Single-cell spatial multiomics reveals tumor microenvironment vulnerabilities in cancer resistance to immunotherapy.

Heterogeneous resistance to immunotherapy remains a major challenge in cancer treatment, often leading to disease progression and death. Using CITE-seq and matched 40-plex PhenoCycler tissue imaging, we performed longitudinal multimodal single-cell analysis of tumors from metastatic melanoma patients with innate resistance, acquired resistance, or response to immunotherapy. We established the multimodal integration toolkit to align transcriptomic features, cellular epitopes, and spatial information to provide deeper insights into the tumors. With longitudinal analysis, we identified an "immune-striving" tumor microenvironment marked by peri-tumor lymphoid aggregates and low infiltration of T cells in the tumor and the emergence of MITF+SPARCL1+ and CENPF+ melanoma subclones after therapy. The enrichment of B cell-associated signatures in the molecular composition of lymphoid aggregates was associated with better survival. These findings provide further insights into the establishment of microenvironmental cell interactions and molecular composition of spatial structures that could inform therapeutic intervention.

Single-cell and spatial profiling identify three response trajectories to pembrolizumab and radiation therapy in triple negative breast cancer.

Strategies are needed to better identify patients that will benefit from immunotherapy alone or who may require additional therapies like chemotherapy or radiotherapy to overcome resistance. Here we employ single-cell transcriptomics and spatial proteomics to profile triple negative breast cancer biopsies taken at baseline, after one cycle of pembrolizumab, and after a second cycle of pembrolizumab given with radiotherapy. Non-responders lack immune infiltrate before and after therapy and exhibit minimal therapy-induced immune changes. Responding tumors form two groups that are distinguishable by a classifier prior to therapy, with one showing high major histocompatibility complex expression, evidence of tertiary lymphoid structures, and displaying anti-tumor immunity before treatment. The other responder group resembles non-responders at baseline and mounts a maximal immune response, characterized by cytotoxic T cell and antigen presenting myeloid cell interactions, only after combination therapy, which is mirrored in a murine model of triple negative breast cancer.

A machine learning one-class logistic regression model to predict stemness for single cell transcriptomics and spatial omics.

Cell annotation is a crucial methodological component to interpreting single cell and spatial omics data. These approaches were developed for single cell analysis but are often biased, manually curated and yet unproven in spatial omics. Here we apply a stemness model for assessing oncogenic states to single cell and spatial omic cancer datasets. This one-class logistic regression machine learning algorithm is used to extract transcriptomic features from non-transformed stem cells to identify dedifferentiated cell states in tumors. We found this method identifies single cell states in metastatic tumor cell populations without the requirement of cell annotation. This machine learning model identified stem-like cell populations not identified in single cell or spatial transcriptomic analysis using existing methods. For the first time, we demonstrate the application of a ML tool across five emerging spatial transcriptomic and proteomic technologies to identify oncogenic stem-like cell types in the tumor microenvironment.

A spatially resolved single-cell genomic atlas of the adult human breast.

The adult human breast is comprised of an intricate network of epithelial ducts and lobules that are embedded in connective and adipose tissue1-3. Although most previous studies have focused on the breast epithelial system4-6, many of the non-epithelial cell types remain understudied. Here we constructed the comprehensive Human Breast Cell Atlas (HBCA) at single-cell and spatial resolution. Our single-cell transcriptomics study profiled 714,331 cells from 126 women, and 117,346 nuclei from 20 women, identifying 12 major cell types and 58 biological cell states. These data reveal abundant perivascular, endothelial and immune cell populations, and highly diverse luminal epithelial cell states. Spatial mapping using four different technologies revealed an unexpectedly rich ecosystem of tissue-resident immune cells, as well as distinct molecular differences between ductal and lobular regions. Collectively, these data provide a reference of the adult normal breast tissue for studying mammary biology and diseases such as breast cancer.

An N-Cadherin 2 expressing epithelial cell subpopulation predicts response to surgery, chemotherapy and immunotherapy in bladder cancer.

Neoadjuvant chemotherapy (NAC) prior to surgery and immune checkpoint therapy (ICT) have revolutionized bladder cancer management. However, stratification of patients that would benefit most from these modalities remains a major clinical challenge. Here, we combine single nuclei RNA sequencing with spatial transcriptomics and single-cell resolution spatial proteomic analysis of human bladder cancer to identify an epithelial subpopulation with therapeutic response prediction ability. These cells express Cadherin 12 (CDH12, N-Cadherin 2), catenins, and other epithelial markers. CDH12-enriched tumors define patients with poor outcome following surgery with or without NAC. In contrast, CDH12-enriched tumors exhibit superior response to ICT. In all settings, patient stratification by tumor CDH12 enrichment offers better prediction of outcome than currently established bladder cancer subtypes. Molecularly, the CDH12 population resembles an undifferentiated state with inherently aggressive biology including chemoresistance, likely mediated through progenitor-like gene expression and fibroblast activation. CDH12-enriched cells express PD-L1 and PD-L2 and co-localize with exhausted T-cells, possibly mediated through CD49a (ITGA1), providing one explanation for ICT efficacy in these tumors. Altogether, this study describes a cancer cell population with an intriguing diametric response to major bladder cancer therapeutics. Importantly, it also provides a compelling framework for designing biomarker-guided clinical trials.