Dissociative single-cell approaches enable detailed characterization of the different cell types and states that compose a heterogeneous tumor but sacrifices the spatial relationships among cells that represent an important layer of information regarding intercellular collaborations within the tumor microenvironment (TME). To uncover how neoadjuvant treatment and cancer cell- and fibroblast-intrinsic programs modulate the composition of multicellular neighborhoods in situ, we performed digital spatial profiling (DSP; NanoString GeoMx) on 21 formalin-fixed paraffin-embedded sections using the human whole transcriptome atlas (WTA) (Hwang, Jagadeesh, Guo, Hoffman, Nat Genetics 2022). Each tumor showed intra-tumoral heterogeneity in tissue architecture and regions of interest (ROIs) with diverse patterns of neoplastic cells, cancer-associated fibroblasts (CAFs), and immune cells were selected for profiling. We deconvolved the WTA data with our snRNA-seq cell type signatures and mapped expression programs onto the tumor architecture to reveal three distinct multicellular
neighborhoods, which we annotated as classical, squamoid-basaloid, and treatment-enriched (Hwang, Jagadeesh, Guo, Hoffman, Nat Genetics 2022). The observed enrichment in post-treatment residual disease of multiple spatially-defined receptor-ligand interactions and a neighborhood featuring the NRP malignant program, neurotropic CAF program, and CD8 T cells may open new opportunities to improve therapy for PDAC and provides a paradigm that can be applied to the study of many other malignancies.
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Discovering mechanisms of therapeutic resistance at the tumor-stromal interface using single-cell spatial transcriptomics and tumoroids
To understand the single-cell spatial transcriptome landscape of the PDAC TME, we are performing spatial molecular imaging (SMI; NanoString CosMx Technology Access Program) using a ~1000-plex custom RNA panel. SMI augmented the DSP observations and provided further elucidation of transcriptional profiles but at single cell/subcellular resolution with broader tissue coverage. SMI enabled cell typing and mapping of complex cell populations while preserving spatial context and highlighted differential cell type distributions in PDAC specimens that either received neoadjuvant therapy or were treatment-naïve. Ultimately, our goal is to develop a translational spatial biology paradigm that enables (1) mapping pancreatic cancer subtypes onto the tumor architecture at single-cell resolution; (2) defining functional neighborhoods at single-cell resolution by creating robust spatial consensus non-negative matrix factorization methods; (3) identifying candidate receptor-ligand pairs that are associated with therapeutic resistance using novel computational approaches; and (4) dissecting these candidate interactions through CRISPR-based genetic engineering approaches in stromal tumoroids to validate their roles in mediating treatment resistance and identify new therapeutic strategies. This will enable high-resolution, spatially-guided discovery of critical mediators of therapeutic resistance in pancreatic cancer, as well as other malignancies.
Integrating liquid and spatial tissue biomarkers to assess response to therapy
While tissue-based analyses provide rich in situ information, they require invasive biopsies and cannot be performed as frequently as peripheral liquid biopsies. Recently, there has been significant advances in the potential clinical translation of high-plex molecular information gleaned from cell-free nucleic acids, extracellular vesicles, circulating tumor cells, peripheral blood mononuclear cells, plasma proteomics and metabolomics. However, it is not known how to combine analyses of liquid and tissue biopsies to determine what information about the tumor and its microenvironment can be obtained from the peripheral approach. We will utilize matched pre- and post-treatment blood and tissue specimens from various clinical trials enrolling patients with gastrointestinal malignancies as an ideal platform to develop such integrated methods, which we anticipate will establish a new paradigm for the combined use of liquid biopsies and spatially-resolved tissue proteotranscriptomics with broad clinical applicability. In parallel, we are interested in developing other ways of translating our single-cell and spatial information to the clinic. For example, we are working on developing low-plex protein/RNA panels and deep learning algorithms to identify subtypes in routinely stained clinical tumor sections.
Uncovering distinct mechanisms of immune evasion employed by pancreatic cancer subtypes through high-dimensional spatial analysis
Digital spatial profiling revealed that CD8 T cells are enriched in regions of tumor with NRP differentiation, which was surprising since high CD8 T cell infiltration has generally been associated with improved prognosis. Hence, we are characterizing the immune landscapes associated with each of the distinct PDAC malignant subtypes we identified (Hwang, Jagadeesh, Guo, Hoffman, Nat Genetics 2022) using spatial proteotranscriptomics applied to patient tumors and preclinical models using the isogenic murine organoids engineered to overexpress subtype-defining transcription factors.