Zoltan Szallasi, MD
Senior Research Scientist, Informatics Program
Children's Hospital/Harvard Medical School
2013-2014 BCRF Project(s):
(The Hamptons Paddle & Party for Pink Award)
Dr. Szallasi’s group will continue their efforts to identify DNA aberration patterns that are specifically associated with individual DNA repair inefficacies, which also predict response to individual chemotherapeutic agents. Breast cancer, similarly to other cancer types, is driven by one or more of the various genomic instability mechanisms. In fact, the biology of a given breast cancer and often its sensitivity or resistance to a given chemotherapeutic agent is determined to a large extent by the dominant genomic instability )DNA repair pathway aberration present in the tumor). The type and level of this type of aberration can often be determined only through the analysis of the various genome scale molecular profiles such as CGH or SNP arrays, simultaneously detecting all chromosomal copy number aberrations in the same cancer sample or by next generation sequencing directly detecting the majority of small scale DNA sequence aberrations in tumor biopsies. Dr. Szallasi’s group is developing computational methods to perform the analysis of such genome scale molecular profiles of breast cancer and correlate those results with the genomic instability subtype and other essential biology of the given cancer. Their results have produced a predictor of response to platinum-based therapy in hormone receptor negative breast cancer, a diagnostic tool currently under clinical evaluation. They are applying the same research principles to identify predictors of chemotherapy sensitivity to other genotoxic agents as well. Dr. Szallasi’s team will identify subgroups of breast cancer patients that are particularly sensitive to a given therapeutic agent and, therefore, can be treated and cured effectively without the toxic side effects of ineffective therapy.
Dr. Szallasi received his Doctor of Medicine (MD) degree from the University of Medicine in Debrecen, Hungary in 1988. He did his postdoctoral research in the field of molecular pharmacology of cancer at the National Cancer Institute. As a faculty member, first at the Uniformed Services University of Health Sciences and currently at the Children's Hospital, Boston at Harvard Medical School, he has become active in the high throughput analysis of breast cancer. He has published over 60 peer reviewed articles, mainly on the molecular pharmacology and high throughput analysis of cancer.
Genome scale molecular analysis, such as microarray based gene expression profiling, offers a more complete view of the biochemical changes associated with cancer. However, more data means more noise, more uncertainty and an explosion of the hypothesis space, all impeding association based learning often applied both in basic and clinical cancer research. Dr. Szallasi's group is interested in the meaningful and responsible application of high throughput measurements for cancer research. They implemented several methods that increased the reliability of microarray measurements. They are also interested in approaches that combine high throughput measurements in a manner that describe essential biology in a robust fashion, such as developing a gene expression signature of chromosomal instability.
His earlier projects have led Dr. Szallasi to the current main focus of his research: How is the robust phenotype of a given cancer type coded in gene expression networks? This problem could (and perhaps should) be approached both from a computational and an experimental direction. The success of genome scale analysis of breast cancer may in fact depend on the effective combination of developing experimental models yielding robust information about human tumors and their statistically sound exploitation. Dr. Szallasi's group is working on such "dual" approaches to answer whether one can identify which patient will respond to a given chemotherapeutic agent or whether there exist different subtypes of genomic instability in breast cancer with prognostic and therapeutic relevance.