During the reporting period, we achieved several milestones as outlined in the research proposal: On the technology side, we developed a highly multiplexed X-ray imaging method, multi-element Z-tag X-ray fluorescence (MEZ-XRF) imaging. This technique enabled parallel imaging of 20 Z-tag or SABER Z-tag antibodies at subcellular resolution in various human tissues, including breast cancer. We showcased the nondestructive, multiscale repeat imaging capabilities of MEZ-XRF, starting with rapid tissue overview scans followed by more sensitive imaging of low-abundance markers like immune checkpoint proteins. The multiscale, nondestructive nature of MEZ-XRF facilitates highly multiplexed bioimaging across biological scales. To achieve kHz scanning speeds, we combined MEZ-XRF with signal amplification by exchange reaction (SABER)-amplified Z-tag reagents. This method is also highly useful for other metal-based imaging techniques, such as imaging mass cytometry.
On the data analysis side we realized that no multiplexed tissue imaging method is fast enough to analyze large tissue sections across extensive patient cohorts. We therefore developed a statistical framework to determine the number and area necessary to accurately identify all cell phenotypes in a tissue for a given biological or clincial question. We introduced a measure called tissue spatial segregation to improve the design of multiplexed imaging studies. Furthermore, we developed a full data analysis pipeline, automating multiplexed tissue imaging processing and downstream analysis.
On the biomedical side, our proposal aims to describe complex (metastatic) breast cancer ecosystems as multi-cellular assemblies (functional tissue motifs) executing defined functions, and relate these motifs to clinical parameters. To identify such multi-cellular assemblies, multiscale tissue analysis from molecules to single-cell phenotypes, cell-to-cell interactions, and morphology is necessary. We first conducted single-cell RNA sequencing and imaging mass cytometry to comprehensively determine single-cell phenotypes and states in primary and metastatic breast cancer. Comparing single-cell phenotypes of primary breast tumors and matched lymph node metastases in 205 patients, we identified phenotypes and spatial organizations of disseminated tumor cells associated with patient survival. Notably, using the lymph node metastasis tumor cells, we identified high-risk patients within a patient group normally considered low risk. This suggests that molecular characterization of locally disseminated tumor cells is a valuable source of clinically relevant prognostic information for breast cancer. We also examined matched pairs of primary breast tumors and distant metastases using 75 unique antibody targets. Our findings indicate that primary tumors and metastatic sites typically share tumor cell phenotypes, supporting a linear spread of metastases. Except for brain metastases, the metastatic site didn’t significantly influence tumor phenotype composition. We observed higher concentrations of myeloid cells, exhausted and cytotoxic T cells in metastatic sites. This analysis of tumor and immune cell phenotypic composition in distant metastatic breast cancer underscores the heterogeneity of metastatic disease within patients and across distant sites, highlighting myeloid cells as key immune modulators that could be targeted in metastatic sites. We aim to validate some of these findings in viable ex vivo breast cancer samples and hopefully in the future in a clinical study.
