Multiple lines of evidence across several tumor types indicate that genomic, phenotypic, and morphologic intratumoral heterogeneity (ITH) among malignant cells, non-malignant cells (e.g., immune cells, cancer associated fibroblasts (CAFs), endothelial cells), and their localized interactions within the tumor microenvironment (TME) are critical determinants of disease progression landmarks that include metastasis, immune evasion, therapeutic response, and drug resistance. The coevolution of these heterotypic signaling networks reciprocally selects for spatial ITH evident as distinct regions within the same primary tumor or metastases. The prevalence of spatial ITH and its correlation with clinical outcomes have defined an unmet need to deconvolve and exploit this complexity to determine the underpinnings of metastasis, the major cause of cancer mortality.
To meet the challenge of understanding the mechanistic basis of metastasis for the purpose of optimizing prognosis and informing novel therapeutic strategies for individual patients, the UPDDI has spearheaded the development of transdisciplinary teams comprised of members both within and outside the Pitt/UPMC community. For example, our collaboration with the Hillman Cancer Institute and General Electric Global Research Center (GEGRC) has led to the integration of a novel hyperplexed (~50 protein biomarkers) in situ immunofluorescence labeling and imaging technology with cutting-edge machine learning tools to generate a comprehensive systems pathology platform to define the TME of metastatic breast and colon cancer. This platform will also be used to gain mechanistic insights for metastatic melanoma and head and neck cancer. To complement this systems pathology platform, we have developed vascularized patient-derived microphysiological liver metastasis models that complement preclinical in vivo models to test causal hypotheses resulting from our metastatic breast (Figure 1) and colon cancer studies.
We expect to identify and characterize spatial ITH and the pathogenic signaling networks within the TME that confer metastatic potential and development. We anticipate that the knowledge gained from these studies will identify 1) predictive biomarkers mechanistically linked to metastatic disease progression and 2) emergent tumor dependencies that will inform novel therapeutic strategies for efficacious remodeling of the TME to a less permissive metastatic state.