T cell-mediated curation and restructuring of tumor tissue coordinates an effective immune response

A Spatial Multi-Omic Framework Identifies Gliomas Permissive to TIL Expansion

Tumor-infiltrating lymphocyte (TIL) therapy, recently approved by the FDA for melanoma, is an emerging modality for cell-based immunotherapy. However, its application in immunologically "cold" tumors such as glioblastoma remains limited due to sparse T cell infiltration, antigenic heterogeneity, and a suppressive tumor microenvironment. To identify genomic and spatial determinants of TIL expandability, we performed integrated, multimodal profiling of high-grade gliomas using spectral flow cytometry, TCR sequencing, single-cell RNA-seq, Xenium in situ transcriptomics, and CODEX spatial proteomics. Comparative analysis of TIL-generating (TIL+) versus non-generating (TIL-) tumors revealed that IL7Rexpression, structured perivascular immune clustering, and tumor-intrinsic metabolic programs such as ACSS3 were associated with successful TIL expansion. In contrast, TIL-tumors were enriched for neuronal lineage signatures, immunosuppressive transcripts including TOX and FERMT1, and tumor-connected macrophages. This study defines spatial and molecular correlates of TIL manufacturing success and establishes a genomics-enabled selection platform for adoptive T cell therapy. The profiling approach is now being prospectively implemented in the GIANT clinical trial (NCT06816927), supporting its translational relevance and scalability across glioblastoma and other immune-excluded cancers.

A Spatial Multi-Omic Framework Identifies Gliomas Permissive to TIL Expansion

Tumor-infiltrating lymphocyte (TIL) therapy, recently approved by the FDA for melanoma, is an emerging modality for cell-based immunotherapy. However, its application in immunologically 'cold' tumors such as glioblastoma remains limited due to sparse T cell infiltration, antigenic heterogeneity, and a suppressive tumor microenvironment. To identify genomic and spatial determinants of TIL expandability, we performed integrated, multimodal profiling of high-grade gliomas using spectral flow cytometry, TCR sequencing, single-cell RNA-seq, Xenium in situ transcriptomics, and CODEX spatial proteomics. Comparative analysis of TIL-generating (TIL+) versus non-generating (TIL-) tumors revealed that IL7R expression, structured perivascular immune clustering, and tumor-intrinsic metabolic programs such as ACSS3 were associated with successful TIL expansion. In contrast, TIL-; tumors were enriched for neuronal lineage signatures, immunosuppressive transcripts including TOX and FERMT1, and tumor-connected macrophages. This study defines spatial and molecular correlates of TIL manufacturing success and establishes a genomics-enabled selection platform for adoptive T cell therapy. The profiling approach is now being prospectively implemented in the GIANT clinical trial ( NCT06816927 ), supporting its translational relevance and scalability across glioblastoma and other immune-excluded cancers.

The Bidirectional Interplay between T Cell-Based Immunotherapies and the Tumor Microenvironment

T cell-based therapies, including tumor-infiltrating lymphocyte therapy, T-cell receptor-engineered T cells, and chimeric antigen receptor T cells, are powerful therapeutic approaches for cancer treatment. Whereas these therapies are primarily known for their direct cytotoxic effects on cancer cells, accumulating evidence indicates that they also influence the tumor microenvironment (TME) by altering the cytokine milieu and recruiting additional effector populations to help orchestrate the antitumor immune response. Conversely, the TME itself can modulate the behavior of these therapies within the host by either supporting or inhibiting their activity. In this review, we provide an overview of clinical and preclinical data on the bidirectional influences between T-cell therapies and the TME. Unraveling the interactions between T cell-based therapies and the TME is critical for a better understanding of their mechanisms of action, resistance, and toxicity, with the goal of optimizing efficacy and safety.

Quantitative characterization of tissue states using multiomics and ecological spatial analysis

The spatial organization of cells in tissues underlies biological function, and recent advances in spatial profiling technologies have enhanced our ability to analyze such arrangements to study biological processes and disease progression. We propose MESA (multiomics and ecological spatial analysis), a framework drawing inspiration from ecological concepts to delineate functional and spatial shifts across tissue states. MESA introduces metrics to systematically quantify spatial diversity and identify hot spots, linking spatial patterns to phenotypic outcomes, including disease progression. Furthermore, MESA integrates spatial and single-cell multiomics data to facilitate an in-depth, molecular understanding of cellular neighborhoods and their spatial interactions within tissue microenvironments. Applying MESA to diverse datasets demonstrates additional insights it brings over prior methods, including newly identified spatial structures and key cell populations linked to disease states. Available as a Python package, MESA offers a versatile framework for quantitative decoding of tissue architectures in spatial omics across health and disease.

Multi-omics in immunotherapy research for HNSCC: present situation and future perspectives

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide, significantly impacting patient survival and quality of life. The recent emergence of immunotherapy has provided new hope for HNSCC patients, improving survival rates; however, only 15%-20% of patients benefit, and side effects are inevitable. With advancements in omics technologies and the growing prevalence of bioinformatics research, the immune microenvironment of HNSCC has become increasingly well understood, and the molecular mechanisms underlying immunotherapy responses continue to be elucidated. In this review, we summarize commonly used omics techniques and their applications in the research of HNSCC immunotherapy, including predicting and enhancing efficacy, formulating personalized treatment plans, establishing robust preclinical research models, and identifying new immunotherapy targets. Finally, we explore future perspective in terms of sequencing samples, data integration analysis, emerging technologies, clinicopathological features, and interdisciplinary approaches.

Integrative spatial analysis reveals tumor heterogeneity and immune colony niche related to clinical outcomes in small cell lung cancer

Recent advances have shed light on the molecular heterogeneity of small cell lung cancer (SCLC), yet the spatial organizations and cellular interactions in tumor immune microenvironment remain to be elucidated. Here, we employ co-detection by indexing (CODEX) and multi-omics profiling to delineate the spatial landscape for 165 SCLC patients, generating 267 high-dimensional images encompassing over 9.3 million cells. Integrating CODEX and genomic data reveals a multi-positive tumor cell neighborhood within ASCL1+ (SCLC-A) subtype, characterized by high SLFN11 expression and associated with poor prognosis. We further develop a cell colony detection algorithm (ColonyMap) and reveal a spatially assembled immune niche consisting of antitumoral macrophages, CD8+ T cells and natural killer T cells (MT2) which highly correlates with superior survival and predicts improving immunotherapy response in an independent cohort. This study serves as a valuable resource to study SCLC spatial heterogeneity and offers insights into potential patient stratification and personalized treatments.

Profiling immune cell tissue niches in the spatial -omics era

Immune responses require complex, spatially coordinated interactions between immune cells and their tissue environment. For decades, we have imaged tissue sections to visualize a limited number of immune-related macromolecules in situ, functioning as surrogates for cell types or processes of interest. However, this inevitably provides a limited snapshot of the tissue's immune landscape. Recent developments in high-throughput spatial -omics technologies, particularly spatial transcriptomics, and its application to human samples has facilitated a more comprehensive understanding of tissue immunity by mapping fine-grained immune cell states to their precise tissue location while providing contextual information about their immediate cellular and tissue environment. These data provide opportunities to investigate mechanisms underlying the spatial distribution of immune cells and its functional implications, including the identification of immune niches, although the criteria used to define this term have been inconsistent. Here, we review recent technological and analytic advances in multiparameter spatial profiling, focusing on how these methods have generated new insights in translational immunology. We propose a 3-step framework for the definition and characterization of immune niches, which is powerfully facilitated by new spatial profiling methodologies. Finally, we summarize current approaches to analyze adaptive immune repertoires and lymphocyte clonal expansion in a spatially resolved manner.

Immunological characterization and prognostic of colon cancer evaluated by angiogenesis-related features: a computational analysis and in vitro experiments

BACKGROUND

Diseases are often caused by multiple factors, angiogenesis-related genes (ARGs) have been shown to be associated with cancer, however, their role in colon cancer had not been fully explored. This study investigated potential biomarkers based on ARGs to improve prognosis and treatment effect in colon cancer.

METHODS

ARGs associated with colon cancer prognosis were identified using Cox regression analysis and LASSO analysis. Furthermore, a prognostic model was constructed in colon cancer based on the 3 ARGs, and its biological function were analyzed. We evaluated the differences in tumor immune microenvironment based on prognostic signature. Finally, cell experiments confirmed the function of genes in colon cancer.

RESULTS

The prognostic value of ARGs in colon cancer patients has been comprehensively analyzed for the first time and identified 3 ARGs with prognostic values. A prognosis risk model was constructed based on 3 ARGs and its prognostic value was validated on an independent external colon cancer dataset. In colon cancer patients, this prognostic feature was an independent risk factor and was significantly correlated with clinical feature information of colon cancer patients. This feature was also related to the immune microenvironment of colon cancer. Cell experiments showed that high expression of TNF Receptor Superfamily Member 1B (TNFRSF1B) significantly promoted apoptosis and inhibited proliferation of colon cancer cells. Therefore, TNFRSF1B may become an important regulatory factor in the progression of colon cancer by participating in intracellular functional regulation.

CONCLUSIONS

This study constructed a prognostic risk model based on three ARGs and for the first time discovered that TNFRSF1B may become an important regulatory factor in cancer progression by participating in intracellular functional regulation.

mRNA lipid nanoparticle-incorporated nanofiber-hydrogel composite generates a local immunostimulatory niche for cancer immunotherapy

Hydrogel materials have emerged as versatile platforms for various biomedical applications. Notably, the engineered nanofiber-hydrogel composite (NHC) has proven effective in mimicking the soft tissue extracellular matrix, facilitating substantial recruitment of host immune cells and the formation of a local immunostimulatory microenvironment. Leveraging this feature, here we report an mRNA lipid nanoparticle (LNP)-incorporated NHC microgel matrix, termed LiNx, by incorporating LNPs loaded with mRNA encoding tumour antigens. Harnessing the potent transfection efficiency of LNPs in antigen-presenting cells (APCs), LiNx demonstrates remarkable immune cell recruitment, antigen expression and presentation, and cellular interaction. These attributes collectively create an immunostimulating milieu and yield a potent immune response achievable with a single dose, comparable to the conventional three-dose LNP immunization regimen. Further investigations reveal that the LiNx not only generates heightened Th1 and Th2 responses but also elicits a distinctive Type 17 T helper cell-mediated response pivotal for bolstering antitumour efficacy. Our findings elucidate the mechanism underlying LiNx's role in potentiating antigen-specific immune responses, presenting a new strategy for cancer immunotherapy.

T cell receptor signaling pathway subgroups and construction of a novel prognostic model in osteosarcoma

Background

T cell receptor (TCR) signaling pathway is closely related to tumor progress and immunotherapy. This study aimed to explore the clinical significance, prognosis, immune infiltration and chemotherapy sensitivity of TCR in osteosarcoma (OS).

Material and methods

OS data were obtained from TARGET and GEO database. TCR signaling pathway-related genes (TCRGs) were extracted from Molecular Signatures Database. Unsupervised non-negative matrix factorization clustering analysis was used to identify OS molecular subtypes. Differential expressed TCRGs between molecular subtypes were screened with univariate Cox regression, LASSO regression and multivariate Cox regression. Subsequently, an OS-associated prognostic model was constructed and validated. Nomogram was established and verified. Immune landscape analysis including immune infiltration analysis, ESTIMATE algorithm and immune checkpoints expression levels of molecular subtypes and different risk groups were analyzed. Finally, chemotherapy sensitivity and potential therapeutic agents between different risk groups was identified.

Results

Two TCRGs related subclusters were identified. Two hundred and seventy-two Differential expressed TCRGs were screened between two subclusters. A robust prognostic model were constructed. High and low risk groups were stratified. Low risk group showed higher ESTIMATE, immune and stromal scores, while high risk group exhibited higher tumor purity and the lower expression levels of immune checkpoints. A nomogram comprising metastasis and risk score was successfully built. The sensitivity to chemotherapy agents were different across high and low risk groups.

Conclusions

Our study proposed TCR related molecular subtypes and provided a prognostic model for OS. Our findings may bring a new insight into the immunotherapy for OS patients.

Powerful microscopy technologies decode spatially organized cellular networks that drive response to immunotherapy in humans

In tumors, immune cells organize into networks of different sizes and composition, including complex tertiary lymphoid structures and recently identified networks centered around the chemokines CXCL9/10/11 and CCL19. New commercially available highly multiplexed microscopy using cyclical RNA in situ hybridization and antibody-based approaches have the potential to establish the organization of the immune response in human tissue and serve as a foundation for future immunology research.

Spatial multiplexed immunofluorescence analysis reveals coordinated cellular networks associated with overall survival in metastatic osteosarcoma

Patients diagnosed with advanced osteosarcoma, often in the form of lung metastases, have abysmal five-year overall survival rates. The complexity of the osteosarcoma immune tumor microenvironment has been implicated in clinical trial failures of various immunotherapies. The purpose of this exploratory study was to spatially characterize the immune tumor microenvironment of metastatic osteosarcoma lung specimens. Knowledge of the coordinating cellular networks within these tissues could then lead to improved outcomes when utilizing immunotherapy for treatment of this disease. Importantly, various cell types, interactions, and cellular neighborhoods were associated with five-year survival status. Of note, increases in cellular interactions between T lymphocytes, positive for programmed cell death protein 1, and myeloid-derived suppressor cells were observed in the 5-year deceased cohort. Additionally, cellular neighborhood analysis identified an Immune-Cold Parenchyma cellular neighborhood, also associated with worse 5-year survival. Finally, the Osteosarcoma Spatial Score, which approximates effector immune activity in the immune tumor microenvironment through the spatial proximity of immune and tumor cells, was increased within 5-year survivors, suggesting improved effector signaling in this patient cohort. Ultimately, these data represent a robust spatial multiplexed immunofluorescence analysis of the metastatic osteosarcoma immune tumor microenvironment. Various communication networks, and their association with survival, were described. In the future, identification of these networks may suggest the use of specific, combinatory immunotherapeutic strategies for improved anti-tumor immune responses and outcomes in osteosarcoma.

Constructing a prognostic model for hepatocellular carcinoma based on bioinformatics analysis of inflammation-related genes

Background

This study aims to screen inflammation-related genes closely associated with the prognosis of hepatocellular carcinoma (HCC) to accurately forecast the prognosis of HCC patients.

Methods

Gene expression matrices and clinical information for liver cancer samples were obtained from the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC). An intersection of differentially expressed genes of HCC and normal and GeneCards yielded inflammation-related genes associated with HCC. Cox regression and the minor absolute shrinkage and selection operator (LASSO) regression analysis to filter genes associated with HCC prognosis. The prognostic value of the model was confirmed by drawing Kaplan-Meier and ROC curves. Select differentially expressed genes between the high-risk and low-risk groups and perform GO and KEGG pathways analyses. CIBERSORT analysis was conducted to assess associations of risk models with immune cells and verified using real-time qPCR.

Results

A total of six hub genes (C3, CTNNB1, CYBC1, DNASE1L3, IRAK1, and SERPINE1) were selected using multivariate Cox regression to construct a prognostic model. The validation evaluation of the prognostic model showed that it has an excellent ability to predict prognosis. A line plot was drawn to indicate the HCC patients' survival, and the calibration curve revealed satisfactory predictability. Among the six hub genes, C3 and DNASE1L3 are relatively low expressed in HCCLM3 and 97H liver cancer cell lines, while CTNNB1, CYBC1, IRAK1, and SERPINE1 are relatively overexpressed in liver cancer cell lines.

Conclusion

One new inflammatory factor-associated prognostic model was constructed in this study. The risk score can be an independent predictor for judging the prognosis of HCC patients' survival.

Integrating multiplexed imaging and multiscale modeling identifies tumor phenotype conversion as a critical component of therapeutic T cell efficacy

Cancer progression is a complex process involving interactions that unfold across molecular, cellular, and tissue scales. These multiscale interactions have been difficult to measure and to simulate. Here, we integrated CODEX multiplexed tissue imaging with multiscale modeling software to model key action points that influence the outcome of T cell therapies with cancer. The initial phenotype of therapeutic T cells influences the ability of T cells to convert tumor cells to an inflammatory, anti-proliferative phenotype. This T cell phenotype could be preserved by structural reprogramming to facilitate continual tumor phenotype conversion and killing. One takeaway is that controlling the rate of cancer phenotype conversion is critical for control of tumor growth. The results suggest new design criteria and patient selection metrics for T cell therapies, call for a rethinking of T cell therapeutic implementation, and provide a foundation for synergistically integrating multiplexed imaging data with multiscale modeling of the cancer-immune interface. A record of this paper's transparent peer review process is included in the supplemental information.