Welcome to the Perou Lab
Tumor Evolution in Two Patients with Basallike Breast Cancer: A Retrospective GenomicsStudy of Multiple Metastases
Katherine A. Hoadley, Marni B. Siegel, Krishna L. Kanchi, Et al.
|Genomic Analysis of Immune Cell Infiltrates Across 11 Tumor Types
Michael D. Iglesia, Joel S. Parker, Katherine A. Hoadley, Et al.
SynthEx: a synthetic-normal-based DNA sequencing tool for copy number alteration detection and tumor heterogeneity profiling
Grace O. Silva, Marni B. Siegel, Lisle E. Mose, Et al.
|Comparison of Breast Cancer Molecular Features and Survival
by African and European Ancestry in The Cancer Genome Atlas
Dezheng Huo,MD, PhD, Hai Hu, PhD, Suhn K. Rhie, PhD, Et al.
Human carcinomas show great diversity in their morphologies, clinical histories and responsiveness to therapy. This wide tumor diversity poses the main challenge for the effective treatment of cancer patients. The focus of my lab is to characterize the biological diversity of human tumors using genomics, genetics, and cell biology, and then to use this information to develop improved treatments that are specific for each tumor subtype and for each patient. A significant contribution of ours towards the goal of personalized medicine has been in the genomic characterization of human breast tumors, which identified the Intrinsic Subtypes of Breast Cancer. These Intrinsic Subtypes are predictive of relapse-free survival, overall survival and responsiveness to chemotherapy and some molecularly targeted agents.
We study many human solid tumor disease types using multiple experimental approaches including RNA-sequencing (RNA-seq), DNA exome sequencing, Whole Genome Sequencing (WGS), cell/tissue culturing, and Proteomics, with a particular focus on the Basal-like/Triple Negative Breast Cancer subtype. In addition, we are mimicking these human tumor alterations in Genetically Engineered Mouse Models, and using established primary tumor Patient-Derived Xenografts (PDXs), to investigate the efficacy of new drugs and new drug combinations. All of these genomic and genetic studies generate large volumes of data; thus, a significant portion of my lab is devoted to using genomic data and a systems biology approach to create computational predictors of complex cancer phenotypes, which will ultimately be applied in the clinic.
My lab utilizes a multi-disciplinary team spanning cancer biology, genomics, genetics, bioinformatics, statistics, systems biology, and the clinical treatment of cancer patients. In addition to our experimental approaches, we are also developing novel computational approaches and algorithms that will predict patient survival and complex phenotypes including tumor responsiveness to a variety of novel drugs. Some of our assays are already used in the clinic and we have more in development. I am actively seeking new graduate students, medical fellows, and postdocs and have opportunities available for both experimental and computational scientists.