Zoltan Szallasi, MD

2012-2013 BCRF Project(s):
Dr. Szallasi's group is developing computational methods to analyze the various genome scale molecular profiles of breast cancer samples. The specific molecular mechanisms driving a given cancer leave their footprints on the DNA aberration patterns. This team has generated gene expression microarrays that simultaneously measured all gene expression levels in a cancer sample. They have also performed CGH arrays, which simultaneously detected all chromosomal aberrations in the same cancer sample or directly detecting a wide array of all possible DNA sequence aberrations in tumors using next generation sequencing.

The main goal of Dr. Szallasi's work continues to be the characterization of the type and level of genomic instability subtype driving a given case of breast cancer. The reliable detection of these is able to predict response for at least two classes of chemotherapeutic agents, taxanes and platinum agents. In 2012-2013, Dr. Szallasi's team will continue to develop methods to detect and quantify all relevant genome instability and DNA repair incompetence subtypes in breast cancer and match those with their most effective therapeutic counterpart. They are also investigating the relevance of intratumor heterogeneity in the diagnosis and treatment of breast cancer and exploring genomic data-driven avenues for the immunotherapy of this disease.

Mid-year Progress: Dr. Szallasi's group continues to develop computational methods to analyze the various genome scale molecular profiles of breast cancer samples using such technology as gene expression microarrays, which simultaneously measures all gene expression levels in a cancer sample, and CGH arrays, which simultaneously detects all chromosomal aberrations in the same cancer sample or directly detects a wide array of all possible DNA sequence aberrations in tumors with next generation sequencing. The specific molecular mechanisms driving a given cancer often leave their footprints on the DNA aberration patterns. Dr. Szallasi's team is using developed predictors to platinum and taxane based therapy, which are currently being turned into actual clinical tests. In their research, they are developing methods to detect and quantify further relevant genome instability and DNA repair incompetence subtypes in breast cancer and match those with their most effective therapeutic counterpart. Dr. Szallasi is also investigating the relevance of intratumor heterogeneity in the diagnosis and treatment of breast cancer and also exploring genomic data driven avenues for the immunotherapy of this disease.

Bio:
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.