Atlas of clinically distinct cell states and ecosystems across human solid tumors

The Identification of Novel Prognostic and Predictive Biomarkers in Breast Cancer via the Elucidation of Tumor Ecotypes Using Ecotyper

Background

Breast cancer is a highly heterogeneous disease, characterized by tumor and nontumor cells at various cell states. Ecotyper is an innovative machine learning framework that quantifies the tumor microenvironment and delineates the tumor ecosystem, demonstrating clinical significance. However, further validation is needed in breast cancer.

Methods

Ecotyper was applied to identify multiple cellular states and tumor ecotypes using large-scale breast cancer bulk sequencing data, followed by a detailed analysis of their associations with clinical classification, molecular subtypes, survival prognosis, and immunotherapy response. Identified subtypes were further characterized using single-cell and spatial data sets to reveal molecular profiles.

Results

In a comprehensive analysis of 6578 breast cancer samples from four data sets, Ecotyper identified 69 cellular states and 10 tumor ecotypes. Of these, 37 cellular states significantly correlated with overall survival. Notably, specific states within epithelial cells, macrophages/monocytes, and fibroblasts were linked to a worse prognosis. CE2 abundance was identified as the most significant marker indicating unfavorable prognosis and was further validated in an additional data set of 116 HER2-negative patients. These biomarkers also indicated the efficacy of neoadjuvant immunotherapy in breast cancer. CE2-high cancers were characterized by an abundance of basal-like epithelial cells, scant lymphocytic infiltration, and activation of hypoxia signaling. Single-cell analysis showed that CE2-high areas were rich in SPP1-positive tumor-associated macrophages(TAM), basal-like epithelial cells, and hypoxic cancer-associated fibroblasts(CAF). Spatially, these regions were often peripheral in triple-negative breast cancer, adjacent to fibrotic/necrotic zones. Multiplex immunofluorescence confirmed the enrichment of SPP1+CD68+TAM and HIF1A+SMA+CAF in hypoxic triple-negative breast cancer (TNBC) regions.

Conclusions

Ecotyper identified novel biomarkers for breast cancer prognosis and treatment prediction. The CE2-high region may represent a hypoxic immune-suppressive niche.

Systemic inflammation in response to radiation drives the genesis of an immunosuppressed tumor microenvironment

The composition of the tumor immune microenvironment has become a major determinant of response to therapy, particularly immunotherapy. Clinically, a tumor microenvironment lacking lymphocytes, so-called "cold" tumors, are considered poor candidates for immune checkpoint inhibition. In this review, we describe the diversity of the tumor immune microenvironment in breast cancer and how radiation exposure alters carcinogenesis. We review the development and use of a radiation-genetic mammary chimera model to clarify the mechanism by which radiation acts. Using the chimera model, we demonstrate that systemic inflammation elicited by a low dose of radiation is key to the construction of an immunosuppressive tumor microenvironment, resulting in aggressive, rapidly growing tumors lacking lymphocytes. Our experimental studies inform the non-mutagenic mechanisms by which radiation affects cancer and provide insight into the genesis of cold tumors.

Deep Learning and Radiomic Signatures Associated with Tumor Immune Heterogeneity Predict Microvascular Invasion in Colon Cancer

RATIONALE AND OBJECTIVES

This study aims to develop and validate a deep learning radiomics signature (DLRS) that integrates radiomics and deep learning features for the non-invasive prediction of microvascular invasion (MVI) in patients with colon cancer (CC). Furthermore, the study explores the potential association between DLRS and tumor immune heterogeneity.

MATERIALS AND METHODS

This study is a multi-center retrospective study that included a total of 1007 patients with colon cancer (CC) from three medical centers and The Cancer Genome Atlas (TCGA-COAD) database. Patients from Medical Centers 1 and 2 were divided into a training cohort (n = 592) and an internal validation cohort (n = 255) in a 7:3 ratio. Medical Center 3 (n = 135) and the TCGA-COAD database (n = 25) were used as external validation cohorts. Radiomics and deep learning features were extracted from contrast-enhanced venous-phase CT images. Feature selection was performed using machine learning algorithms, and three predictive models were developed: a radiomics model, a deep learning (DL) model, and a combined deep learning radiomics (DLR) model. The predictive performance of each model was evaluated using multiple metrics, including the area under the curve (AUC), sensitivity, and specificity. Additionally, differential gene expression analysis was conducted on RNA-seq data from the TCGA-COAD dataset to explore the association between the DLRS and tumor immune heterogeneity within the tumor microenvironment.

RESULTS

Compared to the standalone radiomics and deep learning models, DLR fusion model demonstrated superior predictive performance. The AUC for the internal validation cohort was 0.883 (95% CI: 0.828-0.937), while the AUC for the external validation cohort reached 0.855 (95% CI: 0.775-0.935). Furthermore, stratifying patients from the TCGA-COAD dataset into high-risk and low-risk groups based on the DLRS revealed significant differences in immune cell infiltration and immune checkpoint expression between the two groups (P < 0.05).

CONCLUSION

The contrast-enhanced CT-based DLR fusion model developed in this study effectively predicts the MVI status in patients with CC. This model serves as a non-invasive preoperative assessment tool and reveals a potential association between the DLRS and immune heterogeneity within the tumor microenvironment, providing insights to optimize individualized treatment strategies.

Expression of MASTL (Greatwall) associates with good prognosis and response to radiotherapy in pharyngeal squamous cell carcinoma

Head and neck squamous cell carcinomas (HNSCCs) are highly heterogeneous in both disease progression and treatment outcome, with hardly any molecular biomarker in clinical practice. Thus, the aim of this study was to investigate the potential prognostic and predictive value of MASTL/Greatwall, a mitotic kinase also involved in PI3K-mTOR signaling and the DNA damage response, in HNSCC. MASTL expression was evaluated by immunohistochemistry in a cohort of 346 surgically treated HPV-negative pharyngeal and laryngeal squamous cell carcinoma patients, as well as in pre-treatment biopsies from a separate cohort of 64 patients treated with induction chemotherapy (ICT). In addition, MASTL mRNA expression was analyzed in 135 patients from The Cancer Genome Atlas (TCGA) database. High MASTL expression was significantly associated with improved disease-specific survival (DSS) (P = 0.029), specifically in well-differentiated pharyngeal squamous cell carcinoma (PSCC) tumors (P = 0.002). Notably, this association was restricted to patients who received adjuvant radiotherapy (RT) (P = 0.009). Consistently, a similar correlation was found at the mRNA level in PSCC tumors from the TCGA dataset. Moreover, the combined expression of MASTL and p21 was significantly associated with better DSS, specifically among patients receiving RT (P = 0.014). Multivariate Cox regression analysis further confirmed that high MASTL expression was independently associated with favorable prognosis in patients who received post-operative RT (HR= 0.65; 95 % CI: 0.45-0.94; P = 0.021). Collectively, these findings unprecedentedly revealed the association between high MASTL expression and favorable outcome in advanced HPV-negative PSCC, in marked contrast to previous reports in other tumor types. Importantly, MASTL expression emerges as an independent predictor of good prognosis in RT-treated PSCC patients.

Identification of progression related LncRNAs in colorectal cancer aggressiveness

Colorectal cancer (CRC) progression involves complex molecular alterations, including the dysregulation of long non-coding RNAs (lncRNAs). In this study, we identified key progression-related lncRNAs in CRC by integrating transcriptomic data from TCGA and single-cell RNA sequencing (scRNA-seq). Differential expression analysis revealed numerous lncRNAs associated with CRC progression. To systematically prioritize these lncRNAs, we developed a scoring system incorporating multiple progression-related signatures, differential expression, and survival analysis. This approach identified 198 key lncRNAs, including both known (e.g., LINC01615) and novel candidates (e.g., AC007998.3). Experimental validation confirmed that LINC01615 was significantly upregulated in CRC tissues, whereas AC007998.3 was downregulated. Further analyses indicated that these lncRNAs influence CRC progression through cis-, trans-, and post-transcriptional regulation. Patients were classified into distinct molecular subgroups based on lncRNA expression, exhibiting significant differences in prognosis and immune microenvironment composition. The enrichment of progression-related lncRNAs among differentially expressed lncRNAs was statistically significant, reinforcing their functional relevance. Validation across independent datasets demonstrated the robustness of our findings. Our research provides novel insights into the molecular mechanisms underlying CRC progression and highlights the potential of progression-related lncRNAs as prognostic biomarkers and therapeutic targets.

Multi-dimensional characterization of cellular states reveals clinically relevant immunological subtypes and therapeutic vulnerabilities in ovarian cancer

BACKGROUND

Diverse cell types and cellular states in the tumor microenvironment (TME) are drivers of biological and therapeutic heterogeneity in ovarian cancer (OV). Characterization of the diverse malignant and immunology cellular states that make up the TME and their associations with clinical outcomes are critical for cancer therapy. However, we are still lack of knowledge about the cellular states and their clinical relevance in OV.

METHODS

We manually collected the comprehensive transcriptomes of OV samples and characterized the cellular states and ecotypes based on a machine-learning framework. The robustness of the cellular states was validated in independent cohorts and single-cell transcriptomes. The functions and regulators of cellular states were investigated. Meanwhile, we thoroughly examined the associations between cellular states and various clinical factors, including clinical prognosis and drug responses.

RESULTS

We depicted and characterized an immunophenotypic landscape of 3,099 OV samples and 80,044 cells based on a machine learning framework. We identified and validated 32 distinct transcriptionally defined cellular states from 12 cell types and three cellular communities or ecotypes, extending the current immunological subtypes in OV. Functional enrichment and upstream transcriptional regulator analyses revealed cancer hallmark-related pathways and potential immunological biomarkers. We further investigated the spatial patterns of identified cellular states by integrating the spatially resolved transcriptomes. Moreover, prognostic landscape and drug sensitivity analysis exhibited clinically relevant immunological subtypes and therapeutic vulnerabilities.

CONCLUSION

Our comprehensive analysis of TME helps leveraging various immunological subtypes to highlight new directions and targets for the treatment of cancer.

A tissue-specific atlas of protein-protein associations enables prioritization of candidate disease genes

Despite progress in mapping protein-protein interactions, their tissue specificity is understudied. Here, given that protein coabundance is predictive of functional association, we compiled and analyzed protein abundance data of 7,811 proteomic samples from 11 human tissues to produce an atlas of tissue-specific protein associations. We find that this method recapitulates known protein complexes and the larger structural organization of the cell. Interactions of stable protein complexes are well preserved across tissues, while cell-type-specific cellular structures, such as synaptic components, are found to represent a substantial driver of differences between tissues. Over 25% of associations are tissue specific, of which <7% are because of differences in gene expression. We validate protein associations for the brain through cofractionation experiments in synaptosomes, curation of brain-derived pulldown data and AlphaFold2 modeling. We also construct a network of brain interactions for schizophrenia-related genes, indicating that our approach can functionally prioritize candidate disease genes in loci linked to brain disorders.

Advances in Spatial Omics Technologies

Rapidly developing spatial omics technologies provide us with new approaches to deeply understanding the diversity and functions of cell types within organisms. Unlike traditional approaches, spatial omics technologies enable researchers to dissect the complex relationships between tissue structure and function at the cellular or even subcellular level. The application of spatial omics technologies provides new perspectives on key biological processes such as nervous system development, organ development, and tumor microenvironment. This review focuses on the advancements and strategies of spatial omics technologies, summarizes their applications in biomedical research, and highlights the power of spatial omics technologies in advancing the understanding of life sciences related to development and disease.

Spatial organization of B lymphocytes and prognosis prediction in patients with gastric cancer

BACKGROUND

Within the tumor microenvironment (TME), the association of B lymphocytes (B cells) with prognosis and therapy response in gastric cancer (GC) remains poorly characterized. We investigated the predictive and prognostic value of B cells, including their spatial organization within the TME, in one of the largest multi-cohort studies to date.

METHODS

Using CD20 immunohistochemistry, we evaluated B cell density in resection specimens from 977 patients with resectable GC across three cohorts, including the randomized phase III Korean CLASSIC trial. The relationship between CD20 density, clinicopathological characteristics, and overall survival (OS) was analyzed. Digital spatial profiling of 1063 regions of interest from 15 patients was performed to characterize B cell distribution within different regions of interest (ROIs) using the NanoString GeoMx platform.

RESULTS

CD20 density was significantly higher in diffuse-type GC compared to intestinal-type (p = 0.000012). Patients with CD20-low diffuse-type GC had the shortest OS in the CLASSIC trial (median OS: 49 vs 62 months, HR: 1.9, 95% CI: 1.2-3.0, p = 0.003) and in a Japanese cohort (median OS: 49 vs 67 months, HR: 2.2, 95% CI: 1.2-4.0, p = 0.011). This survival difference was not seen in patients treated with adjuvant chemotherapy (median OS: 62 vs 63 months, HR: 1.8, 95% CI: 0.88-3.5, p = 0.108). Spatial profiling revealed significant B cell enrichment within tumor ROIs compared to the stroma, particularly in diffuse-type GC.

CONCLUSIONS

Low CD20 positivity, especially in diffuse-type GC, is linked to poor prognosis and may identify patients who could benefit from chemotherapy. These findings underscore the role of B cells in GC.

Heightened IDO1 levels predict Bacillus Calmette-Guèrin failure in high-risk non-muscle-invasive bladder cancer patients

Recent studies have indicated a potential link between immune-related gene expression and Bacillus Calmette-Guèrin (BCG) treatment response in non-muscle-invasive bladder cancer (NMIBC) patients, however, prognostic gene signatures have not significantly improved risk stratification beyond clinical characteristics. To identify predictive biomarkers in T1 high-risk (HR) bladder cancer (BC) patients responding to BCG treatment, a gene signature was derived from a discovery cohort of 73 BCG-naïve patients, both responders and non-responders, using the publicly available dataset GSE1542618. Among the identified genes, Indoleamine 2,3-dioxygenase (IDO1), an immunosuppressive enzyme, emerged as a crucial determinant of treatment outcomes. The association between IDO1 expression and worse prognosis was subsequently validated in a cohort of 75 BC patients using formalin-fixed paraffin-embedded (FFPE) BC specimens collected prior BCG treatment. This research revealed significant insights into the mechanisms underlying unsatisfactory responses to BCG treatment in HR patients, posing IDO1 as a promising prognostic biomarker and therapeutic target for NMIBC.

Leveraging TME features and multi-omics data with an advanced deep learning framework for improved Cancer survival prediction

Glioma, a malignant intracranial tumor with high invasiveness and heterogeneity, significantly impacts patient survival. This study integrates multi-omics data to improve prognostic prediction and identify therapeutic targets. Using single-cell data from glioblastoma (GBM) and low-grade glioma (LGG) samples, we identified 55 distinct cell states via the EcoTyper framework, validated for stability and prognostic impact in an independent cohort. We constructed multi-omics datasets of 620 samples, integrating transcriptomic, copy number variation (CNV), somatic mutation (MUT), Microbe (MIC), EcoTyper result data. A scRNA-seq enhanced Self-Normalizing Network-based glioma prognosis model achieved a C-index of 0.822 (training) and 0.817 (test), with AUC values of 0.867, 0.876, and 0.844 at 1, 3, and 5 years in the training set, and 0.820, 0.947, and 0.936 in the test set. Gradient attribution analysis enhanced the interpretability of the model and identified key molecular markers. The classification into high- and low-risk groups was validated as an independent prognostic factor. HDAC inhibitors are proposed as potential treatments. This study demonstrates the potential of integrating scRNA-seq and multi-omics data for robust glioma prognosis and clinical decision-making support.

The single-cell spatial landscape of stage III colorectal cancers

We conducted a spatial analysis of stage III colorectal adenocarcinomas using Hyperion Imaging Mass Cytometry, examining 52 tumors to assess the tumor microenvironment at the single-cell level. This approach identified 10 distinct cell phenotypes in the tumor microenvironment, including stromal and immune cells, with a subset showing a proliferative phenotype. By focusing on spatial neighborhood interactions and tissue niches, particularly regions with tumor-infiltrating lymphocytes, we investigated how cellular organization relates to clinicopathological and molecular features such as microsatellite instability (MSI) and recurrence. We determined that microsatellite stable (MSS) colorectal cancers had an increased risk of recurrence if they had the following features: 1) a low level of stromal tumor-infiltrating lymphocytes, and 2) low interactions between CD4 + T cells and stromal cells. Our results point to the utility of spatial single-cell interaction analysis in defining novel features of the tumor immune microenvironments and providing useful clinical cell-related spatial biomarkers.

Integrated proteogenomic characterization of localized prostate cancer identifies biological insights and subtype-specific therapeutic strategies

Localized prostate cancer (PCa) is highly variable in their response to therapies. Although a fraction of this heterogeneity can be explained by clinical factors or genomic and transcriptomic profiling, the proteomic-based profiling of aggressive PCa remains poorly understood. Here, we profiled the genome, transcriptome, proteome and phosphoproteome of 145 cases of localized PCa in Chinese patients. Proteome-based stratification of localized PCa revealed three subtypes with distinct molecular features: immune subgroup, arachidonic acid metabolic subgroup and sialic acid metabolic subgroup with highest biochemical recurrence (BCR) rates. Further, we nominated NANS protein, a key enzyme in sialic acid synthesis as a potential prognostic biomarker for aggressive PCa and validated in two independent cohorts. Finally, taking advantage of cell-derived orthotopic transplanted mouse models, single-cell RNA sequencing (scRNA-seq) and immunofluorescence analysis, we revealed that targeting NANS can reverse the immunosuppressive microenvironment through restricting the sialoglycan-sialic acid-recognizing immunoglobulin superfamily lectin (Siglec) axis, thereby inhibiting tumor growth of PCa. In sum, we integrate multi-omic data to refine molecular subtyping of localized PCa, and identify NANS as a potential prognostic biomarker and therapeutic option for aggressive PCa.

Transcriptome of Anaplastic Thyroid Cancer Reveals Two Molecular Subtypes with Distinct Tumor Microenvironment and Prognosis

Background: Although patients with anaplastic thyroid cancer (ATC) generally have a poor prognosis and there are currently no effective treatment options, survival and response to therapy vary between patients. Genomic and transcriptomic profiles of ATC have been reported; however, a comprehensive study of the tumor microenvironment (TME) of ATC is still lacking. This study aimed to elucidate the TME characteristics associated with ATC and their prognostic implications. Methods: We analyzed bulk RNA transcriptomic data from 1,634 samples-including 476 normal thyroid tissues, 25 benign thyroid adenomas, 340 RAS-like and 719 BRAFV600E-like differentiated thyroid cancers (DTC-R and DTC-B, respectively), and 74 ATCs. We assessed the TME and molecular characteristics of these thyroid cancer subtypes using deconvolution analysis. Results: The TME of ATC was characterized by a high abundance of immune cells and fibroblasts and a low abundance of epithelial cells compared to other thyroid histologies. During its malignant evolution, ATC exhibited an ecotype more closely related to DTC-B than RAS-like DTC (DTC-R). Furthermore, we identified two distinct molecular subtypes within ATC with significant differences in their TMEs. We termed the subtype with increased immune cells and fibroblasts as ATC-immune-fibroblast (ATC-IF) and the subtype with elevated epithelial and endothelial cells as ATC-epithelial-endothelial (ATC-E). The ATC-IF group had worse disease-specific survival (log-rank p = 0.035), higher ERK scores, and lower thyroid differentiation scores than the ATC-E group. While both ATC subtypes had elevated immune cells and fibroblasts compared to DTC-R and DTC-B, this increase was more pronounced in ATC-IF, with a marked rise in myeloid lineage cells and promigratory fibroblasts. Immune checkpoint gene expression and epithelial-mesenchymal transition scores were significantly higher in the ATC-IF group than in the ATC-E group. Conclusion: ATC shows a TME distinct from that of DTC and can be further divided into two molecular subtypes-each with its own unique TME. The ATC-IF group, with a poorer prognosis and higher ERK score, is enriched in immune cells and fibroblasts, which may represent potential therapeutic targets.

scMalignantFinder distinguishes malignant cells in single-cell and spatial transcriptomics by leveraging cancer signatures

Single-cell RNA sequencing (scRNA-seq) is a powerful tool for characterizing tumor heterogeneity, yet accurately identifying malignant cells remains challenging. Here, we propose scMalignantFinder, a machine learning tool specifically designed to distinguish malignant cells from their normal counterparts using a data- and knowledge-driven strategy. To develop the tool, multiple cancer datasets were collected, and the initially annotated malignant cells were calibrated using nine carefully curated pan-cancer gene signatures, resulting in over 400,000 single-cell transcriptomes for training. The union of differentially expressed genes across datasets was taken as the features for model construction to comprehensively capture tumor transcriptional diversity. scMalignantFinder outperformed existing automated methods across two gold-standard and eleven patient-derived scRNA-seq datasets. The capability to predict malignancy probability empowers scMalignantFinder to capture dynamic characteristics during tumor progression. Furthermore, scMalignantFinder holds the potential to annotate malignant regions in tumor spatial transcriptomics. Overall, we provide an efficient tool for detecting heterogeneous malignant cell populations.

Pan-cancer analysis shapes the understanding of cancer biology and medicine

Advances in multi-omics datasets and analytical methods have revolutionized cancer research, offering a comprehensive, pan-cancer perspective. Pan-cancer studies identify shared mechanisms and unique traits across different cancer types, which are reshaping diagnostic and treatment strategies. However, continued innovation is required to refine these approaches and deepen our understanding of cancer biology and medicine. This review summarized key findings from pan-cancer research and  explored their potential to drive future advancements in oncology.

Comprehensive immunophenotyping of gastric adenocarcinoma identifies an inflamed class of tumors amenable to immunotherapies

BACKGROUND

Gastric adenocarcinoma (GAC) imposes a considerable global health burden. Molecular profiling of GAC from the tumor microenvironment perspective through a multi-omics approach is eagerly awaited in order to allow a more precise application of novel therapies in the near future.

METHODS

To better understand the tumor-immune interface of GAC, we identified an internal cohort of 82 patients that allowed an integrative molecular analysis including mutational profiling by whole-exome sequencing, RNA gene expression of 770 genes associated with immune response, and multiplex protein expression at spatial resolution of 34 immuno-oncology targets at different compartments (tumorous cells and immune cells). Molecular findings were validated in 595 GAC from the TCGA and ACRG external cohorts with available multiomics data. Prediction of response to immunotherapies of the discovered immunophenotypes was assessed in 1039 patients with cancer from external cohorts with available transcriptome data.

RESULTS

Unsupervised clustering by gene expression identified a subgroup of GAC that includes 52% of the tumors, the so-called Inflamed class, characterized by high tumor immunogenicity and cytotoxicity, particularly in the tumor center at protein level, with enrichment of PIK3CA and ARID1A mutations and increased presence of exhausted CD8+ T cells as well as co-inhibitory receptors such as PD1, CTLA4, LAG3, and TIGIT. The remaining 48% of tumors were called non-inflamed based on the observed exclusion of T cell infiltration, with an overexpression of VEGFA and higher presence of TP53 mutations, resulting in a worse clinical outcome. A 10-gene RNA signature was developed for the identification of tumors belonging to these classes, demonstrating in evaluated datasets comparable clinical utility in predicting response to current immunotherapies when tested against other published gene signatures.

CONCLUSIONS

Comprehensive immunophenotyping of GAC identifies an inflamed class of tumors that complements previously proposed tumor-based molecular clusters. Such findings may provide the rationale for exploring novel immunotherapeutic approaches for biomarker-enriched populations in order to improve GAC patient's survival.

NLRP4 unlocks an NK/macrophages-centered ecosystem to suppress non-small cell lung cancer

BACKGROUND

Tumor immune evasion extends beyond T cells, affecting innate immune elements like natural killer cells (NK) and macrophages within the tumor-immune microenvironment (TIME). Nevertheless, translational strategies to trigger collaboration of NK cells and macrophages to initiate sufficient anti-tumor cytoxicity remain scarce and are urgently needed.

METHODS

In this study, TCGA datasets was used to confirm the prognosis value of the expression level of NLR family pyrin domain containing 4 (NLRP4) in NSCLC and the tumor tissues microarray was used to further check its clinical-relevance at protein-level. Subsequently, a tumor cell line with stable NLRP4 overexpression was established and subcutaneous tumor models in C57BL/6J mice were used to validate the anti-tumor characteristics of NLRP4. After analyzing the tumor microenvironment using flow cytometry and multiplex immunofluorescence, we further validated our findings through co-culture transwell assays and TCGA analysis. Utilizing bulk-RNA sequencing, proteomics, and mass spectrometry of mouse tumor tissues, we innovatively identified the downstream pathways of NLRP4 and verified them through co-immunoprecipitation (co-IP) and Western blot (WB) experiments.

RESULTS

NLRP4 could trigger a distinct anti-tumor ecosystem organized by TIGIT+TNFA+ NK and iNOS+ M1 in lung cancer, discovered in TCGA analysis and verified in murine model. NLRP4-eco exerted tumor-suppression capacity through chemokine reprogramming including CCL5 and CXCL2. Meanwhile, the cytoxicity of NK could be facilitated by iNOS+M1. Mechanistically, NLRP4 stimulated PI3K/Akt-NF-kB axis through suppression of the activity of PP2A. Besides, knockdown of CCL5 and blockade of CXCL2-CXCR2 axis abolished chemotaxis of TIGIT+TNFA+ NK and iNOS+ M1 respectively, as well as for LB-100, a PP2A inhibitor.

CONCLUSION

Altogether, we delineated NLRP4's unexplored facets and discovered an NLRP4-driven anti-tumor ecosystem composed of TIGIT+TNFA+ NK and iNOS+ M1. Finally, targeting PP2A by its inhibitor successfully mimicked the anti-tumor capacity of the overexpression of NLRP4.

Single-cell spatial transcriptomics unravels cell states and ecosystems associated with clinical response to immunotherapy

BACKGROUND

The tumor microenvironment (TME) is a complex and dynamic ecosystem that is known to influence responses to immunotherapy. We leveraged single-cell spatial transcriptomics to systematically dissect the intricate complexity of the TME, in particular the cellular heterogeneity and spatial interactions. Their collective impact on immunotherapy efficacy was studied in the context of a homogeneous group of patients with vulvar high-grade squamous intraepithelial lesions (vHSIL) treated with an immunotherapeutic tumor-specific peptide vaccine.

METHODS

We performed single-cell spatial transcriptomics on 20 pretreatment vHSIL lesions, stratified by clinical response to immunotherapeutic vaccination into complete responders (CR), partial responders (PR) and non-responders (NR). Using a 1,000-gene panel, we mapped over 274,000 single cells in situ, identifying 18 cell clusters and 99 distinct non-epithelial cell states. Findings were validated against public single-cell transcriptomic data sets to assess their broader relevance across tumor types.

RESULTS

Profound heterogeneity within the TME was detected across the response groups. CR lesions exhibited a higher ratio of immune-supportive to immune-suppressive cells-a pattern mirrored in other solid tumors following neoadjuvant checkpoint blockade. Key immune populations enriched in CRs included CD4+CD161+ effector T cells and chemotactic CD4+ and CD8+ T cells. Conversely, PRs were characterized by increased proportions of T helper 2 cells and CCL18-expressing macrophages, which are associated with the recruitment of type 2 T cells and regulatory T cells. NRs displayed preferential infiltration with immunosuppressive fibroblasts. Distinct spatial immune ecosystems further defined response groups. Although a number of immune cells were detected in all patients, type 1 effector cells dominated interactions in CRs, type 2 cells were prominently interacting in PRs, while NRs lacked organized immune cell interactions.

CONCLUSIONS

This study underscores the dual importance of both cellular composition and spatial organization in steering clinical response to immunotherapy.

Population-specific patterns in assessing molecular subtypes of young black females with triple-negative breast cancer

We determined triple-negative breast cancer (TNBC) subtypes, genetic ancestry, and immune features in a cohort of self-reported Black females with TNBC diagnosed at or below age 50. Among 104 tumors, 34.6% were basal-like 1 (BL1), 17.3% basal-like 2 (BL2), 9.6% luminal androgen receptor (LAR), 26.9% mesenchymal (M), and 11.5% unsubtyped (UNS). Subtypes resembled those seen in Europeans or East Asians, with less LAR (9.6% vs. 14.6-24.4%) and more UNS (11.5% vs. 0-7.5%). "High" proportion of West African ancestry was associated with more LAR (14.9% vs. 4.9%) and less M (25.5% vs. 34.2%). M demonstrated reduced immune activity and was marginally associated with worse overall survival in a multivariate model including stage, West African ancestry, BMI, and TILs, meriting future research. Our study is the largest to date of TNBC subtypes in young Black females. These results reinforce TNBC subtypes' application across populations and potential use as a prognostic biomarker.

Profiling Multiple CD8+ T-cell Functional Dimensions Enhances Breast Cancer Immune Assessment

CD8+ T-cell abundance is insufficient to assess antitumor immunity and shows poor performance in predicting breast cancer prognosis and immunotherapy response, presumably owing to the complexity of CD8+ T-cell functionalities. Although single-cell RNA sequencing can dissect the multifaceted functions of CD8+ T cells for better immune assessment, its clinical application is limited. In this study, we developed bulk RNA sequencing-based FuncDimen models from integrative analysis of single-cell RNA sequencing and matched bulk RNA sequencing data to evaluate CD8+ T-cell functionalities across five dimensions: tumor reactivity, cytotoxicity, IFNγ secretion, proliferation, and apoptosis. The FuncDimen models quantifying different functional dimensions of CD8+ T cells were validated in our breast cancer cohort and external databases using immunofluorescence and imaging mass cytometry. We calculated the FuncAggre score by weighted aggregation of all five FuncDimen models to encapsulate the overall antitumor immunity. In our breast cancer cohort and external databases, the FuncAggre score demonstrated superior predictive performance for breast cancer prognosis (time-dependent AUC: 0.56-0.70) and immunotherapy response (AUC: 0.71-0.83) over other immune biomarkers, regardless of the breast cancer molecular subtype. Together, the FuncDimen models offer a refined assessment of antitumor immunity mediated by CD8+ T cells in the clinic, enhancing prognostic prediction and aiding personalized immunotherapy in breast cancer.

Predicting Survival Rates in Brain Metastases Patients from Non-Small Cell Lung Cancer Using Radiomic Signatures Associated with Tumor Immune Heterogeneity

Non-small cell lung cancer (NSCLC) frequently metastasizes to the brain, significantly worsened prognoses. This study aimed to develop an interpretable model for predicting survival in NSCLC patients with brain metastases (BM) integrating radiomic features and RNA sequencing data. 292 samples are collected and analyzed utilizing T1/T2 MRIs. Bidirectional stepwise logistic regression is employed to identify significant variables, facilitating the construction of a prognostic model, which is benchmarked against four machine learning algorithms. BM tissue samples are processed for RNA extraction and sequencing. The optimal model achieved an AUC of 0.96 and a C-index of 0.89 in the train set and an AUC of 0.84 with a C-index of 0.78 in the test set, indicating strong predictive performance and generalizability. Patients from Xiangya Hospital are stratified into high-risk (n = 11) and low-risk (n = 30) groups. RNA sequencing revealed an enrichment of immune-related pathways, particularly the interferon (IFN) pathway in the low-risk group. Immune cell infiltration analysis identified a significant presence of CD8+-T cells, IFNγ-6/-18 in the low-risk group, suggesting an immunologically favorable tumor microenvironment. These findings highlight the potential of combining radiomic and RNA sequencing data for improved survival predictions and personalized treatment strategies in BM patients from NSCLC.

eIF3a function in immunity and protection against severe sepsis by regulating B cell quantity and function through m6A modification

eIF3a is a N 6-methyladenosine (m6A) reader that regulates mRNA translation by recognizing m6A modifications of these mRNAs. It has been suggested that eIF3a may play an important role in regulating translation initiation via m6A during infection when canonical cap-dependent initiation is inhibited. However, the death of animal model studies impedes our understanding of the functional significance of eIF3a in immunity and regulation in vivo. In this study, we investigated the in vivo function of eIF3a using eIF3a knockout and knockdown mouse models and found that eIF3a deficiency resulted in splenic tissue structural disruption and multi-organ damage, which contributed to severe sepsis induced by Lipopolysaccharide (LPS). Ectopic eIF3a overexpression in the eIF3a knockdown mice rescued mice from LPS-induced severe sepsis. We further showed that eIF3a maintains a functional and healthy immune system by regulating B cell function and quantity through m6A modification of mRNAs. These findings unveil a novel mechanism underlying sepsis, implicating the pivotal role of B cells in this complex disease process regulated by eIF3a. Furthermore, eIF3a may be used to develop a potential strategy for treating sepsis.

High-dimensional deconstruction of HNSC reveals clinically distinct cellular states and ecosystems that are associated with prognosis and therapy response

BACKGROUND

Characterizing the variety of cell types in the tumor microenvironment (TME) and their organization into cellular communities is vital for elucidating the biological diversity of cancer and informing therapeutic strategies.

METHODS

Here, we employed a machine learning-based algorithm framework, EcoTyper, to analyze single-cell transcriptomes from 139 patients with head and neck squamous cell carcinoma (HNSC)and gene expression profiles from 983 additional HNSC patients, aiming to delineate the fundamental cell states and ecosystems integral to HNSC.

RESULTS

A diverse landscape of 66 cell states and 9 ecosystems within the HNSC microenvironment was identified, revealing classical cell types while also expanding upon previous immune classifications. Survival analysis revealed that specific cell states and ecotypes (ecosystems) are associated with patient prognosis, underscoring their potential as indicators of clinical outcomes. Moreover, distinct cell states and ecotypes exhibited varying responses to immunotherapy and chemotherapy, with several showing promise as predictive biomarkers for treatment efficacy.

CONCLUSION

Our large-scale integrative transcriptome analysis provides high-resolution insights into the cellular states and ecosystems of HNSC, facilitating the discovery of novel biomarkers and supporting the development of precision therapies.

Application of CIBERSORTx and BayesPrism to deconvolution of bulk RNA-seq data from human myocardium and skeletal muscle

RNA-sequencing (RNA-seq) is an important tool to explore molecular mechanisms of disease. Technological advances mean this can be performed at the single-cell level, but the large sample sizes needed in clinical studies are currently prohibitively expensive and complex. Deconvolution of bulk RNA-seq offers an opportunity to bridge this gap by defining the cell lineage composition of samples. This approach is widely used in immunology studies, but currently there are no validated pipelines for researchers analysing human myocardium or skeletal muscle. Here, we describe the application and in silico validation of two pipelines to deconvolute human right atrium, left ventricle and skeletal muscle bulk RNA-seq data. Specifically, we have defined the major cell lineages of these tissues using single cell/nucleus RNA-seq data from the Heart Cell Atlas, which are then applied during deconvolution using the CIBERSORTx or BayesPrism deconvolution packages. Both pipelines gave robust estimates of the proportion of all major cell lineages in these tissues. We demonstrate their value in defining age- and sex-differences in tissue composition using bulk RNA-seq data from the GTEx consortium. Our validated pipelines can be rapidly applied by researchers working with existing or novel bulk RNA-seq of myocardium or skeletal muscle to gain novel insights.

Systematic inference of super-resolution cell spatial profiles from histology images

Inferring cell spatial profiles from histology images is critical for cancer diagnosis and treatment in clinical settings. In this study, we report a weakly-supervised deep-learning method, HistoCell, to directly infer super-resolution cell spatial profiles consisting of cell types, cell states and their spatial network from histology images at the single-nucleus-level. Benchmark analysis demonstrates that HistoCell robustly achieves state-of-the-art performance in terms of cell type/states prediction solely from histology images across multiple cancer tissues. HistoCell can significantly enhance the deconvolution accuracy for the spatial transcriptomics data and enable accurate annotation of subtle cancer tissue architectures. Moreover, HistoCell is applied to de novo discovery of clinically relevant spatial organization indicators, including prognosis and drug response biomarkers, across diverse cancer types. HistoCell also enable image-based screening of cell populations that drives phenotype of interest, and is applied to discover the cell population and corresponding spatial organization indicators associated with gastric malignant transformation risk. Overall, HistoCell emerges as a powerful and versatile tool for cancer studies in histology image-only cohorts.

Building Spatiotemporal Understanding of Mycobacterium tuberculosis-Host Interactions

Heterogeneity during Mycobacterium tuberculosis (Mtb) infection greatly impacts disease outcome and complicates treatment. This heterogeneity encompasses many facets, spanning local differences in the host immune response to Mtb and the environment experienced by the bacterium, to nonuniformity in Mtb replication state. All of these facets are interlinked and each can affect Mtb susceptibility to antibiotic treatment. In-depth spatiotemporal understanding of Mtb-host interactions is thus critical to both fundamental comprehension of Mtb infection biology and for the development of effective therapeutic regimens. Such spatiotemporal understanding dictates the need for analysis at the single bacterium/cell level in the context of intact tissue architecture, which has been a significant technical challenge. Excitingly, innovations in spatial single cell methodology have opened the door to such studies, beginning to illuminate aspects ranging from intergranuloma differences in cellular composition and phenotype, to sublocation differences in Mtb physiology and replication state. In this perspective, we discuss recent studies that demonstrate the potential of these methodological advancements to reveal critical spatiotemporal insight into Mtb-host interactions, and highlight future avenues of research made possible by these advances.

Spatially distinct cellular and molecular landscapes define prognosis in triple negative breast cancer

Background-

Triple-negative breast cancer is a prevalent breast cancer subtype with the lowest 5-year survival. Several factors contribute to its treatment response, but the inherent molecular and cellular tumor heterogeneity are increasingly acknowledged as crucial determinants.

Methods-

Spatial transcriptomic profiling was performed on FFPE tissues from a retrospective, treatment-naive group of women with differential prognoses (17 with >15 years survival- good prognosis (GPx) and 15 with <3 years survival-poor prognosis (PPx)) using GeoMX® Digital Spatial Profiler. Regions of interest were segmented on pan-cytokeratin and analyzed for tumor and stromal components, probed using GeoMx human whole transcriptome atlas (WTA) panel. Data quality control, normalization, and differential analysis was performed in R using GeomxTools and linear mixed models. Additional analyses including cell-type deconvolution, spatial entropy, functional enrichment, TF-target / ligand-receptor analysis and convolution neural networks were employed to identify significant gene signatures contributing to differential prognosis.

Results-

Here we report on the spatial and molecular heterogeneity underlying differential prognosis. We observe that the state of the epithelia and its microenvironment (TME) are transcriptionally distinct between the two groups. Invasive epithelia in GPx show a significant increase in immune transcripts with the TME exhibiting increased immune cell presence (via IF), while in PPx they are more metabolically and translationally active, with the TME being more mesenchymal/fibrotic. Specifically, pre-cancerous epithelia in PPx display a prescience of aggressiveness as evidenced by increased EMT-signaling. We identify distinct epithelial gene signatures for PPx and GPx, that can, with high accuracy, classify samples at the time of diagnosis and likely inform therapy.

Conclusions-

To the best of our knowledge, this is the first study to leverage spatial transcriptomics for an in-depth delineation of the cellular and molecular underpinnings of differential prognosis in TNBC. Our study highlights the potential of spatial transcriptomics to not only uncover the molecular drivers of differential prognosis in TNBC but also to pave the way for precision diagnostics and tailored therapeutic strategies, transforming the clinical landscape for this aggressive breast cancer subtype.

Dynamic changes in peripheral blood immunophenotyping and its prognostic value in cervical cancer patients undergoing immune checkpoint blockade therapy

BACKGROUND

Immune checkpoint blockade (ICB) therapy, including antibodies targeting the programmed cell death protein 1 (PD-1) pathway, has significantly prolonged the overall survival (OS) in patients with advanced cervical cancer (CC). ICB treatment affects both target cells and various components released by immune cells, which can be observed in peripheral blood. However, there has been limited research on the dynamics of peripheral blood immunophenotyping and its association with OS in CC patients receiving ICB therapy.

METHODS

Patients with persistent, recurrent, or metastatic CC treated with ICB were enrolled between December 2019 and September 2022. The dynamic changes in peripheral blood immune cells, immunoglobulins, and complement components were analyzed at baseline (within 30 days prior to the first ICB cycle) and after the second cycle of ICB treatment (4-6 weeks after the first ICB treatment). Associations of the baseline levels of peripheral blood immune cells, immunoglobulins, complement components with OS were analyzed using multivariable Cox regression analysis.

RESULTS

In this retrospective cohort study, 119 patients who received at least two cycles of ICB were included. Data on peripheral blood immune cells, immunoglobulins, and complement components were available for 70 of these patients. The percentages of suppressor T (Ts) cells and natural killer (NK) cells in peripheral blood increased significantly post-ICB treatment, whereas the Th/Ts ratio and IgM levels decreased. The percentages of cytotoxic T (Tc) cells, Ts cells, the Th/Ts ratio, and levels of IgM, IgA, C3, and C4 were significantly associated with the OS of patients. Furthermore, multivariable Cox regression analysis found that a high level of IgA was associated with poor OS of the patients (HR = 2.918; 95% CI, 1.081-7.877, P = 0.035).

CONCLUSION

Our study demonstrated the potential proliferation of peripheral blood anti-tumor T cells in some CC patients undergoing ICB therapy. The observed associations between peripheral blood immunophenotyping and OS suggest that these biomarkers might have potential as prognostic tools.

Plasma miRNAs in polycystic ovary syndrome drive endometrial cancer progression: insights into molecular pathways and therapeutic targets

Polycystic ovary syndrome (PCOS) is a known risk factor for uterine endometrial cancer (UCEC), but its underlying mechanisms remain unclear. MicroRNAs (miRNAs) could provide insights into these mechanisms and reveal potential therapeutic targets. Differential miRNA expression was analyzed in plasma exosomes from 15 PCOS and 15 control samples. Survival analysis assessed the prognostic value of these miRNAs in UCEC. MiRNA-target gene interaction networks and gene co-expression analyses were used to explore molecular mechanisms. Validation was performed using experimental data from Ishikawa cells treated with six candidate drugs. Among the 15 differentially expressed miRNAs, 12 were up-regulated and 3 were down-regulated in PCOS. Twelve of these miRNAs were associated with UCEC overall survival, with miR-142, miR-424, and miR-331 acting as protective factors, while the remaining 9 miRNAs were identified as risk factors. MiRNA-target network highlighted key genes such as PHF8, LCOR, SFT2D3, E2F1, and ESR1, which were found to be prognostic for patient survival. Further gene expression and co-expression analyses based on miR-424 and miR-330 expression revealed significant alterations in gene expression and cellular processes related to UCEC. Two-sample Mendelian randomization analysis identified potential causal relationships between AURKA gene expression and PCOS or UCEC. Testosterone and estradiol might have adverse roles in UCEC, while drugs like troglitazone, valproic acid, retinoic acid, and progesterone demonstrated various effects on gene expression and cellular processes. Our findings suggest that aberrant miRNA expression, particularly miR-330 and miR-424, may play crucial roles in UCEC progression. The identified miRNAs and candidate drugs may serve as potential therapeutic targets for UCEC, but further research is required to validate and explore their clinical applications.

Multiomics Analysis Reveals Molecular Changes during Early Progression of Precancerous Lesions to Lung Adenocarcinoma in Never-Smokers

Lung cancer is the most common cause of cancer mortality globally, and the prevalence of lung adenocarcinoma, the most common lung cancer subtype, has increased sharply in East Asia. Early diagnosis leads to better survival rates, but this requires an improved understanding of the molecular changes during early tumorigenesis, particularly in nonsmokers. In this study, we performed whole-exome sequencing and RNA sequencing of samples from 94 East Asian patients with precancerous lesions [25 with atypical adenomatous hyperplasia (AAH); 69 with adenocarcinoma in situ (AIS)] and 73 patients with early invasive lesions [minimally invasive adenocarcinoma (MIA)]. Cellular analysis revealed that the activities of endothelial and stromal cells could be used to categorize tumors into molecular subtypes within pathologically defined types of lesions. The subtypes were linked with the radiologically defined type of lesions and corresponded to immune cell infiltration throughout the early progression of lung adenocarcinoma. Spatial transcriptomic analysis revealed the distribution of epithelial cells, endothelial cells, fibroblasts, and plasma cells within MIA samples. Characterization of the molecular lesion subtypes identified positively selected mutational patterns and suggested that angiogenesis in the late-stage AIS type potentially contributes to tissue invasion of the MIA type. This study offers a resource that may help improve early diagnosis and patient prognosis, and the findings suggest possible approaches for early disease interception. Significance: Integrative analysis of multiomics data revealed coordination between immune and nonimmune cells during early progression of precancerous lesions to lung adenocarcinomas and shed light on the molecular characteristics of clinically defined subtypes.

Recent advancement in the spatial immuno-oncology

Recent advancements in spatial transcriptomics and spatial proteomics enabled the high-throughput profiling of single or multi-cell types and cell states with spatial information. They transformed our understanding of the higher-order architectures and paired cell-cell interactions within a tumor microenvironment (TME). Within less than a decade, this rapidly emerging field has discovered much crucial fundamental knowledge and significantly improved clinical diagnosis in the field of immuno-oncology. This review summarizes the conceptual frameworks to understand spatial omics data and highlights the updated knowledge of spatial immuno-oncology.

Integrated Analysis of Serum and Tissue microRNA Transcriptome for Biomarker Discovery in Gastric Cancer

Gastric cancer (GC) poses a significant global health challenge, demanding a detailed exploration of its molecular landscape. Studies suggest that exposure to environmental pollutants can lead to changes in microRNA (miRNA) expression patterns, which may contribute to the development and progression of GC. MiRNAs have emerged as crucial regulators implicated in GC pathogenesis. The largest GC serum miRNA dataset to date, comprising 1417 non-cancer controls and 1417 GC samples was used. We conducted a comprehensive analysis of miRNA expression profiles. Differential expression analysis, co-expression network construction, and machine learning models were employed to identify key serum miRNAs and their association with clinical parameters. Weighted Gene Co-expression Network Analysis (WGCNA) and immune infiltration analysis were used to validate the importance of the key miRNA. A total of 1766 differentially expressed miRNAs were identified, with miR-1290, miR-1246, and miR-451a among the top up-regulated, and miR-6875-5p, miR-6784-5p, miR-1228-5p, and miR-6765-5p among the top down-regulated. WGCNA revealed that modules M1 and M5 were significantly associated with GC subtypes and disease status. MiRNA-target gene network analysis identified prognostically significant genes TP53, EMCN, CBX8, and ALDH1A3. Machine learning models LASSO, SVM, randomforest, and XGBOOST demonstrated the diagnostic potential of miRNA profiles. Tissue and serum miR-187 emerged as an independent prognostic factor, influencing patient survival across clinical parameters. Gene expression and immune cell infiltration were different in tissues stratified by miR-187 expression. In summary, the integration of differential gene expression, co-expression analysis, and immune cell profiling provided insights into the molecular intricacies of GC progression.

Spatial transcriptomic analysis reveals significant differences in tumor microenvironment in HPV-dependent and HPV-independent vulvar squamous cell carcinoma

OBJECTIVE

Vulvar squamous cell carcinoma (VSCC) can be either HPV-dependent (HPVd) or HPV-independent (HPVi). HPVd VSCC typically occurs in younger women, has a more favorable prognosis, and develops from high-grade squamous intraepithelial lesions (HSIL). HPVi VSCC predominantly affects older women and arises within areas of chronic inflammation, particularly lichen sclerosis (LS). We utilized sequencing-based spatial transcriptomics to explore gene expression in a cohort of patients with HPVi and HPVd VSCC.

METHODS

We analysed gene expression in distinct areas (SCC, inflammation, LS, HSIL) from four early-stage VSCC cases (two HPVi, two HPVd) using the 10× Genomics Visium spatial transcriptomics platform. Cell-specific type expression was inferred using CIBERSORTx.

RESULTS

28,183 Visium spots were detected; each contained an estimated 20-50 cells. Reads per spot ranged from 9903 to 68,527. More genes were upregulated in HPVd (N = 601) than HPVi (N = 72) with distinct differences in Keratin and Collagen genes between etiologies. Gene expression was strikingly similar between SCC and adjacent inflammatory areas, regardless of etiology. IL-17 signaling was upregulated in HPVd samples. Surprisingly, CIBERSORTx inferred significantly more CD45+ cells in HPVi tissues than HPVd, especially CD4+ resting memory and follicular helper T cells in SCC areas. Immune cells moved from resting states in the pre-invasive tissues to activated states in the SCC and peri-tumoral inflammatory areas.

CONCLUSIONS

This study represents the first application of spatial transcriptomics in VSCC, with significantly more immune cells identified in HPVi SCC than in HPVd SCC. These data will act as a baseline for future studies.

A deconvolution framework that uses single-cell sequencing plus a small benchmark data set for accurate analysis of cell type ratios in complex tissue samples

Bulk deconvolution with single-cell/nucleus RNA-seq data is critical for understanding heterogeneity in complex biological samples, yet the technological discrepancy across sequencing platforms limits deconvolution accuracy. To address this, we utilize an experimental design to match inter-platform biological signals, hence revealing the technological discrepancy, and then develop a deconvolution framework called DeMixSC using this well-matched, that is, benchmark, data. Built upon a novel weighted nonnegative least-squares framework, DeMixSC identifies and adjusts genes with high technological discrepancy and aligns the benchmark data with large patient cohorts of matched-tissue-type for large-scale deconvolution. Our results using two benchmark data sets of healthy retinas and ovarian cancer tissues suggest much-improved deconvolution accuracy. Leveraging tissue-specific benchmark data sets, we applied DeMixSC to a large cohort of 453 age-related macular degeneration patients and a cohort of 30 ovarian cancer patients with various responses to neoadjuvant chemotherapy. Only DeMixSC successfully unveiled biologically meaningful differences across patient groups, demonstrating its broad applicability in diverse real-world clinical scenarios. Our findings reveal the impact of technological discrepancy on deconvolution performance and underscore the importance of a well-matched data set to resolve this challenge. The developed DeMixSC framework is generally applicable for accurately deconvolving large cohorts of disease tissues, including cancers, when a well-matched benchmark data set is available.

The spatially informed mFISHseq assay resolves biomarker discordance and predicts treatment response in breast cancer

Current assays fail to address breast cancer's complex biology and accurately predict treatment response. On a retrospective cohort of 1082 female breast tissues, we develop and validate mFISHseq, which integrates multiplexed RNA fluorescent in situ hybridization with RNA-sequencing, guided by laser capture microdissection. This technique ensures tumor purity, unbiased whole transcriptome profiling, and explicitly quantifies intratumoral heterogeneity. Here we show mFISHseq has 93% accuracy compared to immunohistochemistry. Our consensus subtyping and risk groups mitigate single sample discordance, provide early and late prognostic information, and identify high risk patients with enriched immune signatures, which predict response to neoadjuvant immunotherapy in the multicenter, phase II, prospective I-SPY2 trial. We identify putative antibody-drug conjugate (ADC)-responsive patients, as evidenced by a 19-feature T-DM1 classifier, validated on I-SPY2. Deploying mFISHseq as a research-use only test on 48 patients demonstrates clinical feasibility, revealing insights into the efficacy of targeted therapies, like CDK4/6 inhibitors, immunotherapies, and ADCs.

Multi-omic profiling highlights factors associated with resistance to immuno-chemotherapy in non-small-cell lung cancer

Although immune checkpoint blockade (ICB) therapies have shifted the treatment paradigm for non-small-cell lung cancer (NSCLC), many patients remain resistant. Here we characterize the tumor cell states and spatial cellular compositions of the NSCLC tumor microenvironment (TME) by analyzing single-cell transcriptomes of 232,080 cells and spatially resolved transcriptomes of tumors from 19 patients before and after ICB-chemotherapy. We find that tumor cells and secreted phosphoprotein 1-positive macrophages interact with collagen type XI alpha 1 chain-positive cancer-associated fibroblasts to stimulate the deposition and entanglement of collagen fibers at tumor boundaries, obstructing T cell infiltration and leading to poor prognosis. We also reveal distinct states of tertiary lymphoid structures (TLSs) in the TME. Activated TLSs are associated with improved prognosis, whereas a hypoxic microenvironment appears to suppress TLS development and is associated with poor prognosis. Our study provides novel insights into different cellular and molecular components corresponding to NSCLC ICB-chemotherapeutic responsiveness, which will benefit future individualized immuno-chemotherapy.

Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics

Single-cell transcriptomics has broadened our understanding of cellular diversity and gene expression dynamics in healthy and diseased tissues. Recently, spatial transcriptomics has emerged as a tool to contextualize single cells in multicellular neighbourhoods and to identify spatially recurrent phenotypes, or ecotypes. These technologies have generated vast datasets with targeted-transcriptome and whole-transcriptome profiles of hundreds to millions of cells. Such data have provided new insights into developmental hierarchies, cellular plasticity and diverse tissue microenvironments, and spurred a burst of innovation in computational methods for single-cell analysis. In this Review, we discuss recent advancements, ongoing challenges and prospects in identifying and characterizing cell states and multicellular neighbourhoods. We discuss recent progress in sample processing, data integration, identification of subtle cell states, trajectory modelling, deconvolution and spatial analysis. Furthermore, we discuss the increasing application of deep learning, including foundation models, in analysing single-cell and spatial transcriptomics data. Finally, we discuss recent applications of these tools in the fields of stem cell biology, immunology, and tumour biology, and the future of single-cell and spatial transcriptomics in biological research and its translation to the clinic.

Integrated Analysis of Single-Cell and Bulk RNA-Sequencing Based on EcoTyper Machine Learning Framework Identifies Cell-State-Specific M2 Macrophage Markers Associated with Gastric Cancer Prognosis

Background

Tumor is a complex and dynamic ecosystem formed by the interaction of numerous diverse cells types and the microenvironments they inhabit. Determining how cellular states change and develop distinct cellular communities in response to the tumor microenvironment is critical to understanding cancer progression. Tumour-associated macrophages (TAMs) are an important component of the tumour microenvironment and play a crucial role in cancer progression. This study was designed to identify cell-state-specific M2 macrophage markers associated with gastric cancer (GC) prognosis through integrative analysis of single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data using a machine learning framework named EcoTyper.

Results

The results showed that TAMs were classified into M1 macrophages, M2 macrophages, monocytes, undefined macrophages and dendritic cells, with M2 macrophages predominating. EcoTyper assigned macrophages to different cell states and ecotypes. A total of 168 cell-state-specific M2 macrophage markers were obtained by integrative analysis of scRNA-seq and bulk RNA-seq data. These markers could categorize GC patients into two clusters (clusters A and B) with different survival and M2 macrophages infiltration abundance. Cell adhesion molecules, cytokine-cytokine receptor interaction, JAK/STAT pathway, MAPK pathway were significantly enriched in cluster A, which had worse survival and higher M2 macrophages infiltration.

Conclusion

In conclusion, this study profiles a single-cell atlas of intratumor heterogeneity and defines the cell states and ecotypes of TAMs in GC. Furthermore, we have identified prognostically relevant cell-state-specific M2 macrophage markers. These findings provide novel insights into the tumor ecosystem and cancer progression.

GPS-Net: Discovering prognostic pathway modules based on network regularized kernel learning

The search for prognostic biomarkers capable of predicting patient outcomes, by analyzing gene expression in tissue samples and other molecular profiles, remains largely focused on single-gene-based or global-gene-search approaches. Gene-centric approaches, while foundational, fail to capture the higher-order dependencies that reflect the activities of co-regulated processes, pathway alterations, and regulatory networks, all of which are crucial in determining the patient outcomes in complex diseases like cancer. Here, we introduce GPS-Net, a computational framework that fills the gap in efficiently identifying prognostic modules by incorporating the holistic pathway structures and the network of gene interactions. By innovatively incorporating advanced multiple kernel learning techniques and network-based regularization, the proposed method not only enhances the accuracy of biomarker and pathway identification but also significantly reduces computational complexity, as demonstrated by extensive simulation studies. Applying GPS-Net, we identified key pathways that are predictive of patient outcomes in a cancer immunotherapy study. Overall, our approach provides a novel framework that renders genome-wide pathway-level prognostic analysis both feasible and scalable, synergizing both mechanism-driven and data-driven methodologies for precision genomics.

The importance of type I interferon in orchestrating the cytotoxic T-cell response to cancer

Both type I interferon (IFN-I) and CD4+ T-cell help are required to generate effective CD8+ T-cell responses to cancer. We here outline based on existing literature how IFN-I signaling and CD4+ T-cell help are connected. Both impact on the functional state of dendritic cells (DCs), particularly conventional (c)DC1. The cDC1s are critical for crosspresentation of cell-associated antigens and for delivery of CD4+ T-cell help for cytotoxic T-lymphocyte (CTL) effector and memory differentiation. In infection, production of IFN-I is prompted by pathogen-associated molecular patterns (PAMPs), while in cancer it relies on danger-associated molecular patterns (DAMPs). IFN-I production by tumor cells and pDCs in the tumor micro-environment (TME) is often limited. IFN-I signals increase the ability of migratory cDC1s and cDC2s to transport tumor antigens to tumor-draining lymph nodes (tdLNs). IFN-I also enables cDC1s to form and sustain the platform for help delivery by stimulating the production of chemokines that attract CD4+ and CD8+ T cells. IFN-I promotes delivery of help in concert with CD40 signals by additive and synergistic impact on cross-presentation and provision of critical costimulatory and cytokine signals for CTL effector and memory differentiation. The scenario of CD4+ T-cell help therefore depends on IFN-I signaling. This scenario can play out in tdLNs as well as in the TME, thereby contributing to the cancer immunity cycle. The collective observations may explain why both IFN-I and CD4+ T-cell help signatures in the TME correlate with good prognosis and response to PD-1 targeting immunotherapy in human cancer. They also may explain why a variety of tumor types in which IFN-I signaling is attenuated, remain devoid of functional CTLs.

Deciphering the Significance of Platelet-Derived Chloride Ion Channel Gene (BEST3) Through Platelet-Related Subtypes Mining for Non-Small Cell Lung Cancer

This study investigates platelet-related subtypes in non-small cell lung cancer (NSCLC) and seeks to identify genes associated with prognosis, focusing on the clinical significance of the chloride ion channel gene BEST3. We utilised sequencing and clinical data from GEO, TCGA and the Xena platform, building a risk model based on genetic features. TCGA and GSE37745 served as training cohorts, while GSE50081, GSE13213, GSE30129 and GSE42127 were validation cohorts. Immunotherapy datasets (GSE135222, TCGA-SKCM) were also analysed. Differentially expressed genes (DEGs) were identified using Limma, subtypes through ConsensusClusterPlus and key prognostic genes using COX regression, Random Forest and LASSO-COX. BEST3 expression was validated by flow cytometry (FCM) and functional assays in A549 cells with lentiviral overexpression evaluated its impact on apoptosis, proliferation and migration. Three platelet-related subtypes were identified, with ten key prognostic genes (including BEST3). Gene Ontology (GO) analysis showed six genes involved in platelet pathways. BEST3 was highly expressed in the platelet subtype 1. Flow cytometry confirmed elevated BEST3 levels in NSCLC (35.9% vs. 27.3% in healthy individuals). Overexpression of BEST3 in NSCLC cells suppressed apoptosis and promoted proliferation and migration. The discovery of three platelet subtypes and the role of BEST3 in promoting tumour growth and migration highlights its potential as a therapeutic target and prognostic marker in NSCLC.

Genetic and therapeutic heterogeneity shape the baseline and longitudinal immune ecosystem of ovarian clear cell carcinoma

BACKGROUND

Ovarian clear cell carcinoma (OCCC) is a rare and chemo-resistant subtype of ovarian cancer. While immunotherapy has demonstrated effectiveness in some OCCC cases, the mechanisms for heterogeneous immunoreactivity and potential combinatory strategies remain unclear.

METHODS

Tumor samples from 13 patients with OCCC underwent single-cell mRNA-seq and TCR-seq to generate 1 40 683 cells transcriptome, while additionally 31 formalin-fixed paraffin-embedded samples were used for immunohistochemistry. Spatial transcriptomics of two OCCC samples and bulk RNA-seq of 58 patients were incorporated for spatial and interpatient level explorations. Serum tumor markers and radiologic images of three patients with OCCC who received combinatory VEGF and PD-1 inhibition were retrospectively analyzed.

RESULTS

OCCC exhibited a dynamic immune architecture shaped by genetic and therapeutic pressure. ARID1A mutation linked to baseline immune activation, correlated with an enrichment of neoantigen-reactive CXCL13+ CTLA4+ CD8+ T cells (p<0.001) and enhanced FASLG-FAS interactions. Recurrent OCCC was fibrotic, angiogenic, and immunosuppressive, exhibiting metabolic reprogramming towards activated activity in fatty acid metabolism. High CD36 (log-rank p=0.012, HR: 4.515) and CD47 expression (log-rank p=0.037, HR: 3.246) indicated worse progression-free survival. Treatment with bevacizumab increased intratumoral T cell infiltration and activated T cell interferon-γ signaling. Retrospective analysis of clinical cases revealed that combination therapy with anti-VEGF (vascular endothelial growth factor) and anti-PD-1 agents exerted clinical benefits in patients with OCCC with persistent, recurrent, and metastatic disease.

CONCLUSIONS

ARID1A mutation correlated with OCCC baseline immune activation. Stromal reconstruction and tumor metabolic reprogramming functioned as key processes of OCCC dynamic progression. VEGF inhibition remodeled OCCC stroma, restored T cell function and potentiated immunotherapy. CD36 and CD47 might be potential therapeutic targets for recurrent OCCC.

Immunotherapy that improves response to chemotherapy in high-grade serous ovarian cancer

Single-cell RNA sequencing (scRNAseq) of tumour-infiltrating immune cells in high-grade serous ovarian cancer (HGSOC) omental biopsies reveals potential targets that could enhance response to neo-adjuvant chemotherapy (NACT). Analysis of 64,097 cells identifies NACT-induced overexpression of stabilin-1 (clever-1) on macrophages and FOXP3 in Tregs that is confirmed at the protein level. STAB1 inhibition in vitro induces anti-tumour macrophages. FOXP3 anti-sense oligonucleotide (FOXP3-ASO), repolarises Tregs to an effector T cell phenotype. ScRNAseq on 69,781 cells from an HGSOC syngeneic mouse model recapitulates the patients' data. Combining chemotherapy with anti-stabilin1 antibody and/or Foxp3-ASO significantly increases survival of mice with established peritoneal disease in two HGSOC syngeneic models and progression-free survival in a third model. Long-term survivors (300 days + ) are resistant to tumour rechallenge. Anti-stabilin1 antibody enriches the tumours with CXCL9+ macrophages and Foxp3-ASO increases TBET cell infiltration. Our results suggest that targeting these molecules in immune cells may improve chemotherapy response in patients.

Dual decoding of cell types and gene expression in spatial transcriptomics with PANDA

Sequencing-based spatial transcriptomics technologies have revolutionized our understanding of complex biological systems by enabling transcriptome profiling while preserving spatial context. However, spot-level expression measurements often amalgamate signals from diverse cells, obscuring potential heterogeneity. Existing methods aim to deconvolute spatial transcriptomics data into cell type proportions for each spot using single-cell RNA sequencing references but overlook cell-type-specific gene expression, essential for uncovering intra-type heterogeneity. We present PANDA (ProbAbilistic-based decoNvolution with spot-aDaptive cell type signAtures), a novel method that concurrently deciphers spot-level gene expression into both cell type proportions and cell-type-specific gene expression. PANDA integrates archetypal analysis to capture within-cell-type heterogeneity and dynamically learns cell type signatures for each spot during deconvolution. Simulations demonstrate PANDA's superior performance. Applied to real spatial transcriptomics data from diverse tissues, including tumor, brain, and developing heart, PANDA reconstructs spatial structures and reveals subtle transcriptional variations within specific cell types, offering a comprehensive understanding of tissue dynamics.

The single-cell spatial landscape of stage III colorectal cancers

We conducted a spatial analysis using imaging mass cytometry applied to stage III colorectal adenocarcinomas. This study used multiplexed markers to distinguish individual cells and their spatial organization from 52 colorectal cancers. We determined the landscape features of cellular spatial features in the CRC tumor microenvironment. This spatial single-cell analysis identified 10 unique cell phenotypes in the tumor microenvironment that included stromal and immune cells with a subset which had a proliferative phenotype. These special features included spatial neighborhood interactions between single cells as well as different tissue niches, especially the tumor infiltrating lymphocyte regions. We applied a robust statistical analysis to identify significant correlations of cell features with phenotypes such as microsatellite instability or recurrence. We determined that microsatellite stable (MSS) colorectal cancers had an increased risk of recurrence if they had the following features: 1) a low level of stromal tumor-infiltrating lymphocytes, and 2) low interactions between CD4+ T cells and stromal cells. Our results point to the utility of spatial single-cell interaction analysis in defining novel features of the tumor immune microenvironments and providing useful clinical cell-related spatial biomarkers.

Tumor-Associated Antigen Burden Correlates with Immune Checkpoint Blockade Benefit in Tumors with Low Levels of T-cell Exhaustion

Tumor-associated antigens (TAA) are important targets for cancer vaccines. However, TAA-based vaccines have not yet achieved their full potential in clinical trials. In contrast, immune checkpoint blockade (ICB) has emerged as an effective therapy, leading to durable responses in selected patients with cancer. To date, few generalizable associations between TAAs and ICB benefit have been reported, with most studies focusing on melanoma, which has the highest mutation rate in cancer. In this study, we developed a TAA burden (TAB) algorithm based on known and putative TAAs and investigated the association of TAB with ICB benefit. Analysis of the IMvigor210 patient cohort of urothelial carcinoma treated with anti-PDL1 revealed that high tumor mutation burden weakened the association of TAB with ICB benefit. Furthermore, TAB correlated with ICB efficacy in tumors characterized by negative PDL1 staining on immune cells; however, high levels of PDL1 staining on immune cells were linked to T-cell exhaustion. Validation across independent clinical datasets-including urothelial carcinoma cohorts treated with anti-PD1/PDL1 agents and neoadjuvant anti-PD1 trials for head and neck cancers-corroborated the finding that TAB correlates with ICB benefit in tumors with low T-cell exhaustion. Pan-cancer analyses revealed that in most cancer entities, tumors with higher T-cell exhaustion exhibited lower TAB levels, implying possible immunoediting of TAAs in tumors with established antitumor immunity. Our study challenges the prevailing notion of a lack of association between TAAs and ICB response. It also underscores the need for future investigations into the immunogenicity of TAAs and TAA-based vaccine strategies in tumors with low levels of T-cell exhaustion.

Systems immunology insights into brain metastasis

Brain metastasis poses formidable clinical challenges due to its intricate interactions with the brain's unique immune environment, often resulting in poor prognoses. This review delves into systems immunology's role in uncovering the dynamic interplay between metastatic cancer cells and brain immunity. Leveraging spatial and single-cell technologies, along with advanced computational modeling, systems immunology offers unprecedented insights into mechanisms of immune evasion and tumor proliferation. Recent studies highlight potential immunotherapeutic targets, suggesting strategies to boost antitumor immunity and counteract cancer cell evasion in the brain. Despite substantial progress, challenges persist, particularly in accurately simulating human conditions. This review underscores the need for interdisciplinary collaboration to harness systems immunology's full potential, aiming to dramatically improve outcomes for patients with brain metastasis.

Transcriptomics of Human Brain Tissue in Parkinson's Disease: a Comparison of Bulk and Single-cell RNA Sequencing

Parkinson's disease (PD) is a chronic and progressive neurodegenerative disease leading to motor dysfunction and, in some cases, dementia. Transcriptome analysis is one promising approach for characterizing PD and other neurodegenerative disorders by informing how specific disease events influence gene expression and contribute to pathogenesis. With the emergence of single-cell and single-nucleus RNA sequencing (scnRNA-seq) technologies, the transcriptional landscape of neurodegenerative diseases can now be described at the cellular level. As the application of scnRNA-seq is becoming routine, it calls to question how results at a single-cell resolution compare to those obtained from RNA sequencing of whole tissues (bulk RNA-seq), whether the findings are compatible, and how the assays are complimentary for unraveling the elusive transcriptional changes that drive neurodegenerative disease. Herein, we review the studies that have leveraged RNA-seq technologies to investigate PD. Through the integration of bulk and scnRNA-seq findings from human, post-mortem brain tissue, we use the PD literature as a case study to evaluate the compatibility of the results generated from each assay and demonstrate the complementarity of the sequencing technologies. Finally, through the lens of the PD transcriptomic literature, we evaluate the current feasibility of bulk and scnRNA-seq technologies to illustrate the necessity of both technologies for achieving a comprehensive insight into the mechanism by which gene expression promotes neurodegenerative disease. We conclude that the continued application of both assays will provide the greatest insight into neurodegenerative disease pathology, providing both cell-specific and whole-tissue level information.

COADREADx: A comprehensive algorithmic dissection of colorectal cancer unravels salient biomarkers and actionable insights into its discrete progression

Background

Colorectal cancer is a common condition with an uncommon burden of disease, heterogeneity in manifestation, and no definitive treatment in the advanced stages. Renewed efforts to unravel the genetic drivers of colorectal cancer progression are paramount. Early-stage detection contributes to the success of cancer therapy and increases the likelihood of a favorable prognosis. Here, we have executed a comprehensive computational workflow aimed at uncovering the discrete stagewise genomic drivers of colorectal cancer progression.

Methods

Using the TCGA COADREAD expression data and clinical metadata, we constructed stage-specific linear models as well as contrast models to identify stage-salient differentially expressed genes. Stage-salient differentially expressed genes with a significant monotone trend of expression across the stages were identified as progression-significant biomarkers. The stage-salient genes were benchmarked using normals-augmented dataset, and cross-referenced with existing knowledge. The candidate biomarkers were used to construct the feature space for learning an optimal model for the digital screening of early-stage colorectal cancers. The candidate biomarkers were also examined for constructing a prognostic model based on survival analysis.

Results

Among the biomarkers identified are: CRLF1, CALB2, STAC2, UCHL1, KCNG1 (stage-I salient), KLHL34, LPHN3, GREM2, ADCY5, PLAC2, DMRT3 (stage-II salient), PIGR, HABP2, SLC26A9 (stage-III salient), GABRD, DKK1, DLX3, CST6, HOTAIR (stage-IV salient), and CDH3, KRT80, AADACL2, OTOP2, FAM135B, HSP90AB1 (top linear model genes). In particular the study yielded 31 genes that are progression-significant such as ESM1, DKK1, SPDYC, IGFBP1, BIRC7, NKD1, CXCL13, VGLL1, PLAC1, SPERT, UPK2, and interestingly three members of the LY6G6 family. Significant monotonic linear model genes included HIGD1A, ACADS, PEX26, and SPIB. A feature space of just seven biomarkers, namely ESM1, DHRS7C, OTOP3, AADACL2, LPHN3, GABRD, and LPAR1, was sufficient to optimize a RandomForest model that achieved > 98% balanced accuracy (and performant recall) of cancer vs. normal on external validation. Design of an optimal multivariate model based on survival analysis yielded a prognostic panel of three stage-IV salient genes, namely HOTAIR, GABRD, and DKK1. Based on the above sparse signatures, we have developed COADREADx, a web-server for potentially assisting colorectal cancer screening and patient risk stratification. COADREADx provides uncertainty measures for its predictions and needs clinical validation. It has been deployed for experimental non-commercial use at: https://apalanialab.shinyapps.io/coadreadx/.