella, R., VanAelst, L., Cordon-Cardo, C., Singer, S., Kuehnel, F., Wigler, M., Powers, S., Zender, L., Lowe, S.W.  2008. DLC1 is a chromosome 8p tumor suppressor whose loss promotes hepatocellular carcinoma.  Genes Dev. 22: 1439 -1444.

Zender, L.,  Xue, W., Zuber, J., Semighini, C.P., Krasnitz, A., Ma, B., Zender, P., Kubicka, S., Luk, J.M., Schirmacher, P., McCombie, R.W., Wigler, M., Hicks, J., Hannon, G.J., Powers, S., Lowe, S.W. 2008 An Oncogenomics-Based In Vivo RNAi Screen Identifies Tumor Suppressors in Liver Cancer. Cell 135: 852-864.

Grubor, V., Krasnitz, A., Troge, J.E., Meth,J.L., Lakshmi, B., Kendall, J.T., Yamrom, B., Alex, G., Pai, D., Navin, N., Hufnagel, L.A., Lee, Y-H., Cook, K., Allen, S., Rai, K.R., Damle, R.N., Calissano, C., Chiorazzi, N., Wigler, M. and Esposito, D. 2008.  Novel genomic alterations and clonal evolution in Chronic Lymphocytic Leukemia revealed by Representational Oligonucleotide Microarray Analysis (ROMA).  Blood 11: 1294 -1303.

Bric., A., Miething., C., Bialucha, C.U., Scuoppo., C., Zender, L., Krasnitz, A., Xuan, Z., Zuber, J., Wigler, M., Hicks, J., McCombie, R.W., Hemann, M.T., Hannon, G.J., Powers, S., Lowe, S.W. 2009. Functional identification of tumor-suppressor genes through an in vivo RNA interference screen in a mouse lymphoma model.  Cancer Cell 4:324-35.