Non-small cell lung cancer (NSCLC) remains a significant cause of cancer mortality and morbidity each year. Treatments harnessing the body’s own immune system to eliminate tumorous tissue have provided a novel way to treat NSCLC, but despite such breakthroughs, patients often develop resistance to these therapies and succumb to their disease. One mechanism of resistance is through “immune exclusion” by the tumor, where through a complex interplay of cancer cells, stromal cells, and elements of the extracellular matrix (ECM), the tumor establishes an environment which excludes T cells. However, the complexity of these interacting systems has made immune exclusion difficult to model in the lab, and thus it has been challenging to stratify patients for enrollment in different treatment regimens based on their risk developing resistance, and to identify points of vulnerability to target with new therapeutic options when such resistance arises.
To study these questions in more detail, Erik Sahai and Helen Byrne will develop and implement a workflow to examine ECM patterning and its relationship to the immune environment in lung adenocarcinomas. By combining microscopy and omics data from an unprecedently large number of samples with the mathematical framework of topological data analysis, they will be able to more deeply probe the complex interplay of cells and ECM in lung tumors than was previously possible. To do so, the team will establish quantitative metrics of ECM organization and correlate them with genomic and transcriptomic data from tumors and the distribution of immune cells in the microenvironment. They will develop and optimize a panel of antibodies targeting components of the ECM and use these to acquire multiplexed images of the ECM from 100 lung adenocarcinoma samples. Finally, they will develop and apply sophisticated methods of analysis to determine how particular features in the ECM are linked to T-cell distribution, cancer subtype, and patient outcomes. The application of quantitative metrics to ECM architecture, and the integration of these metrics with cellular and clinical features, will enable new discoveries about both the causes and consequences of different patterns of tumor organization. This framework will have broad utility across cancer types to uncover new insights into the factors governing leukocyte distribution in tumors, therapy responses, and patient outcomes across solid malignancies.