Data-Driven
Cancer Biology

News and Events


05-15-2019
Congrats to Dr. Gunsagar Gulati for successfully defending his PhD dissertation!
Gun in action >>
05-06-2019
CIBERSORTx is published in Nature Biotechnology!
Paper >>
03-15-2019
Congrats to Chloe for receiving an AACR postdoctoral fellowship!
03-07-2019
Congrats to Anoop for being selected as a 2019 Knight-Hennessy Scholar!
Press release >>

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Our Research

Demystifying Tumor Cells

Our group develops computational strategies to study the phenotypic diversity, differentiation hierarchies, and clinical significance of tumor cell subsets. Key results are further explored experimentally, both in our lab and through collaboration, with the ultimate goal of translating promising findings into the clinic.

As a member of the Institute for Stem Cell Biology and Regenerative Medicine, the Department of Biomedical Data Science, and as an affiliate of graduate programs in Biomedical Informatics, Cancer Biology, and Immunology, we are also interested in the development of impactful biomedical data science tools in areas beyond our immediate research focus, including developmental biology, regenerative medicine, and systems immunology.

Ongoing Projects

Tumor Initiating Cells

Just as normal cell types are ultimately derived from stem cells, increasing evidence suggests that human tumors may also arise from cancer stem cells, which have been implicated in tumor development, maintenance, and metastasis. Despite their importance, our fundamental understanding of "tumor initiating cells" remains incomplete, and therapies that target these cells are lacking. To address this need, we are developing new cellular and genetic analysis methods to discover and better understand tumor initiating cells in primary human malignancies, with the ultimate goal of improving cancer patient outcomes.


Digital Cytometry

Tissue composition is a major determinant of phenotypic variation and a key factor influencing disease outcomes. Although single cell RNA sequencing has emerged as a powerful technique for characterizing cellular heterogeneity, it is currently impractical for large sample cohorts and cannot be applied to fixed specimens collected as part of routine clinical care. To overcome these challenges, we have developed a new platform for in silico cytometry, called CIBERSORTx. Our approach leverages single cell RNA sequencing and expression deconvolution to link unbiased cell type discovery to large-scale tissue dissection. In addition, CIBERSORTx enables the simultaneous inference of cell type-specific gene expression profiles and cell type abundance from bulk tissue transcriptomes.  We are currently applying this integrated framework to address multiple biological and clinical questions of interest across diverse tumor types.