Spatial Transcriptomic Analysis of a Diverse Patient Cohort Reveals a Conserved Architecture in Triple-Negative Breast Cancer

Sociodemographic Disparities in Rectal Cancer Outcomes within Academic Cancer Centers

BACKGROUND

Sociodemographic disparities in cancer care outcomes are often related to delayed or limited care access. However, it is unknown whether outcome differences persist after establishing high-level care. This study evaluated the relationship between rectal cancer outcomes and sociodemographic status at academic cancer centers.

PATIENTS AND METHODS

A retrospective cohort study of patients with rectal cancer treated at academic cancer centers was conducted utilizing the National Cancer Database. Primary outcome was overall survival, while secondary outcomes included 30- and 90-day mortality, time from diagnosis to treatments, hospital readmission rates, and hospital length of stay.

RESULTS

Of the 127,023 patients, median age was 62.7 years (SD 11.92), 59.3% were male, 80.3% were white, and 39.4% presented with stage III disease. After adjustment, Black patients had the worst overall survival (HR 1.10, 95% CI 1.01-1.19, p = 0.016). Private insurance status conferred overall survival benefit (HR 0.66, 95% CI 0.58-0.75, p < 0.001) as well as the best protection against 30- and 90- day postoperative mortality (30-day OR 0.31, 95% CI 0.10-0.97, p = 0.044; 90-day OR 0.37, 95% CI 0.16-0.83, p = 0.015). Black patients experienced longer time to first treatment than their white counterparts, with a delay of 3.23 days (95% CI 1.87-4.58, p < 0.001).

CONCLUSIONS

This study demonstrated the existence of sociodemographic disparities even within the walls of academic institutions, where care should be evidence-based, standardized, comprehensive, and equitable. When analyzing causal pathways, delays in time to treatment initiation may be contributing to these outcomes, but may be modifiable.

A METTL3-NFE2L3 axis mediates tumor stemness and progression in lung adenocarcinoma

The progression of lung adenocarcinoma is primarily driven by cancer stem cells (CSCs), which have self-renewal capabilities and confer resistance to therapies, including neoadjuvant treatments combining chemotherapy and immune checkpoint inhibitors. In this study, we identified that OV6+ tumor cells exhibit stem-like characteristics and are notably enriched in patients with non-major pathological response, closely associated with resistance to combination therapies. Hypoxia and HIF1α were found to drive the formation of OV6+ CSCs. METTL3, a methyltransferase, was revealed as a critical regulator of OV6+ CSCs by stabilizing NFE2L3 messenger RNA via an N6-methyladenosine-dependent manner, thereby up-regulating NFE2L3 and activating the intrinsic WNT signaling pathway essential for maintaining stemness. OV6+ tumor cells promoted M2 macrophage infiltration and the formation of an immunosuppressive tumor microenvironment (TME). Targeting METTL3 effectively eliminated OV6+ CSCs and suppressed tumor progression. Moreover, the combination of STM2457 with cisplatin overcame chemoresistance, remodeled the TME, and provided promising insights for enhancing the efficacy of neoadjuvant combination therapies.

Spatial transcriptomics reveals tryptophan metabolism restricting maturation of intratumoral tertiary lymphoid structures

Tertiary lymphoid structures (TLSs) are ectopic lymphoid aggregates found in numerous cancers, often linked to enhanced immunotherapy responses and better clinical outcomes. However, the factors driving TLS maturation are not fully understood. Using near single-cell spatial transcriptomic mapping, we comprehensively profile TLSs under various maturation stages and their microenvironment in hepatocellular carcinoma (HCC). Based on their developmental trajectories, we classify immature TLSs into two groups: conforming and deviating TLSs. Our findings indicate that conforming TLSs, similar to mature TLSs, possess a niche function for immunotherapy responses, while deviating TLSs do not. We discover that the tryptophan-enriched metabolic microenvironment shaped by malignant cells contributes to the deviation of TLS maturation. Inhibiting tryptophan metabolism promotes intratumoral TLS maturation and enhances tumor control, synergizing with anti-PD-1 treatments. Therefore, promoting TLS maturation represents a potential strategy to improve antitumor responses and immunotherapy outcomes.

Spatial multi-omics analysis of tumor-stroma boundary cell features for predicting breast cancer progression and therapy response

Background

The tumor boundary of breast cancer represents a highly heterogeneous region. In this area, the interactions between malignant and non-malignant cells influence tumor progression, immune evasion, and drug resistance. However, the spatial transcriptional profile of the tumor boundary and its role in the prognosis and treatment response of breast cancer remain unclear.

Method

Utilizing the Cottrazm algorithm, we reconstructed the intricate boundaries and identified differentially expressed genes (DEGs) associated with these regions. Cell-cell co-positioning analysis was conducted using SpaCET, which revealed key interactions between tumor-associated macrophage (TAMs) and cancer-associated fibroblasts (CAFs). Additionally, Lasso regression analysis was employed to develop a malignant body signature (MBS), which was subsequently validated using the TCGA dataset for prognosis prediction and treatment response assessment.

Results

Our research indicates that the tumor boundary is characterized by a rich reconstruction of the extracellular matrix (ECM), immunomodulatory regulation, and the epithelial-to-mesenchymal transition (EMT), underscoring its significance in tumor progression. Spatial colocalization analysis reveals a significant interaction between CAFs and M2-like tumor-associated macrophage (TAM), which contributes to immune exclusion and drug resistance. The MBS score effectively stratifies patients into high-risk groups, with survival outcomes for patients exhibiting high MBS scores being significantly poorer. Furthermore, drug sensitivity analysis demonstrates that high-MB tumors had poor response to chemotherapy strategies, highlighting the role of the tumor boundary in modulating therapeutic efficacy.

Conclusion

Collectively, we investigate the spatial transcription group and bulk data to elucidate the characteristics of tumor boundary molecules in breast cancer. The CAF-M2 phenotype emerges as a critical determinant of immunosuppression and drug resistance, suggesting that targeting this interaction may improve treatment responses. Furthermore, the MBS serves as a novel prognostic tool and offers potential strategies for guiding personalized treatment approaches in breast cancer.

Integrative spatial omics reveals distinct tumor-promoting multicellular niches and immunosuppressive mechanisms in Black American and White American patients with TNBC

Racial disparities in the clinical outcomes of triple-negative breast cancer (TNBC) have been well-documented, but the underlying biological mechanisms remain poorly understood. To investigate these disparities, we employed a multi-omic approach integrating imaging mass cytometry and spatial transcriptomics to characterize the tumor microenvironment (TME) in self-identified Black American (BA) and White American (WA) TNBC patients. Our analysis revealed that the TME in BA patients is marked by a network of endothelial cells, macrophages, and mesenchymal-like cells, which correlates with reduced patient survival. In contrast, the WA TNBC microenvironment is enriched in T-cells and neutrophils, indicative of T-cell exhaustion and suppressed immune responses. Ligand-receptor and pathway analyses further demonstrated that BA TNBC tumors exhibit a relatively "immune-cold" profile, while WA TNBC tumors display features of an "inflamed" TME, suggesting the evolution of a unique immunosuppressive mechanism. These findings provide insight into racially distinct tumor-promoting and immunosuppressive microenvironments, which may contribute to the observed differences in clinical outcomes among BA and WA TNBC patients.

Statement of Significance

This study identifies distinct tumor microenvironment (TME) profiles in Black and White American TNBC patients, providing new insights into the biological mechanisms driving outcome disparities. Our findings highlight the role of the tumor-endothelial-macrophage niche in these disparities, offering a potential therapeutic target for race-inclusive strategies aimed at improving clinical outcomes. By revealing racial differences in treatment response profiles, this work underscores the necessity for tailored therapies in TNBC. These insights lay the groundwork for the development of inclusive, precision-driven treatment approaches that may help mitigate racial disparities and enhance patient outcomes.

Mapping molecular landscapes in triple-negative breast cancer: insights from spatial transcriptomics

The tumor microenvironment (TME) of triple-negative breast cancer (TNBC) is a highly heterogeneous and very aggressive form of the disease that has few suitable treatment options; however, spatial transcriptomics (ST) is a powerful tool for elucidation of the TME in TNBC. Because of its spatial context preservation, ST has a unique capability to map tumor-stroma interactions, immune infiltration, and therapy resistance mechanisms (which are key to understanding TNBC progression), compared with conventional transcriptomics. This review shows the use of ST in TNBC, its utilization in spatial biomarker identification, intratumoral heterogeneity definition, molecular subtyping refinement, and prediction of immunotherapy responses. Recent insight from ST-driven insights has explained the key spatial patterns on immune evasion, chemotherapy resistance, racial disparities of TNBC, and aspects for patient stratification and therapeutic decision. With the integration of ST with the subjects of proteomics and imaging mass cytometry, this approach has been enlarged and is now applied in precision medicine and biomarker discovery. Recently, advancements in AI-based spatial analysis for tumor classification, identification of biomarkers, and creation of therapy prediction models have occurred. However, continued developments in ST technologies, computational tools, and partnerships amongst multiple centers to facilitate the integration of ST into clinical routine practice are needed to unlock novel therapeutic targets.

Analysis of single-cell and spatial transcriptomics in TNBC cell-cell interactions

Triple-negative breast cancer (TNBC) is a highly malignant tumor in women, characterized by high morbidity, mortality, and recurrence rates. Although surgical treatment, radiotherapy, and chemotherapy are the mainstays of current treatment methods, the high heterogeneity of TNBC results in unsatisfactory outcomes with low 5-year survival rates. Rapid advancements in omics technology have propelled the understanding of TNBC molecular biology. The emergence of single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) has significantly enhanced knowledge of tumor heterogeneity and the distribution, functionality, and intercellular interactions of various cell types within the tumor microenvironment, including tumor cells, T cells, B cells, macrophages, and fibroblasts. The present study provides an overview of the technical characteristics of scRNA-seq and ST, highlighting their applications in exploring TNBC heterogeneity, cell spatial distribution patterns, and intercellular interactions. This review aims to enhance the comprehension of TNBC at the cellular level for the development of effective therapeutic targets.

Deciphering the role of CNIH4 in pan-cancer landscapes and its significance in breast cancer progression

Background

The escalating global cancer burden necessitates the development of biomarkers with enhanced specificity and sensitivity for early diagnosis and therapeutic efficacy monitoring. The CNIH4 gene, an emerging biomarker, is increasingly recognized for its role in the malignant progression across various cancers.

Methods

We conducted a comprehensive multi-omics analysis of CNIH4, including pan-cancer expression profiles, epigenetic alterations, immune microenvironment characteristics, and therapeutic response patterns. Our focus was on clinical features, molecular underpinnings, and drug sensitivity in breast cancer (BRCA) associated with CNIH4. In vitro studies were also performed to assess the effects of CNIH4 knockdown on cell proliferation and cell cycle in the MDA-MB-231 cell line.

Results

CNIH4 upregulation was observed in multiple cancers, significantly correlating with genomic instability. High CNIH4 expression levels were linked to poor prognosis across cancers and associated with key cancer-related pathways, particularly those in cell cycle regulation and DNA repair. Correlation analyses suggest a role for CNIH4 in the tumor immune microenvironment, as evidenced by its association with immune subtypes, immune-related genes, and immune cell infiltration. Single-cell and spatial transcriptome analyses confirmed that CNIH4 expression in BRCA predicts tumor malignancy. Drug sensitivity analysis revealed a significant correlation between CNIH4 and responsiveness to various kinase inhibitors and chemotherapeutic agents. In vitro experiments demonstrated that CNIH4 knockdown significantly impacts the proliferation and cell cycle of MDA-MB-231 cells.

Conclusion

Our study highlights CNIH4 as a promising pan-cancer biomarker with significant implications for tumor progression and a critical role in cell cycle regulation in BRCA.

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.

Learning Phenotype Associated Signature in Spatial Transcriptomics with PASSAGE

Spatially resolved transcriptomics (SRT) is poised to advance the understanding of cellular organization within complex tissues under various physiological and pathological conditions at unprecedented resolution. Despite the development of numerous computational tools that facilitate the automatic identification of statistically significant intra-/inter-slice patterns (like spatial domains), these methods typically operate in an unsupervised manner, without leveraging sample characteristics like physiological/pathological states. Here PASSAGE (Phenotype Associated Spatial Signature Analysis with Graph-based Embedding), a rationally-designed deep learning framework is presented for characterizing phenotype-associated signatures across multiple heterogeneous spatial slices effectively. In addition to its outstanding performance in systematic benchmarks, PASSAGE's unique capability in calling sophisticated signatures has been demonstrated in multiple real-world cases. The full package of PASSAGE is available at https://github.com/gao-lab/PASSAGE.

Exploring the role of metabolic pathways in TNBC immunotherapy: insights from single-cell and spatial transcriptomics

The article provides an overview of the current understanding of the interplay between metabolic pathways and immune function in the context of triple-negative breast cancer (TNBC). It highlights recent advancements in single-cell and spatial transcriptomics technologies, which have revolutionized the analysis of tumor heterogeneity and the immune microenvironment in TNBC. The review emphasizes the crucial role of metabolic reprogramming in modulating immune cell function, discussing how specific metabolic pathways, such as glycolysis, lipid metabolism, and amino acid metabolism, can directly impact the activity and phenotypes of various immune cell populations within the TNBC tumor microenvironment. Furthermore, the article explores the implications of these metabolic-immune interactions for the efficacy of immune checkpoint inhibitor (ICI) therapies in TNBC, suggesting that strategies targeting metabolic pathways may enhance the responsiveness to ICI treatments. Finally, the review outlines future directions and the potential for combination therapies that integrate metabolic modulation with immunotherapeutic approaches, offering promising avenues for improving clinical outcomes for TNBC patients.

Spatial transcriptomics in breast cancer: providing insight into tumor heterogeneity and promoting individualized therapy

A comprehensive understanding of tumor heterogeneity, tumor microenvironment and the mechanisms of drug resistance is fundamental to advancing breast cancer research. While single-cell RNA sequencing has resolved the issue of "temporal dynamic expression" of genes at the single-cell level, the lack of spatial information still prevents us from gaining a comprehensive understanding of breast cancer. The introduction and application of spatial transcriptomics addresses this limitation. As the annual technical method of 2020, spatial transcriptomics preserves the spatial location of tissues and resolves RNA-seq data to help localize and differentiate the active expression of functional genes within a specific tissue region, enabling the study of spatial location attributes of gene locations and cellular tissue environments. In the context of breast cancer, spatial transcriptomics can assist in the identification of novel breast cancer subtypes and spatially discriminative features that show promise for individualized precise treatment. This article summarized the key technical approaches, recent advances in spatial transcriptomics and its applications in breast cancer, and discusses the limitations of current spatial transcriptomics methods and the prospects for future development, with a view to advancing the application of this technology in clinical practice.

Spatial transcriptomics reveals substantial heterogeneity in triple-negative breast cancer with potential clinical implications

While triple-negative breast cancer (TNBC) is known to be heterogeneous at the genomic and transcriptomic levels, spatial information on tumor organization and cell composition is still lacking. Here, we investigate TNBC tumor architecture including its microenvironment using spatial transcriptomics on a series of 92 patients. We perform an in-depth characterization of tumor and stroma organization and composition using an integrative approach combining histomorphological and spatial transcriptomics. Furthermore, a detailed molecular characterization of tertiary lymphoid structures leads to identify a gene signature strongly associated to disease outcome and response to immunotherapy in several tumor types beyond TNBC. A stepwise clustering analysis identifies nine TNBC spatial archetypes, further validated in external datasets. Several spatial archetypes are associated with disease outcome and characterized by potentially actionable features. In this work, we provide a comprehensive insight into the complexity of TNBC ecosystem with potential clinical relevance, opening avenues for treatment tailoring including immunotherapy.

Comprehensive mapping of somatotroph pituitary neuroendocrine tumour heterogeneity using spatial and single-cell transcriptomics

BACKGROUND

Pituitary neuroendocrine tumours (PitNETs) are common intracranial tumours that are highly heterogeneous with unpredictable growth patterns. The driver genes and mechanisms that are crucial for tumour progression in somatotroph PitNETs are poorly understood.

METHODS

In this study, we performed integrative spatial transcriptomics (ST) and single-cell RNA sequencing (scRNA-seq) analysis on somatotroph tumours and normal pituitary samples to comprehensively characterize the differences in cellular characteristics.

RESULTS

By analyzing combined copy number variations (CNVs), tumour tissues were divided into two regions, which included the CNVhigh and CNVlow areas. The protumour genes DLK1 and RCN1 were highly expressed in the CNVhigh area, which might be related to tumour progression and could be targeted for precision therapy. We also found that the transforming growth factor beta signalling pathway participated in tumour progression and identified heterogeneity in the expression profiles of key genes. We assessed the intertumoral and intratumoral heterogeneity in somatotroph PitNETs and emphasized the importance of individualized treatment.

CONCLUSION

In summary, we visualized the cellular distribution and transcriptional differences in normal pituitary and somatotroph PitNETs by ST and scRNA-seq for the first time. This study provides a strong theoretical foundation to comprehensively understand the crucial mechanisms involved in tumour progression and develop new strategies to treat somatotroph PitNETs.

KEY POINTS

The first-ever visualization of cellular distributions in normal and tumor pituitary tissues. The inter- and intra-tumoral transcriptomic heterogeneity of somatotroph PitNETs was comprehensively revealed. Identification of potential protumor factors and critical signaling pathways, opening new avenues for therapeutic intervention.

AKAP12 positive fibroblast determines immunosuppressive contexture and immunotherapy response in patients with TNBC by promoting macrophage M2 polarization

BACKGROUND

Triple-negative breast cancer (TNBC) is a molecular subtype of breast cancer with high aggressiveness and poor prognosis. Cancer-associated fibroblasts (CAFs) are major components of the TNBC microenvironment and play an important role in tumor progression and treatment responses. Our goal is to identify specific CAFs subpopulations contributing to TNBC development.

METHODS

Multiomics analyses were applied to identify the CAFs-specific genes related to immunotherapy response. The clinical significance of a CAFs subset with A-kinase anchoring protein 12 (AKAP12) positive was explored in 80 patients with TNBC through double-labeling immunofluorescence assay. Cytometry by time-of-flight and RNA sequencing were performed to elucidate the immune landscape of TNBC microenvironment and functional mechanism of AKAP12+ CAFs.

RESULTS

Multiomics analyses identified an AKAP12+ CAFs subset associated with the immunotherapy response of TNBC, and a high population of these cells is correlated with poor prognosis in patients with TNBC. Intratumoral AKAP12+ CAFs promote formation of an immunosuppressive tumor microenvironment by spatially mediating macrophage M2 polarization via interleukin-34 (IL-34)/macrophage-colony stimulating factor receptor (CSF1R) signaling in TNBC. Single-cell RNA sequencing analyses revealed that AKAP12+ fibroblasts interact with macrophages through the PI3K/AKT/IL-34 axis. In addition, pharmacological blockade of the IL-34/CSF1R signaling enhances the efficacy of anti-programmed cell death protein-1 antibody in TNBC rodent models.

CONCLUSIONS

AKAP12 is mainly expressed in fibroblasts in TNBC. AKAP12+ CAFs population is negatively associated with the prognosis of patients with TNBC. AKAP12+ CAFs shape the immunosuppressive TNBC microenvironment by releasing IL-34 to promote macrophage M2 polarization. Targeting IL-34 may boost the immunotherapeutic efficacy for TNBC.

Anti-EGFR Antibody-Drug Conjugate Carrying an Inhibitor Targeting CDK Restricts Triple-Negative Breast Cancer Growth

PURPOSE

Anti-EGFR antibodies show limited response in breast cancer, partly due to activation of compensatory pathways. Furthermore, despite the clinical success of cyclin-dependent kinase (CDK) 4/6 inhibitors in hormone receptor-positive tumors, aggressive triple-negative breast cancers (TNBC) are largely resistant due to CDK2/cyclin E expression, whereas free CDK2 inhibitors display normal tissue toxicity, limiting their therapeutic application. A cetuximab-based antibody drug conjugate (ADC) carrying a CDK inhibitor selected based on oncogene dysregulation, alongside patient subgroup stratification, may provide EGFR-targeted delivery.

EXPERIMENTAL DESIGN

Expressions of G1/S-phase cell cycle regulators were evaluated alongside EGFR in breast cancer. We conjugated cetuximab with CDK inhibitor SNS-032, for specific delivery to EGFR-expressing cells. We assessed ADC internalization and its antitumor functions in vitro and in orthotopically grown basal-like/TNBC xenografts.

RESULTS

Transcriptomic (6,173 primary, 27 baseline, and matched post-chemotherapy residual tumors), single-cell RNA sequencing (150,290 cells, 27 treatment-naïve tumors), and spatial transcriptomic (43 tumor sections, 22 TNBCs) analyses confirmed expression of CDK2 and its cyclin partners in basal-like/TNBCs, associated with EGFR. Spatiotemporal live-cell imaging and super-resolution confocal microscopy demonstrated ADC colocalization with late lysosomal clusters. The ADC inhibited cell cycle progression, induced cytotoxicity against high EGFR-expressing tumor cells, and bystander killing of neighboring EGFR-low tumor cells, but minimal effects on immune cells. Despite carrying a small molar fraction (1.65%) of the SNS-032 inhibitor, the ADC restricted EGFR-expressing spheroid and cell line/patient-derived xenograft tumor growth.

CONCLUSIONS

Exploiting EGFR overexpression, and dysregulated cell cycle in aggressive and treatment-refractory tumors, a cetuximab-CDK inhibitor ADC may provide selective and efficacious delivery of cell cycle-targeted agents to basal-like/TNBCs, including chemotherapy-resistant residual disease.

Spatial molecular profiling of mixed invasive ductal and lobular breast cancers reveals heterogeneity in intrinsic molecular subtypes, oncogenic signatures, and mutations

Mixed invasive ductal and lobular carcinoma (MDLC) is a rare histologic subtype of breast cancer displaying both E-cadherin positive ductal and E-cadherin negative lobular morphologies within the same tumor, posing challenges with regard to anticipated clinical management. It remains unclear whether these distinct morphologies also have distinct biology and risk of recurrence. Our spatially resolved transcriptomic, genomic, and single-cell profiling revealed clinically significant differences between ductal and lobular tumor regions including distinct intrinsic subtype heterogeneity - e.g., MDLC with triple-negative breast cancer (TNBC) or basal ductal and estrogen receptor positive (ER+) luminal lobular regions, distinct enrichment of cell cycle arrest/senescence and oncogenic (ER and MYC) signatures, genetic and epigenetic CDH1 inactivation in lobular but not ductal regions, and single-cell ductal and lobular subpopulations with unique oncogenic signatures further highlighting intraregional heterogeneity. Altogether, we demonstrated that the intratumoral morphological/histological heterogeneity within MDLC is underpinned by intrinsic subtype and oncogenic heterogeneity which may result in prognostic uncertainty and therapeutic dilemma.

Tumor necrosis factor receptor 2 promotes endothelial cell-mediated suppression of CD8+ T cells through tuning glycolysis in chemoresistance of breast cancer

BACKGROUND

T cells play a pivotal role in chemotherapy-triggered anti-tumor effects. Emerging evidence underscores the link between impaired anti-tumor immune responses and resistance to paclitaxel therapy in triple-negative breast cancer (TNBC). Tumor-related endothelial cells (ECs) have potential immunoregulatory activity. However, how ECs regulate T cell activity during TNBC chemotherapy remains poorly understood.

METHODS

Single-cell analysis of ECs in patients with TNBC receiving paclitaxel therapy was performed using an accessible single-cell RNA sequencing (scRNA-seq) dataset to identify key EC subtypes and their immune characteristics. An integrated analysis of a tumor-bearing mouse model, immunofluorescence, and a spatial transcriptome dataset revealed the spatial relationship between ECs, especially Tumor necrosis factor receptor (TNFR) 2+ ECs, and CD8+ T cells. RNA sequencing, CD8+ T cell proliferation assays, flow cytometry, and bioinformatic analyses were performed to explore the immunosuppressive function of TNFR2 in ECs. The downstream metabolic mechanism of TNFR2 was further investigated using RNA sequencing, cellular glycolysis assays, and western blotting.

RESULTS

In this study, we identified an immunoregulatory EC subtype, characterized by enhanced TNFR2 expression in non-responders. By a mouse model of TNBC, we revealed a dynamic reduction in the proportion of the CD8+ T cell-contacting tumor vessels that could co-localize spatially with CD8+ T cells during chemotherapy and an increased expression of TNFR2 by ECs. TNFR2 suppresses glycolytic activity in ECs by activating NF-κB signaling in vitro. Tuning endothelial glycolysis enhances programmed death-ligand (PD-L) 1-dependent inhibitory capacity, thereby inducing CD8+ T cell suppression. In addition, TNFR2+ ECs showed a greater spatial affinity for exhausted CD8+ T cells than for non-exhausted CD8+ T cells. TNFR2 blockade restores impaired anti-tumor immunity in vivo, leading to the loss of PD-L1 expression by ECs and enhancement of CD8+ T cell infiltration into the tumors.

CONCLUSIONS

These findings reveal the suppression of CD8+ T cells by ECs in chemoresistance and indicate the critical role of TNFR2 in driving the immunosuppressive capacity of ECs via tuning glycolysis. Targeting endothelial TNFR2 may serve as a potent strategy for treating TNBC with paclitaxel.

Crosstalk between tumor and microenvironment: Insights from spatial transcriptomics

Cancer is a dynamic disease, and clonal heterogeneity plays a fundamental role in tumor development, progression, and resistance to therapies. Single-cell and spatial multimodal technologies can provide a high-resolution molecular map of underlying genomic, epigenomic, and transcriptomic alterations involved in inter- and intra-tumor heterogeneity and interactions with the microenvironment. In this review, we provide a perspective on factors driving cancer heterogeneity, tumor evolution, and clonal states. We briefly describe spatial transcriptomic technologies and summarize recent literature that sheds light on the dynamical interactions between tumor states, cell-to-cell communication, and remodeling local microenvironment.

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.

Advances in spatial transcriptomics and its applications in cancer research

Malignant tumors have increasing morbidity and high mortality, and their occurrence and development is a complicate process. The development of sequencing technologies enabled us to gain a better understanding of the underlying genetic and molecular mechanisms in tumors. In recent years, the spatial transcriptomics sequencing technologies have been developed rapidly and allow the quantification and illustration of gene expression in the spatial context of tissues. Compared with the traditional transcriptomics technologies, spatial transcriptomics technologies not only detect gene expression levels in cells, but also inform the spatial location of genes within tissues, cell composition of biological tissues, and interaction between cells. Here we summarize the development of spatial transcriptomics technologies, spatial transcriptomics tools and its application in cancer research. We also discuss the limitations and challenges of current spatial transcriptomics approaches, as well as future development and prospects.

Spatial molecular profiling of mixed invasive ductal-lobular breast cancers reveals heterogeneity in intrinsic molecular subtypes, oncogenic signatures, and mutations

Mixed invasive ductal and lobular carcinoma (MDLC) is a rare histologic subtype of breast cancer displaying both E-cadherin positive ductal and E-cadherin negative lobular morphologies within the same tumor, posing challenges with regard to anticipated clinical management. It remains unclear whether these distinct morphologies also have distinct biology and risk of recurrence. Our spatially-resolved transcriptomic, genomic, and single-cell profiling revealed clinically significant differences between ductal and lobular tumor regions including distinct intrinsic subtype heterogeneity (e.g., MDLC with TNBC/basal ductal and ER+/luminal lobular regions), distinct enrichment of senescence/dormancy and oncogenic (ER and MYC) signatures, genetic and epigenetic CDH1 inactivation in lobular, but not ductal regions, and single-cell ductal and lobular sub-populations with unique oncogenic signatures further highlighting intra-regional heterogeneity. Altogether, we demonstrated that the intra-tumoral morphological/histological heterogeneity within MDLC is underpinned by intrinsic subtype and oncogenic heterogeneity which may result in prognostic uncertainty and therapeutic dilemma.

Significance

MDLC displays both ductal and lobular tumor regions. Our multi-omic profiling approach revealed that these morphologically distinct tumor regions harbor distinct intrinsic subtypes and oncogenic features that may cause prognostic uncertainty and therapeutic dilemma. Thus histopathological/molecular profiling of individual tumor regions may guide clinical decision making and benefit patients with MDLC, particularly in the advanced setting where there is increased reliance on next generation sequencing.

Spatial transcriptomics in cancer research and potential clinical impact: a narrative review

Spatial transcriptomics (ST) provides novel insights into the tumor microenvironment (TME). ST allows the quantification and illustration of gene expression profiles in the spatial context of tissues, including both the cancer cells and the microenvironment in which they are found. In cancer research, ST has already provided novel insights into cancer metastasis, prognosis, and immunotherapy responsiveness. The clinical precision oncology application of next-generation sequencing (NGS) and RNA profiling of tumors relies on bulk methods that lack spatial context. The ability to preserve spatial information is now possible, as it allows us to capture tumor heterogeneity and multifocality. In this narrative review, we summarize precision oncology, discuss tumor sequencing in the clinic, and review the available ST research methods, including seqFISH, MERFISH (Vizgen), CosMx SMI (NanoString), Xenium (10x), Visium (10x), Stereo-seq (STOmics), and GeoMx DSP (NanoString). We then review the current ST literature with a focus on solid tumors organized by tumor type. Finally, we conclude by addressing an important question: how will spatial transcriptomics ultimately help patients with cancer?

Comprehensive characterization of stemness-related lncRNAs in triple-negative breast cancer identified a novel prognostic signature related to treatment outcomes, immune landscape analysis and therapeutic guidance: a silico analysis with in vivo experiments

BACKGROUND

Cancer stem cells (CSCs) and long non-coding RNAs (lncRNAs) are known to play a crucial role in the growth, migration, recurrence, and drug resistance of tumor cells, particularly in triple-negative breast cancer (TNBC). This study aims to investigate stemness-related lncRNAs (SRlncRNAs) as potential prognostic indicators for TNBC patients.

METHODS

Utilizing RNA sequencing data and corresponding clinical information from the TCGA database, and employing Weighted Gene Co-expression Network Analysis (WGCNA) on TNBC mRNAsi sourced from an online database, stemness-related genes (SRGs) and SRlncRNAs were identified. A prognostic model was developed using univariate Cox and LASSO-Cox analysis based on SRlncRNAs. The performance of the model was evaluated using Kaplan-Meier analysis, ROC curves, and ROC-AUC. Additionally, the study delved into the underlying signaling pathways and immune status associated with the divergent prognoses of TNBC patients.

RESULTS

The research identified a signature of six SRlncRNAs (AC245100.6, LINC02511, AC092431.1, FRGCA, EMSLR, and MIR193BHG) for TNBC. Risk scores derived from this signature were found to correlate with the abundance of plasma cells. Furthermore, the nominated chemotherapy drugs for TNBC exhibited considerable variability between different risk score groups. RT-qPCR validation confirmed abnormal expression patterns of these SRlncRNAs in TNBC stem cells, affirming the potential of the SRlncRNAs signature as a prognostic biomarker.

CONCLUSION

The identified signature not only demonstrates predictive power in terms of patient outcomes but also provides insights into the underlying biology, signaling pathways, and immune status associated with TNBC prognosis. The findings suggest the possibility of guiding personalized treatments, including immune checkpoint gene therapy and chemotherapy strategies, based on the risk scores derived from the SRlncRNA signature. Overall, this research contributes valuable knowledge towards advancing precision medicine in the context of TNBC.

Multiplex Digital Spatial Profiling in Breast Cancer Research: State-of-the-Art Technologies and Applications across the Translational Science Spectrum

While RNA sequencing and multi-omic approaches have significantly advanced cancer diagnosis and treatment, their limitation in preserving critical spatial information has been a notable drawback. This spatial context is essential for understanding cellular interactions and tissue dynamics. Multiplex digital spatial profiling (MDSP) technologies overcome this limitation by enabling the simultaneous analysis of transcriptome and proteome data within the intact spatial architecture of tissues. In breast cancer research, MDSP has emerged as a promising tool, revealing complex biological questions related to disease evolution, identifying biomarkers, and discovering drug targets. This review highlights the potential of MDSP to revolutionize clinical applications, ranging from risk assessment and diagnostics to prognostics, patient monitoring, and the customization of treatment strategies, including clinical trial guidance. We discuss the major MDSP techniques, their applications in breast cancer research, and their integration in clinical practice, addressing both their potential and current limitations. Emphasizing the strategic use of MDSP in risk stratification for women with benign breast disease, we also highlight its transformative potential in reshaping the landscape of breast cancer research and treatment.

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.

Serum protein profiling reveals an inflammation signature as a predictor of early breast cancer survival

BACKGROUND

Breast cancers exhibit considerable heterogeneity in their biology, immunology, and prognosis. Currently, no validated, serum protein-based tools are available to evaluate the prognosis of patients with early breast cancer.

METHODS

The study population consisted of 521 early-stage breast cancer patients with a median follow-up of 8.9 years. Additionally, 61 patients with breast fibroadenoma or atypical ductal hyperplasia were included as controls. We used a proximity extension assay to measure the preoperative serum levels of 92 proteins associated with inflammatory and immune response processes. The invasive cancers were randomly split into discovery (n = 413) and validation (n = 108) cohorts for the statistical analyses.

RESULTS

Using LASSO regression, we identified a nine-protein signature (CCL8, CCL23, CCL28, CSCL10, S100A12, IL10, IL10RB, STAMPB2, and TNFβ) that predicted various survival endpoints more accurately than traditional prognostic factors. In the time-dependent analyses, the prognostic power of the model remained rather stable over time. We also developed and validated a 17-protein model with the potential to differentiate benign breast lesions from malignant lesions (Wilcoxon p < 2.2*10- 16; AUC 0.94).

CONCLUSIONS

Inflammation and immunity-related serum proteins have the potential to rise above the classical prognostic factors of early-stage breast cancer. They may also help to distinguish benign from malignant breast lesions.

Multi-omics analysis in developmental bone biology

Single-cell omics and multi-omics have revolutionized our understanding of molecular and cellular biological processes at a single-cell level. In bone biology, the combination of single-cell RNA-sequencing analyses and in vivo lineage-tracing approaches has successfully identified multi-cellular diversity and dynamics of skeletal cells. This established a new concept that bone growth and regeneration are regulated by concerted actions of multiple types of skeletal stem cells, which reside in spatiotemporally distinct niches. One important subtype is endosteal stem cells that are particularly abundant in young bone marrow. The discovery of this new skeletal stem cell type has been facilitated by single-cell multi-omics, which simultaneously measures gene expression and chromatin accessibility. Using single-cell omics, it is now possible to computationally predict the immediate future state of individual cells and their differentiation potential. In vivo validation using histological approaches is the key to interpret the computational prediction. The emerging spatial omics, such as spatial transcriptomics and epigenomics, have major advantage in retaining the location of individual cells within highly complex tissue architecture. Spatial omics can be integrated with other omics to further obtain in-depth insights. Single-cell multi-omics are now becoming an essential tool to unravel intricate multicellular dynamics and intercellular interactions of skeletal cells.

Tryptophan metabolism regulates inflammatory macrophage polarization as a predictive factor for breast cancer immunotherapy

Metabolic reprogramming plays a pivotal role in regulating macrophage polarization and function. However, the impact of macrophage tryptophan metabolism on polarization within the breast cancer microenvironment remains elusive. In this study, we used single-cell transcriptome analysis and found that macrophages had the highest tryptophan metabolic activity in breast cancer, melanoma, and head and neck squamous cell carcinoma (HNSC). Further analysis revealed that the tryptophan metabolic activity of macrophages was positively correlated with the M1 macrophage scores in breast cancer. Pancancer analysis found positive correlations between tryptophan metabolism and the M1 macrophage score in almost all tumor types. Spatial transcriptome analysis revealed higher tryptophan metabolism in regions with higher M1 macrophage score in breast cancer tissues. Immune infiltration analysis revealed that the high tryptophan metabolism group exhibited a higher immune score, an increased proportion of CD8+ T cells, augmented cytolytic activity mediated by CD8+ T cells, and elevated expression of immune checkpoint molecules. Spatial immunophenotype cohort analysis exhibited that breast cancer patients expected to respond to immunotherapy had stronger tryptophan metabolism, with a 73.8 % area under the ROC curve. Single-cell transcriptome analysis of the immunotherapy cohort found that patients responding to immunotherapy had higher macrophage tryptophan metabolism prior to treatment initiation. Finally, in vitro experiments demonstrated elevated expression of tryptophan metabolic enzymes in M1 macrophages. Moreover, tryptophan facilitated the expression of M1 polarization markers, whereas inhibitors of tryptophan metabolic enzymes, such as NLG919, LM10, and Ro 61-8048, inhibited the expression of M1 polarization markers. In conclusion, this study identified a dual role for macrophage tryptophan metabolism in breast cancer; on the one hand, it promotes macrophage M1 polarization, while on the other hand, it serves as a promising predictor for the effectiveness of immunotherapy in breast cancer.

Chromatin Accessibility Landscape of Human Triple-negative Breast Cancer Cell Lines Reveals Variation by Patient Donor Ancestry

African American (AA) women have an excessive risk of developing triple-negative breast cancer (TNBC). We employed Assay for Transposase-Accessible Chromatin using sequencing to characterize differences in chromatin accessibility between nine commonly used TNBC cell lines derived from patients of European and African ancestry. Principal component and chromosome mapping analyses of accessibility peaks with the most variance revealed separation of chromatin profiles by patient group. Motif enrichment and footprinting analyses of disparate open chromatin regions revealed differences in transcription factor activity, identifying 79 with ancestry-associated binding patterns (FDR < 0.01). AA TNBC cell lines exhibited increased accessibility for 62 transcription factors associated with epithelial-to-mesenchymal transition, cancer stemness/chemotherapeutic resistance, proliferation, and aberrant p53 regulation, as well as KAISO, which has been previously linked to aggressive tumor characteristics in AA patients with cancer. Differential Assay for Transposase-Accessible Chromatin signal analysis identified 1,596 genes located within promoters of differentially open chromatin regions in AA-derived TNBC, identifying DNA methyltransferase 1 as the top upregulated gene associated with African ancestry. Pathway analyses with these genes revealed enrichment in several pathways, including hypoxia. Culturing cells under hypoxia showed ancestry-specific stress responses that led to the identification of a core set of AA-associated transcription factors, which included members of the Kruppel-like factor and Sp subfamilies, as well as KAISO, and identified ZDHHC1, a gene previously implicated in immunity and STING activation, as the top upregulated AA-specific gene under hypoxia. Together, these data reveal a differential chromatin landscape in TNBC associated with donor ancestry. The open chromatin structure of AA TNBC may contribute to a more lethal disease.

SIGNIFICANCE

We identify an ancestry-associated open chromatin landscape and related transcription factors that may contribute to aggressive TNBC in AA women. Furthermore, this study advocates for the inclusion of diversely sourced cell lines in experimental in vitro studies to advance health equity at all levels of scientific research.

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.

Omics Technologies Improving Breast Cancer Research and Diagnostics

Breast cancer (BC) has yielded approximately 2.26 million new cases and has caused nearly 685,000 deaths worldwide in the last two years, making it the most common diagnosed cancer type in the world. BC is an intricate ecosystem formed by both the tumor microenvironment and malignant cells, and its heterogeneity impacts the response to treatment. Biomedical research has entered the era of massive omics data thanks to the high-throughput sequencing revolution, quick progress and widespread adoption. These technologies-liquid biopsy, transcriptomics, epigenomics, proteomics, metabolomics, pharmaco-omics and artificial intelligence imaging-could help researchers and clinicians to better understand the formation and evolution of BC. This review focuses on the findings of recent multi-omics-based research that has been applied to BC research, with an introduction to every omics technique and their applications for the different BC phenotypes, biomarkers, target therapies, diagnosis, treatment and prognosis, to provide a comprehensive overview of the possibilities of BC research.

Implications of tumour heterogeneity on cancer evolution and therapy resistance: lessons from breast cancer

Tumour heterogeneity is pervasive amongst many cancers and leads to disease progression, and therapy resistance. In this review, using breast cancer as an exemplar, we focus on the recent advances in understanding the interplay between tumour cells and their microenvironment using single cell sequencing and digital spatial profiling technologies. Further, we discuss the utility of lineage tracing methodologies in pre-clinical models of breast cancer, and how these are being used to unravel new therapeutic vulnerabilities and reveal biomarkers of breast cancer progression. © 2023 The Pathological Society of Great Britain and Ireland.