Breast tumor microenvironment structures are associated with genomic features and clinical outcome

Flow-on-repellent biofabrication of fibrous decellularized breast tumor-stroma models

On-the-fly biofabrication of reproducible 3D tumor models at a pre-clinical level is highly desirable to level-up their applicability and predictive potential. Incorporating ECM biomolecular cues and its complex 3D bioarchitecture in the design stages of such in vitro platforms is essential to better recapitulate the native tumor microenvironment. To materialize these needs, herein we describe an innovative flow-on-repellent (FLORE) 3D extrusion bioprinting technique that leverages expedited and automatized bioink deposition onto a customized superhydrophobic printing bed. We demonstrate that this approach enables the rapid generation of quasi-spherical breast cancer-stroma hybrid models in a mode governed by surface wettability rather than bioink rheological features. For this purpose, an ECM-mimetic bioink comprising breast tissue-specific decellularized matrix in the form of microfiber bundles (dECM-μF) and photocrosslinkable hyaluronan (HAMA), was formulated to generate triple negative breast tumor-stroma models. Leveraging on the FLORE bioprinting approach, a rapid, automated, and reproducible fabrication of physiomimetic breast cancer hydrogel beads was successfully demonstrated. Hydrogel beads size with and without dECM-μF was easily tailored by modelling droplet deposition time on the superhydrophobic bed. Interestingly, in heterotypic breast cancer-stroma beads a self-arrangement of different cellular populations was observed, independent of dECM-μF inclusion, with CAFs clustering overtime within the fabricated models. Drug screening assays showed that the inclusion of CAFs and dECM-μF also impacted the overall response of these living constructs when incubated with gemcitabine chemotherapeutics, with dECM-μF integration promoting a trend for higher resistance in ECM-enriched models. Overall, we developed a rapid fabrication approach leveraging on extrusion bioprinting and superhydrophobic surfaces to process photocrosslinkable dECM bioinks and to generate increasingly physiomimetic tumor-stroma-matrix platforms for drug screening.

Spatial Architecture of Single-cell and Vasculature in Tumor Microenvironment Predicts Clinical Outcomes in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with limited treatment options, which warrants the identification of novel therapeutic targets. Deciphering nuances in the tumor microenvironment (TME) may unveil insightful links between anti-tumor immunity and clinical outcomes, yet such connections remain underexplored. Here we employed a dataset derived from imaging mass cytometry of 71 TNBC patient specimens at single-cell resolution and performed in-depth quantifications with a suite of multi-scale computational algorithms. The TNBC TME reflected a heterogeneous ecosystem with high spatial and compositional heterogeneity. Spatial analysis identified ten recurrent cellular neighborhoods (CNs) - a collection of local TME characteristics with unique cell components. The prevalence of CNs enriched with B cells, fibroblasts, and tumor cells, in conjunction with vascular density and perivasculature immune profiles, could significantly enrich for long-term survivors. Furthermore, relative spatial colocalization of SMAhi fibroblasts and tumor cells compared to B cells correlated significantly with favorable clinical outcomes. Using a deep learning model trained on engineered spatial data, we can predict with high accuracy (mean AUC of 5-fold cross-validation = 0.71) how a separate cohort of patients in the NeoTRIP clinical trial will respond to treatment based on baseline TME features. These data reinforce that the TME architecture is structured in cellular compositions, spatial organizations, vasculature biology, and molecular profiles, and suggest novel imaging-based biomarkers for treatment development in the context of TNBC.

Spatial multiplex analysis of lung cancer reveals that regulatory T cells attenuate KRAS-G12C inhibitor-induced immune responses

Kirsten rat sarcoma virus (KRAS)-G12C inhibition causes remodeling of the lung tumor immune microenvironment and synergistic responses to anti-PD-1 treatment, but only in T cell infiltrated tumors. To investigate mechanisms that restrain combination immunotherapy sensitivity in immune-excluded tumors, we used imaging mass cytometry to explore cellular distribution in an immune-evasive KRAS mutant lung cancer model. Cellular spatial pattern characterization revealed a community where CD4+ and CD8+ T cells and dendritic cells were gathered, suggesting localized T cell activation. KRAS-G12C inhibition led to increased PD-1 expression, proliferation, and cytotoxicity of CD8+ T cells, and CXCL9 expression by dendritic cells, indicating an effector response. However, suppressive regulatory T cells (Tregs) were also found in frequent contact with effector T cells within this community. Lung adenocarcinoma clinical samples showed similar communities. Depleting Tregs led to enhanced tumor control in combination with anti-PD-1 and KRAS-G12C inhibitor. Combining Treg depletion with KRAS inhibition shows therapeutic potential for increasing antitumoral immune responses.

A heterogeneous graph transformer framework for accurate cancer driver gene prediction and downstream analysis

Accurately predicting cancer driver genes remains a formidable challenge amidst the burgeoning volume and intricacy of cancer genomic data. In this investigation, we propose HGTDG, an innovative heterogeneous graph transformer framework tailored for precisely predicting cancer driver genes and exploring downstream tasks. A heterogeneous graph construction module is central to the framework, which assembles a gene-protein heterogeneous network leveraging the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and protein-protein interactions sourced from the STRING (search tool for recurring instances of neighboring genes) database. Moreover, our framework introduces a pioneering heterogeneous graph transformer module, harnessing multi-head attention mechanisms for nuanced node embedding. This transformative module proficiently captures distinct representations for both nodes and edges, thereby enriching the model's predictive capacity. Subsequently, the generated node embeddings are seamlessly integrated into a classification module, facilitating the discrimination between driver and non-driver genes. Our experimental findings evince the superiority of HGTDG over existing methodologies, as evidenced by the enhanced performance metrics, including the area under the receiver operating characteristic curves (AUROC) and the area under the precision-recall curves (AUPRC). Furthermore, the downstream analysis utilizing the newly identified cancer driver genes underscores the efficacy and versatility of our proposed framework.

Invasion/chemotaxis- and extravasation-chip models for breast cancer bone metastasis

Bone is one of the most frequently targeted organs in metastatic cancers including the breast. Breast cancer bone metastasis often results in devastating outcomes as limited treatment options are currently available. Therefore, innovative methods are needed to provide earlier detection and thus better treatment and prognosis. Here, we present a new approach to model bone-like microenvironments to detect invasion and extravasation of breast cancer cells using invasion/chemotaxis (IC-) and extravasation (EX-) chips, respectively. Our results show that the behaviors of MDA-MB-231 breast cancer cells on IC- and EX-chip models correlate with their in vivo metastatic potential. Our culture model constitutes cell lines representing osteoblasts, bone marrow stromal cells, and monocytes embedded in three-dimensional (3D) collagen I-based extracellular matrices of varying composition and stiffness. We show that collagen I offers a better bone-like environment for bone cells and matrix composition and stiffness regulate the invasion of breast cancer cells. Using in situ contactless rheological measurements under cell culture conditions, we show that the presence of cells increased the stiffness values of the matrices up to 1200 Pa when monitored for five days. This suggests that the cellular composition has a significant effect on regulating matrix mechanical properties, which in turn contribute to the invasiveness. The platforms we present here enable the investigation of the underlying molecular mechanisms in breast cancer bone metastasis and provide the groundwork of developing preclinical tools for the prediction of bone metastasis risk.

Spatial oncology: Translating contextual biology to the clinic

Microscopic examination of cells in their tissue context has been the driving force behind diagnostic histopathology over the past two centuries. Recently, the rise of advanced molecular biomarkers identified through single cell profiling has increased our understanding of cellular heterogeneity in cancer but have yet to significantly impact clinical care. Spatial technologies integrating molecular profiling with microenvironmental features are poised to bridge this translational gap by providing critical in situ context for understanding cellular interactions and organization. Here, we review how spatial tools have been used to study tumor ecosystems and their clinical applications. We detail findings in cell-cell interactions, microenvironment composition, and tissue remodeling for immune evasion and therapeutic resistance. Additionally, we highlight the emerging role of multi-omic spatial profiling for characterizing clinically relevant features including perineural invasion, tertiary lymphoid structures, and the tumor-stroma interface. Finally, we explore strategies for clinical integration and their augmentation of therapeutic and diagnostic approaches.

Bi-level Graph Learning Unveils Prognosis-Relevant Tumor Microenvironment Patterns in Breast Multiplexed Digital Pathology

The tumor microenvironment is widely recognized for its central role in driving cancer progression and influencing prognostic outcomes. There have been increasing efforts dedicated to characterizing this complex and heterogeneous environment, including developing potential prognostic tools by leveraging modern deep learning methods. However, the identification of generalizable data-driven biomarkers has been limited, in part due to the inability to interpret the complex, black-box predictions made by these models. In this study, we introduce a data-driven yet interpretable approach for identifying patterns of cell organizations in the tumor microenvironment that are associated with patient prognoses. Our methodology relies on the construction of a bi-level graph model: (i) a cellular graph, which models the intricate tumor microenvironment, and (ii) a population graph that captures inter-patient similarities, given their respective cellular graphs, by means of a soft Weisfeiler-Lehman subtree kernel. This systematic integration of information across different scales enables us to identify patient subgroups exhibiting unique prognoses while unveiling tumor microenvironment patterns that characterize them. We demonstrate our approach in a cohort of breast cancer patients and show that the identified tumor microenvironment patterns result in a risk stratification system that provides new complementary information with respect to standard stratification systems. Our results, which are validated in two independent cohorts, allow for new insights into the prognostic implications of the breast tumor microenvironment. This methodology could be applied to other cancer types more generally, providing insights into the cellular patterns of organization associated with different outcomes.

Single-cell and spatial omics unravel the spatiotemporal biology of tumour border invasion and haematogenous metastasis

Solid tumours exhibit a well-defined architecture, comprising a differentiated core and a dynamic border that interfaces with the surrounding tissue. This border, characterised by distinct cellular morphology and molecular composition, serves as a critical determinant of the tumour's invasive behaviour. Notably, the invasive border of the primary tumour represents the principal site for intravasation of metastatic cells. These cells, known as circulating tumour cells (CTCs), function as 'seeds' for distant dissemination and display remarkable heterogeneity. Advancements in spatial sequencing technology are progressively unveiling the spatial biological features of tumours. However, systematic investigations specifically targeting the characteristics of the tumour border remain scarce. In this comprehensive review, we illuminate key biological insights along the tumour body-border-haematogenous metastasis axis over the past five years. We delineate the distinctive landscape of tumour invasion boundaries and delve into the intricate heterogeneity and phenotype of CTCs, which orchestrate haematogenous metastasis. These insights have the potential to explain the basis of tumour invasion and distant metastasis, offering new perspectives for the development of more complex and precise clinical interventions and treatments.

Statistical analysis of multiple regions-of-interest in multiplexed spatial proteomics data

Multiplexed spatial proteomics reveals the spatial organization of cells in tumors, which is associated with important clinical outcomes such as survival and treatment response. This spatial organization is often summarized using spatial summary statistics, including Ripley's K and Besag's L. However, if multiple regions of the same tumor are imaged, it is unclear how to synthesize the relationship with a single patient-level endpoint. We evaluate extant approaches for accommodating multiple images within the context of associating summary statistics with outcomes. First, we consider averaging-based approaches wherein multiple summaries for a single sample are combined in a weighted mean. We then propose a novel class of ensemble testing approaches in which we simulate random weights used to aggregate summaries, test for an association with outcomes, and combine the $P$-values. We systematically evaluate the performance of these approaches via simulation and application to data from non-small cell lung cancer, colorectal cancer, and triple negative breast cancer. We find that the optimal strategy varies, but a simple weighted average of the summary statistics based on the number of cells in each image often offers the highest power and controls type I error effectively. When the size of the imaged regions varies, incorporating this variation into the weighted aggregation may yield additional power in cases where the varying size is informative. Ensemble testing (but not resampling) offered high power and type I error control across conditions in our simulated data sets.

VisualZoneR: A computational protocol to identify compartmental zones from single-cell spatial transcriptomics using R

VisualZoneR is an R-based technique used to analyze spatial transcriptomics data generated by employing Visium or Visium HD technology. Here, we present a protocol to identify compartmental zones from single-cell spatial transcriptomics using VisualZoneR. We describe steps for identifying distinct zones ranging from healthy liver tissue to inner metastatic areas and measuring transcriptomic changes. We then detail procedures for integrating distinct samples and grouping transcriptomic spots into compartmental zones according to their relative distance from the tumor/liver parenchyma boundary.

Multiplex imaging of localized prostate tumors reveals altered spatial organization of AR-positive cells in the microenvironment

Mapping the spatial interactions of cancer, immune, and stromal cell states presents novel opportunities for patient stratification and for advancing immunotherapy. While single-cell studies revealed significant molecular heterogeneity in prostate cancer cells, the impact of spatial stromal cell heterogeneity remains poorly understood. Here, we used cyclic immunofluorescent imaging on whole-tissue sections to uncover novel spatial associations between cancer and stromal cells in low- and high-grade prostate tumors and tumor-adjacent normal tissues. Our results provide a spatial map of single cells and recurrent cellular neighborhoods in the prostate tumor microenvironment of treatment-naive patients. We report unique populations of mast cells that show distinct spatial associations with M2 macrophages and regulatory T cells. Our results show disease-specific neighborhoods that are primarily driven by androgen receptor-positive (AR+) stromal cells and identify inflammatory gene networks active in AR+ prostate stroma.

A Novel Tumor-Associated Neutrophil-Related Risk Signature Based on Single-Cell and Bulk RNA-Sequencing Analyses Predicts the Prognosis and Immune Landscape of Breast Cancer

Tumor-associated neutrophils (TANs) are increasingly recognized as contributors to cancer prognosis and therapeutics. However, TAN-related targets of breast cancer (BRCA) remain scarce. This study aimed to develop a novel TAN-associated risk signature (TANRS) of BRCA using single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing data. Eighty-six TAN-related genes (TANRGs) were derived from the intersection of TAN marker genes identified from scRNA-seq with modular genes identified by weighted gene co-expression network analysis (WGCNA). The TANRS consisting of nine TANRGs (TAGLN2, IGF2R, LAMP2, TBL1X, ASAP1, DENND5A, SNRK, BCL3, and CEBPD) was constructed using Cox regression and the least absolute shrinkage and selection operator (LASSO) regression. The TANRS efficiently predicted the survival prognosis and clinicopathological progression of patients across multiple cohorts. Significant differences in immune infiltration landscapes between TANRS groups were observed. Additionally, patients with high TANRS exhibited tumor immunosuppression, enhanced cancer hallmarks, and unfavorable therapeutic effects. Four promising compounds for treating high-TANRS BRCA were also presented. SNRK was identified as a key prognostic TANRG, and its expression profile and correlation with TANs were validated using immunohistochemical assays of BRCA samples and spatial transcriptomic sections. This novel TAN-based signature exhibited promising predictive capabilities, with the potential to contribute to personalized medicine for BRCA patients.

Spatial landscapes of cancers: insights and opportunities

Solid tumours comprise many different cell types organized in spatially structured arrangements, with substantial intratumour and intertumour heterogeneity. Advances in spatial profiling technologies over the past decade hold promise to capture the complexity of these cellular architectures to build a holistic view of the intricate molecular mechanisms that shape the tumour ecosystem. Some of these mechanisms act at the cellular scale and are controlled by cell-autonomous programmes or communication between nearby cells, whereas other mechanisms result from coordinated efforts between large networks of cells and extracellular molecules organized into tissues and organs. In this Review we provide insights into the application of single-cell and spatial profiling tools, with a focus on spatially resolved transcriptomic tools developed to understand the cellular architecture of the tumour microenvironment and identify opportunities to use them to improve clinical management of cancers.

Mapping the breast tumor microenvironment: proximity analysis reveals spatial relationships between macrophage subtypes and metastasis-initiating cancer cells

Metastasis is responsible for the majority of cancer-related fatalities. We previously identified specific cancer cell populations responsible for metastatic events which are cytokeratin-14 (CK14) and E-cadherin positive in luminal tumors, and E-cadherin and vimentin positive in triple-negative tumors. Since cancer cells evolve within a complex ecosystem comprised of immune cells and stromal cells, we sought to decipher the spatial interactions of these aggressive cancer cell populations within the tumor microenvironment (TME). We used imaging mass cytometry to detect 36 proteins in tumor microarrays containing paired primary and metastatic lesions from luminal or triple-negative breast cancers (TNBC), resulting in a dataset of 1,477,337 annotated cells. Focusing on metastasis-initiating cell populations, we observed close proximity to specific fibroblast and macrophage subtypes, a relationship maintained between primary and metastatic tumors. Notably, high CK14 in luminal cancer cells and high vimentin in TNBC cells correlated with close proximity to specific macrophage subtypes (CD163intCD206intPDL1intHLA-DR+ or PDL1highARG1high). Our in-depth spatial analysis demonstrates that metastasis-initiating cancer cells consistently colocalizes with distinct cell populations within the TME, suggesting a role for these cell-cell interactions in promoting metastasis.

Lymphatic-localized Treg-mregDC crosstalk limits antigen trafficking and restrains anti-tumor immunity

The tumor microenvironment (TME) has a significant impact on tumor growth and immunotherapy efficacies. However, the precise cellular interactions and spatial organizations within the TME that drive these effects remain elusive. Using advanced multiplex imaging techniques, we have discovered that regulatory T cells (Tregs) accumulate around lymphatic vessels in the peripheral tumor stroma. This localized accumulation is facilitated by mature dendritic cells enriched in immunoregulatory molecules (mregDCs), which promote chemotaxis of Tregs, establishing a peri-lymphatic Treg-mregDC niche. Within this niche, mregDCs facilitate Treg activation, which in turn restrains the trafficking of tumor antigens to the draining mesenteric lymph nodes, thereby impeding the initiation of anti-tumor adaptive immune responses. Disrupting Treg recruitment to mregDCs inhibits tumor progression. Our study provides valuable insights into the organization of TME and how local crosstalk between lymphoid and myeloid cells suppresses anti-tumor immune responses.

Chemokine receptors and their ligands in breast cancer: The key roles in progression and metastasis

Chemokines and their receptors are a family of chemotactic cytokines with important functions in the immune response in both health and disease. Their known physiological roles such as the regulation of leukocyte trafficking and the development of immune organs generated great interest when it was found that they were also related to the control of early and late inflammatory stages in the tumor microenvironment. In fact, in breast cancer, an imbalance in the synthesis of chemokines and/or in the expression of their receptors was attributed to be involved in the regulation of disease progression, including invasion and metastasis. Research in this area is progressing rapidly and the development of new agents based on chemokine and chemokine receptor antagonists are emerging as attractive alternative strategies. This chapter provides a snapshot of the different functions reported for chemokines and their receptors with respect to the potential to regulate breast cancer progression.

Copy number alterations: a catastrophic orchestration of the breast cancer genome

Breast cancer (BCa) is a prevalent malignancy that predominantly affects women around the world. Somatic copy number alterations (CNAs) are tumor-specific amplifications or deletions of DNA segments that often drive BCa development and therapy resistance. Hence, the complex patterns of CNAs complement BCa classification systems. In addition, understanding the precise contributions of CNAs is essential for tailoring personalized treatment approaches. This review highlights how tumor evolution drives the acquisition of CNAs, which in turn shape the genomic landscapes of BCas. It also discusses advanced methodologies for identifying recurrent CNAs, studying CNAs in BCa and their clinical impact.

Towards targeting the breast cancer immune microenvironment

The tumour immune microenvironment is shaped by the crosstalk between cancer cells, immune cells, fibroblasts, endothelial cells and other stromal components. Although the immune tumour microenvironment (TME) serves as a source of therapeutic targets, it is also considered a friend or foe to tumour-directed therapies. This is readily illustrated by the importance of T cells in triple-negative breast cancer (TNBC), culminating in the advent of immune checkpoint therapy in combination with cytotoxic chemotherapy as standard of care for both early and advanced-stage TNBC, as well as recent promising signs of efficacy in a subset of hormone receptor-positive disease. In this Review, we discuss the various components of the immune TME in breast cancer and therapies that target or impact the immune TME, as well as the complexity of host physiology.

Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology

Advancements in imaging technologies have revolutionized our ability to deeply profile pathological tissue architectures, generating large volumes of imaging data with unparalleled spatial resolution. This type of data collection, namely, spatial proteomics, offers invaluable insights into various human diseases. Simultaneously, computational algorithms have evolved to manage the increasing dimensionality of spatial proteomics inherent in this progress. Numerous imaging-based computational frameworks, such as computational pathology, have been proposed for research and clinical applications. However, the development of these fields demands diverse domain expertise, creating barriers to their integration and further application. This review seeks to bridge this divide by presenting a comprehensive guideline. We consolidate prevailing computational methods and outline a roadmap from image processing to data-driven, statistics-informed biomarker discovery. Additionally, we explore future perspectives as the field moves toward interfacing with other quantitative domains, holding significant promise for precision care in immuno-oncology.

IMmuneCite: an integrated workflow for analysis of immune enriched spatial proteomic data

Spatial proteomics enable detailed analysis of tissue at single cell resolution. However, creating reliable segmentation masks and assigning accurate cell phenotypes to discrete cellular phenotypes can be challenging. We introduce IMmuneCite, a computational framework for comprehensive image pre-processing and single-cell dataset creation, focused on defining complex immune landscapes when using spatial proteomics platforms. We demonstrate that IMmuneCite facilitates the identification of 32 discrete immune cell phenotypes using data from human liver samples while substantially reducing nonbiological cell clusters arising from co-localization of markers for different cell lineages. We established its versatility and ability to accommodate any antibody panel and different species by applying IMmuneCite to data from murine liver tissue. This approach enabled deep characterization of different functional states in each immune compartment, uncovering key features of the immune microenvironment in clinical liver transplantation and murine hepatocellular carcinoma. In conclusion, we demonstrated that IMmuneCite is a user-friendly, integrated computational platform that facilitates investigation of the immune microenvironment across species, while ensuring the creation of an immune focused, spatially resolved single-cell proteomic dataset to provide high fidelity, biologically relevant analyses.

Spatial Dissection of the Immune Landscape of Solid Tumors to Advance Precision Medicine

Chemotherapeutics, radiation, targeted therapeutics, and immunotherapeutics each demonstrate clinical benefits for a small subset of patients with solid malignancies. Immune cells infiltrating the tumor and the surrounding stroma play a critical role in shaping cancer progression and modulating therapy response. They do this by interacting with the other cellular and molecular components of the tumor microenvironment. Spatial multi-omics technologies are rapidly evolving. Currently, such technologies allow high-throughput RNA and protein profiling and retain geographical information about the tumor microenvironment cellular architecture and the functional phenotype of tumor, immune, and stromal cells. An in-depth spatial characterization of the heterogeneous tumor immune landscape can improve not only the prognosis but also the prediction of therapy response, directing cancer patients to more tailored and efficacious treatments. This review highlights recent advancements in spatial transcriptomics and proteomics profiling technologies and the ways these technologies are being applied for the dissection of the immune cell composition in solid malignancies in order to further both basic research in oncology and the implementation of precision treatments in the clinic.

Phosphoglycerate mutase 1 promotes breast cancer progression through inducing immunosuppressive M2 macrophages

Immunosuppressive tumor microenvironment (TME) contributes to tumor progression and causes major obstacles for cancer therapy. Phosphoglycerate mutase 1 (PGAM1) is a key enzyme involved in cancer metabolism while its role in remodeling TME remains unclear. In this study, we reported that PGAM1 suppression in breast cancer (BC) cells led to a decrease in M2 polarization, migration, and interleukin-10 (IL-10) production of macrophages. PGAM1 regulation on CCL2 expression was essential to macrophage recruitment, which further mediated by activating JAK-STAT pathway. Additionally, the CCL2/CCR2 axis was observed to participate in PGAM1-mediated immunosuppression via regulating PD-1 expression in macrophages. Combined targeting of PGAM1 and the CCL2/CCR2 axis led to a reduction in tumor growth in vivo. Furthermore, clinical validation in BC tissues indicated a positive correlation between PGAM1, CCL2 and macrophage infiltration. Our study provides novel insights into the induction of immunosuppressive TME by PGAM1 and propose a new strategy for combination therapies targeting PGAM1 and macrophages in BC.

Spatial distribution pattern of immune cells is associated with patient prognosis in colorectal cancer

BACKGROUND

The spatial context of tumor-infiltrating immune cells (TIICs) is important in predicting colorectal cancer (CRC) patients' clinical outcomes. However, the prognostic value of the TIIC spatial distribution is unknown. Thus, we aimed to investigate the association between TIICs in situ and patient prognosis in a large CRC sample.

METHODS

We implemented multiplex immunohistochemistry staining technology in 190 CRC samples to quantify 14 TIIC subgroups in situ. To delineate the spatial relationship of TIICs to tumor cells, tissue slides were segmented into tumor cell and microenvironment compartments based on image recognition technology, and the distance between immune and tumor cells was calculated by implementing the computational pipeline phenoptr.

RESULTS

MPO+ neutrophils and CD68+IDO1+ tumor-associated macrophages (TAMs) were enriched in the epithelial compartment, and myeloid lineage cells were located nearest to tumor cells. Except for CD68+CD163+ TAMs, other cells were all positively associated with favorable prognosis. The prognostic predictive power of TIICs was highly related to their distance to tumor cells. Unsupervised clustering analysis divided colorectal cancer into three subtypes with distinct prognostic outcomes, and correlation analysis revealed the synergy among B cells, CD68+IDO1+TAMs, and T lineage cells in producing an effective immune response.

CONCLUSIONS

Our study suggests that the integration of spatial localization with TIIC abundance is important for comprehensive prognostic assessment.

Navigating the Intricacies of Tumor Heterogeneity: An Insight into Potential Prognostic Breast Cancer Biomarkers

Breast cancer is a heterogeneous disease with diverse histological and molecular subtypes. Luminal breast tumors are the most diagnosed subtype. In luminal breast cancer, hormone receptors (including ER, PR, HER2) play a diagnostic and prognostic role. Despite the effectiveness of endocrine therapy in luminal breast tumors, tumor recurrence and resistance occur, and this may highlight evolutionary strategies for survival driven by stemness. In this review we thus consider the association between estrogen signaling and stemness in mediating tumor processes. Many studies report stemness as one of the factors promoting tumor progression. Its association with estrogen signaling warrants further investigation and provides an opportunity for the identification of novel biomarkers which may be used for diagnostic, prognostic, and therapeutic purposes. Breast cancer stem cells have been characterized (CD44+ CD24-) and their role in promoting treatment resistance and tumor recurrence widely studied; however, the complexity of tumor progression which also involve microenvironmental factors suggests the existence of more varied cell phenotypes which mediate stemness and its role in tumor progression.

Cytotoxic Effects of Doxorubicin on Cancer Cells and Macrophages Depend Differently on the Microcarrier Structure

Microparticles are versatile carriers for controlled drug delivery in personalized, targeted therapy of various diseases, including cancer. The tumor microenvironment contains different infiltrating cells, including immune cells, which can affect the efficacy of antitumor drugs. Here, prototype microparticle-based systems for the delivery of the antitumor drug doxorubicin (DOX) were developed, and their cytotoxic effects on human epidermoid carcinoma cells and macrophages derived from human leukemia monocytic cells were compared in vitro. DOX-containing calcium carbonate microparticles with or without a protective polyelectrolyte shell and polyelectrolyte microcapsules of about 2.4-2.5 μm in size were obtained through coprecipitation and spontaneous loading. All the microstructures exhibited a prolonged release of DOX. An estimation of the cytotoxicity of the DOX-containing microstructures showed that the encapsulation of DOX decreased its toxicity to macrophages and delayed the cytotoxic effect against tumor cells. The DOX-containing calcium carbonate microparticles with a protective polyelectrolyte shell were more toxic to the cancer cells than DOX-containing polyelectrolyte microcapsules, whereas, for the macrophages, the microcapsules were most toxic. It is concluded that DOX-containing core/shell microparticles with an eight-layer polyelectrolyte shell are optimal drug microcarriers due to their low toxicity to immune cells, even upon prolonged incubation, and strong delayed cytotoxicity against tumor cells.

NSUN2/YBX1 promotes the progression of breast cancer by enhancing HGH1 mRNA stability through m5C methylation

BACKGROUND

RNA m5C methylation has been extensively implicated in the occurrence and development of tumors. As the main methyltransferase, NSUN2 plays a crucial regulatory role across diverse tumor types. However, the precise impact of NSUN2-mediated m5C modification on breast cancer (BC) remains unclear. Our study aims to elucidate the molecular mechanism underlying how NSUN2 regulates the target gene HGH1 (also known as FAM203) through m5C modification, thereby promoting BC progression. Additionally, this study targets at preliminarily clarifying the biological roles of NSUN2 and HGH1 in BC.

METHODS

Tumor and adjacent tissues from 5 BC patients were collected, and the m5C modification target HGH1 in BC was screened through RNA sequencing (RNA-seq) and single-base resolution m5C methylation sequencing (RNA-BisSeq). Methylation RNA immunoprecipitation-qPCR (MeRIP-qPCR) and RNA-binding protein immunoprecipitation-qPCR (RIP-qPCR) confirmed that the methylation molecules NSUN2 and YBX1 specifically recognized and bound to HGH1 through m5C modification. In addition, proteomics, co-immunoprecipitation (co-IP), and Ribosome sequencing (Ribo-Seq) were used to explore the biological role of HGH1 in BC.

RESULTS

As the main m5C methylation molecule, NSUN2 is abnormally overexpressed in BC and increases the overall level of RNA m5C. Knocking down NSUN2 can inhibit BC progression in vitro or in vivo. Combined RNA-seq and RNA-BisSeq analysis identified HGH1 as a potential target of abnormal m5C modifications. We clarified the mechanism by which NSUN2 regulates HGH1 expression through m5C modification, a process that involves interactions with the YBX1 protein, which collectively impacts mRNA stability and protein synthesis. Furthermore, this study is the first to reveal the binding interaction between HGH1 and the translation elongation factor EEF2, providing a comprehensive understanding of its ability to regulate transcript translation efficiency and protein synthesis in BC cells.

CONCLUSIONS

This study preliminarily clarifies the regulatory role of the NSUN2-YBX1-m5C-HGH1 axis from post-transcriptional modification to protein translation, revealing the key role of abnormal RNA m5C modification in BC and suggesting that HGH1 may be a new epigenetic biomarker and potential therapeutic target for BC.

Spatial Architecture of Myeloid and T Cells Orchestrates Immune Evasion and Clinical Outcome in Lung Cancer

Understanding the role of the tumor microenvironment (TME) in lung cancer is critical to improving patient outcomes. We identified four histology-independent archetype TMEs in treatment-naïve early-stage lung cancer using imaging mass cytometry in the TRACERx study (n = 81 patients/198 samples/2.3 million cells). In immune-hot adenocarcinomas, spatial niches of T cells and macrophages increased with clonal neoantigen burden, whereas such an increase was observed for niches of plasma and B cells in immune-excluded squamous cell carcinomas (LUSC). Immune-low TMEs were associated with fibroblast barriers to immune infiltration. The fourth archetype, characterized by sparse lymphocytes and high tumor-associated neutrophil (TAN) infiltration, had tumor cells spatially separated from vasculature and exhibited low spatial intratumor heterogeneity. TAN-high LUSC had frequent PIK3CA mutations. TAN-high tumors harbored recently expanded and metastasis-seeding subclones and had a shorter disease-free survival independent of stage. These findings delineate genomic, immune, and physical barriers to immune surveillance and implicate neutrophil-rich TMEs in metastasis.

SIGNIFICANCE

This study provides novel insights into the spatial organization of the lung cancer TME in the context of tumor immunogenicity, tumor heterogeneity, and cancer evolution. Pairing the tumor evolutionary history with the spatially resolved TME suggests mechanistic hypotheses for tumor progression and metastasis with implications for patient outcome and treatment. This article is featured in Selected Articles from This Issue, p. 897.

Hypoxia coordinates the spatial landscape of myeloid cells within glioblastoma to affect survival

Myeloid cells are highly prevalent in glioblastoma (GBM), existing in a spectrum of phenotypic and activation states. We now have limited knowledge of the tumor microenvironment (TME) determinants that influence the localization and the functions of the diverse myeloid cell populations in GBM. Here, we have utilized orthogonal imaging mass cytometry with single-cell and spatial transcriptomic approaches to identify and map the various myeloid populations in the human GBM tumor microenvironment (TME). Our results show that different myeloid populations have distinct and reproducible compartmentalization patterns in the GBM TME that is driven by tissue hypoxia, regional chemokine signaling, and varied homotypic and heterotypic cellular interactions. We subsequently identified specific tumor subregions in GBM, based on composition of identified myeloid cell populations, that were linked to patient survival. Our results provide insight into the spatial organization of myeloid cell subpopulations in GBM, and how this is predictive of clinical outcome.

Cancer-associated fibroblast-derived gene signature discriminates distinct prognoses by integrated single-cell and bulk RNA-seq analyses in breast cancer

BACKGROUND

Cancer-associated fibroblasts (CAFs) are one of the most predominant cellular subpopulations in the tumor stroma and play an integral role in cancer occurrence and progression. However, the prognostic role of CAFs in breast cancer remains poorly understood.

METHODS

We identified a number of CAF-related biomarkers in breast cancer by combining single-cell and bulk RNA-seq analyses. Based on univariate Cox regression as well as Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, a novel CAF-associated prognostic model was developed. Breast cancer patients were grouped according to the median risk score and further analyzed for outcome, clinical characteristic, pathway activity, genomic feature, immune landscape, and drug sensitivity.

RESULTS

A total of 341 CAF-related biomarkers were identified from single-cell and bulk RNA-seq analyses. We eventually screened eight candidate prognostic genes, including CERCAM, EMP1, SDC1, PRKG1, XG, TNN, WLS, and PDLIM4, and constructed the novel CAF-related prognostic model. Grouped by the median risk score, high-risk patients showed a significantly worse prognosis and exhibited distinct pathway activities such as uncontrolled cell cycle progression, angiogenesis, and activation of glycolysis. In addition, the combined risk score and tumor mutation burden significantly improved the ability to predict patient prognosis. Importantly, patients in the high-risk group had a higher infiltration of M2 macrophages and a lower infiltration of CD8+ T cells and activated NK cells. Finally, we calculated the IC50 for a range of anticancer drugs and personalized the treatment regimen for each patient.

CONCLUSION

Integrating single-cell and bulk RNA-seq analyses, we identified a list of compositive CAF-associated biomarkers and developed a novel CAF-related prognostic model for breast cancer. This robust CAF-derived gene signature acts as an excellent predictor of patient outcomes and treatment responses in breast cancer.

Spatial Biology of Breast Cancer

Spatial findings have shaped on our understanding of breast cancer. In this review, we discuss how spatial methods, including spatial transcriptomics and proteomics and the resultant understanding of spatial relationships, have contributed to concepts regarding cancer progression and treatment. In addition to discussing traditional approaches, we examine how emerging multiplex imaging technologies have contributed to the field and how they might influence future research.

Epigenetic deregulation in breast cancer microenvironment: Implications for tumor progression and therapeutic strategies

Breast cancer comprises a substantial proportion of cancer diagnoses in women and is a primary cause of cancer-related mortality. While hormone-responsive cases generally have a favorable prognosis, the aggressive nature of triple-negative breast cancer presents challenges, with intrinsic resistance to established treatments being a persistent issue. The complexity intensifies with the emergence of acquired resistance, further complicating the management of breast cancer. Epigenetic changes, encompassing DNA methylation, histone and RNA modifications, and non-coding RNAs, are acknowledged as crucial contributors to the heterogeneity of breast cancer. The unique epigenetic landscape harbored by each cellular component within the tumor microenvironment (TME) adds great diversity to the intricate regulations which influence therapeutic responses. The TME, a sophisticated ecosystem of cellular and non-cellular elements interacting with tumor cells, establishes an immunosuppressive microenvironment and fuels processes such as tumor growth, angiogenesis, and extracellular matrix remodeling. These factors contribute to challenging conditions in cancer treatment by fostering a hypoxic environment, inducing metabolic stress, and creating physical barriers to drug delivery. This article delves into the complex connections between breast cancer treatment response, underlying epigenetic changes, and vital interactions within the TME. To restore sensitivity to treatment, it emphasizes the need for combination therapies considering epigenetic changes specific to individual members of the TME. Recognizing the pivotal role of epigenetics in drug resistance and comprehending the specificities of breast TME is essential for devising more effective therapeutic strategies. The development of reliable biomarkers for patient stratification will facilitate tailored and precise treatment approaches.

Redefining clinical practice through spatial profiling: a revolution in tissue analysis

Spatial biology, which combines molecular biology and advanced imaging, enhances our understanding of tissue cellular organisation. Despite its potential, spatial omics encounters challenges related to data complexity, computational requirements and standardisation of analysis. In clinical applications, spatial omics has the potential to revolutionise biomarker discovery, disease stratification and personalised treatments. It can identify disease-specific cell patterns, and could help risk stratify patients for clinical trials and disease-appropriate therapies. Although there are challenges in adopting it in clinical practice, spatial omics has the potential to significantly enhance patient outcomes. In this paper, we discuss the recent evolution of spatial biology, and its potential for improving our tissue level understanding and treatment of disease, to help advance precision and effectiveness in healthcare interventions.

Deciphering the spatial landscape and plasticity of immunosuppressive fibroblasts in breast cancer

Although heterogeneity of FAP+ Cancer-Associated Fibroblasts (CAF) has been described in breast cancer, their plasticity and spatial distribution remain poorly understood. Here, we analyze trajectory inference, deconvolute spatial transcriptomics at single-cell level and perform functional assays to generate a high-resolution integrated map of breast cancer (BC), with a focus on inflammatory and myofibroblastic (iCAF/myCAF) FAP+ CAF clusters. We identify 10 spatially-organized FAP+ CAF-related cellular niches, called EcoCellTypes, which are differentially localized within tumors. Consistent with their spatial organization, cancer cells drive the transition of detoxification-associated iCAF (Detox-iCAF) towards immunosuppressive extracellular matrix (ECM)-producing myCAF (ECM-myCAF) via a DPP4- and YAP-dependent mechanism. In turn, ECM-myCAF polarize TREM2+ macrophages, regulatory NK and T cells to induce immunosuppressive EcoCellTypes, while Detox-iCAF are associated with FOLR2+ macrophages in an immuno-protective EcoCellType. FAP+ CAF subpopulations accumulate differently according to the invasive BC status and predict invasive recurrence of ductal carcinoma in situ (DCIS), which could help in identifying low-risk DCIS patients eligible for therapeutic de-escalation.

Cancer-cell derived S100A11 promotes macrophage recruitment in ER+ breast cancer

Macrophages are pivotal in driving breast tumor development, progression, and resistance to treatment, particularly in estrogen receptor-positive (ER+) tumors, where they infiltrate the tumor microenvironment (TME) influenced by cancer cell-secreted factors. By analyzing single-cell RNA-sequencing data from 25 ER+ tumors, we elucidated interactions between cancer cells and macrophages, correlating macrophage density with epithelial cancer cell density. We identified that S100A11, a previously unexplored factor in macrophage-cancer crosstalk, predicts high macrophage density and poor outcomes in ER+ tumors. We found that recombinant S100A11 enhances macrophage infiltration and migration in a dose-dependent manner. Additionally, in 3D models, we showed that S100A11 expression levels in ER+ cancer cells predict macrophage infiltration patterns. Neutralizing S100A11 decreased macrophage recruitment, both in cancer cell lines and in a clinically relevant patient-derived organoid model, underscoring its role as a paracrine regulator of cancer-macrophage interactions in the protumorigenic TME. This study offers novel insights into the interplay between macrophages and cancer cells in ER+ breast tumors, highlighting S100A11 as a potential therapeutic target to modulate the macrophage-rich tumor microenvironment.

Spatially Resolved Tumor Microenvironment Predicts Treatment Outcomes in Relapsed/Refractory Hodgkin Lymphoma

PURPOSE

About a third of patients with relapsed or refractory classic Hodgkin lymphoma (r/r CHL) succumb to their disease after high-dose chemotherapy followed by autologous stem-cell transplantation (HDC/ASCT). Here, we aimed to describe spatially resolved tumor microenvironment (TME) ecosystems to establish novel biomarkers associated with treatment failure in r/r CHL.

PATIENTS AND METHODS

We performed imaging mass cytometry (IMC) on 71 paired primary diagnostic and relapse biopsies using a marker panel specific to CHL biology. For each cell type in the TME, we calculated a spatial score measuring the distance of nearest neighbor cells to the malignant Hodgkin Reed Sternberg cells within the close interaction range. Spatial scores were used as features in prognostic model development for post-ASCT outcomes.

RESULTS

Highly multiplexed IMC data revealed shared TME patterns in paired diagnostic and early r/r CHL samples, whereas TME patterns were more divergent in pairs of diagnostic and late relapse samples. Integrated analysis of IMC and single-cell RNA sequencing data identified unique architecture defined by CXCR5+ Hodgkin and Reed Sternberg (HRS) cells and their strong spatial relationship with CXCL13+ macrophages in the TME. We developed a prognostic assay (RHL4S) using four spatially resolved parameters, CXCR5+ HRS cells, PD1+CD4+ T cells, CD68+ tumor-associated macrophages, and CXCR5+ B cells, which effectively separated patients into high-risk versus low-risk groups with significantly different post-ASCT outcomes. The RHL4S assay was validated in an independent r/r CHL cohort using a multicolor immunofluorescence assay.

CONCLUSION

We identified the interaction of CXCR5+ HRS cells with ligand-expressing CXCL13+ macrophages as a prominent crosstalk axis in relapsed CHL. Harnessing this TME biology, we developed a novel prognostic model applicable to r/r CHL biopsies, RHL4S, opening new avenues for spatial biomarker development.

Cellular interactions in tumor microenvironment during breast cancer progression: new frontiers and implications for novel therapeutics

The breast cancer tumor microenvironment (TME) is dynamic, with various immune and non-immune cells interacting to regulate tumor progression and anti-tumor immunity. It is now evident that the cells within the TME significantly contribute to breast cancer progression and resistance to various conventional and newly developed anti-tumor therapies. Both immune and non-immune cells in the TME play critical roles in tumor onset, uncontrolled proliferation, metastasis, immune evasion, and resistance to anti-tumor therapies. Consequently, molecular and cellular components of breast TME have emerged as promising therapeutic targets for developing novel treatments. The breast TME primarily comprises cancer cells, stromal cells, vasculature, and infiltrating immune cells. Currently, numerous clinical trials targeting specific TME components of breast cancer are underway. However, the complexity of the TME and its impact on the evasion of anti-tumor immunity necessitate further research to develop novel and improved breast cancer therapies. The multifaceted nature of breast TME cells arises from their phenotypic and functional plasticity, which endows them with both pro and anti-tumor roles during tumor progression. In this review, we discuss current understanding and recent advances in the pro and anti-tumoral functions of TME cells and their implications for developing safe and effective therapies to control breast cancer progress.

Multiplex protein imaging in tumour biology

Tissue imaging has become much more colourful in the past decade. Advances in both experimental and analytical methods now make it possible to image protein markers in tissue samples in high multiplex. The ability to routinely image 40-50 markers simultaneously, at single-cell or subcellular resolution, has opened up new vistas in the study of tumour biology. Cellular phenotypes, interaction, communication and spatial organization have become amenable to molecular-level analysis, and application to patient cohorts has identified clinically relevant cellular and tissue features in several cancer types. Here, we review the use of multiplex protein imaging methods to study tumour biology, discuss ongoing attempts to combine these approaches with other forms of spatial omics, and highlight challenges in the field.

Human Breast Cancer Cell Lines Differentially Modulate Signaling from Distant Microenvironments, Which Reflects Their Metastatic Potential

Metastasis is the stage at which the prognosis substantially decreases for many types of cancer. The ability of tumor cells to metastasize is dependent upon the characteristics of the tumor cells, and the conditioning of distant tissues that support colonization by metastatic cells. In this report, we investigated the systemic alterations in distant tissues caused by multiple human breast cancer cell lines and the impact of these alterations on the tumor cell phenotype. We observed that the niche within the lung, a common metastatic site, was significantly altered by MDA-MB-231, MCF7, and T47 tumors, and that the lung microenvironment stimulated, to differing extents, an epithelial-to-mesenchymal transition (EMT), reducing proliferation, increasing transendothelial migration and senescence, with no significant impact on cell death. We also investigated the ability of an implantable scaffold, which supports the formation of a distant tissue, to serve as a surrogate for the lung to identify systemic alterations. The scaffolds are conditioned by the primary tumor similarly to the lung for each tumor type, evidenced by promoting a pro-EMT profile. Collectively, we demonstrate that metastatic and non-metastatic breast cancers condition distant tissues, with distinct effects on tumor cell responses, and that a surrogate tissue can distinguish the metastatic potential of human breast cancer cell lines in an accessible site that avoids biopsy of a vital organ.

Multiplex Single-Cell Bioprinting for Engineering of Heterogeneous Tissue Constructs with Subcellular Spatial Resolution

Tissue development, function, and disease are largely driven by the spatial organization of individual cells and their cell-cell interactions. Precision engineered tissues with single-cell spatial resolution, therefore, have tremendous potential for next generation disease models, drug discovery, and regenerative therapeutics. Despite significant advancements in biofabrication approaches to improve feature resolution, strategies to fabricate tissues with the exact same organization of individual cells in their native cellular microenvironment have remained virtually non-existent to date. Here we report a method to spatially pattern single cells with up to eight cell phenotypes and subcellular spatial precision. As proof-of-concept we first demonstrate the ability to systematically assess the influence of cellular microenvironments on cell behavior by controllably altering the spatial arrangement of cell types in bioprinted precision cell-cell interaction arrays. We then demonstrate, for the first time, the ability to produce high-fidelity replicas of a patient's annotated cancer biopsy with subcellular resolution. The ability to replicate native cellular microenvironments marks a significant advancement for precision biofabricated in-vitro models, where heterogenous tissues can be engineered with single-cell spatial precision to advance our understanding of complex biological systems in a controlled and systematic manner.

TNF-α induced NF-κB mediated LYRM7 expression modulates the tumor growth and metastatic ability in breast cancer

Tumor microenvironment (TME) of solid tumors including breast cancer is complex and contains a distinct cytokine pattern including TNF-α, which determines the progression and metastasis of breast tumors. The metastatic potential of triple negative breast cancer subtypes is high as compared to other subtypes of breast cancer. NF-κB is key transcription factor regulating inflammation and mitochondrial bioenergetics including oxidative phosphorylation (OXPHOS) genes which determine its oxidative capacity and generating reducing equivalents for synthesis of key metabolites for proliferating breast cancer cells. The differential metabolic adaptation and OXPHOS function of breast cancer subtypes in inflammatory conditions and its contribution to metastasis is not well understood. Here we demonstrated that different subunits of NF-κB are differentially expressed in subtypes of breast cancer patients. RELA, one of the major subunits in regulation of the NF-κB pathway is positively correlated with high level of TNF-α in breast cancer patients. TNF-α induced NF-κB regulates the expression of LYRM7, an assembly factor for mitochondrial complex III. Downregulation of LYRM7 in MDA-MB-231 cells decreases mitochondrial super complex assembly and enhances ROS levels, which increases the invasion and migration potential of these cells. Further, in vivo studies using Infliximab, a monoclonal antibody against TNF-α showed decreased expression of LYRM7 in tumor tissue. Large scale breast cancer databases and human patient samples revealed that LYRM7 levels decreased in triple negative breast cancer patients compared to other subtypes and is determinant of survival outcome in patients. Our results indicate that TNF-α induced NF-κB is a critical regulator of LYRM7, a major factor for modulating mitochondrial functions under inflammatory conditions, which determines growth and survival of breast cancer cells.

Breast Cancer Immunity: It is TIME for the Next Chapter

Our ability to interrogate the tumor immune microenvironment (TIME) at an ever-increasing granularity has uncovered critical determinants of disease progression. Not only do we now have a better understanding of the immune response in breast cancer, but it is becoming possible to leverage key mechanisms to effectively combat this disease. Almost every component of the immune system plays a role in enabling or inhibiting breast tumor growth. Building on early seminal work showing the involvement of T cells and macrophages in controlling breast cancer progression and metastasis, single-cell genomics and spatial proteomics approaches have recently expanded our view of the TIME. In this article, we provide a detailed description of the immune response against breast cancer and examine its heterogeneity in disease subtypes. We discuss preclinical models that enable dissecting the mechanisms responsible for tumor clearance or immune evasion and draw parallels and distinctions between human disease and murine counterparts. Last, as the cancer immunology field is moving toward the analysis of the TIME at the cellular and spatial levels, we highlight key studies that revealed previously unappreciated complexity in breast cancer using these technologies. Taken together, this article summarizes what is known in breast cancer immunology through the lens of translational research and identifies future directions to improve clinical outcomes.

Stabilized mosaic single-cell data integration using unshared features

Currently available single-cell omics technologies capture many unique features with different biological information content. Data integration aims to place cells, captured with different technologies, onto a common embedding to facilitate downstream analytical tasks. Current horizontal data integration techniques use a set of common features, thereby ignoring non-overlapping features and losing information. Here we introduce StabMap, a mosaic data integration technique that stabilizes mapping of single-cell data by exploiting the non-overlapping features. StabMap first infers a mosaic data topology based on shared features, then projects all cells onto supervised or unsupervised reference coordinates by traversing shortest paths along the topology. We show that StabMap performs well in various simulation contexts, facilitates 'multi-hop' mosaic data integration where some datasets do not share any features and enables the use of spatial gene expression features for mapping dissociated single-cell data onto a spatial transcriptomic reference.

Stimuli-responsive hydrogel based on natural polymers for breast cancer

Aims: Breast cancer is the most common malignancy among women in both high- and low-resource settings. Conventional breast cancer therapies were inefficient and had low patient compliance. Stimuli-responsive hydrogels possessing similar physicochemical features as soft tissue facilitate diagnostic and therapeutic approaches for breast cancer subtypes. Scope: Polysaccharides and polypeptides are major natural polymers with unique biocompatibility, biodegradability, and feasible modification approaches utilized frequently for hydrogel fabrication. Alternating the natural polymer-based hydrogel properties in response to external stimuli such as pH, temperature, light, ultrasonic, enzyme, glucose, magnetic, redox, and electric have provided great potential for the evolution of novel drug delivery systems (DDSs) and various advanced technologies in medical applications. Stimuli-responsive hydrogels are triggered by specific cancer tissue features, promote target delivery techniques, and modify release therapeutic agents at localized sites. This narrative review presented innovation in preparing and characterizing the most common stimuli-responsive natural polymer-based hydrogels for diagnostic and therapeutic applications in the breast cancer area. Conclusion: Stimuli-responsive hydrogels display bioinspiration products as DDSs for breast cancer subtypes, protect the shape of breast tissue, provide modified drug release, enhance therapeutic efficacy, and minimize chemotherapy agents' side effects. The potential benefits of smart natural polymer-based hydrogels make them an exciting area of practice for breast cancer diagnosis and treatment.

Cancer Evolution: A Multifaceted Affair

Cancer cells adapt and survive through the acquisition and selection of molecular modifications. This process defines cancer evolution. Building on a theoretical framework based on heritable genetic changes has provided insights into the mechanisms supporting cancer evolution. However, cancer hallmarks also emerge via heritable nongenetic mechanisms, including epigenetic and chromatin topological changes, and interactions between tumor cells and the tumor microenvironment. Recent findings on tumor evolutionary mechanisms draw a multifaceted picture where heterogeneous forces interact and influence each other while shaping tumor progression. A comprehensive characterization of the cancer evolutionary toolkit is required to improve personalized medicine and biomarker discovery.

SIGNIFICANCE

Tumor evolution is fueled by multiple enabling mechanisms. Importantly, genetic instability, epigenetic reprogramming, and interactions with the tumor microenvironment are neither alternative nor independent evolutionary mechanisms. As demonstrated by findings highlighted in this perspective, experimental and theoretical approaches must account for multiple evolutionary mechanisms and their interactions to ultimately understand, predict, and steer tumor evolution.

Multiplex Imaging Reveals Novel Subcellular, Microenvironmental, and Racial Patterns of MRTFA/B Activation in Invasive Breast Cancers and Metastases

Breast cancer progression and metastasis involve the action of multiple transcription factors in tumors and in the cells of the tumor microenvironment (TME) and understanding how these transcription factors are coordinated can guide novel therapeutic strategies. Myocardin related transcription factors A and B (MRTFA/B) are two related transcription factors that redundantly control cancer cell invasion and metastasis in mouse models of breast cancer, but their roles in human cancer are incompletely understood. Here, we used a combination of multiplexed immunofluorescence and bioinformatics analyses to show that MRTFA/B are concurrently activated in tumor cells, but they show distinct patterns of expression across different histological subtypes and in the TME. Importantly, MRTFA expression was elevated in metastatic tumors of African American patients, who disproportionately die from breast cancer. Interestingly, in contrast to publicly available mRNA expression data, MRTFA was similarly expressed across estrogen receptor (ER) positive and negative breast tumors, while MRTFB expression was highest in ER+ breast tumors. Furthermore, MRTFA was specifically expressed in the perivascular antigen presenting cells (APCs) and its expression correlated with the expression of the immune checkpoint protein V-set immunoregulatory receptor (VSIR). These results provide unique insights into how MRTFA and MRTFB can promote metastasis in human cancer, into the racial disparities of their expression patterns, and their function within the complex breast cancer TME.

CellCharter reveals spatial cell niches associated with tissue remodeling and cell plasticity

Tissues are organized in cellular niches, the composition and interactions of which can be investigated using spatial omics technologies. However, systematic analyses of tissue composition are challenged by the scale and diversity of the data. Here we present CellCharter, an algorithmic framework to identify, characterize, and compare cellular niches in spatially resolved datasets. CellCharter outperformed existing approaches and effectively identified cellular niches across datasets generated using different technologies, and comprising hundreds of samples and millions of cells. In multiple human lung cancer cohorts, CellCharter uncovered a cellular niche composed of tumor-associated neutrophil and cancer cells expressing markers of hypoxia and cell migration. This cancer cell state was spatially segregated from more proliferative tumor cell clusters and was associated with tumor-associated neutrophil infiltration and poor prognosis in independent patient cohorts. Overall, CellCharter enables systematic analyses across data types and technologies to decode the link between spatial tissue architectures and cell plasticity.

The cell stress and immunity cycle in cancer: Toward next generation of cancer immunotherapy

The cellular stress and immunity cycle is a cornerstone of organismal homeostasis. Stress activates intracellular and intercellular communications within a tissue or organ to initiate adaptive responses aiming to resolve the origin of this stress. If such local measures are unable to ameliorate this stress, then intercellular communications expand toward immune activation with the aim of recruiting immune cells to effectively resolve the situation while executing tissue repair to ameliorate any damage and facilitate homeostasis. This cellular stress-immunity cycle is severely dysregulated in diseased contexts like cancer. On one hand, cancer cells dysregulate the normal cellular stress responses to reorient them toward upholding growth at all costs, even at the expense of organismal integrity and homeostasis. On the other hand, the tumors severely dysregulate or inhibit various components of organismal immunity, for example, by facilitating immunosuppressive tumor landscape, lowering antigenicity, and increasing T-cell dysfunction. In this review we aim to comprehensively discuss the basis behind tumoral dysregulation of cellular stress-immunity cycle. We also offer insights into current understanding of the regulators and deregulators of this cycle and how they can be targeted for conceptualizing successful cancer immunotherapy regimen.

AI-powered interpretable imaging phenotypes noninvasively characterize tumor microenvironment associated with diverse molecular signatures and survival in breast cancer

BACKGROUND AND OBJECTIVES

Tumor microenvironment (TME) is a determining factor in decision-making and personalized treatment for breast cancer, which is highly intra-tumor heterogeneous (ITH). However, the noninvasive imaging phenotypes of TME are poorly understood, even invasive genotypes have been largely known in breast cancer.

METHODS

Here, we develop an artificial intelligence (AI)-driven approach for noninvasively characterizing TME by integrating the predictive power of deep learning with the explainability of human-interpretable imaging phenotypes (IMPs) derived from 4D dynamic imaging (DCE-MRI) of 342 breast tumors linked to genomic and clinical data, which connect cancer phenotypes to genotypes. An unsupervised dual-attention deep graph clustering model (DGCLM) is developed to divide bulk tumor into multiple spatially segregated and phenotypically consistent subclusters. The IMPs ranging from spatial heterogeneity to kinetic heterogeneity are leveraged to capture architecture, interaction, and proximity between intratumoral subclusters.

RESULTS

We demonstrate that our IMPs correlate with well-known markers of TME and also can predict distinct molecular signatures, including expression of hormone receptor, epithelial growth factor receptor and immune checkpoint proteins, with the performance of accuracy, reliability and transparency superior to recent state-of-the-art radiomics and 'black-box' deep learning methods. Moreover, prognostic value is confirmed by survival analysis accounting for IMPs.

CONCLUSIONS

Our approach provides an interpretable, quantitative, and comprehensive perspective to characterize TME in a noninvasive and clinically relevant manner.