Ph.D., Tel Aviv University, 1990
How does cancer arise? It evolves from innocuous beginnings, as healthy cells accumulate mutations and transform into lethal tumor cells. I am developing mathematical and statistical tools to discover key genetic elements involved in the evolution of cancer, and in particular, metastatic tumors.
Alexander Krasnitz and colleagues use mathematical and statistical tools to discover key genetic elements involved in cancer and to understand how cancer cells evolve. Array-based comparative genome hybridization, a technique honed in the Wigler lab, and, more recently, sequencing experiments, have revealed subtle patterns of frequent and widespread aberration in cancer genomes. Krasnitz hypothesizes that recurrent, aberrant genomic loci observed in a range of cancer types are under selection and therefore are enriched in important cancer genes. He has developed a novel, comprehensive methodology to discover such “cores” and has used it to analyze multiple genome data sets in breast, liver, ovarian, and prostate cancer. The results have been shared with cancer biology labs across CSHL, and they have been a key enabling agent of functional studies using mouse models and RNA interference. Krasnitz has begun to apply these novel statistical tools to the latest generation of experimental data, which have characterized tumor samples down to the level of single cells. By interpreting single-cell genomes, he and colleagues seek to learn how specific tumors evolve and how cancer cells migrate to invade adjacent tissues and metastasize.
Navin, N., Kendall, J., Troge, J., Andrews, P., Rodgers, L., McIndoo, J., Cook, K., Stepansky, A., Levy, D., Esposito, D., Muthuswamy, L., Krasnitz, A., McCombie, W.R., Hicks, J., and Wigler, M. 2011. Tumour evolution inferred by single-cell sequencing. Nature 472: 90–94.
Navin, N., Krasnitz, A., Rodgers, L., Cook, K., Meth, J., Kendall, J., Riggs, M., Eberling, Y., Troge, J., Grubor, V., Levy, D., Lundin, P., Månér, S., Zetterberg, A., Hicks, J., and Wigler, M. 2010. Inferring tumor progression from genomic heterogeneity.Genome Res. 20: 68–80.
Xue, W., Krasnitz, A., Lucito, R., Sordella, R., VanAelst, L., Cordon-Cardo, C., Singer, S., Kuehnel, F., Wigler, M., Powers, S., Zender, L., and 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., and Lowe, S.W. 2008 An Oncogenomics-Based In Vivo RNAi Screen Identifies Tumor Suppressors in Liver Cancer. Cell 135: 852–864.
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.
Mathematical technique de-clutters cancer-cell data, revealing tumor evolution, treatment leads
June 6, 2013