To understand how the diversity and composition of T cells affect the outcome of cancer treatment, we are developing tools for analysis of multi-omic data from bulk, single cell, and spatial T cell receptor (TCR) sequencing assays. TCRseq can identify variable regions on receptors on the surface of each T cell, enabling statistical analysis of ...

In the emerging field of precision cancer immunotherapies, understanding the basis for immune system recognition of tumor cells has become crucial. The primary signal with which T cells recognize tumor and professional antigen-presenting cells (APCs) is the binding of T cell receptors (TCRs) to neoantigen peptides presented by Major Histocompatibility Complex (MHC molecules) on the ...

We develop algorithms to reconstruct tumor clonal architectures and evolutionary trees from somatic alterations across single tumors or multiple spatial/longitudinal samples. Our reconstructions model clonal architecture and estimate subclone proportions in each sample, allowing us to track lineages that expand or contract over space and time; the most expanded subclones are expected to have the ...