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The results of these analyses are available in a web portal.


We have also used this approach, coupled with additional genomic information, to identify prognostic indicators in endometrial cancers and predictors of pathway dependency several cancer types, including glioblastoma and renal cancer.

We have used this approach to identify new oncogenes in several cancer types, including lung, esophageal, and colorectal cancers.  We have also applied it across thousands of cancer copy-number profiles from multiple histologic types, enabling us to determine that most copy-number changes are not unique to individual cancer types, but shared across multiple cancer types.

Our research focuses on understanding the somatic genetic changes that occur in cancer, and what these genetic changes can tell us about how different cancers will behave. 


We have undertaken a variety of genomic approaches to profiling large numbers of cancers, including the use of SNP arrays, expression arrays, and sequencing, and have developed several computational approaches to understand these data. 


For copy-number changes, which are some of the most frequent somatic genetic events in cancer, we developed an approach (GISTIC, for Genomic Identification of Significant Targets In Cancer) that simultaneously identifies those events that are most likely to drive cancer development and profiles individual specimens for the set of events they have undergone.  GISTIC is available for download or through GenePattern.


Schematic of GISTIC algorithm

We currently have particular interests in the following areas:


  1. 1)The use of high-throughput sequencing technologies to profile somatic genetic changes in brain and other cancers


  1. 2)The development and use of analytic methods to interpret somatic genetic data across multiple cancer types


  1. 3)Understanding the implications of these somatic genetic changes on cancer biology and phenotype


  1. 4)Applying these approaches to brain cancers in the context of clinical trials.

Correlation between genomic and clinical features of endometrial cancer