Predicting colon cancer metastasis through spatial molecular characterization of the tumor immune microenvironment

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Colorectal Cancer (CRC) is both the third most common form of cancer and cause of cancer-related deaths in the United States. Examination of axillary lymph nodes at the time of surgical resection is essential for prognostication and while it is important to maximize the number of lymph nodes assessed, recent population-based studies have shown that evaluation of lymph node involvement is usually incomplete or inadequate. This can impact the accuracy of tumor staging and downstream disease management options, such as whether the patient should receive adjuvant chemotherapy. Developing alternative assessment methods which assess lymph node involvement through indirect mechanisms would be illuminating in cases where resection is inadequate. Tumor-infiltrating lymphocytes (TIL) and other immune cell types are important prognostic indicators in CRC. 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 prognosis. Furthermore, spatially dependent, immune cell specific, proteomic and transcriptomic expression patterns inside and around tumor – the Tumor Immune Microenvironment (TIME) – can discern the coordinated immune response to tumor metastasis. The comprehensive characterization of TILs is possible using highly multiplexed spatial omics technologies, but high cost and low throughput prevent their clinical deployment. Virtual staining can infer molecular information at low cost from tissue histology where the morphology allows. We aim to design a low-cost Virtual Staining test, distilled from highly multiplexed spatial molecular information, that could complement surgical lymph node dissection for recurrence risk assessments and compete with other emerging predictors (e.g., circulating tumor DNA). In a set of stage III tumors with or without nodal and/or distant metastases, we will identify spatial proteomic and whole transcriptomic markers of metastasis with digital spatial profiling and Visium spatial transcriptomics of immune cells. We will also assess upstream cell-type specific DNA methylation alterations concomitant with spatial architectural TIME changes. Identified markers will be validated through lower-cost multiplexed immunofluorescence staining. Finally, we will establish histological correspondence to identified spatial metastasis markers and develop virtual staining algorithms to convert H&E-stained tissue into validated multiplexed immunofluorescent and whole transcriptomic markers. Spatial and cell-type specific patterns of molecular markers that indicate whether a patient has or is likely to develop metastasis will be identified under this framework. Inferring such information from tissue morphology can provide a low-cost and highly interpretable adjunct molecular assessment for lymph node resection, to predict recurrence risk and response to adjuvant chemotherapy. We expect that our findings will provide preliminary data for an R01 clinical trial to compare identified markers prospectively to independent metastasis predictors (e.g., liquid biopsy) for their ability to assess patient prognosis and treatment options.