Category: Cancer evolution and the immune system

T cell repertoire
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 ...
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Neoantigen prediction
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 ...
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Tumor evolution
We have developed algorithms to reconstruct tumor clonal architectures and evolutionary trees from bulk DNA sequencing, using small somatic alterations as clonal markers. The algorithms were designed to model either single or multiple tumor samples from the same individual, either spatial or temporal. The reconstructions include estimates of the subclone proportions in each sample, which ...
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