Distinct phenotypic states and spatial distribution of CD8+ T cell clonotypes in human brain metastases

Reactivation of CTLA4-expressing T cells accelerates resolution of lung fibrosis in a humanized mouse model

Tissue regenerative responses involve complex interactions between resident structural and immune cells. Recent reports indicate that accumulation of senescent cells during injury repair contributes to pathological tissue fibrosis. Using tissue-based spatial transcriptomics and proteomics, we identified upregulation of the immune checkpoint protein, cytotoxic T lymphocyte-associated protein 4 (CTLA4), on CD8+ T cells adjacent to regions of active fibrogenesis in human idiopathic pulmonary fibrosis and in a repetitive bleomycin lung injury murine model of persistent fibrosis. In humanized CTLA4-knockin mice, treatment with ipilimumab, an FDA-approved drug that targets CTLA4, resulted in accelerated lung epithelial regeneration and diminished fibrosis from repetitive bleomycin injury. Ipilimumab treatment resulted in the expansion of Cd3e+ T cells, diminished accumulation of senescent cells, and robust expansion of type 2 alveolar epithelial cells, facultative progenitor cells of the alveolar epithelium. Ex vivo activation of isolated CTLA4-expressing CD8+ cells from mice with established fibrosis resulted in enhanced cytolysis of senescent cells, suggesting that impaired immune-mediated clearance of these cells contributes to persistence of lung fibrosis in this murine model. Our studies support the concept that endogenous immune surveillance of senescent cells may be essential in promoting tissue regenerative responses that facilitate the resolution of fibrosis.

Spatial Transcriptomics: Biotechnologies, Computational Tools, and Neuroscience Applications

Spatial transcriptomics (ST) represents a revolutionary approach in molecular biology, providing unprecedented insights into the spatial organization of gene expression within tissues. This review aims to elucidate advancements in ST technologies, their computational tools, and their pivotal applications in neuroscience. It is begun with a historical overview, tracing the evolution from early image-based techniques to contemporary sequence-based methods. Subsequently, the computational methods essential for ST data analysis, including preprocessing, cell type annotation, spatial clustering, detection of spatially variable genes, cell-cell interaction analysis, and 3D multi-slices integration are discussed. The central focus of this review is the application of ST in neuroscience, where it has significantly contributed to understanding the brain's complexity. Through ST, researchers advance brain atlas projects, gain insights into brain development, and explore neuroimmune dysfunctions, particularly in brain tumors. Additionally, ST enhances understanding of neuronal vulnerability in neurodegenerative diseases like Alzheimer's and neuropsychiatric disorders such as schizophrenia. In conclusion, while ST has already profoundly impacted neuroscience, challenges remain issues such as enhancing sequencing technologies and developing robust computational tools. This review underscores the transformative potential of ST in neuroscience, paving the way for new therapeutic insights and advancements in brain research.

Single-cell and spatial genomic landscape of non-small cell lung cancer brain metastases

Brain metastases frequently develop in patients with non-small cell lung cancer (NSCLC) and are a common cause of cancer-related deaths, yet our understanding of the underlying human biology is limited. Here we performed multimodal single-nucleus RNA and T cell receptor, single-cell spatial and whole-genome sequencing of brain metastases and primary tumors of patients with treatment-naive NSCLC. Chromosomal instability (CIN) is a distinguishing genomic feature of brain metastases compared with primary tumors, which we validated through integrated analysis of molecular profiling and clinical data in 4,869 independent patients, and a new cohort of 12,275 patients with NSCLC. Unbiased analyses revealed transcriptional neural-like programs that strongly enriched in cancer cells from brain metastases, including a recurring, CINhigh cell subpopulation that preexists in primary tumors but strongly enriched in brain metastases, which was also recovered in matched single-cell spatial transcriptomics. Using multiplexed immunofluorescence in an independent cohort of treatment-naive pairs of primary tumors and brain metastases from the same patients with NSCLC, we validated genomic and tumor-microenvironmental findings and identified a cancer cell population characterized by neural features strongly enriched in brain metastases. This comprehensive analysis provides insights into human NSCLC brain metastasis biology and serves as an important resource for additional discovery.

Practice Variation in Perioperative Dexamethasone Use and Outcomes in Brain Metastasis Resection

Importance

Variations in perioperative dexamethasone dosing are common in brain metastasis resection, but their impact on patient outcomes remains unclear.

Objective

To evaluate the association between perioperative dexamethasone dosing and patient outcomes, focusing on overall survival (OS) and progression-free survival (PFS).

Design, Setting, and Participants

This retrospective multicenter comparative effectiveness study used data collected from January 2010 to December 2023. Patients with symptomatic brain metastases undergoing primary surgical resection at 7 neurological centers in Germany and 1 in Austria and who had complete records of perioperative dexamethasone dosing were included. Propensity score matching (PSM) was used to control for confounders. Analysis was conducted from March to June 2024.

Exposures

Cumulative perioperative dexamethasone administration over 27 days, dichotomized at 122 mg using maximally selected rank statistics.

Main Outcomes and Measures

The primary outcome was OS. Secondary outcomes included extracranial PFS (ecPFS) and intracranial PFS (icPFS) as well as incidence of wound revision surgery after brain metastasis resection. Hazard ratios (HRs) were calculated using Cox proportional hazards models.

Results

A total of 1064 patients were included in the analysis. The median (IQR) age was 64 (56-72) years, with 489 female patients (49%) and 541 male patients (51%). Non-small cell lung cancer (NSCLC) was the most common tumor entity (564 patients [53%]), followed by breast cancer (146 patients [14%]) and melanoma (138 patients [13%]). After PSM, patients receiving cumulative dexamethasone doses less than 122 mg had a median OS of 19.1 (95% CI, 15.2-22.4) months compared with 12.0 (95% CI, 9.1-14.7) months for those receiving 122 mg or more (P = .002). Multivariable analysis showed an independent association between higher cumulative dexamethasone doses and reduced OS (HR, 1.40; 95% CI, 1.18-1.66; P < .001). Secondary analyses demonstrated consistent findings with icPFS and ecPFS and a dose-response association between cumulative dexamethasone and hazard for death.

Conclusions and Relevance

In this study, higher cumulative perioperative dexamethasone was associated with reduced OS, icPFS, and ecPFS in patients undergoing brain metastasis resection. These findings suggest that stricter dosing protocols could improve outcomes. Prospective trials are warranted to confirm these associations and guide evidence-based practice.

Mucin 5AC Promotes Breast Cancer Brain Metastasis through cMET/CD44v6

PURPOSE

Breast cancer brain metastasis remains a significant clinical problem. Mucins have been implicated in metastasis; however, whether they are also involved in breast cancer brain metastasis remains unknown. We queried databases of patients with brain metastasis and found mucin 5AC (MUC5AC) to be upregulated and therefore sought to define the role of MUC5AC in breast cancer brain metastasis.

EXPERIMENTAL DESIGN

In silico dataset analysis, RNA-sequence profiling of patient samples and cell lines, analysis of patient serum samples, and in vitro/in vivo knockdown experiments were performed to determine the function of MUC5AC in breast cancer brain metastasis. Coimmunoprecipitation was used to unravel the interactions that can be therapeutically targeted.

RESULTS

Global in silico transcriptomic analysis showed that MUC5AC is significantly higher in patients with breast cancer brain metastasis. Analysis of archived breast cancer brain metastasis tissue further revealed significantly higher expression of MUC5AC in all breast cancer subtypes, and high MUC5AC expression predicted poor survival in HER2+ breast cancer brain metastasis. We validated these observations in breast cancer brain metastatic cell lines and tissue samples. Interestingly, elevated levels of MUC5AC were detected in the sera of patients with breast cancer brain metastasis. MUC5AC silencing in breast cancer brain metastatic cells reduced their migration and adhesion in vitro and in brain metastasis in the intracardiac injection mouse model. We found high expression of cMET and CD44v6 in breast cancer brain metastasis, which increased MUC5AC expression via hepatocyte growth factor signaling. In addition, MUC5AC interacts with cMET and CD44v6, suggesting that MUC5AC promotes breast cancer brain metastasis via the cMET/CD44v6 axis. Inhibition of the MUC5AC/cMET/CD44v6 axis with the blood-brain barrier-permeable cMET inhibitor bozitinib (PLB1001) effectively inhibits breast cancer brain metastasis.

CONCLUSIONS

Our study establishes that the MUC5AC/cMET/CD44v6 axis is critical for breast cancer brain metastasis, and blocking this axis will be a novel therapeutic approach for breast cancer brain metastasis.

Deciphering key roles of B cells in prognostication and tailored therapeutic strategies for lung adenocarcinoma: a multi-omics and machine learning approach towards predictive, preventive, and personalized treatment strategies

Background

Lung adenocarcinoma (LUAD) remains a significant global health challenge, with an urgent need for innovative predictive, preventive, and personalized medicine (PPPM) strategies to improve patient outcomes. This study leveraged multi-omics and machine learning approaches to uncover the prognostic roles of B cells in LUAD, thereby reinforcing the PPPM approach.

Methods

We integrated multi-omics data, including bulk RNA, ATAC-seq, single-cell RNA, and spatial transcriptomics sequencing, to characterize the B cell landscape in LUAD within the PPPM framework. Subsequently, we developed an integrative machine learning program that generated the Scissor+ related B cell score (SRBS). This score was validated in the training and validation sets, and its prognostic value was assessed along with clinical features to develop predictive nomograms. This study further assessed the role of SRBS and SRBS genes in response to immunotherapy and identified personalized drug targets for distinct risk subgroups, with gene expression verified experimentally to ensure tailored medical interventions.

Results

Our analysis identified 79 Scissor+ B cell genes linked to LUAD prognosis, supporting the predictive aspect of PPPM. The SRBS model, which utilizes multiple machine learning algorithms, performed excellently in predicting prognosis and clinical transformation, embodying the preventive and personalized aspects of PPPM. Multifactorial analysis confirmed that SRBS was an independent prognostic factor. We observed varying biological functions and immune cell infiltration in the tumor immune microenvironment (TIME) between the high- and low-SRBS groups, underscoring personalized treatment approaches. Notably, patients with elevated SRBS may exhibit resistance to immunotherapy but show increased sensitivity to chemotherapy and targeted therapies. Additionally, we found that LDHA, as an SRBS gene with significant clinical implications, may regulate the sensitivity of LUAD cells to cisplatin.

Conclusion

This study presents a B cell-associated gene signature that serves as a prognostic marker to facilitate personalized treatment for patients with LUAD, adhering to the principles of PPPM.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-024-00390-4.

Detection of human brain cancers using genomic and immune cell characterization of cerebrospinal fluid through CSF-BAM

Patients who have radiographically detectable lesions in their brain or other symptoms compatible with brain tumors pose challenges for diagnosis. The only definitive way to diagnose such patients is through brain biopsy, an obviously invasive and dangerous procedure. Here we present a new workflow termed "CSF-BAM" that simultaneously identifies B cell or T cell receptor rearrangements, A neuploidy, and M utations using PCR-mediated amplification of both strands of the DNA from CSF samples. We first describe the details of the molecular genetic assessments and then establish thresholds for positivity using training sets of libraries from patients with or without cancer. We then applied CSF-BAM to an independent set of 206 DNA samples from patients with common, aggressive cancer types as well as other forms of brain cancers. Among the 126 samples from patients with the most common aggressive cancer types (high grade gliomas, medulloblastomas, or metastatic cancers to the brain), the sensitivity of detection was >81%. None of 33 CSF-BAM assays (100% specificity, 90% to 100% credible interval) were positive in CSF samples from patients without brain cancers. The sensitivity of CSF-BAM was considerably higher than that achieved with cytology. CSF-BAM provides an integrated multi-analyte approach to identify neoplasia in the central nervous system, provides information about the immune environment in patients with or without cancer, and has the potential to inform the subsequent management of such patients.

Statement of significance

There is a paucity of technologies beyond surgical biopsy that can accurately diagnose central nervous system neoplasms. We developed a novel, sensitive and highly specific assay that can detect brain cancers by comprehensively identifying somatic mutations, chromosomal copy number changes, and adaptive immunoreceptor repertoires from samples of cerebrospinal fluid.

The immune landscape in brain metastasis

The prognosis for patients with brain metastasis remains dismal despite intensive therapy including surgical resection, radiotherapy, chemo-, targeted, and immunotherapy. Thus, there is a high medical need for new therapeutic options. Recent advances employing high-throughput and spatially resolved single-cell analyses have provided unprecedented insights into the composition and phenotypes of the diverse immune cells in the metastatic brain, revealing a unique immune landscape starkly different from that of primary brain tumors or other metastatic sites. This review summarizes the current evidence on the composition and phenotypes of the most prominent immune cells in the brain metastatic niche, along with their dynamic interactions with metastatic tumor cells and each other. As the most abundant immune cell types in this niche, we explore in detail the phenotypic heterogeneity and functional plasticity of tumor-associated macrophages, including both resident microglia and monocyte-derived macrophages, as well as the T-cell compartment. We also review preclinical and clinical trials evaluating the therapeutic potential of targeting the immune microenvironment in brain metastasis. Given the substantial evidence highlighting a significant role of the immune microenvironmental niche in brain metastasis pathogenesis, a comprehensive understanding of the key molecular and cellular factors within this niche holds great promise for developing novel therapeutic approaches as well as innovative combinatory treatment strategies for brain metastasis.

Pan-cancer single cell and spatial transcriptomics analysis deciphers the molecular landscapes of senescence related cancer-associated fibroblasts and reveals its predictive value in neuroblastoma via integrated multi-omics analysis and machine learning

Introduction

Cancer-associated fibroblasts (CAFs) are a diverse group of cells that significantly contribute to reshaping the tumor microenvironment (TME), and no research has systematically explored the molecular landscapes of senescence related CAFs (senes CAF) in NB.

Methods

We utilized pan-cancer single cell and spatial transcriptomics analysis to identify the subpopulation of senes CAFs via senescence related genes, exploring its spatial distribution characteristics. Harnessing the maker genes with prognostic significance, we delineated the molecular landscapes of senes CAFs in bulk-seq data. We established the senes CAFs related signature (SCRS) by amalgamating 12 and 10 distinct machine learning (ML) algorithms to precisely diagnose stage 4 NB and to predict prognosis in NB. Based on risk scores calculated by prognostic SCRS, patients were categorized into high and low risk groups according to median risk score. We conducted comprehensive analysis between two risk groups, in terms of clinical applications, immune microenvironment, somatic mutations, immunotherapy, chemotherapy and single cell level. Ultimately, we explore the biological function of the hub gene JAK1 in pan-cancer multi-omics landscape.

Results

Through integrated analysis of pan-cancer spatial and single-cell transcriptomics data, we identified distinct functional subgroups of CAFs and characterized their spatial distribution patterns. With marker genes of senes CAF and leave-one-out cross-validation, we selected RF algorithm to establish diagnostic SCRS, and SuperPC algorithm to develop prognostic SCRS. SCRS demonstrated a stable predictive capability, outperforming the previously published NB signatures and clinic variables. We stratified NB patients into high and low risk group, which showed the low-risk group with a superior survival outcome, an abundant immune infiltration, a different mutation landscape, and an enhanced sensitivity to immunotherapy. Single cell analysis reveals biologically cellular variations underlying model genes of SCRS. Spatial transcriptomics delineated the molecular variant expressions of hub gene JAK1 in malignant cells across cancers, while immunohistochemistry validated the differential protein levels of JAK1 in NB.

Conclusion

Based on multi-omics analysis and ML algorithms, we successfully developed the SCRS to enable accurate diagnosis and prognostic stratification in NB, which shed light on molecular landscapes of senes CAF and clinical utilization of SCRS.

PD-L1 and VEGF dual blockade enhances anti-tumor effect on brain metastasis in hematogenous metastasis model

Immunotherapy improves survival outcomes in cancer patients, but there is still an unmet clinical need in the treatment of brain metastases. Here, we used a mouse model to investigate the antitumor effect of programmed death-ligand 1 (PD-L1) and vascular endothelial growth factor (VEGF) dual blockade on metastatic brain tumors and evaluated immune responses during treatment. After establishing hematogenous brain metastasis by transplanting murine bladder carcinoma MBT2 cells stably expressing secNLuc reporter via the internal carotid artery of C3H/HeNCrl mice, we observed the formation of metastases not only in the brain parenchyma but also in the ventricles. The observed pathological areas showed that metastases in the ventricle were histologically larger than that in the brain parenchyma. Regarding the total tumor burden in the whole brain as revealed by Nluc activities, the combination of anti-PD-L1 antibody and anti-VEGF antibody showed a stronger anti-tumor effect than each single agent. Anti-PD-L1 antibody alone enhanced CD8+ T cell priming in regional lymph nodes, increased the proportion of activated CD8+ T cells in whole brain, and increased the density of CD8+ cells in the brain parenchyma. Furthermore, anti-VEGF antibody alone decreased microvessel density (MVD) in ventricular metastases, and the combination treatment increased intratumoral CD8+ cell density in the brain parenchyma and ventricular metastases. These results suggest that PD-L1 blockade enhanced cancer immunity not only in brain metastases lesions but also in the regional lymph nodes of the metastases, and that the addition of VEGF blockade increased the antitumor effect by increasing the infiltration of activated CD8+ T cell and decreasing MVD.

Holliday junction recognition protein (HJURP) could reflect the clinical outcomes of lung adenocarcinoma patients, and impact the choice of precision therapy

Background

Lung adenocarcinoma (LUAD) is the most prevalent subtype of non-small cell lung cancer (NSCLC), characterized by poor prognosis and a high mortality rate. Identifying reliable prognostic biomarkers and potential therapeutic targets is crucial for improving patient outcomes.

Methods

We conducted a comprehensive analysis of HJURP expression in LUAD using data from four cohorts: TCGA-LUAD (n = 453), GSE31210 (n = 226), GSE68465 (n = 442), and GSE72094 (n = 386). Univariate Cox regression analysis was employed to identify prognostic genes, with Kaplan-Meier survival analysis used to assess the predictive power of HJURP. Functional enrichment analyses were performed using MetaScape and FGSEA, and spatial transcriptomics and single-cell sequencing data were analyzed to explore HJURP's distribution and potential functions. Additionally, correlations between HJURP expression and genetic alterations, immune cell infiltration, and potential therapeutic responses were evaluated.

Results

HJURP was identified as a significant prognostic biomarker in all four cohorts, with high expression associated with increased risk of overall survival (OS) death (TCGA-LUAD: HR = 1.93, 95% CI: 1.321-2.815, P < 0.001; GSE31210: HR = 2.75, 95% CI: 1.319-5.735, P = 0.007; GSE68465: HR = 1.57, 95% CI: 1.215-2.038, P < 0.001; GSE72094: HR = 2.2, 95% CI: 1.485-3.27, P < 0.001). Functional analyses indicated that HJURP is involved in DNA metabolic processes, cell cycle regulation, and mitotic processes, with significant activation of pathways related to MYC targets, G2M checkpoint, and DNA repair. High HJURP expression was associated with higher mutation frequencies in TP53, CSMD3, TTN, and MUC16, and positively correlated with pro-inflammatory immune cell infiltration and several immune checkpoints, including PD-L1 and PD-L2. Chemotherapeutic agents such as gefitinib and sorafenib were predicted to be effective against high HJURP-expressing tumors.

Conclusion

HJURP is a pivotal biomarker for LUAD, consistently associated with poor prognosis and advanced disease stages. Its high expression correlates with specific genetic alterations and immune profiles, highlighting its potential as a therapeutic target. Future studies should validate these findings in larger cohorts.

Understanding the interplay between extracellular matrix topology and tumor-immune interactions: Challenges and opportunities

Modern cancer management comprises a variety of treatment strategies. Immunotherapy, while successful at treating many cancer subtypes, is often hindered by tumor immune evasion and T cell exhaustion as a result of an immunosuppressive tumor microenvironment (TME). In solid malignancies, the extracellular matrix (ECM) embedded within the TME plays a central role in T cell recognition and cancer growth by providing structural support and regulating cell behavior. Relative to healthy tissues, tumor associated ECM signatures include increased fiber density and alignment. These and other differentiating features contributed to variation in clinically observed tumor-specific ECM configurations, collectively referred to as Tumor-Associated Collagen Signatures (TACS) 1-3. TACS is associated with disease progression and immune evasion. This review explores our current understanding of how ECM geometry influences the behaviors of both immune cells and tumor cells, which in turn impacts treatment efficacy and cancer evolutionary progression. We discuss the effects of ECM remodeling on cancer cells and T cell behavior and review recent in silico models of cancer-immune interactions.

Spatial Multiomics Reveals Intratumoral Immune Heterogeneity with Distinct Cytokine Networks in Lung Cancer Brain Metastases

The tumor microenvironment of brain metastases has become a focus in the development of immunotherapeutic drugs. However, countless patients with brain metastasis have not experienced clinical benefit. Thus, understanding the immune cell composition within brain metastases and how immune cells interact with each other and other microenvironmental cell types may be critical for optimizing immunotherapy. We applied spatial whole-transcriptomic profiling with extensive multiregional sampling (19-30 regions per sample) and multiplex IHC on formalin-fixed, paraffin-embedded lung cancer brain metastasis samples. We performed deconvolution of gene expression data to infer the abundances of immune cell populations and inferred spatial relationships from the multiplex IHC data. We also described cytokine networks between immune and tumor cells and used a protein language model to predict drug-target interactions. Finally, we performed deconvolution of bulk RNA data to assess the prognostic significance of immune-metastatic tumor cellular networks. We show that immune cell infiltration has a negative prognostic role in lung cancer brain metastases. Our in-depth multiomics analyses further reveal recurring intratumoral immune heterogeneity and the segregation of myeloid and lymphoid cells into distinct compartments that may be influenced by distinct cytokine networks. By using computational modeling, we identify drugs that may target genes expressed in both tumor core and regions bordering immune infiltrates. Finally, we illustrate the potential negative prognostic role of our immune-metastatic tumor cell networks. Our findings advocate for a paradigm shift from focusing on individual genes or cell types toward targeting networks of immune and tumor cells.

SIGNIFICANCE

Immune cell signatures are conserved across lung cancer brain metastases, and immune-metastatic tumor cell networks have a prognostic effect, implying that targeting cytokine networks between immune and metastatic tumor cells may generate more precise immunotherapeutic approaches.

Mapping intratumoral myeloid-T cell interactomes at single-cell resolution reveals targets for overcoming checkpoint inhibitor resistance

Effective cancer immunotherapies restore anti-tumor immunity by rewiring cell-cell communication. Treatment-induced changes in communication can be inferred from single-cell RNA-sequencing (scRNA-seq) data, but current methods do not effectively manage heterogeneity within cell types. Here we developed a computational approach to efficiently analyze scRNA-seq-derived, single-cell-resolved cell-cell interactomes, which we applied to determine how agonistic CD40 (CD40ag) alters immune cell crosstalk alone, across tumor models, and in combination with immune checkpoint blockade (ICB). Our analyses suggested that CD40ag improves responses to ICB by targeting both immuno-stimulatory and immunosuppressive macrophage subsets communicating with T cells, and we experimentally validated a spatial basis for these subsets with immunofluorescence and spatial transcriptomics. Moreover, treatment with CD40ag and ICB established coordinated myeloid-T cell interaction hubs that are critical for reestablishing antitumor immunity. Our work advances the biological significance of hypotheses generated from scRNA-seq-derived cell-cell interactomes and supports the clinical translation of myeloid-targeted therapies for ICB-resistant tumors.

Pre-operative stereotactic radiosurgery and peri-operative dexamethasone for resectable brain metastases: a two-arm pilot study evaluating clinical outcomes and immunological correlates

Enhancing the efficacy of immunotherapy in brain metastases (BrM) requires an improved understanding of the immune composition of BrM and how this is affected by radiation and dexamethasone. Our two-arm pilot study (NCT04895592) allocated 26 patients with BrM to either low (Arm A) or high (Arm B) dose peri-operative dexamethasone followed by pre-operative stereotactic radiosurgery (pSRS) and resection (n= 13 per arm). The primary endpoint, a safety analysis at 4 months, was met. The secondary clinical endpoints of overall survival, distant brain failure, leptomeningeal disease and local recurrence at 12-months were 66%, 37.3%, 6%, and 0% respectively and were not significantly different between arms (p= 0.7739, p= 0.3884, p= 0.3469). Immunological data from two large retrospective BrM datasets and confirmed by correlates from both arms of this pSRS prospective trial revealed that BrM CD8 T cells were composed of predominantly PD1+ TCF1+ stem-like and PD1+ TCF1-TIM3+ effector-like cells. Clustering of TCF1+ CD8 T cells with antigen presenting cells in immune niches was prognostic for local control, even without pSRS. Following pSRS, CD8 T cell and immune niche density were transiently reduced compared to untreated BrM, followed by a rebound 6+ days post pSRS with an increased frequency of TCF1- effector-like cells. In sum, pSRS is safe and therapeutically beneficial, and these data provide a framework for how pSRS may be leveraged to maximize intracranial CD8 T cell responses.

Exploit Spatially Resolved Transcriptomic Data to Infer Cellular Features from Pathology Imaging Data

Digital pathology is a rapidly advancing field where deep learning methods can be employed to extract meaningful imaging features. However, the efficacy of training deep learning models is often hindered by the scarcity of annotated pathology images, particularly images with detailed annotations for small image patches or tiles. To overcome this challenge, we propose an innovative approach that leverages paired spatially resolved transcriptomic data to annotate pathology images. We demonstrate the feasibility of this approach and introduce a novel transfer-learning neural network model, STpath (Spatial Transcriptomics and pathology images), designed to predict cell type proportions or classify tumor microenvironments. Our findings reveal that the features from pre-trained deep learning models are associated with cell type identities in pathology image patches. Evaluating STpath using three distinct breast cancer datasets, we observe its promising performance despite the limited training data. STpath excels in samples with variable cell type proportions and high-resolution pathology images. As the influx of spatially resolved transcriptomic data continues, we anticipate ongoing updates to STpath, evolving it into an invaluable AI tool for assisting pathologists in various diagnostic tasks.

Comprehensive integrated single-cell RNA sequencing analysis of brain metastasis and glioma microenvironment: Contrasting heterogeneity landscapes

Understanding the specific type of brain malignancy, source of brain metastasis, and underlying transformation mechanisms can help provide better treatment and less harm to patients. The tumor microenvironment plays a fundamental role in cancer progression and affects both primary and metastatic cancers. The use of single-cell RNA sequencing to gain insights into the heterogeneity profiles in the microenvironment of brain malignancies is useful for guiding treatment decisions. To comprehensively investigate the heterogeneity in gliomas and brain metastasis originating from different sources (lung and breast), we integrated data from three groups of single-cell RNA-sequencing datasets obtained from GEO. We gathered and processed single-cell RNA sequencing data from 90,168 cells obtained from 17 patients. We then employed the R package Seurat for dataset integration. Next, we clustered the data within the UMAP space and acquired differentially expressed genes for cell categorization. Our results underscore the significance of macrophages as abundant and pivotal constituents of gliomas. In contrast, lung-to-brain metastases exhibit elevated numbers of AT2, cytotoxic CD4+ T, and exhausted CD8+ T cells. Conversely, breast-to-brain metastases are characterized by an abundance of epithelial and myCAF cells. Our study not only illuminates the variation in the TME between brain metastasis with different origins but also opens the door to utilizing established markers for these cell types to differentiate primary brain metastatic cancers.

HEARTSVG: a fast and accurate method for identifying spatially variable genes in large-scale spatial transcriptomics

Identifying spatially variable genes (SVGs) is crucial for understanding the spatiotemporal characteristics of diseases and tissue structures, posing a distinctive challenge in spatial transcriptomics research. We propose HEARTSVG, a distribution-free, test-based method for fast and accurately identifying spatially variable genes in large-scale spatial transcriptomic data. Extensive simulations demonstrate that HEARTSVG outperforms state-of-the-art methods with higherF 1 scores (averageF 1 Score=0.948), improved computational efficiency, scalability, and reduced false positives (FPs). Through analysis of twelve real datasets from various spatial transcriptomic technologies, HEARTSVG identifies a greater number of biologically significant SVGs (average AUC = 0.792) than other comparative methods without prespecifying spatial patterns. Furthermore, by clustering SVGs, we uncover two distinct tumor spatial domains characterized by unique spatial expression patterns, spatial-temporal locations, and biological functions in human colorectal cancer data, unraveling the complexity of tumors.

Deep 3D histology powered by tissue clearing, omics and AI

To comprehensively understand tissue and organism physiology and pathophysiology, it is essential to create complete three-dimensional (3D) cellular maps. These maps require structural data, such as the 3D configuration and positioning of tissues and cells, and molecular data on the constitution of each cell, spanning from the DNA sequence to protein expression. While single-cell transcriptomics is illuminating the cellular and molecular diversity across species and tissues, the 3D spatial context of these molecular data is often overlooked. Here, I discuss emerging 3D tissue histology techniques that add the missing third spatial dimension to biomedical research. Through innovations in tissue-clearing chemistry, labeling and volumetric imaging that enhance 3D reconstructions and their synergy with molecular techniques, these technologies will provide detailed blueprints of entire organs or organisms at the cellular level. Machine learning, especially deep learning, will be essential for extracting meaningful insights from the vast data. Further development of integrated structural, molecular and computational methods will unlock the full potential of next-generation 3D histology.

STimage-1K4M: A histopathology image-gene expression dataset for spatial transcriptomics

Recent advances in multi-modal algorithms have driven and been driven by the increasing availability of large image-text datasets, leading to significant strides in various fields, including computational pathology. However, in most existing medical image-text datasets, the text typically provides high-level summaries that may not sufficiently describe sub-tile regions within a large pathology image. For example, an image might cover an extensive tissue area containing cancerous and healthy regions, but the accompanying text might only specify that this image is a cancer slide, lacking the nuanced details needed for in-depth analysis. In this study, we introduce STimage-1K4M, a novel dataset designed to bridge this gap by providing genomic features for sub-tile images. STimage-1K4M contains 1,149 images derived from spatial transcriptomics data, which captures gene expression information at the level of individual spatial spots within a pathology image. Specifically, each image in the dataset is broken down into smaller sub-image tiles, with each tile paired with 15,000 - 30,000 dimensional gene expressions. With 4,293,195 pairs of sub-tile images and gene expressions, STimage-1K4M offers unprecedented granularity, paving the way for a wide range of advanced research in multi-modal data analysis an innovative applications in computational pathology, and beyond.

Gene representation bias in spatial transcriptomics

For sequencing-based spatial transcriptomics data, the gene-spot count matrix is highly sparse. This feature is similar to scRNA-seq. The goal of this paper is to identify whether there exist genes that are frequently under-detected in Visium compared to bulk RNA-seq, and the underlying potential mechanism of under-detection in Visium. We collected paired Visium and bulk RNA-seq data for 28 human samples and 19 mouse samples, which covered diverse tissue sources. We compared the two data types and observed that there indeed exists a collection of genes frequently under-detected in Visium compared to bulk RNA-seq. We performed a motif search to examine the last 350 bp of the frequently under-detected genes, and we observed that the poly (T) motif was significantly enriched in genes identified from both human and mouse data, which matches with our previous finding about frequently under-detected genes in scRNA-seq. We hypothesized that the poly (T) motif may be able to form a hairpin structure with the poly (A) tails of their mRNA transcripts, making it difficult for their mRNA transcripts to be captured during Visium library preparation.

Crosstalk between CD8+ T cells and mesenchymal stromal cells in intestine homeostasis and immunity

The intestine represents the most complex cellular network in the whole body. It is constantly faced with multiple types of immunostimulatory agents encompassing from food antigen, gut microbiome, metabolic waste products, and dead cell debris. Within the intestine, most T cells are found in three primary compartments: the organized gut-associated lymphoid tissue, the lamina propria, and the epithelium. The well-orchestrated epithelial-immune-microbial interaction is critically important for the precise immune response. The main role of intestinal mesenchymal stromal cells is to support a structural framework within the gut wall. However, recent evidence from stromal cell studies indicates that they also possess significant immunomodulatory functions, such as maintaining intestinal tolerance via the expression of PDL1/2 and MHC-II molecules, and promoting the development of CD103+ dendritic cells, and IgA+ plasma cells, thereby enhancing intestinal homeostasis. In this review, we will summarize the current understanding of CD8+ T cells and stromal cells alongside the intestinal tract and discuss the reciprocal interactions between T subsets and mesenchymal stromal cell populations. We will focus on how the tissue residency, migration, and function of CD8+ T cells could be potentially regulated by mesenchymal stromal cell populations and explore the molecular mediators, such as TGF-β, IL-33, and MHC-II molecules that might influence these processes. Finally, we discuss the potential pathophysiological impact of such interaction in intestine hemostasis as well as diseases of inflammation, infection, and malignancies.

The Web-Based Portal SpatialTME Integrates Histological Images with Single-Cell and Spatial Transcriptomics to Explore the Tumor Microenvironment

The tumor microenvironment (TME) represents a complex network in which tumor cells communicate not only with each other but also with stromal and immune cells. The intercellular interactions in the TME contribute to tumor initiation, progression, metastasis, and treatment outcome. Recent advances in spatial transcriptomics (ST) have revolutionized the molecular understanding of the TME at the spatial level. A comprehensive interactive analysis resource specifically designed for characterizing the spatial TME could facilitate further advances using ST. In this study, we collected 296 ST slides covering 19 cancer types and developed a computational pipeline to delineate the spatial structure along the malignant-boundary-nonmalignant axis. The pipeline identified differentially expressed genes and their functional enrichment, deconvoluted the cellular composition of the TME, reconstructed cell type-specific gene expression profiles at the sub-spot level, and performed cell-cell interaction analysis. Finally, the user-friendly database SpatialTME (http://www.spatialtme.yelab.site/) was constructed to provide search, visualization, and downloadable results. These detailed analyses are able to reveal the heterogeneous regulatory network of the spatial microenvironment and elucidate associations between spatial features and tumor development or response to therapy, offering a valuable resource to study the complex TME.

SIGNIFICANCE

SpatialTME provides spatial structure, cellular composition, expression, function, and cell-cell interaction information to enable investigations into the tumor microenvironment at the spatial level to advance understanding of cancer development and treatment.

Novel insights into TCR-T cell therapy in solid neoplasms: optimizing adoptive immunotherapy

Adoptive immunotherapy in the T cell landscape exhibits efficacy in cancer treatment. Over the past few decades, genetically modified T cells, particularly chimeric antigen receptor T cells, have enabled remarkable strides in the treatment of hematological malignancies. Besides, extensive exploration of multiple antigens for the treatment of solid tumors has led to clinical interest in the potential of T cells expressing the engineered T cell receptor (TCR). TCR-T cells possess the capacity to recognize intracellular antigen families and maintain the intrinsic properties of TCRs in terms of affinity to target epitopes and signal transduction. Recent research has provided critical insight into their capability and therapeutic targets for multiple refractory solid tumors, but also exposes some challenges for durable efficacy. In this review, we describe the screening and identification of available tumor antigens, and the acquisition and optimization of TCRs for TCR-T cell therapy. Furthermore, we summarize the complete flow from  laboratory to clinical applications of TCR-T cells. Last, we emerge future prospects for improving therapeutic efficacy in cancer world with combination therapies or TCR-T derived products. In conclusion, this review depicts our current understanding of TCR-T cell therapy in solid neoplasms, and provides new perspectives for expanding its clinical applications and improving therapeutic efficacy.

Enhanced Photodynamic Therapy Synergizing with Inhibition of Tumor Neutrophil Ferroptosis Boosts Anti-PD-1 Therapy of Gastric Cancer

For tumor treatment, the ultimate goal in tumor therapy is to eliminate the primary tumor, manage potential metastases, and trigger an antitumor immune response, resulting in the complete clearance of all malignant cells. Tumor microenvironment (TME) refers to the local biological environment of solid tumors and has increasingly become an attractive target for cancer therapy. Neutrophils within TME of gastric cancer (GC) spontaneously undergo ferroptosis, and this process releases oxidized lipids that limit T cell activity. Enhanced photodynamic therapy (PDT) mediated by di-iodinated IR780 (Icy7) significantly increases the production of reactive oxygen species (ROS). Meanwhile, neutrophil ferroptosis can be triggered by increased ROS generation in the TME. In this study, a liposome encapsulating both ferroptosis inhibitor Liproxstatin-1 and modified photosensitizer Icy7, denoted LLI, significantly inhibits tumor growth of GC. LLI internalizes into MFC cells to generate ROS causing immunogenic cell death (ICD). Simultaneously, liposome-deliver Liproxstatin-1 effectively inhibits the ferroptosis of tumor neutrophils. LLI-based immunogenic PDT and neutrophil-targeting immunotherapy synergistically boost the anti-PD-1 treatment to elicit potent TME and systemic antitumor immune response with abscopal effects. In conclusion, LLI holds great potential for GC immunotherapy.

Predicting patient outcomes after treatment with immune checkpoint blockade: A review of biomarkers derived from diverse data modalities

Immune checkpoint blockade (ICB) therapy targeting cytotoxic T-lymphocyte-associated protein 4, programmed death 1, and programmed death ligand 1 has shown durable remission and clinical success across different cancer types. However, patient outcomes vary among disease indications. Studies have identified prognostic biomarkers associated with immunotherapy response and patient outcomes derived from diverse data types, including next-generation bulk and single-cell DNA, RNA, T cell and B cell receptor sequencing data, liquid biopsies, and clinical imaging. Owing to inter- and intra-tumor heterogeneity and the immune system's complexity, these biomarkers have diverse efficacy in clinical trials of ICB. Here, we review the genetic and genomic signatures and image features of ICB studies for pan-cancer applications and specific indications. We discuss the advantages and disadvantages of computational approaches for predicting immunotherapy effectiveness and patient outcomes. We also elucidate the challenges of immunotherapy prognostication and the discovery of novel immunotherapy targets.

Harnessing immunotherapy for brain metastases: insights into tumor-brain microenvironment interactions and emerging treatment modalities

Brain metastases signify a deleterious milestone in the progression of several advanced cancers, predominantly originating from lung, breast and melanoma malignancies, with a median survival timeframe nearing six months. Existing therapeutic regimens yield suboptimal outcomes; however, burgeoning insights into the tumor microenvironment, particularly the immunosuppressive milieu engendered by tumor-brain interplay, posit immunotherapy as a promising avenue for ameliorating brain metastases. In this review, we meticulously delineate the research advancements concerning the microenvironment of brain metastases, striving to elucidate the panorama of their onset and evolution. We encapsulate three emergent immunotherapeutic strategies, namely immune checkpoint inhibition, chimeric antigen receptor (CAR) T cell transplantation and glial cell-targeted immunoenhancement. We underscore the imperative of aligning immunotherapy development with in-depth understanding of the tumor microenvironment and engendering innovative delivery platforms. Moreover, the integration with established or avant-garde physical methodologies and localized applications warrants consideration in the prevailing therapeutic schema.

From bench to bedside via bytes: Multi-omic immunoprofiling and integration using machine learning and network approaches

A significant surge in research endeavors leverages the vast potential of high-throughput omic technology platforms for broad profiling of biological responses to vaccines and cutting-edge immunotherapies and stem-cell therapies under development. These profiles capture different aspects of core regulatory and functional processes at different scales of resolution from molecular and cellular to organismal. Systems approaches capture the complex and intricate interplay between these layers and scales. Here, we summarize experimental data modalities, for characterizing the genome, epigenome, transcriptome, proteome, metabolome, and antibody-ome, that enable us to generate large-scale immune profiles. We also discuss machine learning and network approaches that are commonly used to analyze and integrate these modalities, to gain insights into correlates and mechanisms of natural and vaccine-mediated immunity as well as therapy-induced immunomodulation.

Spatial transcriptomics of B cell and T cell receptors reveals lymphocyte clonal dynamics

The spatial distribution of lymphocyte clones within tissues is critical to their development, selection, and expansion. We have developed spatial transcriptomics of variable, diversity, and joining (VDJ) sequences (Spatial VDJ), a method that maps B cell and T cell receptor sequences in human tissue sections. Spatial VDJ captures lymphocyte clones that match canonical B and T cell distributions and amplifies clonal sequences confirmed by orthogonal methods. We found spatial congruency between paired receptor chains, developed a computational framework to predict receptor pairs, and linked the expansion of distinct B cell clones to different tumor-associated gene expression programs. Spatial VDJ delineates B cell clonal diversity and lineage trajectories within their anatomical niche. Thus, Spatial VDJ captures lymphocyte spatial clonal architecture across tissues, providing a platform to harness clonal sequences for therapy.

Decoding the tumor microenvironment with spatial technologies

Visualization of the cellular heterogeneity and spatial architecture of the tumor microenvironment (TME) is becoming increasingly important to understand mechanisms of disease progression and therapeutic response. This is particularly relevant in the era of cancer immunotherapy, in which the contexture of immune cell positioning within the tumor landscape has been proven to affect efficacy. Although single-cell technologies have mostly replaced conventional approaches to analyze specific cellular subsets within tumors, those that integrate a spatial dimension are now on the rise. In this Review, we assess the strengths and limitations of emerging spatial technologies with a focus on their applications in tumor immunology, as well as forthcoming opportunities for artificial intelligence (AI) and the value of integrating multiomics datasets to achieve a holistic picture of the TME.

Localization, tissue biology and T cell state - implications for cancer immunotherapy

Tissue localization is a critical determinant of T cell immunity. CD8+ T cells are contact-dependent killers, which requires them to physically be within the tissue of interest to kill peptide-MHC class I-bearing target cells. Following their migration and extravasation into tissues, T cells receive many extrinsic cues from the local microenvironment, and these signals shape T cell differentiation, fate and function. Because major organ systems are variable in their functions and compositions, they apply disparate pressures on T cells to adapt to the local microenvironment. Additional complexity arises in the context of malignant lesions (either primary or metastatic), and this has made understanding the factors that dictate T cell function and longevity in tumours challenging. Moreover, T cell differentiation state influences how cues from the microenvironment are interpreted by tissue-infiltrating T cells, highlighting the importance of T cell state in the context of tissue biology. Here, we review the intertwined nature of T cell differentiation state, location, survival and function, and explain how dysfunctional T cell populations can adopt features of tissue-resident memory T cells to persist in tumours. Finally, we discuss how these factors have shaped responses to cancer immunotherapy.

Opportunities for High-plex Spatial Transcriptomics in Solid Organ Transplantation

The last 5 y have seen the development and widespread adoption of high-plex spatial transcriptomic technology. This technique detects and quantifies mRNA transcripts in situ, meaning that transcriptomic signatures can be sampled from specific cells, structures, lesions, or anatomical regions while conserving the physical relationships that exist within complex tissues. These methods now frequently implement next-generation sequencing, enabling the simultaneous measurement of many targets, up to and including the whole mRNA transcriptome. To date, spatial transcriptomics has been foremost used in the fields of neuroscience and oncology, but there is potential for its use in transplantation sciences. Transplantation has a clear dependence on biopsies for diagnosis, monitoring, and research. Transplant patients represent a unique cohort with multiple organs of interest, clinical courses, demographics, and immunosuppressive regimens. Obtaining high complexity data on the disease processes underlying rejection, tolerance, infection, malignancy, and injury could identify new opportunities for therapeutic intervention and biomarker identification. In this review, we discuss currently available spatial transcriptomic technologies and how they can be applied to transplantation.

High-sensitive spatially resolved T cell receptor sequencing with SPTCR-seq

Spatial resolution of the T cell repertoire is essential for deciphering cancer-associated immune dysfunction. Current spatially resolved transcriptomic technologies are unable to directly annotate T cell receptors (TCR). We present spatially resolved T cell receptor sequencing (SPTCR-seq), which integrates optimized target enrichment and long-read sequencing for highly sensitive TCR sequencing. The SPTCR computational pipeline achieves yield and coverage per TCR comparable to alternative single-cell TCR technologies. Our comparison of PCR-based and SPTCR-seq methods underscores SPTCR-seq's superior ability to reconstruct the entire TCR architecture, including V, D, J regions and the complementarity-determining region 3 (CDR3). Employing SPTCR-seq, we assess local T cell diversity and clonal expansion across spatially discrete niches. Exploration of the reciprocal interaction of the tumor microenvironmental and T cells discloses the critical involvement of NK and B cells in T cell exhaustion. Integrating spatially resolved omics and TCR sequencing provides as a robust tool for exploring T cell dysfunction in cancers and beyond.

The immune cell landscape of glioblastoma patients highlights a myeloid-enriched and immune suppressed microenvironment compared to metastatic brain tumors

Introduction

Brain metastases (BrM), which commonly arise in patients with melanoma, breast cancer and lung cancer, are associated with a poor clinical prognosis. In this context, the tumor microenvironment (TME) plays an important role since it either promotes or inhibits tumor progression. Our previous studies have characterized the immunosuppressive microenvironment of glioblastoma (GBM). The aim of this study is to compare the immune profiles of BrM and GBM in order to identify potential differences that may be exploited in their differential treatment.

Methods

Tumor and/or blood samples were taken from 20 BrM patients and 19 GBM patients. Multi-parametric flow cytometry was used to evaluate myeloid and lymphoid cells, as well as the expression of immune checkpoints in the TME and blood. In selected cases, the immunosuppressive ability of sorted myeloid cells was tested, and the ex vivo proliferation of myeloid, lymphoid and tumor cell populations was analyzed.

Results

High frequencies of myeloid cells dominated both the BrM and GBM landscapes, but a higher presence of tumor-associated macrophages was observed in GBM, while BrM were characterized by a significant presence of tumor-infiltrating lymphocytes. Exhaustion markers were highly expressed in all T cells from both primary and metastatic brain tumors. Ex vivo analysis of the cell cycle of a single sample of a BrM and of a GBM revealed subsets of proliferating tumor cells and blood-derived macrophages, but quiescent resident microglial cells and few proliferating lymphocytes. Macrophages sorted from a single lung BrM exhibited a strong immunosuppressive activity, as previously shown for primary GBM. Finally, a significant expansion of some myeloid cell subsets was observed in the blood of both GBM and BrM patients.

Discussion

Our results define the main characteristics of the immune profile of BrM and GBM, which are distinguished by different levels of immunosuppressive myeloid cells and lymphocytes devoid of effector function. Understanding the role of the different cells in establishing the metastatic setting is critical for improving the therapeutic efficacy of new targeted immunotherapy strategies.

The local microenvironment drives activation of neutrophils in human brain tumors

Neutrophils are abundant immune cells in the circulation and frequently infiltrate tumors in substantial numbers. However, their precise functions in different cancer types remain incompletely understood, including in the brain microenvironment. We therefore investigated neutrophils in tumor tissue of glioma and brain metastasis patients, with matched peripheral blood, and herein describe the first in-depth analysis of neutrophil phenotypes and functions in these tissues. Orthogonal profiling strategies in humans and mice revealed that brain tumor-associated neutrophils (TANs) differ significantly from blood neutrophils and have a prolonged lifespan and immune-suppressive and pro-angiogenic capacity. TANs exhibit a distinct inflammatory signature, driven by a combination of soluble inflammatory mediators including tumor necrosis factor alpha (TNF-ɑ) and Ceruloplasmin, which is more pronounced in TANs from brain metastasis versus glioma. Myeloid cells, including tumor-associated macrophages, emerge at the core of this network of pro-inflammatory mediators, supporting the concept of a critical myeloid niche regulating overall immune suppression in human brain tumors.

Spatially Resolved Transcriptomics Technology Facilitates Cancer Research

Single cell RNA sequencing (scRNA-seq) provides a great convenience for studying tumor occurrence and development for its ability to study gene expression at the individual cell level. However, patient-derived tumor tissues are composed of multiple types of cells including tumor cells and adjacent non-malignant cells such as stromal cells and immune cells. The spatial locations of various cells in situ tissues plays a pivotal role in the occurrence and development of tumors, which cannot be elucidated by scRNA-seq alone. Spatially resolved transcriptomics (SRT) technology emerges timely to explore the unrecognized relationship between the spatial background of a particular cell and its functions, and is increasingly used in cancer research. This review provides a systematic overview of the SRT technologies that are developed, in particular the more widely used cutting-edge SRT technologies based on next-generation sequencing (NGS). In addition, the main achievements by SRT technologies in precisely unveiling the underappreciated spatial locations on gene expression and cell function with unprecedented high-resolution in cancer research are emphasized, with the aim of developing more effective clinical therapeutics oriented to a deeper understanding of the interaction between tumor cells and surrounding non-malignant cells.

Phenotypic diversity of T cells in human primary and metastatic brain tumors revealed by multiomic interrogation

The immune-specialized environment of the healthy brain is tightly regulated to prevent excessive neuroinflammation. However, after cancer development, a tissue-specific conflict between brain-preserving immune suppression and tumor-directed immune activation may ensue. To interrogate potential roles of T cells in this process, we profiled these cells from individuals with primary or metastatic brain cancers via integrated analyses on the single-cell and bulk population levels. Our analysis revealed similarities and differences in T cell biology between individuals, with the most pronounced differences observed in a subgroup of individuals with brain metastasis, characterized by accumulation of CXCL13-expressing CD39+ potentially tumor-reactive T (pTRT) cells. In this subgroup, high pTRT cell abundance was comparable to that in primary lung cancer, whereas all other brain tumors had low levels, similar to primary breast cancer. These findings indicate that T cell-mediated tumor reactivity can occur in certain brain metastases and may inform stratification for treatment with immunotherapy.

The Tumor-Specific Immune Landscape in HPV+ Head and Neck Cancer

Human papillomaviruses (HPVs) are the causative agent of several anogenital cancers as well as head and neck cancers, with HPV+ head and neck squamous cell carcinoma (HNSCC) becoming a rapidly growing public health issue in the Western world. Due its viral etiology and potentially its subanatomical location, HPV+ HNSCC exhibits an immune microenvironment which is more inflamed and thus distinct from HPV-negative HNSCC. Notably, the antigenic landscape in most HPV+ HNSCC tumors extends beyond the classical HPV oncoproteins E6/7 and is extensively targeted by both the humoral and cellular arms of the adaptive immune system. Here, we provide a comprehensive overview of HPV-specific immune responses in patients with HPV+ HNSCC. We highlight the localization, antigen specificity, and differentiation states of humoral and cellular immune responses, and discuss their similarities and differences. Finally, we review currently pursued immunotherapeutic treatment modalities that attempt to harness HPV-specific immune responses for improving clinical outcomes in patients with HPV+ HNSCC.

Immune microenvironment remodeling after radiation of a progressing brain metastasis

Radiation is commonly used in the treatment of many cancers. However, its effects on anti-tumor immune responses are incompletely understood. Here, we present a detailed immunological analysis of two tumors from a patient with multiple non-small cell lung cancer metastases to the brain. One tumor was resected without treatment; the second was irradiated to a total dose of 30 Gy and resected following further progression. Comprehensive single-cell analysis reveals a substantially reduced immune cell fraction in the irradiated tumor, including the depletion of tissue-resident macrophages and infiltration of pro-inflammatory monocytes. Despite the presence of similar somatic mutations in both tumors, radiation is associated with the depletion of exhausted, tumor-resident T cell clones and their replacement by circulating clones unlikely to contribute to tumor-specific immunity. These results provide insight into the local effects of radiation on anti-tumor immunity and raise important considerations for the combination of radiation and immunotherapy.

Analysis of RNA processing directly from spatial transcriptomics data reveals previously unknown regulation

Technical advances have led to an explosion in the amount of biological data available in recent years, especially in the field of RNA sequencing. Specifically, spatial transcriptomics (ST) datasets, which allow each RNA molecule to be mapped to the 2D location it originated from within a tissue, have become readily available. Due to computational challenges, ST data has rarely been used to study RNA processing such as splicing or differential UTR usage. We apply the ReadZS and the SpliZ, methods developed to analyze RNA process in scRNA-seq data, to analyze spatial localization of RNA processing directly from ST data for the first time. Using Moran's I metric for spatial autocorrelation, we identify genes with spatially regulated RNA processing in the mouse brain and kidney, re-discovering known spatial regulation in Myl6 and identifying previously-unknown spatial regulation in genes such as Rps24, Gng13, Slc8a1, Gpm6a, Gpx3, ActB, Rps8, and S100A9. The rich set of discoveries made here from commonly used reference datasets provides a small taste of what can be learned by applying this technique more broadly to the large quantity of Visium data currently being created.

Technology meets TILs: Deciphering T cell function in the -omics era

T cells are at the center of cancer immunology because of their ability to recognize mutations in tumor cells and directly mediate cancer cell killing. Immunotherapies to rejuvenate exhausted T cell responses have transformed the clinical management of several malignancies. In parallel, the development of novel multidimensional analysis platforms, such as single-cell RNA sequencing and high-dimensional flow cytometry, has yielded unprecedented insights into immune cell biology. This convergence has revealed substantial heterogeneity of tumor-infiltrating immune cells in single tumors, across tumor types, and among individuals with cancer. Here we discuss the opportunities and challenges of studying the complex tumor microenvironment with -omics technologies that generate vast amounts of data, highlighting the opportunities and limitations of these technologies with a particular focus on interpreting high-dimensional studies of CD8+ T cells in the tumor microenvironment.

T-cell repertoire diversity: friend or foe for protective antitumor response?

Profiling the T-Cell Receptor (TCR) repertoire is establishing as a potent approach to investigate autologous and treatment-induced antitumor immune response. Technical and computational breakthroughs, including high throughput next-generation sequencing (NGS) approaches and spatial transcriptomics, are providing unprecedented insight into the mechanisms underlying antitumor immunity. A precise spatiotemporal variation of T-cell repertoire, which dynamically mirrors the functional state of the evolving host-cancer interaction, allows the tracking of the T-cell populations at play, and may identify the key cells responsible for tumor eradication, the evaluation of minimal residual disease and the identification of biomarkers of response to immunotherapy. In this review we will discuss the relationship between global metrics characterizing the TCR repertoire such as T-cell clonality and diversity and the resultant functional responses. In particular, we will explore how specific TCR repertoires in cancer patients can be predictive of prognosis or response to therapy and in particular how a given TCR re-arrangement, following immunotherapy, can predict a specific clinical outcome. Finally, we will examine current improvements in terms of T-cell sequencing, discussing advantages and challenges of current methodologies.

Single-cell RNA sequencing reveals cellular and molecular reprograming landscape of gliomas and lung cancer brain metastases

BACKGROUND

Brain malignancies encompass gliomas and brain metastases originating from extracranial tumours including lung cancer. Approximately 50% of patients with lung adenocarcinoma (LUAD) will eventually develop brain metastases. However, the specific characteristics of gliomas and lung-to-brain metastases (LC) are largely unknown.

METHODS

We applied single-cell RNA sequencing to profile immune and nonimmune cells in 4 glioma and 10 LC samples.

RESULTS

Our analysis revealed that tumour microenvironment (TME) cells are present in heterogeneous subpopulations. LC reprogramed cells into immune suppressed state, including microglia, macrophages, endothelial cells, and CD8+ T cells, with unique cell proportions and gene signatures. Particularly, we identified that a subset of macrophages was associated with poor prognosis. ROS (reactive oxygen species)-producing neutrophils was found to participant in angiogenesis. Furthermore, endothelial cells participated in active communication with fibroblasts. Metastatic epithelial cells exhibited high heterogeneity in chromosomal instability (CIN) and cell population.

CONCLUSIONS

Our findings provide a comprehensive understanding of the heterogenicity of the tumor microenvironment and tumour cells and it will be crucial for successful immunotherapy development for brain metastasis of lung cancer.

Space Is the Place: Mapping the Cell–Cell Interactions That Predict Immunotherapy Responses in Melanoma

Although immune checkpoint inhibition (ICI) has revolutionized the treatment of advanced melanoma, reliable predictive biomarkers are still lacking. In this issue of Cancer Research, Antoranz and colleagues used RNA sequencing and multiplexed IHC to study the spatial immune landscape of pretreatment melanoma specimens from patients who either responded or did not respond to antiprogrammed death protein 1 (PD-1) therapy. The authors identified the spatial interaction between cytotoxic T cells and M1-like macrophages expressing PD-L1 at the tumor boundary as predictive of responses to immune checkpoint inhibition. These studies pave the way for the development of new spatial biomarkers to identify patients most likely to benefit from ICI therapy.

See related article by Antoranz et al., p. 3275

Clinical application of advanced multi-omics tumor profiling: Shaping precision oncology of the future

Next-generation DNA sequencing technology has dramatically advanced clinical oncology through the identification of therapeutic targets and molecular biomarkers, leading to the personalization of cancer treatment with significantly improved outcomes for many common and rare tumor entities. More recent developments in advanced tumor profiling now enable dissection of tumor molecular architecture and the functional phenotype at cellular and subcellular resolution. Clinical translation of high-resolution tumor profiling and integration of multi-omics data into precision treatment, however, pose significant challenges at the level of prospective validation and clinical implementation. In this review, we summarize the latest advances in multi-omics tumor profiling, focusing on spatial genomics and chromatin organization, spatial transcriptomics and proteomics, liquid biopsy, and ex vivo modeling of drug response. We analyze the current stages of translational validation of these technologies and discuss future perspectives for their integration into precision treatment.

Localization of T cell clonotypes using the Visium spatial transcriptomics platform

We present a protocol to localize T cell receptor clones using the Visium spatial transcriptomics platform. This approach permits simultaneous localization of both gene expression and T cell clonotypes in situ within tissue sections. T cell receptor sequences identified by this protocol are readily recapitulated by single-cell sequencing. This technique enables detailed studies of the spatial organization of the human T cell repertoire, such as the localization of infiltrating T cell clones within the tumor microenvironment. For complete details on the use and execution of this protocol, please refer to Sudmeier et al. (2022).