Single cell profiling of primary and paired metastatic lymph node tumors in breast cancer patients

Single-cell Rapid Capture Hybridization sequencing reliably detects isoform usage and coding mutations in targeted genes

Single-cell long-read sequencing has transformed our understanding of isoform usage and the mutation heterogeneity between cells. Despite unbiased in-depth analysis, the low sequencing throughput often results in insufficient read coverage, thereby limiting our ability to perform mutation calling for specific genes. Here, we developed a single-cell Rapid Capture Hybridization sequencing (scRaCH-seq) method that demonstrates high specificity and efficiency in capturing targeted transcripts using long-read sequencing, allowing an in-depth analysis of mutation status and transcript usage for genes of interest. The method includes creating a probe panel for transcript capture, using barcoded primers for pooling and efficient sequencing via Oxford Nanopore Technologies platforms. scRaCH-seq is applicable to stored and indexed single-cell cDNA, which allows analysis to be combined with existing short-read RNA-seq data sets. In our investigation of BTK and SF3B1 genes in samples from patients with chronic lymphocytic leukemia (CLL), we detect SF3B1 isoforms and mutations with high sensitivity. Integration with short-read single-cell RNA sequencing (scRNA-seq) data reveals significant gene expression differences in SF3B1-mutated CLL cells, although it does not impact the sensitivity of the anticancer drug venetoclax. scRaCH-seq's capability to study long-read transcripts of multiple genes makes it a powerful tool for single-cell genomics.

Fibroblast hierarchy dynamics during mammary gland morphogenesis and tumorigenesis

Fibroblasts form a major component of the stroma in normal mammary tissue and breast tumors. Here, we have applied longitudinal single-cell transcriptome profiling of >45,000 fibroblasts in the mouse mammary gland across five different developmental stages and during oncogenesis. In the normal gland, diverse stromal populations were resolved, including lobular-like fibroblasts, committed preadipocytes and adipogenesis-regulatory, as well as cycling fibroblasts in puberty and pregnancy. These specialized cell types appear to emerge from CD34high mesenchymal progenitor cells, accompanied by elevated Hedgehog signaling. During late tumorigenesis, heterogeneous cancer-associated fibroblasts (CAFs) were identified in mouse models of breast cancer, including a population of CD34- myofibroblastic CAFs (myCAFs) that were transcriptionally and phenotypically similar to senescent CAFs. Moreover, Wnt9a was demonstrated to be a regulator of senescence in CD34- myCAFs. These findings reflect a diverse and hierarchically organized stromal compartment in the normal mammary gland that provides a framework to better understand fibroblasts in normal and cancerous states.

Establishment of multiple machine learning prognostic model for gene differences between primary tumors and lymph nodes in luminal breast cancer

BACKGROUND

This study aimed to explore the correlation between primary tumors (PT) and paired metastatic lymph nodes (LN) and to develop a predictive model to provide evidence for forecasting patient prognoses.

METHODS

We obtained single-cell and bulk transcriptome data from the Gene Expression Omnibus database. Furthermore, mRNA transcriptomic data, encompassing 112 normal tissues and 1066 breast cancer samples, along with survival, clinical, and mutation information for breast cancer patients, were acquired from The Cancer Genome Atlas (TCGA). Employing a machine learning integration framework incorporating ten distinct algorithms, we developed and validated a prognostic model.

RESULTS

We constructed a prognostic model named Lymph Node Metastasis-Related Scores (LMRS) using 26 differentially expressed genes trained on eight TCGA datasets. Across validation sets, the model demonstrated a high C-index, signifying its stability and effectiveness, outperforming 64 models from other studies. Notably, cytolytic activity and T cell co-stimulation were downregulated in the high LMRS group, alongside a downregulation of immune cells, including B cells, CD8 + T cells, iDCs, and TILs. Similarly, most immune checkpoints exhibited a decreasing trend with high LMRS expression. Finally, we selected the hub biomarkers PGK1 and HSP90 for pathological verification. Results indicated higher expression levels in PT and LN compared to normal and benign tumors, with higher expression levels in LN than in PT.

CONCLUSION

This comprehensive analysis sheds light on gene expression differences between PT and LN in breast cancer, culminating in the development of a multiple-gene prognostic model with high clinical accuracy for prognosis prediction.

Current state of cancer immunity cycle: new strategies and challenges of using precision hydrogels to treat breast cancer

The cancer-immunity cycle provides a framework for a series of events in anti-cancer immune responses, initiated by T cell-mediated tumor cell killing, which leads to antigen presentation and T cell stimulation. Current immunomodulatory therapies for breast cancer are often associated with short duration, poor targeting to sites of action, and severe side effects. Hydrogels, with their extracellular matrix-mimicking properties, tunable characteristics, and diverse bioactivities, have garnered significant attention for their ability to locally deliver immunomodulators and cells, providing an immunomodulatory microenvironment to recruit, activate, and expand host immune cells. This review focuses on the design considerations of hydrogel platforms, including polymer backbone, crosslinking mechanisms, physicochemical properties, and immunomodulatory components. The immunomodulatory effects and therapeutic outcomes of various hydrogel systems in breast cancer treatment and tissue regeneration are highlighted, encompassing hydrogel depots for immunomodulator delivery, hydrogel scaffolds for cell delivery, and immunomodulatory hydrogels dependent on inherent material properties. Finally, the challenges that persist in current systems and future directions for immunomodulatory hydrogels are discussed.

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.

Masked language modeling pretraining dynamics for downstream peptide: T-cell receptor binding prediction

Motivation

Predicting antigen peptide and T-cell receptor (TCR) binding is difficult due to the combinatoric nature of peptides and the scarcity of labeled peptide-binding pairs. The masked language modeling method of pretraining is reliably used to increase the downstream performance of peptide:TCR binding prediction models by leveraging unlabeled data. In the literature, binding prediction models are commonly trained until the validation loss converges. To evaluate this method, cited transformer model architectures pretrained with masked language modeling are investigated to assess the benefits of achieving lower loss metrics during pretraining. The downstream performance metrics for these works are recorded after each subsequent interval of masked language modeling pretraining.

Results

The results demonstrate that the downstream performance benefit achieved from masked language modeling peaks substantially before the pretraining loss converges. Using the pretraining loss metric is largely ineffective for precisely identifying the best downstream performing pretrained model checkpoints (or saved states). However, the pretraining loss metric in these scenarios can be used to mark a threshold in which the downstream performance benefits from pretraining have fully diminished. Further pretraining beyond this threshold does not negatively impact downstream performance but results in unpredictable bilateral deviations from the post-threshold average downstream performance benefit.

Availability and implementation

The datasets used in this article for model training are publicly available from each original model's authors at https://github.com/SFGLab/bertrand, https://github.com/wukevin/tcr-bert, https://github.com/NKI-AI/STAPLER, and https://github.com/barthelemymp/TULIP-TCR.

Breast cancer: pathogenesis and treatments

Breast cancer, characterized by unique epidemiological patterns and significant heterogeneity, remains one of the leading causes of malignancy-related deaths in women. The increasingly nuanced molecular subtypes of breast cancer have enhanced the comprehension and precision treatment of this disease. The mechanisms of tumorigenesis and progression of breast cancer have been central to scientific research, with investigations spanning various perspectives such as tumor stemness, intra-tumoral microbiota, and circadian rhythms. Technological advancements, particularly those integrated with artificial intelligence, have significantly improved the accuracy of breast cancer detection and diagnosis. The emergence of novel therapeutic concepts and drugs represents a paradigm shift towards personalized medicine. Evidence suggests that optimal diagnosis and treatment models tailored to individual patient risk and expected subtypes are crucial, supporting the era of precision oncology for breast cancer. Despite the rapid advancements in oncology and the increasing emphasis on the clinical precision treatment of breast cancer, a comprehensive update and summary of the panoramic knowledge related to this disease are needed. In this review, we provide a thorough overview of the global status of breast cancer, including its epidemiology, risk factors, pathophysiology, and molecular subtyping. Additionally, we elaborate on the latest research into mechanisms contributing to breast cancer progression, emerging treatment strategies, and long-term patient management. This review offers valuable insights into the latest advancements in Breast Cancer Research, thereby facilitating future progress in both basic research and clinical application.

Genetic insights into the shared molecular mechanisms of Crohn's disease and breast cancer: a Mendelian randomization and deep learning approach

The objective of this study was to explore the potential genetic link between Crohn's disease and breast cancer, with a focus on identifying druggable genes that may have therapeutic relevance. We assessed the causal relationship between these diseases through Mendelian randomization and investigated gene-drug interactions using computational predictions. This study sought to identify common genetic pathways possibly involved in immune responses and cancer progression, providing a foundation for future targeted treatment research. The dataset comprises single nucleotide polymorphisms used as instrumental variables for Crohn's disease, analyzed to explore their possible impact on breast cancer risk. Gene ontology and pathway enrichment analyses were conducted to identify genes shared between the two conditions, supported by protein-protein interaction networks, colocalization analyses, and deep learning-based predictions of gene-drug interactions. The identified hub genes and predicted gene-drug interactions offer preliminary insights into possible therapeutic targets for breast cancer and immune-related conditions. This dataset may be valuable for researchers studying genetic links between autoimmune diseases and cancer and for those interested in the early identification of potential drug targets.

Activity-Regulated Cytoskeleton-Associated Protein Gene Expression Is Associated With High Infiltration of Stromal Cells and Immune Cells, but With Less Cancer Cell Proliferation and Better Overall Survival in Estrogen Receptor-Positive/Human Epidermal Growth Factor Receptor 2-Negative Breast Cancers

Background

Peritumoral lidocaine infiltration prior to excision is associated with better survival in breast cancer (BC), which led us to hypothesize that innervation to the tumor affects its biology and patient survival. Activity-regulated cytoskeleton-associated protein (ARC) gene expression is known to be regulated by neuronal activity. Therefore, we studied the clinical relevance of ARC gene expression as a surrogate of neuronal activity in BC.

Methods

Sweden Cancerome Analysis Network - Breast (SCAN-B (GSE96058), n = 3,273) cohort and The Cancer Genome Atlas (TCGA, n = 1,069) were analyzed.

Results

High ARC expression was significantly associated with smaller tumor size, without lymph node metastasis, and less stage IV disease in one cohort, but not validated by the other. Estrogen receptor-positive (ER+)/human epidermal growth factor receptor 2-negative (HER2-) and luminal A expressed significantly higher ARC compared to the other subtypes in both cohorts (P < 0.005). High ARC BC was significantly associated with lower Nottingham histological grade and lower Ki67 gene expression consistently in ER+/HER2- but not triple negative breast cancer (TNBC) in both cohorts (P < 0.001). Cell proliferation-related gene sets in the Hallmark collection (E2F targets, G2M checkpoint, and mitotic spindle) were significantly enriched to low ARC BC in ER+/HER2- but not TNBC in TCGA. The stromal cells (fibroblasts, vascular endothelial cells, and adipocytes) were all significantly infiltrated in high ARC ER+/HER2-, but not in TNBC, except for neurons. Homologous recombination deficiency, intratumor heterogeneity, fraction altered, silent or non-silent mutation rate were all significantly lower in high ARC ER+/HER2- but not TNBC. Although there was no difference in single nucleotide variant or indel neoantigens, tumor infiltrating lymphocytes, and cytolytic activity by ARC expression regardless of subtype, multiple immune cells were significantly infiltrated in high ARC ER+/HER2-, including CD8, CD4 memory cells, helper type II T cells, regulatory T cells, M2 macrophages, and B cells (all P < 0.03 in both cohorts), but not in TNBC. Disease-specific and overall survival were significantly improved in high ARC ER+/HER2- consistently in both cohorts (all P < 0.05), but this was not the case in TNBC.

Conclusion

ARC gene expression was associated with less cancer cell proliferation, high infiltration of stromal cells and immune cells, and better survival in the ER+/HER2- but not TNBC subtype.

Integrative mapping of human CD8+ T cells in inflammation and cancer

CD8+ T cells exhibit remarkable phenotypic diversity in inflammation and cancer. However, a comprehensive understanding of their clonal landscape and dynamics remains elusive. Here we introduce scAtlasVAE, a deep-learning-based model for the integration of large-scale single-cell RNA sequencing data and cross-atlas comparisons. scAtlasVAE has enabled us to construct an extensive human CD8+ T cell atlas, comprising 1,151,678 cells from 961 samples across 68 studies and 42 disease conditions, with paired T cell receptor information. Through incorporating information in T cell receptor clonal expansion and sharing, we have successfully established connections between distinct cell subtypes and shed light on their phenotypic and functional transitions. Notably, our approach characterizes three distinct exhausted T cell subtypes and reveals diverse transcriptome and clonal sharing patterns in autoimmune and immune-related adverse event inflammation. Furthermore, scAtlasVAE facilitates the automatic annotation of CD8+ T cell subtypes in query single-cell RNA sequencing datasets, enabling unbiased and scalable analyses. In conclusion, our work presents a comprehensive single-cell reference and computational framework for CD8+ T cell research.

Proteomic profiling reveals the significance of lipid metabolism in small cell lung cancer recurrence and metastasis

BACKGROUND

Small cell lung cancer (SCLC) is a lethal and recalcitrant malignancy with early metastases. However, the molecular and cellular mechanisms underlying its aggressive characteristics remain relatively elusive.

METHODS

In this study, we conducted a comprehensive proteomic analysis of 90 primary tumors, 15 patient-matched lymph node metastatic tumors, and 15 brain metastatic tumors derived from a cohort of 105 SCLC patients. The potential mechanism for SCLC metastasis was investigated based on the variety of protein expression profiles.

RESULTS

Primary tumors were divided into two categories according to the their different protein expression profiles, using metastatic tumors as reference. Proteomic comparisons across different groups revealed that lipid metabolism, especially phospholipid metabolism, and immune response had a critical role in SCLC metastasis. Additionally, it was shown that high- and low-density lipoprotein cholesterol were both independent prognostic factors for disease free survival of SCLC patients. To identify critical regulators of metastasis in SCLC, support vector machine was adopted to generate a biomarker combination of ten proteins, all of which significantly correlated with the infiltration of immune cells. Furthermore, it was demonstrated that high expression of phospholipase A2 group IIA in stroma was associated with delayed disease recurrence in limited stage SCLC.

CONCLUSIONS

This study highlighted the critical significance of lipid metabolism, especially phospholipid metabolism in the disease recurrence and metastasis of SCLC.

Cellular components of tumor microenvironment: understanding their role in lymphatic metastasis of tumors

Metastasis is the leading cause of cancer-related death in cancer patients. Tumor cells primarily spread through the hematogenous and lymphatic system. The underlying mechanisms of hematogenous metastasis have been well described over the past few decades. However, the understanding of the molecular mechanisms involved in lymphatic metastasis is still at an early stage. Tumor microenvironment (TME), primarily consisting of T cells, B cells, tumor-associated macrophages, neutrophils, and cancer-associated fibroblasts, has been implicated in the development of lymphatic metastasis. Recent studies have been reported that the dynamic and complex interplay between these cellular components of TME has great effects on lymphatic metastasis. Here, we discussed the paradoxical roles of these cellular component within the TME during lymphatic metastasis, as well as potential therapeutic opportunities to re-educate these cells within the TME to have anti-tumorigenic effects.

Revisiting the CXCL13/CXCR5 axis in the tumor microenvironment in the era of single-cell omics: Implications for immunotherapy

As one of the important members of the family of chemokines and their receptors, the CXCL13/CXCR5 axis is involved in follicle formation in normal lymphoid tissues and the establishment of somatic cavity immunity under physiological conditions, as well as being associated with a wide range of infectious, autoimmune, and tumoral diseases. Here in this review, we focus on its role in tumors. Traditional studies have found the axis to be both pro- and anti-tumorigenic, involving a variety of immune cells, including the tumor cells themselves and those in the tumor microenvironment (TME), and the prognostic significance of this axis is clinical context-dependent. With the development of techniques at the single-cell level, we were able to explain in detail the status of the CXCL13/CXCR5 axis in the TME based on real clinical samples and found that it involves a range of crucial intrinsic anti-tumor immune processes in the TME and is therefore important in tumor immunotherapy. We summarize the cellular subsets, physiological functions, and prognostic significance associated with this axis in the most promising immune checkpoint inhibitor (ICI) therapies of the day and summarize possible therapeutic ideas based on this axis. As with any TME study, the most important takeaway is that the complexity of the CXCL13/CXCR5 axis in TME suggests the importance of personalized therapy in tumor therapy.

B-Lightning: using bait genes for marker gene hunting in single-cell data with complex heterogeneity

In single-cell studies, cells can be characterized with multiple sources of heterogeneity (SOH) such as cell type, developmental stage, cell cycle phase, activation state, and so on. In some studies, many nuisance SOH are of no interest, but may confound the identification of the SOH of interest, and thus affect the accurate annotate the corresponding cell subpopulations. In this paper, we develop B-Lightning, a novel and robust method designed to identify marker genes and cell subpopulations corresponding to an SOH (e.g. cell activation status), isolating it from other SOH (e.g. cell type, cell cycle phase). B-Lightning uses an iterative approach to enrich a small set of trustworthy marker genes to more reliable marker genes and boost the signals of the SOH of interest. Multiple numerical and experimental studies showed that B-Lightning outperforms existing methods in terms of sensitivity and robustness in identifying marker genes. Moreover, it increases the power to differentiate cell subpopulations of interest from other heterogeneous cohorts. B-Lightning successfully identified new senescence markers in ciliated cells from human idiopathic pulmonary fibrosis lung tissues, new T-cell memory and effector markers in the context of SARS-COV-2 infections, and their synchronized patterns that were previously neglected, new AD markers that can better differentiate AD severity, and new dendritic cell functioning markers with differential transcriptomics profiles across breast cancer subtypes. This paper highlights B-Lightning's potential as a powerful tool for single-cell data analysis, particularly in complex data sets where SOH of interest are entangled with numerous nuisance factors.

Characterization of the T-cell receptor repertoire associated with lymph node metastasis in colorectal cancer

Purpose

Colorectal cancer (CRC) is a leading cause of cancer-related mortality worldwide, with lymph node (LN) metastasis playing a pivotal role in disease progression. This study aimed to explore the T-cell receptor (TCR) repertoire among CRC patients, distinguishing those with LN metastasis from those without, in order to uncover potential biomarkers for predicting metastasis.

Methods

We analyzed the TCR repertoire in CRC patients with and without LN metastasis. A classification model utilizing random forest analysis was developed to assess the predictive potential of the TCR repertoire.

Results

The findings demonstrated a significant increase in the number of V-J combinations and immune CDR3 sequences in patients with LN metastasis compared to the control group. The classification model achieved high accuracy in differentiating patients with LN metastasis, with AUC values ranging from 0.514 to 0.794. Specific V-J combinations and CDR3 sequences were identified as significant predictors of the model's predictive accuracy.

Conclusion

These results suggest that the TCR repertoire is altered in CRC patients exhibiting LN metastasis, potentially influencing disease progression. This study highlights the importance of TCR repertoire analysis as a non-invasive biomarker for predicting LN metastasis in CRC patients.

Infiltration of Common Myeloid Progenitor (CMP) Cells is Associated With Less Aggressive Tumor Biology, Lower Risk of Brain Metastasis, Better Response to Immunotherapy, and Higher Patient Survival in Breast Cancer

OBJECTIVE

To investigate the clinical relevance of common myeloid progenitor (CMP) cells in breast tumor microenvironment (TME).

BACKGROUND

The role of rare cells in TME is less studied. In Silico transcriptomic analyses of real-world data enable us to detect and quantify rare cells, including CMP cells.

METHODS

A total of 5176 breast cancer (BC) patients from SCAN-B, METABRIC, and 5 single-cell sequence cohorts were analyzed using the xCell algorithm. The high group was defined as more than two-thirds of the CMP scores in each cohort.

RESULTS

CMP cells consist of 0.07% to 0.25% of bulk breast tumor cells, more in estrogen receptor-positive (ER+) compared with triple-negative (TN) subtype (0.1% to 0.75%, 0.18% to 0.33% of immune cells, respectively). CMP cells did not correlate with any of the myeloid lineages or stem cells in TME. CMP infiltration was higher in smaller tumors, with lower Nottingham grade, and in ER+/HER2- than in TNBC consistently in both SCAN-B and METABRIC cohorts. High CMP was significantly associated with a lower risk of brain metastasis and with better survival, particularly in ER+/HER2-. High CMP enriched epithelial-to-mesenchymal transition and angiogenesis pathways, and less cell proliferation and DNA repair gene sets. High CMP ER+/HER2- was associated with less immune cell infiltration and cytolytic activity ( P <0.001). CMP infiltration correlated with neoadjuvant chemoimmunotherapy response for both ER+/HER2- and TNBC in the ISPY-2 cohort (AUC=0.69 and 0.74, respectively).

CONCLUSIONS

CMP in BC is inversely associated with cell proliferation and brain metastasis, better response to immunotherapy, and survival. This is the first to report the clinical relevance of CMP infiltration in BC.

Single-cell sequencing analysis reveals the dynamic tumour ecosystems of primary and metastatic lymph nodes in nasopharyngeal carcinoma

Lymph node metastasis contributed to the leading cause and treatment failure in nasopharyngeal carcinoma (NPC). The microenvironment and the cellular communications of lymph node metastasized tumours determine the tumour progression and therapeutic effect, but the ecosystems about the lymph node metastasis (LNM) for NPC patients remain poorly characterized. Here, we integrated the transcriptomes of 47,618 single cells from eight samples related to NPC LNM. The dynamic immune ecosystems and immunosuppressive microenvironment including T cells, myeloid cells and B cells were observed in the lymph node metastatic samples compared with primary tumours. Additionally, the heterogeneity of epithelial cells was also revealed, and several clusters with expression programs that were associated with the progression-free survival of NPC patients were identified. Additionally, our data revealed the complex intercellular communications from primary to lymph node metastasis. The rewiring of CCL signalling which plays an important role in tumour metastasis was further identified. Altogether, we systematically characterized the ecosystem of NPC primary and lymph node metastasized tumours, which may shed light on the development of a therapeutic strategy to improve clinical outcomes of NPC patients with lymph node metastasis.

The Observed T Cell Receptor Space database enables paired-chain repertoire mining, coherence analysis, and language modeling

T cell activation is governed through T cell receptors (TCRs), heterodimers of two sequence-variable chains (often an α and β chain) that synergistically recognize antigen fragments presented on cell surfaces. Despite this, there only exist repositories dedicated to collecting single-chain, not paired-chain, TCR sequence data. We addressed this gap by creating the Observed TCR Space (OTS) database, a source of consistently processed and annotated, full-length, paired-chain TCR sequences. Currently, OTS contains 5.35 million redundant (1.63 million non-redundant), predominantly human sequences from across 50 studies and at least 75 individuals. Using OTS, we identify pairing biases, public TCRs, and distinct chain coherence patterns relative to antibodies. We also release a paired-chain TCR language model, providing paired embedding representations and a method for residue in-filling conditional on the partner chain. OTS will be updated as a central community resource and is freely downloadable and available as a web application.

Deciphering breast cancer dynamics: insights from single-cell and spatial profiling in the multi-omics era

As one of the most common tumors in women, the pathogenesis and tumor heterogeneity of breast cancer have long been the focal point of research, with the emergence of tumor metastasis and drug resistance posing persistent clinical challenges. The emergence of single-cell sequencing (SCS) technology has introduced novel approaches for gaining comprehensive insights into the biological behavior of malignant tumors. SCS is a high-throughput technology that has rapidly developed in the past decade, providing high-throughput molecular insights at the individual cell level. Furthermore, the advent of multitemporal point sampling and spatial omics also greatly enhances our understanding of cellular dynamics at both temporal and spatial levels. The paper provides a comprehensive overview of the historical development of SCS, and highlights the most recent advancements in utilizing SCS and spatial omics for breast cancer research. The findings from these studies will serve as valuable references for future advancements in basic research, clinical diagnosis, and treatment of breast cancer.

Anti-4-1BB×PDL1 Bispecific Antibody Reinvigorates Tumor-Specific Exhausted CD8+ T Cells and Enhances the Efficacy of Anti-PD1 Blockade

PURPOSE

To overcome the limited efficacy of immune checkpoint blockade, there is a need to find novel cancer immunotherapeutic strategies for the optimal treatment of cancer. The novel anti-4-1BB×PDL1 bispecific antibody-ABL503 (also known as TJ-L14B)-was designed to simultaneously target PDL1 and 4-1BB and demonstrated strong antitumor T-cell responses without considerable toxicity. In this study, we investigated the mechanisms by which the combination of ABL503 and anti-PD1 blockade affected the reinvigoration of exhausted tumor-infiltrating CD8+ T cells (CD8+ TIL) and antitumor efficacy.

EXPERIMENTAL DESIGN

Single-cell suspensions of hepatocellular carcinoma and ovarian cancer tissues from treatment-naïve patients were used for immunophenotyping of CD8+ TILs and in vitro functional assays. Humanized hPD1/hPDL1/h4-1BB triple-knock-in mice were used to evaluate the effects of ABL503 and anti-PD1 blockade in vivo.

RESULTS

We observed that ABL503 successfully restored the functions of 4-1BB+ exhausted CD8+ TILs, which were enriched for tumor-specific T cells but unresponsive to anti-PD1 blockade. Importantly, compared with anti-PD1 blockade alone, the combination of ABL503 and anti-PD1 blockade further enhanced the functional restoration of human CD8+ TILs in vitro. Consistently, the combination of ABL503 with anti-PD1 in vivo significantly alleviated tumor growth and induced enhanced infiltration and activation of CD8+ TILs.

CONCLUSIONS

ABL503, a PDL1 and 4-1BB dual-targeting bispecific antibody, elicits pronounced additive tumor growth inhibition, with increased infiltration and functionality of exhausted CD8+ T cells, which in turn enhances the anticancer effects of anti-PD1 blockade. These promising findings suggest that ABL503 (TJ-L14B) in combination with PD1 inhibitors will likely further enhance therapeutic benefit in clinical trials. See related commentary by Molero-Glez et al., p. 3971.

DeepKINET: a deep generative model for estimating single-cell RNA splicing and degradation rates

Messenger RNA splicing and degradation are critical for gene expression regulation, the abnormality of which leads to diseases. Previous methods for estimating kinetic rates have limitations, assuming uniform rates across cells. DeepKINET is a deep generative model that estimates splicing and degradation rates at single-cell resolution from scRNA-seq data. DeepKINET outperforms existing methods on simulated and metabolic labeling datasets. Applied to forebrain and breast cancer data, it identifies RNA-binding proteins responsible for kinetic rate diversity. DeepKINET also analyzes the effects of splicing factor mutations on target genes in erythroid lineage cells. DeepKINET effectively reveals cellular heterogeneity in post-transcriptional regulation.

Single-cell analysis of breast cancer metastasis reveals epithelial-mesenchymal plasticity signatures associated with poor outcomes

Metastasis is the leading cause of cancer-related deaths. It is unclear how intratumor heterogeneity (ITH) contributes to metastasis and how metastatic cells adapt to distant tissue environments. The study of these adaptations is challenged by the limited access to patient material and a lack of experimental models that appropriately recapitulate ITH. To investigate metastatic cell adaptations and the contribution of ITH to metastasis, we analyzed single-cell transcriptomes of matched primary tumors and metastases from patient-derived xenograft models of breast cancer. We found profound transcriptional differences between the primary tumor and metastatic cells. Primary tumors upregulated several metabolic genes, whereas motility pathway genes were upregulated in micrometastases, and stress response signaling was upregulated during progression. Additionally, we identified primary tumor gene signatures that were associated with increased metastatic potential and correlated with patient outcomes. Immune-regulatory control pathways were enriched in poorly metastatic primary tumors, whereas genes involved in epithelial-mesenchymal transition were upregulated in highly metastatic tumors. We found that ITH was dominated by epithelial-mesenchymal plasticity (EMP), which presented as a dynamic continuum with intermediate EMP cell states characterized by specific genes such as CRYAB and S100A2. Elevated expression of an intermediate EMP signature correlated with worse patient outcomes. Our findings identified inhibition of the intermediate EMP cell state as a potential therapeutic target to block metastasis.

Spatial landscapes of cancers: insights and opportunities

Solid tumours comprise many different cell types organized in spatially structured arrangements, with substantial intratumour and intertumour heterogeneity. Advances in spatial profiling technologies over the past decade hold promise to capture the complexity of these cellular architectures to build a holistic view of the intricate molecular mechanisms that shape the tumour ecosystem. Some of these mechanisms act at the cellular scale and are controlled by cell-autonomous programmes or communication between nearby cells, whereas other mechanisms result from coordinated efforts between large networks of cells and extracellular molecules organized into tissues and organs. In this Review we provide insights into the application of single-cell and spatial profiling tools, with a focus on spatially resolved transcriptomic tools developed to understand the cellular architecture of the tumour microenvironment and identify opportunities to use them to improve clinical management of cancers.

Intra-patient spatial comparison of non-metastatic and metastatic lymph nodes reveals the reduction of CD169+ macrophages by metastatic breast cancers

BACKGROUND

Breast cancer cells suppress the host immune system to efficiently invade the lymph nodes; however, the underlying mechanism remains incompletely understood. Here, we aimed to comprehensively characterise the effects of breast cancers on immune cells in the lymph nodes.

METHODS

We collected non-metastatic and metastatic lymph node samples from 6 patients with breast cancer with lymph node metastasis. We performed bulk transcriptomics, spatial transcriptomics, and imaging mass cytometry to analyse the obtained lymph nodes. Furthermore, we conducted histological analyses against a larger patient cohort (474 slices from 58 patients).

FINDINGS

The comparison between paired lymph nodes with and without metastasis from the same patients demonstrated that the number of CD169+ lymph node sinus macrophages, an initiator of anti-cancer immunity, was reduced in metastatic lymph nodes (36.7 ± 21.1 vs 7.3 ± 7.0 cells/mm2, p = 0.0087), whereas the numbers of other major immune cell types were unaltered. We also detected that the infiltration of CD169+ macrophages into metastasised cancer tissues differed by section location within tumours, suggesting that CD169+ macrophages were gradually decreased after anti-cancer reactions. Furthermore, CD169+ macrophage elimination was prevalent in major breast cancer subtypes and correlated with breast cancer staging (p = 0.022).

INTERPRETATION

We concluded that lymph nodes with breast cancer metastases have fewer CD169+ macrophages, which may be detrimental to the activity of anti-cancer immunity.

FUNDING

JSPS KAKENHI (16H06279, 20H03451, 20H04842, 22H04925, 19K16770, and 21K15530, 24K02236), JSPS Fellows (JP22KJ1822), AMED (JP21ck0106698), JST FOREST (JPMJFR2062), Caravel, Co., Ltd, Japan Foundation for Applied Enzymology, and Sumitomo Pharma Co., Ltd. under SKIPS.

Single-cell landscape of intratumoral heterogeneity and tumor microenvironment remolding in pre-nodal metastases of breast cancer

BACKGROUND

The metastasis of cancer cells is influenced by both their intrinsic characteristics and the tumor microenvironment (TME). However, the molecular mechanisms underlying pre-nodal metastases of breast cancer remain unclear.

METHODS

We integrated a total of 216,963 cells from 54 samples across 6 single-cell datasets to profile the cellular landscape differences between primary tumors and pre-nodal metastases.

RESULTS

We revealed three distinct metastatic epithelial cell subtypes (Epi1, Epi2 and Epi3), which exhibited different metastatic mechanisms. Specifically, the marker gene KCNK15 of the Epi1 subtype exhibited increased gene expression along the cell differentiation trajectory and was specifically regulated by the transcription factor ASCL1. In the Epi3 subtype, we highlighted NR2F1 as a regulator targeting the marker gene MUCL1. Additionally, we found that the Epi2 and Epi3 subtypes shared some regulons, such as ZEB1 and NR2C1. Similarly, we identified specific subtypes of stromal and immune cells in the TME, and discovered that vascular cancer-associated fibroblasts might promote capillary formation through CXCL9+ macrophages in pre-nodal metastases. All three subtypes of metastatic epithelial cells were associated with poor prognosis.

CONCLUSIONS

In summary, this study dissects the intratumoral heterogeneity and remodeling of the TME in pre-nodal metastases of breast cancer, providing novel insights into the mechanisms underlying breast cancer metastasis.

Mechanism insights and therapeutic intervention of tumor metastasis: latest developments and perspectives

Metastasis remains a pivotal characteristic of cancer and is the primary contributor to cancer-associated mortality. Despite its significance, the mechanisms governing metastasis are not fully elucidated. Contemporary findings in the domain of cancer biology have shed light on the molecular aspects of this intricate process. Tumor cells undergoing invasion engage with other cellular entities and proteins en route to their destination. Insights into these engagements have enhanced our comprehension of the principles directing the movement and adaptability of metastatic cells. The tumor microenvironment plays a pivotal role in facilitating the invasion and proliferation of cancer cells by enabling tumor cells to navigate through stromal barriers. Such attributes are influenced by genetic and epigenetic changes occurring in the tumor cells and their surrounding milieu. A profound understanding of the metastatic process's biological mechanisms is indispensable for devising efficacious therapeutic strategies. This review delves into recent developments concerning metastasis-associated genes, important signaling pathways, tumor microenvironment, metabolic processes, peripheral immunity, and mechanical forces and cancer metastasis. In addition, we combine recent advances with a particular emphasis on the prospect of developing effective interventions including the most popular cancer immunotherapies and nanotechnology to combat metastasis. We have also identified the limitations of current research on tumor metastasis, encompassing drug resistance, restricted animal models, inadequate biomarkers and early detection methods, as well as heterogeneity among others. It is anticipated that this comprehensive review will significantly contribute to the advancement of cancer metastasis research.

CAFs and T cells interplay: The emergence of a new arena in cancer combat

The interaction between the immune system and the tumor matrix has a huge impact on the progression and treatment of cancer. This paper summarizes and discusses the crosstalk between T cells and cancer-associated fibroblasts (CAFs). CAFs can also produce inhibitors that counteract the function of T cells and promote tumor immune escape, while T cells can also engage in complex two-way interactions with CAFs through direct cell contact, the exchange of soluble factors such as cytokines, and the remodeling of the extracellular matrix. Precise targeted intervention can effectively reverse tumor-promoting crosstalk between T cells and CAFs, improve anti-tumor immune response, and provide a new perspective for cancer treatment. Therefore, it is important to deeply understand the mechanism of crosstalk between T cells and CAFs. This review aims to outline the underlying mechanisms of these interactions and discuss potential therapeutic strategies that may become fundamental tools in the treatment of cancer, especially hard-to-cure cancers.

Single-cell transcriptomics analysis of bullous pemphigoid unveils immune-stromal crosstalk in type 2 inflammatory disease

Bullous pemphigoid (BP) is a type 2 inflammation- and immunity-driven skin disease, yet a comprehensive understanding of the immune landscape, particularly immune-stromal crosstalk in BP, remains elusive. Herein, using single-cell RNA sequencing (scRNA-seq) and in vitro functional analyzes, we pinpoint Th2 cells, dendritic cells (DCs), and fibroblasts as crucial cell populations. The IL13-IL13RA1 ligand-receptor pair is identified as the most significant mediator of immune-stromal crosstalk in BP. Notably, fibroblasts and DCs expressing IL13RA1 respond to IL13-secreting Th2 cells, thereby amplifying Th2 cell-mediated cascade responses, which occurs through the specific upregulation of PLA2G2A in fibroblasts and CCL17 in myeloid cells, creating a positive feedback loop integral to immune-stromal crosstalk. Furthermore, PLA2G2A and CCL17 contribute to an increased titer of pathogenic anti-BP180-NC16A autoantibodies in BP patients. Our work provides a comprehensive insight into BP pathogenesis and shows a mechanism governing immune-stromal interactions, providing potential avenues for future therapeutic research.

Proteomic, single-cell and bulk transcriptomic analysis of plasma and tumor tissues unveil core proteins in response to anti-PD-L1 immunotherapy in triple negative breast cancer

BACKGROUND

Anti-PD-1/PD-L1 treatment has achieved durable responses in TNBC patients, whereas a fraction of them showed non-sensitivity to the treatment and the mechanism is still unclear.

METHODS

Pre- and post-treatment plasma samples from triple negative breast cancer (TNBC) patients treated with immunotherapy were measured by tandem mass tag (TMT) mass spectrometry. Public proteome data of lung cancer and melanoma treated with immunotherapy were employed to validate the findings. Blood and tissue single-cell RNA sequencing (scRNA-seq) data of TNBC patients treated with or without immunotherapy were analyzed to identify the derivations of plasma proteins. RNA-seq data from IMvigor210 and other cancer types were used to validate plasma proteins in predicting response to immunotherapy.

RESULTS

A random forest model constructed by FAP, LRG1, LBP and COMP could well predict the response to immunotherapy. The activation of complement cascade was observed in responders, whereas FAP and COMP showed a higher abundance in non-responders and negative correlated with the activation of complements. scRNA-seq and bulk RNA-seq analysis suggested that FAP, COMP and complements were derived from fibroblasts of tumor tissues.

CONCLUSIONS

We constructe an effective plasma proteomic model in predicting response to immunotherapy, and find that FAP+ and COMP+ fibroblasts are potential targets for reversing immunotherapy resistance.

Interactions between LAMP3+ dendritic cells and T-cell subpopulations promote immune evasion in papillary thyroid carcinoma

BACKGROUND

The incidence of papillary thyroid cancer (PTC) continues to rise all over the world, 10-15% of the patients have a poor prognosis. Although immunotherapy has been applied in clinical practice, its therapeutic efficacy remains far from satisfactory, necessitating further investigation of the mechanism of PTC immune remodeling and exploration of novel treatment targets.

METHODS

This study conducted a single-cell RNA sequencing (scRNA-seq) analysis using 18 surgical tissue specimens procured from 14 patients diagnosed with adjacent tissues, non-progressive PTC or progressive PTC. Key findings were authenticated through spatial transcriptomics RNA sequencing, immunohistochemistry, multiplex immunohistochemistry, and an independent bulk RNA-seq data set containing 502 samples.

RESULTS

A total of 151,238 individual cells derived from 18 adjacent tissues, non-progressive PTC and progressive PTC specimens underwent scRNA-seq analysis. We found that progressive PTC exhibits the following characteristics: a significant decrease in overall immune cells, enhanced immune evasion of tumor cells, and disrupted antigen presentation function. Moreover, we identified a subpopulation of lysosomal associated membrane protein 3 (LAMP3+) dendritic cells (DCs) exhibiting heightened infiltration in progressive PTC and associated with advanced T stage and poor prognosis of PTC. LAMP3+ DCs promote CD8+ T cells exhaustion (mediated by NECTIN2-TIGIT) and increase infiltration abundance of regulatory T cells (mediated by chemokine (C-C motif) ligand 17 (CCL17)-chemokine (C-C motif) receptor 4 (CCR4)) establishing an immune-suppressive microenvironment. Ultimately, we unveiled that progressive PTC tumor cells facilitate the retention of LAMP3+ DCs within the tumor microenvironment through NECTIN3-NECTIN2 interactions, thereby rendering tumor cells more susceptible to immune evasion.

CONCLUSION

Our findings expound valuable insights into the role of the interaction between LAMP3+ DCs and T-cell subpopulations and offer new and effective ideas and strategies for immunotherapy in patients with progressive PTC.

Fibroblast heterogeneity and functions: insights from single-cell sequencing in wound healing, breast cancer, ovarian cancer and melanoma

Cancer has been described as the wound that does not heal, in large part due to fibroblast involvement. Activation of cancer-associated fibroblasts (CAFs) contributes to critical features of the tumor microenvironment, including upregulation of key marker proteins, recruitment of immune cells, and deposition of extracellular matrix (ECM)-similar to fibroblast activation in injury-induced wound healing. Prior to the widespread availability of single-cell RNA sequencing (scRNA seq), studies of CAFs or fibroblasts in wound healing largely relied on models guided by individual fibroblast markers, or methods with less resolution to unravel the heterogeneous nature of CAFs and wound healing fibroblasts (especially regarding scarring outcome). Here, insights from the enhanced resolution provided by scRNA sequencing of fibroblasts in normal wound healing, breast cancer, ovarian cancer, and melanoma are discussed. These data have revealed differences in expression of established canonical activation marker genes, epigenetic modifications, fibroblast lineages, new gene and proteins of clinical interest for further experimentation, and novel signaling interactions with other cell types that include spatial information.

Altered tumor signature and T-cell profile after chemotherapy reveal new therapeutic opportunities in high-grade serous ovarian carcinoma

Chemotherapy combined with debulking surgery is the standard treatment protocol for high-grade serous ovarian carcinoma (HGSOC). Nonetheless, a significant number of patients encounter relapse due to the development of chemotherapy resistance. To better understand and address this resistance, we conducted a comprehensive study investigating the transcriptional alterations at the single-cell resolution in tissue samples from patients with HGSOC, using single-cell RNA sequencing and T-cell receptor sequencing techniques. Our analyses unveiled notable changes in the tumor signatures after chemotherapy, including those associated with epithelial-mesenchymal transition and cell cycle arrest. Within the immune compartment, we observed alterations in the T-cell profiles, characterized by naïve or pre-exhausted populations following chemotherapy. This phenotypic change was further supported by the examination of adjoining T-cell receptor clonotypes in paired longitudinal samples. These findings underscore the profound impact of chemotherapy on reshaping the tumor landscape and the immune microenvironment. This knowledge may provide clues for the development of future therapeutic strategies to combat treatment resistance in HGSOC.

SRT-Server: powering the analysis of spatial transcriptomic data

BACKGROUND

Spatial resolved transcriptomics (SRT) encompasses a rapidly developing set of technologies that enable the measurement of gene expression in tissue while retaining spatial localization information. SRT technologies and the enabled SRT studies have provided unprecedent insights into the structural and functional underpinnings of complex tissues. As SRT technologies have advanced and an increasing number of SRT studies have emerged, numerous sophisticated statistical and computational methods have been developed to facilitate the analysis and interpretation of SRT data. However, despite the growing popularity of SRT studies and the widespread availability of SRT analysis methods, analysis of large-scale and complex SRT datasets remains challenging and not easily accessible to researchers with limited statistical and computational backgrounds.

RESULTS

Here, we present SRT-Server, the first webserver designed to carry out comprehensive SRT analyses for a wide variety of SRT technologies while requiring minimal prior computational knowledge. Implemented with cutting-edge web development technologies, SRT-Server is user-friendly and features multiple analytic modules that can perform a range of SRT analyses. With a flowchart-style interface, these different analytic modules on the SRT-Server can be dragged into the main panel and connected to each other to create custom analytic pipelines. SRT-Server then automatically executes the desired analyses, generates corresponding figures, and outputs results-all without requiring prior programming knowledge. We demonstrate the advantages of SRT-Server through three case studies utilizing SRT data collected from two common platforms, highlighting its versatility and values to researchers with varying analytic expertise.

CONCLUSIONS

Overall, SRT-Server presents a user-friendly, efficient, effective, secure, and expandable solution for SRT data analysis, opening new doors for researchers in the field. SRT-Server is freely available at https://spatialtranscriptomicsanalysis.com/ .

Stachydrine represses the proliferation and enhances cell cycle arrest and apoptosis of breast cancer cells via PLA2G2A/DCN axis

Considering the therapeutic efficacy of Stachydrine on breast cancer (BC), this study aims to decipher the relevant mechanism. The effects of Stachydrine on BC cell viability, proliferation and apoptosis were firstly investigated. Then, Bioinformatics was applied to sort out the candidate interacting with Stachydrine as well as its expression and downstream target in BC. Relative expressions of genes of interest as well as proliferation- and apoptosis-related factors in BC cells were quantified through quantitative reverse-transcription PCR and western blot as appropriate. As a result, Stachydrine inhibited the proliferation, down-regulated the expressions of proliferating cell nuclear antigen and CyclinD1, enhanced cell cycle arrest and apoptosis, and up-regulated the levels of Cleaved caspase-3 and Cleaved caspase-9 in BC cells. Phospholipase A2 Group IIA (PLA2G2A) was predicted as the candidate interacting with Stachydrine and to be lowly expressed in BC. PLA2G2A silencing reversed while PLA2G2A overexpression reinforced the effects of Stachydrine. Decorin (DCN) was the downstream target of PLA2G2A and also lowly expressed in BC. PLA2G2A silencing counteracted yet overexpressed PLA2G2A strengthened the promoting effects of Stachydrine on DCN level. Collectively, Stachydrine inhibits the growth of BC cells to promote cell cycle arrest and apoptosis via PLA2G2A/DCN axis.

Single-cell and spatial transcriptomics reveal POSTN+ cancer-associated fibroblasts correlated with immune suppression and tumour progression in non-small cell lung cancer

BACKGROUND

Cancer-associated fibroblasts (CAFs) are potential targets for cancer therapy. Due to the heterogeneity of CAFs, the influence of CAF subpopulations on the progression of lung cancer is still unclear, which impedes the translational advances in targeting CAFs.

METHODS

We performed single-cell RNA sequencing (scRNA-seq) on tumour, paired tumour-adjacent, and normal samples from 16 non-small cell lung cancer (NSCLC) patients. CAF subpopulations were analyzed after integration with published NSCLC scRNA-seq data. SpaTial enhanced resolution omics-sequencing (Stereo-seq) was applied in tumour and tumour-adjacent samples from seven NSCLC patients to map the architecture of major cell populations in tumour microenvironment (TME). Immunohistochemistry (IHC) and multiplexed IHC (mIHC) were used to validate marker gene expression and the association of CAFs with immune infiltration in TME.

RESULTS

A subcluster of myofibroblastic CAFs, POSTN+ CAFs, were significantly enriched in advanced tumours and presented gene expression signatures related to extracellular matrix remodeling, tumour invasion pathways and immune suppression. Stereo-seq and mIHC demonstrated that POSTN+ CAFs were in close localization with SPP1+ macrophages and were associated with the exhausted phenotype and lower infiltration of T cells. POSTN expression or the abundance of POSTN+ CAFs were associated with poor prognosis of NSCLC.

CONCLUSIONS

Our study identified a myofibroblastic CAF subpopulation, POSTN+ CAFs, which might associate with SPP1+ macrophages to promote the formation of desmoplastic architecture and participate in immune suppression. Furthermore, we showed that POSTN+ CAFs associated with cancer progression and poor clinical outcomes and may provide new insights on the treatment of NSCLC.

Multi-omics analyses reveal spatial heterogeneity in primary and metastatic oesophageal squamous cell carcinoma

BACKGROUND

Biopsies obtained from primary oesophageal squamous cell carcinoma (ESCC) guide diagnosis and treatment. However, spatial intra-tumoral heterogeneity (ITH) influences biopsy-derived information and patient responsiveness to therapy. Here, we aimed to elucidate the spatial ITH of ESCC and matched lymph node metastasis (LNmet ).

METHODS

Primary tumour superficial (PTsup ), deep (PTdeep ) and LNmet subregions of patients with locally advanced resectable ESCC were evaluated using whole-exome sequencing (WES), whole-transcriptome sequencing and spatially resolved digital spatial profiling (DSP). To validate the findings, immunohistochemistry was conducted and a single-cell transcriptomic dataset was analysed.

RESULTS

WES revealed 15.72%, 5.02% and 32.00% unique mutations in PTsup , PTdeep and LNmet , respectively. Copy number alterations and phylogenetic trees showed spatial ITH among subregions both within and among patients. Driver mutations had a mixed intra-tumoral clonal status among subregions. Transcriptome data showed distinct differentially expressed genes among subregions. LNmet exhibited elevated expression of immunomodulatory genes and enriched immune cells, particularly when compared with PTsup (all P < .05). DSP revealed orthogonal support of bulk transcriptome results, with differences in protein and immune cell abundance between subregions in a spatial context. The integrative analysis of multi-omics data revealed complex heterogeneity in mRNA/protein levels and immune cell abundance within each subregion.

CONCLUSIONS

This study comprehensively characterised spatial ITH in ESCC, and the findings highlight the clinical significance of unbiased molecular classification based on multi-omics data and their potential to improve the understanding and management of ESCC. The current practices for tissue sampling are insufficient for guiding precision medicine for ESCC, and routine profiling of PTdeep and/or LNmet should be systematically performed to obtain a more comprehensive understanding of ESCC and better inform treatment decisions.

Immunomodulatory properties of the lymphatic endothelium in the tumor microenvironment

The tumor microenvironment (TME) is an intricate complex and dynamic structure composed of various cell types, including tumor, stromal and immune cells. Within this complex network, lymphatic endothelial cells (LECs) play a crucial role in regulating immune responses and influencing tumor progression and metastatic dissemination to lymph node and distant organs. Interestingly, LECs possess unique immunomodulatory properties that can either promote or inhibit anti-tumor immune responses. In fact, tumor-associated lymphangiogenesis can facilitate tumor cell dissemination and metastasis supporting immunoevasion, but also, different molecular mechanisms involved in LEC-mediated anti-tumor immunity have been already described. In this context, the crosstalk between cancer cells, LECs and immune cells and how this communication can shape the immune landscape in the TME is gaining increased interest in recent years. In this review, we present a comprehensive and updated report about the immunomodulatory properties of the lymphatic endothelium within the TME, with special focus on primary tumors and tumor-draining lymph nodes. Furthermore, we outline emerging research investigating the potential therapeutic strategies targeting the lymphatic endothelium to enhance anti-tumor immune responses. Understanding the intricate mechanisms involved in LEC-mediated immune modulation in the TME opens up new possibilities for the development of innovative approaches to fight cancer.

Cellular dynamics in tumour microenvironment along with lung cancer progression underscore spatial and evolutionary heterogeneity of neutrophil

BACKGROUND

The cellular dynamics in the tumour microenvironment (TME) along with non-small cell lung cancer (NSCLC) progression remain unclear.

METHODS

Multiplex immunofluorescence test detecting 10 immune-related markers on 553 primary tumour (PT) samples of NSCLC was conducted and spatial information in TME was assessed by the StarDist depth learning model. The single-cell transcriptomic atlas of PT (n = 4) and paired tumour-draining lymph nodes (TDLNs) (n = 5 for tumour-invaded, n = 3 for tumour-free) microenvironment was profiled. Various bioinformatics analyses based on Gene Expression Omnibus, TCGA and Array-Express databases were also used to validate the discoveries.

RESULTS

Spatial distances of CD4+ T cells-CD38+ T cells, CD4+ T cells-neutrophils and CD38+ T cells-neutrophils prolonged and they were replaced by CD163+ macrophages in PT along with tumour progression. Neutrophils showed unique stage and location-dependent prognostic effects. A high abundance of stromal neutrophils improved disease-free survival in the early-stage, whereas high intratumoural neutrophil infiltrates predicted poor prognosis in the mid-to-late-stage. Significant molecular and functional reprogramming in PT and TDLN microenvironments was observed. Diverse interaction networks mediated by neutrophils were found between positive and negative TDLNs. Five phenotypically and functionally heterogeneous subtypes of tumour-associated neutrophil (TAN) were further identified by pseudotime analysis, including TAN-0 with antigen-presenting function, TAN-1 with strong expression of interferon (IFN)-stimulated genes, the pro-tumour TAN-2 subcluster, the classical subset (TAN-3) and the pro-inflammatory subtype (TAN-4). Loss of IFN-stimulated signature and growing angiogenesis activity were discovered along the transitional trajectory. Eventually, a robust six neutrophil differentiation relevant genes-based model was established, showing that low-risk patients had longer overall survival time and may respond better to immunotherapy.

CONCLUSIONS

The cellular composition, spatial location, molecular and functional changes in PT and TDLN microenvironments along with NSCLC progression were deciphered, highlighting the immunoregulatory roles and evolutionary heterogeneity of TANs.

Tumor heterogeneity: preclinical models, emerging technologies, and future applications

Heterogeneity describes the differences among cancer cells within and between tumors. It refers to cancer cells describing variations in morphology, transcriptional profiles, metabolism, and metastatic potential. More recently, the field has included the characterization of the tumor immune microenvironment and the depiction of the dynamics underlying the cellular interactions promoting the tumor ecosystem evolution. Heterogeneity has been found in most tumors representing one of the most challenging behaviors in cancer ecosystems. As one of the critical factors impairing the long-term efficacy of solid tumor therapy, heterogeneity leads to tumor resistance, more aggressive metastasizing, and recurrence. We review the role of the main models and the emerging single-cell and spatial genomic technologies in our understanding of tumor heterogeneity, its contribution to lethal cancer outcomes, and the physiological challenges to consider in designing cancer therapies. We highlight how tumor cells dynamically evolve because of the interactions within the tumor immune microenvironment and how to leverage this to unleash immune recognition through immunotherapy. A multidisciplinary approach grounded in novel bioinformatic and computational tools will allow reaching the integrated, multilayered knowledge of tumor heterogeneity required to implement personalized, more efficient therapies urgently required for cancer patients.

LAMP3 expression in the liver is involved in T cell activation and adaptive immune regulation in hepatitis B virus infection

BACKGROUND

The disease burden caused by chronic hepatitis B virus (HBV) infection is still heavy, and the current treatment scheme has not achieved a complete cure. Changes in natural and adaptive immunity usually accompany chronic HBV infection. As a marker expressed on dendritic cells (DCs), whether lysosome-associated membrane glycoprotein 3 (LAMP3) participates in chronic HBV infection deserves further analysis.

METHODS

We retrieved chronic HBV infection transcriptional information from the Gene Expression Omnibus (GEO) database. The LAMP3 expression in the liver of patients with chronic hepatitis B (CHB) was analyzed in three GEO datasets and confirmed in our validation cohort (27 patients with CHB). Differentially expressed genes were obtained from one CHB cohort by comparing LAMP3high and LAMP3low expression subgroups. These genes underwent Gene Ontology, Kyoto Encyclopedia of Genes and Genomes analysis, and Gene Set Enrichment Analysis to decipher the influence of LAMP3 on the biological process and immunity changes in HBV infection. Furthermore, we investigated the potential relationship between LAMP3 levels, the abundance of infiltrating immune cells, and liver dysfunction.

RESULTS

Compared to healthy controls, LAMP3 expression was upregulated in the transcriptional profiles of the liver in patients with CHB. The high LAMP3 expression was related to T cell activation and the chemokine signaling pathway. The LAMP3 gene was positively linked to marker sets of infiltrating activated regulatory T cells (Treg), T cell exhaustion, monocytes, and DCs. Moreover, CHB patients with high LAMP3 expression had unfavorable liver dysfunction.

CONCLUSIONS

LAMP3 is a gene related to HBV infection, which might be involved in HBV infection by regulating T cell activation and adaptive immune response.

B Cells in Breast Cancer Pathology

B cells have recently become a focus in breast cancer pathology due to their influence on tumour regression, prognosis, and response to treatment, besides their contribution to antigen presentation, immunoglobulin production, and regulation of adaptive responses. As our understanding of diverse B cell subsets in eliciting both pro- and anti-inflammatory responses in breast cancer patients increases, it has become pertinent to address the molecular and clinical relevance of these immune cell populations within the tumour microenvironment (TME). At the primary tumour site, B cells are either found spatially dispersed or aggregated in so-called tertiary lymphoid structures (TLS). In axillary lymph nodes (LNs), B cell populations, amongst a plethora of activities, undergo germinal centre reactions to ensure humoral immunity. With the recent approval for the addition of immunotherapeutic drugs as a treatment option in the early and metastatic settings for triple-negative breast cancer (TNBC) patients, B cell populations or TLS may resemble valuable biomarkers for immunotherapy responses in certain breast cancer subgroups. New technologies such as spatially defined sequencing techniques, multiplex imaging, and digital technologies have further deciphered the diversity of B cells and the morphological structures in which they appear in the tumour and LNs. Thus, in this review, we comprehensively summarise the current knowledge of B cells in breast cancer. In addition, we provide a user-friendly single-cell RNA-sequencing platform, called "B singLe cEll rna-Seq browSer" (BLESS) platform, with a focus on the B cells in breast cancer patients to interrogate the latest publicly available single-cell RNA-sequencing data collected from diverse breast cancer studies. Finally, we explore their clinical relevance as biomarkers or molecular targets for future interventions.

Combined Single-Cell and Spatial Transcriptomics Reveal the Metabolic Evolvement of Breast Cancer during Early Dissemination

Breast cancer is now the most frequently diagnosed malignancy, and metastasis remains the leading cause of death in breast cancer. However, little is known about the dynamic changes during the evolvement of dissemination. In this study, 65 968 cells from four patients with breast cancer and paired metastatic axillary lymph nodes are profiled using single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics. A disseminated cancer cell cluster with high levels of oxidative phosphorylation (OXPHOS), including the upregulation of cytochrome C oxidase subunit 6C and dehydrogenase/reductase 2, is identified. The transition between glycolysis and OXPHOS when dissemination initiates is noticed. Furthermore, this distinct cell cluster is distributed along the tumor's leading edge. The findings here are verified in three different cohorts of breast cancer patients and an external scRNA-seq dataset, which includes eight patients with breast cancer and paired metastatic axillary lymph nodes. This work describes the dynamic metabolic evolvement of early disseminated breast cancer and reveals a switch between glycolysis and OXPHOS in breast cancer cells as the early event during lymph node metastasis.

Multiomics Integration at Single-Cell Resolution Using Bayesian Networks: A Case Study in Hepatocellular Carcinoma

Multiomics data integration is one of the leading frontiers of complex disease research and integrative biology. The advances in single-cell sequencing technologies offer yet another crucial dimension in multiomics research. The single-cell studies enable the study and integration of multiomics data simultaneously in the same cell. We report in this study multiomics data integration in single-cell resolution using Bayesian networks (BNs) in a case study of hepatocellular carcinoma (HCC). A BN encodes the conditional dependencies/independencies of variables using a graphical model with an accompanying joint probability. RNA-seq and Reduced Representation Bisulfite Sequencing data were analyzed separately, and copy number variations were estimated by the hidden Markov model method. Several BN models were constructed to reveal omics' causal and associational relationships. These methods were subjected to a validation study using an independent data set. We show the heterogeneity of the multiple cellular layers of HCC at single-cell omics resolution by identifying best-fitted BN models of 295 genes. We also provide novel insights into the multiomics mechanistic relationships in the human lymphocyte antigen class I genes in HCC. To the best of our knowledge, this is the first study to focus on integrating omics data using a machine learning algorithm, BNs, at the single-cell resolution using a case study of HCC.