Scientific Premise:

That there exists spatial proteomic patterns in lymphocyte populations indicative of tumor metastasis.

Motivation:

Tumor-infiltrating lymphocytes (TIL) are an important prognostic indicator for colorectal cancer outcomes and have more potent and directed anti-tumor effects than peripheral blood lymphocytes. The type, density, and location of TILs with respect to the tumor, in addition to tumor-specific somatic alteration profiles, can determine TIL’s effect on the tumor microenvironment and prognosis. We aim to understand how spatially dependent lymphocyte expression patterns inside and around tumor informs a coordinated immune response (e.g., spatially dependent recruitment/lysing patterns/communication) to prohibit the development of metastases. We are currently profiling a set of 36 stage III tumors either with or without nodal or distant metastases to establish spatial proteomic markers of metastasis with digital spatial profiling of immune cells within select regions of interest.

Manuscripts:

1. Levy, J. J., Bobak, C. A., Nasir-Moin, M., et al. Mixed Effects Machine Learning Models for Colon Cancer Metastasis Prediction using Spatially Localized Immuno-Oncology Markers. Pac Symp Biocomput 27, 175–186 (2022).
2. Levy, J. J., Bobak, C. A., Nasir-Moin, M., et al. Mixed effects machine learning on spatially localized immuno-oncology markers for colon metastasis prediction. (2021) doi:10.7490/F1000RESEARCH.1118802.1.