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Most research on the cancer metastatic cascade involves tracking cancer cell invasion, migration, arrest, and distant tissue colonization – centered mainly on tumor properties and activities. Our previous work introduced a new paradigm, “lymph node (LN) tolerization,” that redefines LNs from passive way stops in metastasis to active command centers that instigate a systemic switch to immune tolerance. We hypothesize that this switch enables tumor cells to advance unobstructed through previously guarded host defense mechanisms. Knowing how to reprogram LN tolerization may provide novel spatial biomarkers and therapeutic targets.

SUMMARY
Even though LNs are commonly assessed in cancer patients to determine disease stage and treatment plan, they are understudied in the context of metastasis. Our multidisciplinary team is applying genomic and single-cell in-situ imaging technologies to iteratively analyze changes in tumor, immune, and stromal interactions before, during, and after LN colonization in two cancers, head and neck cancer (HNCC) and lung adenocarcinoma (LUAD). These technologies are fueling the growth of spatial systems biology as a distinct field capable of unprecedented characterization of tissue microenvironments as complex ecosystems of interacting cell types including epithelial, immune, stromal and endothelial cells. 

We are dedicated to promoting our early investigators as the next generation thought leaders applying principles of systems biology to the study of metastasis. Our Outreach Core activity will ensure that our Research Center’s scientific and methodological advances in applying the principles of cancer systems biology toward the study of tumor-immune-stromal interactions are fully disseminated among cancer researchers and broader scientific communities.

Biospecimen and Data Core

Both Project 1 and Project 2 will use a shared resource core dedicated to the acquisition of patient samples and associated clinical annotation and data management. These efforts will yield highly multiplexed, multi-scale datasets that will be analyzed by