Tumors are complex ecosystems composed of diverse cell types that interact spatially and functionally to drive disease progression. While the conceptual framework of cancer hallmarks has provided a unifying lens to interpret oncogenic processes, how these hallmarks are spatially organized across tumor compartments remains largely unexplored. Here, we leverage spatial transcriptomics data from 63 untreated tumors spanning 10 cancer types to systematically map the spatial distribution of 13 canonical cancer hallmarks. We show that hallmark activity is spatially patterned and compartmentalized: seven hallmarks, such as genomic instability, apoptosis resistance, and epigenetic reprogramming are predominantly active in cancer cells, while the remaining six, including immune evasion, angiogenesis, and invasion, are enriched in the tumor microenvironment (TME).

By integrating spatial transcriptomics with inferred copy number variation, we reveal that subclonal genetic divergence is associated with hallmark specialization, suggesting that clonal populations may evolve to occupy distinct ecological niches within the tumor. We further uncover structured spatial interdependencies between neoplastic and TME hallmarks using machine learning models, showing that hallmark activity in one compartment often predicts activity in the other. These spatial dependencies form layered architectures across tumors, with hallmark-defined ecological zones conserved across cancer types.

To test clinical relevance, we applied our approach to 33 pre-treatment muscle-invasive bladder cancer samples from the DUTRENEO clinical trial. Spatial hallmark dependencies, particularly those involving angiogenesis, immune evasion, and resistance to cell death correlated with response to neoadjuvant chemotherapy and immunotherapy. For instance, angiogenesis tightly coupled to apoptosis resistance predicted chemoresistance in immune-cold tumors, while spatial decoupling of immune evasion from cancer cell survival predicted immunotherapy response.

Our findings establish the spatial organization of cancer hallmarks as a defining feature of tumor ecosystems, offering a novel framework to interpret functional heterogeneity and predict therapeutic vulnerability.

Deciphering the spatial biology of the tumor microenvironment (TME) can inform researchers about the likelihood of therapeutic success. Of particular interest is the spatial organization of immune cells, especially T cells, which can undergo exhaustion, resulting in reduced effector functions due to expression of inhibitory markers, such as PD-1. Imaging Mass Cytometry™ (IMC™) technology is a quantitative multiplexed spatial imaging technique for the assessment of 40-plus biomarkers simultaneously on the same tissue slide without signal amplification or autofluorescence interference. 

We used IMC technology to investigate the spatial distribution and phenotypic characteristics of immune cells, focusing on exhausted T cells in the TME of multiple cancers. Various IMC imaging modes revealed striking heterogeneity of the TME, including clusters of lymphocytes at the tumor margins, indicative of immune surveillance. Pixel-clustering and single-cell analyses delineated distinct tumor areas based on the extent of T cell exhaustion, immune cell density, cell proliferation, and stromal components. These findings illustrate the power of IMC technology to elucidate the complex spatial landscape of the TME, which may aid in developing prognostic assessments and guide personalized therapeutic strategies for cancer treatments.

For Research Use Only. Not for use in diagnostic procedures.

The tumor microenvironment (TME) is a complex cellular ecosystem, which in turn influences tumor development and treatment response. Understanding cellular interactions within the TME is essential for elucidating disease progression and advancing immunotherapy. Imaging Mass Cytometry™ (IMC™) is a spatial biology imaging technique that enables deep characterization of the TME. This approach offers scalable and high-throughput acquisition while generating high-quality data without fluorescence-based limitations such as spectral overlap and autofluorescence.

We used IMC to map key pathways in metabolic reprogramming and signaling within the TME by utilizing a ready-to-use immuno-oncology IMC panel. This allowed us to investigate processes that regulate energy production, cellular homeostasis and mitogenic signaling pathways. We acquired data using Preview Mode to assess the whole tissue, followed by higher-resolution imaging of selected regions of interest using Cell Mode, or whole tissue section imaging using Tissue Mode.

Data analysis revealed the metabolic profile and organization of cells across cancer tissues. Elevated glycolysis and mTOR pathway activation suggested adaptations to hypoxia and anabolic growth in tumors, while interactions between fibroblasts and immune cells highlighted crosstalk within the TME. Unsupervised pixel and hierarchical clustering using MCD™ SmartViewer offered measurement of metabolic activity and signaling pathway activation within tumors.

Spatial biology profiling using IMC highlights the interconnected roles these pathways play in promoting tumor survival and resistance to therapies. These findings are crucial for developing future prognostic assessments and have the potential to guide more effective, personalized cancer therapies.

For Research Use Only. Not for use in diagnostic procedures.

Thymic epithelial tumors (TETs) are rare and heterogeneous tumors that include thymic carcinoma and five thymoma subtypes (A, AB, B1, B2, B3). Thymomas are characterized by an active intratumor thymopoiesis and by a strong association with autoimmune disorders (AD). Notably, ADs are more frequent in patients with type B thymomas, further suggesting a potential link between specific hihistotypes and immune dysregulation. However, the precise pathophysiological mechanisms underlying this association and the involvment of B-cells are currently poorly understood.

To investigate B-cell involvement, we performed histopathological and immunohistochemical analyses on 126 TET cases. Hematoxylin and eosin staining revealed the presence of tertiary lymphoid structures (TLSs) in a significant proportion of cases, particularly among type B thymomas. The presence of these structures was further confirmed by staining these sections with the B-cell marker CD20. Overall, TLS-like structures were identified in 83% of type B thymomas.

To characterize these lymphoid structures at single-cell resolution, we analyzed the tumor microenvironment of 19 TETs using single-cell RNA sequencing. Unsupervised clustering identified three principal B-cell populations, defined by canonical B-cell genes MS4A1, CD19, and SDC1. Among the different clusters, one exhibited a transcriptional profile resembling pre-pro B-cells, expressing early developmental genes including RAG1, RAG2, and VPREB1, suggesting the presence of developing B-cells within the tumor. Another B-cell cluster displayed a germinal center-like phenotype, characterized by the expression of BCL6 and AICDA, indicative of local B-cell activation and maturation. These populations were enriched in B2/B3 thymomas, providing molecular evidence for the presence of organized TLSs within the tumor microenvironment.

These preliminary findings support a potential link between B-cell organization in thymomas and autoimmune manifestations.

To further dissect the spatial architecture and cellular interactions within TLS-like structures, we plan to perform spatial transcriptomic analyses. This approach may provide novel insights into the comprehension of the immunopathogenic mechanisms underlying thymoma-associated autoimmunity.

Objectives: The immune microenvironment of high-mutational-burden colon cancers is characterized by enhanced immune surveillance including increased cytotoxic T lymphocytes and expression of immune checkpoints. While much is known about adaptive immunity, the impact of mutational status on innate inflammation is less well studied.

Methods: Forty primary human colorectal cancers and surrounding tissues were collected and determined by genomic sequencing to be microsatellite-instability-high (MSI; n=20) or -low (MSS; n=20). Tissues were subjected to multiplex fluorescent immunohistochemistry and analyzed with the Akoya Polaris and InForm software packages.

Results: MSI tumors had a mean mutation count of 72.7 compared to 1.7 in MSS tumors (p<0.001). Additionally, MSI tumors were more frequently found in the right colon (68% vs 15%; p<0.001) and were of an earlier stage (Stage 1 and 2; 56% vs 0%; p<0.001). Low-mutation tumors had increased infiltration of NK cells (CD56+CD16+) compared to high-mutation tumors, and these tended to be spatially associated with tumor cells (mean intercellular distance = 138.8 vs. 340.6 microns; p<0.0001). This was not seen in surrounding tissues. Low-mutation tumors also had a greater infiltration of myeloid cells, with CD11B+ cells found closer to tumor cells (127.6 vs 247.6 microns; p<0.0001). Macrophages (CD11B+CD16+) were more closely associated with tumor cells than neutrophils (CD11B+CD15+). In surrounding non-neoplastic tissue, an opposite association was noted, with myeloid cells spatially distant from epithelial cells (95.8 vs 75.1 microns; p=0.036). When looking at survival, there was an inverse relationship with myeloid cells surrounding tumors where lower infiltration portended a worse prognosis (r² = 0.139; p=0.0046).

Conclusions: We present the first ever spatial characterization of the innate immune microenvironment of primary colorectal cancers and surrounding normal tissue from MSI and MSS tumors. Infiltration and spatial distribution suggest a unique adaptive immune response in low- vs high-mutation colorectal tumors.

Advanced-stage high-grade serous ovarian cancer (HGSC) metastasizes preferentially to the omentum, which is a well-vascularized fold of peritoneal tissue covered by mesothelial cells and a major site of intra-abdominal fat accumulation. We report that HGSC altered the gene expression of mesothelial cells in visceral adipose tissue and significantly downregulated the expression of a novel adipokine, omentin. Circulating levels of omentin are significantly lower in HGSC patients than those in body mass index-matched healthy women. High levels of serum omentin in HGSC patients were associated with longer overall survival time. These findings indicate the prognostic significance of circulating omentin levels in HGSC patients.

We then soughtto delineate the mechanism by which omental adipose tissue interact with ovarian cancer cells to promote tumor growth and progression. We demonstrated previously using a co-culture model that omentin-induced glucose uptake in adipocytes may deplete the surrounding glucose that fuels the glucose-addicted ovarian cancer cells in the omental microenvironment and thus drive metabolic shift in ovarian cancer cells. We further studied the effect of omentin on the metabolic reprograming of ovarian cancer cells in vivo, and performed MALDI-imaging mass spectrometry on omental tumor tissue sections collected from mice treated with omentin. The results showed that tumor cells had markedly reduced hexose-6-phosphate (glucose-6-phosphate and fructose-6-phosphate) levels, and adipocytes adjacent to the tumor cells had increased hexose-6-phosphate levels. These findings suggested that omentin treatment induced a local rapid metabolic coupling between adipocytes and the neighboring metastatic ovarian cancer cells.

In conclusion, we show that adipocytes play an important role in putting competitive nutritional pressure on ovarian cancer cells by depleting glucose through omentin-stimulated uptake. The competitive uptake of glucose by adipocytes leads to glucose starvation of the malignant cells. Further studies to develop omentin as a therapeutic for HGSC to improve patients’ survival rates are warranted.

Background: At the invasive front of primary colorectal cancer (CRC), the tumor growth pattern (TGP) holds a critical role in patient outcome. An infiltrative growth pattern (IGP) is a well-established marker of poor prognosis, in contrast to the more favorable pushing growth pattern (PGP). To elucidate the molecular mechanisms underlying this key clinical observation, we integrated transcriptomics and spatial single-cell proteomics investigations of the tumor microenvironment (TME).

Methods: Large-scale bulk RNA sequencing (n = 154) was applied, followed by validation (n = 104) using multiplex immunofluorescence (mIF). An unsupervised machine learning was applied to the cellular neighbourhood clustering analysis.

Results: The transcriptomic analysis revealed that IGP had a significantly higher abundance of Cancer-Associated Fibroblasts (CAFs) and enrichment of Epithelial–Mesenchymal Transition (EMT)- and Extracellular Matrix (ECM)-related pathways. At the spatial single-cell protein level of PGP, we observed a fibrous wall at the tumor boundary in interface CN, constructed by co-localized clusters of CAF-PDPN and CAF-PDPN-SMA-FAP. This barrier likely limits local invasion and suppresses tumor budding, correlating with a more favorable prognosis. In contrast, IGP revealed that CAF-SMA-FAP created a potent immune-exclusive niche, a key mechanism of immune evasion. This was evidenced by a significantly larger spatial exclusion distance from CD3+ T-cells compared to PGP, suggesting a potential mechanism for resistance to immunotherapies.

Conclusion: Different CAF subtypes govern distinct architectures at the tumor invasive front. In PGP, they form a barrier associated with tumor limitation, while in the more aggressive IGP, they establish an immune-exclusive niche that facilitates tumor progression by shielding the tumor from immune attack. This study identifies CAF-SMA-FAP as a potential therapeutic target to disrupt this niche. Further investigations using spatial transcriptomics are in progress to unravel the precise molecular pathways that can be targeted to overcome immune evasion and improve patient outcomes in CRC.

Introduction
Colorectal cancer liver metastases (CRCLM) display distinct histopathological growth patterns (HGPs),
with the replacement (RHGP) and encapsulated (EHGP) patterns linked to markedly different prognoses.
Despite their clinical relevance, the underlying microenvironmental dynamics shaping these growth
patterns remain incompletely understood.
Materials and Methods
We applied in situ sequencing (ISS) to spatially resolve the transcriptomic landscape of CRCLM tissues,
focusing on the tumor–liver interface in RHGP and EHGP lesions. Cellular populations were identified
through unsupervised clustering and annotated based on spatial context and marker-expression profiles.
All patients provided their informed written consent to participate in the tissue biobank of Region
Västerbotten, Sweden.
Results
Our analysis revealed distinct populations of inflamed hepatocytes enriched in RHGP lesions, displaying
reduced hepatocyte identity and signs of inflammatory reprogramming. These cells were predominantly
located at the tumor–liver interface and may be co-opted by tumor-derived signals to promote progression. In contrast, EHGP lesions were characterized by the presence of well-differentiated, non-
inflamed hepatocytes.

EHGP lesions also showed spatial enrichment of interferon-stimulated gene expression in stromal and
tumor cell populations, supporting immune cell recruitment and activation. Furthermore, fibrotic capsules
in EHGP lesions exhibited a zonated structure, with the tumor-facing side influenced by tumor-derived
signals and the liver-facing side displaying features of hepatic stellate cell activation and immune
involvement. This supports a dynamic, immune–fibrotic niche at the tumor border that may restrict
invasion.
Conclusions
Our findings reveal distinct microenvironmental programs associated with RHGP and EHGP in CRCLM,
suggesting divergent mechanisms of tumor–host interaction with potential relevance for patient
stratification and therapeutic intervention.

Background: Basal cell carcinoma (BCC) represents the most common human malignancy, with aggressive histopathological subtypes posing significant clinical challenges due to their potential for local tissue destruction and rare metastatic capability. While histopathological classification distinguishes low- from high-risk variants, molecular characterization of these subtypes remains limited.

Methods: We examined estrogen receptor alpha (ESR1) expression patterns across BCC subtypes using spatial analysis techniques. Quantitative image analysis was performed to assess ESR1 distribution in both malignant keratinocytes and adjacent stromal fibroblasts, with particular attention to tumor nest architecture and stromal relationships. Cyclic immunofluorescence and single-cell RNA sequencing (scRNAseq) were employed to validate spatial interactions and characterize specific tumor–fibroblast communication pathways.

Results: High-risk BCC subtypes demonstrated significantly elevated focal ESR1 expression compared to low-risk variants. Notably, infiltrative tumor regions exhibited prominent ESR1 positivity at tumor lobe peripheries. Quantitative spatial analysis revealed a positive correlation between tumor nest size and ESR1 expression in the adjacent stroma, suggesting coordinated tumor–stromal signaling. Cyclic immunofluorescence confirmed these spatial interactions, while scRNAseq analysis revealed specific molecular communication pathways between ESR1-positive tumor cells and stromal fibroblasts.

Conclusions: Our findings identify ESR1 as a novel molecular marker distinguishing aggressive BCC subtypes and reveal previously unrecognized estrogen receptor signaling in BCC pathogenesis. The spatial relationship between tumor architecture and stromal ESR1 expression suggests that estrogen signaling may contribute to the invasive behavior characteristic of high-risk BCC variants, offering potential therapeutic targets for aggressive disease.