From bench to bedside: Single-cell analysis for cancer immunotherapy

Cellular Dynamics of Tumor Microenvironment Driving Immunotherapy Resistance in Non-Small-Cell Lung Carcinoma

Immune checkpoint inhibitors (ICIs) have profoundly reshaped the treatment paradigm for non-small cell lung cancer (NSCLC). Despite these advancements, primary and secondary resistance to ICIs remain prevalent challenges in managing advanced NSCLC. Recent studies have highlighted the significant role of the tumor microenvironment (TME) in modulating treatment responses. This review aims to comprehensively examine the interactive roles of immune/stromal cells-such as T cells, B cells, neutrophils, macrophages, and CAFs within the TME, elucidating how these diverse cellular interactions contribute to immunotherapy resistance. It focuses on the dynamic interactions among diverse cell types such as the varying states of T cells under the influence of TME constituents like immune cells and cancer-associated fibroblasts (CAFs). By exploring the mechanisms involved in the complex cellular interactions, we highlight novel therapeutic targets and strategies aimed at overcoming resistance, thereby enhancing the efficacy of ICIs in NSCLC. Our synthesis of recent research provides critical insights into the multifaceted mechanisms of resistance and paves the way for the development of more effective, personalized treatment approaches.

Cancer cell states: Lessons from ten years of single-cell RNA-sequencing of human tumors

Human tumors are intricate ecosystems composed of diverse genetic clones and malignant cell states that evolve in a complex tumor micro-environment. Single-cell RNA-sequencing (scRNA-seq) provides a compelling strategy to dissect this intricate biology and has enabled a revolution in our ability to understand tumor biology over the last ten years. Here we reflect on this first decade of scRNA-seq in human tumors and highlight some of the powerful insights gleaned from these studies. We first focus on computational approaches for robustly defining cancer cell states and their diversity and highlight some of the most common patterns of gene expression intra-tumor heterogeneity (eITH) observed across cancer types. We then discuss ambiguities in the field in defining and naming such eITH programs. Finally, we highlight critical developments that will facilitate future research and the broader implementation of these technologies in clinical settings.

PanIN and CAF transitions in pancreatic carcinogenesis revealed with spatial data integration

This study introduces a new imaging, spatial transcriptomics (ST), and single-cell RNA-sequencing integration pipeline to characterize neoplastic cell state transitions during tumorigenesis. We applied a semi-supervised analysis pipeline to examine premalignant pancreatic intraepithelial neoplasias (PanINs) that can develop into pancreatic ductal adenocarcinoma (PDAC). Their strict diagnosis on formalin-fixed and paraffin-embedded (FFPE) samples limited the single-cell characterization of human PanINs within their microenvironment. We leverage whole transcriptome FFPE ST to enable the study of a rare cohort of matched low-grade (LG) and high-grade (HG) PanIN lesions to track progression and map cellular phenotypes relative to single-cell PDAC datasets. We demonstrate that cancer-associated fibroblasts (CAFs), including antigen-presenting CAFs, are located close to PanINs. We further observed a transition from CAF-related inflammatory signaling to cellular proliferation during PanIN progression. We validate these findings with single-cell high-dimensional imaging proteomics and transcriptomics technologies. Altogether, our semi-supervised learning framework for spatial multi-omics has broad applicability across cancer types to decipher the spatiotemporal dynamics of carcinogenesis.

Epigenetic regulation of tumor immunity

Although cancer has long been considered a genetic disease, increasing evidence shows that epigenetic aberrations play a crucial role in affecting tumor biology and therapeutic response. The dysregulated epigenome in cancer cells reprograms the immune landscape within the tumor microenvironment, thereby hindering antitumor immunity, promoting tumor progression, and inducing immunotherapy resistance. Targeting epigenetically mediated tumor-immune crosstalk is an emerging strategy to inhibit tumor progression and circumvent the limitations of current immunotherapies, including immune checkpoint inhibitors. In this Review, we discuss the mechanisms by which epigenetic aberrations regulate tumor-immune interactions and how epigenetically targeted therapies inhibit tumor progression and synergize with immunotherapy.

Rapidly Evolving Pre- and Post-surgical Systemic Treatment of Melanoma

With the development of effective BRAF-targeted and immune-checkpoint immunotherapies for metastatic melanoma, clinical trials are moving these treatments into earlier adjuvant and perioperative settings. BRAF-targeted therapy is a standard of care in resected stage III-IV melanoma, while anti-programmed death-1 (PD1) immunotherapy is now a standard of care option in resected stage IIB through IV disease. With both modalities, recurrence-free survival and distant-metastasis-free survival are improved by a relative 35-50%, yet no improvement in overall survival has been demonstrated. Neoadjuvant anti-PD1 therapy improves event-free survival by approximately an absolute 23%, although improvements in overall survival have yet to be demonstrated. Understanding which patients are most likely to recur and which are most likely to benefit from treatment is now the highest priority question in the field. Biomarker analyses, such as gene expression profiling of the primary lesion and circulating DNA, are preliminarily exciting as potential biomarkers, though each has drawbacks. As in the setting of metastatic disease, markers that inform positive outcomes include interferon-γ gene expression, PD-L1, and high tumor mutational burden, while negative predictors of outcome include circulating factors such as lactate dehydrogenase, interleukin-8, and C-reactive protein. Integrating and validating these markers into clinically relevant models is thus a high priority. Melanoma therapeutics continues to advance with combination adjuvant approaches now investigating anti-PD1 with lymphocyte activation gene 3 (LAG3), T-cell immunoreceptor with Ig and ITIM domains (TIGIT), and individualized neoantigen therapies. How this progress will be integrated into the management of a unique patient to reduce recurrence, limit toxicity, and avoid over-treatment will dominate clinical research and patient care over the next decade.

Harnessing the potential of reverse-phase protein array technology: Advancing precision oncology strategies

The last few decades have seen remarkable strides in the field of cancer therapy. Precision oncology coupled with comprehensive genomic profiling has become routine clinical practice for solid tumors, the advent of immune checkpoint inhibitors has transformed the landscape of oncology treatment, and the number of cancer drug approvals has continued to increase. Nevertheless, the application of genomics-driven precision oncology has thus far benefited only 10%-20% of cancer patients, leaving the majority without matched treatment options. This limitation underscores the need to explore alternative avenues with regard to selecting patients for targeted therapies. In contrast with genomics-based approaches, proteomics-based strategies offer a more precise understanding of the intricate biological processes driving cancer pathogenesis. This perspective underscores the importance of integrating complementary proteomic analyses into the next phase of precision oncology to establish robust biomarker-drug associations and surmount challenges related to drug resistance. One promising technology in this regard is the reverse-phase protein array (RPPA), which excels in quantitatively detecting protein modifications, even with limited amounts of sample. Its cost-effectiveness and rapid turnaround time further bolster its appeal for application in clinical settings. Here, we review the current status of genomics-driven precision oncology, as well as its limitations, with an emphasis on drug resistance. Subsequently, we explore the application of RPPA technology as a catalyst for advancing precision oncology. Through illustrative examples drawn from clinical trials, we demonstrate its utility for unraveling the molecular mechanisms underlying drug responses and resistance.

Harnessing atomic force microscopy-based single-cell analysis to advance physical oncology

Single-cell analysis is an emerging and promising frontier in the field of life sciences, which is expected to facilitate the exploration of fundamental laws of physiological and pathological processes. Single-cell analysis allows experimental access to cell-to-cell heterogeneity to reveal the distinctive behaviors of individual cells, offering novel opportunities to dissect the complexity of severe human diseases such as cancers. Among the single-cell analysis tools, atomic force microscopy (AFM) is a powerful and versatile one which is able to nondestructively image the fine topographies and quantitatively measure multiple mechanical properties of single living cancer cells in their native states under aqueous conditions with unprecedented spatiotemporal resolution. Over the past few decades, AFM has been widely utilized to detect the structural and mechanical behaviors of individual cancer cells during the process of tumor formation, invasion, and metastasis, yielding numerous unique insights into tumor pathogenesis from the biomechanical perspective and contributing much to the field of cancer mechanobiology. Here, the achievements of AFM-based analysis of single cancer cells to advance physical oncology are comprehensively summarized, and challenges and future perspectives are also discussed. RESEARCH HIGHLIGHTS: Achievements of AFM in characterizing the structural and mechanical behaviors of single cancer cells are summarized, and future directions are discussed. AFM is not only capable of visualizing cellular fine structures, but can also measure multiple cellular mechanical properties as well as cell-generated mechanical forces. There is still plenty of room for harnessing AFM-based single-cell analysis to advance physical oncology.

Leveraging multi-omics data to empower quantitative systems pharmacology in immuno-oncology

Understanding the intricate interactions of cancer cells with the tumor microenvironment (TME) is a pre-requisite for the optimization of immunotherapy. Mechanistic models such as quantitative systems pharmacology (QSP) provide insights into the TME dynamics and predict the efficacy of immunotherapy in virtual patient populations/digital twins but require vast amounts of multimodal data for parameterization. Large-scale datasets characterizing the TME are available due to recent advances in bioinformatics for multi-omics data. Here, we discuss the perspectives of leveraging omics-derived bioinformatics estimates to inform QSP models and circumvent the challenges of model calibration and validation in immuno-oncology.

HyGAnno: hybrid graph neural network-based cell type annotation for single-cell ATAC sequencing data

Reliable cell type annotations are crucial for investigating cellular heterogeneity in single-cell omics data. Although various computational approaches have been proposed for single-cell RNA sequencing (scRNA-seq) annotation, high-quality cell labels are still lacking in single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) data, because of extreme sparsity and inconsistent chromatin accessibility between datasets. Here, we present a novel automated cell annotation method that transfers cell type information from a well-labeled scRNA-seq reference to an unlabeled scATAC-seq target, via a parallel graph neural network, in a semi-supervised manner. Unlike existing methods that utilize only gene expression or gene activity features, HyGAnno leverages genome-wide accessibility peak features to facilitate the training process. In addition, HyGAnno reconstructs a reference-target cell graph to detect cells with low prediction reliability, according to their specific graph connectivity patterns. HyGAnno was assessed across various datasets, showcasing its strengths in precise cell annotation, generating interpretable cell embeddings, robustness to noisy reference data and adaptability to tumor tissues.

Integrative analysis of single-cell and bulk RNA-sequencing data revealed disulfidptosis genes-based molecular subtypes and a prognostic signature in lung adenocarcinoma

BACKGROUND

Disulfidoptosis is an unconventional form of programmed cell death that distinguishes itself from well-established cell death pathways like ferroptosis, pyroptosis, and necroptosis.

METHODS

Initially, we conducted a single-cell analysis of the GSE131907 dataset from the GEO database to identify disulfidoptosis-related genes (DRGs). We utilized differentially expressed DRGs to classify TCGA samples with an unsupervised clustering algorithm. Prognostic models were built using Cox regression and LASSO regression.

RESULTS

Two DRG-related clusters (C1 and C2) were identified based on the DEGs from single-cell sequencing data analysis. In comparison to C1, C2 exhibited significantly worse overall prognosis, along with lower expression levels of immune checkpoint genes (ICGs) and chemoradiotherapy sensitivity-related genes (CRSGs). Furthermore, C2 displayed a notable enrichment in metabolic pathways and cell cycle-associated mechanisms. C2 was also linked to the development and spread of tumors. We created a prognostic risk model known as the DRG score, which relies on the expression levels of five DRGs. Patients were categorized into high-risk and low-risk groups depending on their DRG score, with the former group being linked to a poorer prognosis and higher TMB score. Moreover, the DRG score displayed significant correlations with CRSGs, ICGs, the tumor immune dysfunction and exclusion (TIDE) score, and chemotherapeutic sensitivity. Subsequently, we identified a significant correlation between the DRG score and monocyte macrophages. Additionally, crucial DRGs were additionally validated using qRT-PCR.

CONCLUSIONS

Our new DRG score can predict the immune landscape and prognosis of LUAD, serving as a reference for immunotherapy and chemotherapy.

Transcriptomic profiling of immune cells in murine polymicrobial sepsis

Introduction

Various immune cell types play critical roles in sepsis with numerous distinct subsets exhibiting unique phenotypes even within the same cell population. Single-cell RNA sequencing (scRNA-seq) enables comprehensive transcriptome profiling and unbiased cell classification. In this study, we have unveiled the transcriptomic landscape of immune cells in sepsis through scRNA-seq analysis.

Methods

We induced sepsis in mice by cecal ligation and puncture. 20 h after the surgery, the spleen and peritoneal lavage were collected. Single-cell suspensions were processed using a 10× Genomics pipeline and sequenced on an Illumina platform. Count matrices were generated using the Cell Ranger pipeline, which maps reads to the mouse reference transcriptome, GRCm38/mm10. Subsequent scRNA-seq analysis was performed using the R package Seurat.

Results

After quality control, we subjected the entire data set to unsupervised classification. Four major clusters were identified as neutrophils, macrophages, B cells, and T cells according to their putative markers. Based on the differentially expressed genes, we identified activated pathways in sepsis for each cell type. In neutrophils, pathways related to inflammatory signaling, such as NF-κB and responses to pathogen-associated molecular patterns (PAMPs), cytokines, and hypoxia were activated. In macrophages, activated pathways were the ones related to cell aging, inflammatory signaling, and responses to PAMPs. In B cells, pathways related to endoplasmic reticulum stress were activated. In T cells, activated pathways were the ones related to inflammatory signaling, responses to PAMPs, and acute lung injury. Next, we further classified each cell type into subsets. Neutrophils consisted of four clusters. Some subsets were activated in inflammatory signaling or cell metabolism, whereas others possessed immunoregulatory or aging properties. Macrophages consisted of four clusters, namely, the ones with enhanced aging, lymphocyte activation, extracellular matrix organization, or cytokine activity. B cells consisted of four clusters, including the ones possessing the phenotype of cell maturation or aging. T cells consisted of six clusters, whose phenotypes include molecular translocation or cell activation.

Conclusions

Transcriptomic analysis by scRNA-seq has unveiled a comprehensive spectrum of immune cell responses and distinct subsets in the context of sepsis. These findings are poised to enhance our understanding of sepsis pathophysiology, offering avenues for targeting novel molecules, cells, and pathways to combat infectious diseases.

Macro CD5L+ deteriorates CD8+T cells exhaustion and impairs combination of Gemcitabine-Oxaliplatin-Lenvatinib-anti-PD1 therapy in intrahepatic cholangiocarcinoma

Intratumoral immune status influences tumor therapeutic response, but it remains largely unclear how the status determines therapies for patients with intrahepatic cholangiocarcinoma. Here, we examine the single-cell transcriptional and TCR profiles of 18 tumor tissues pre- and post- therapy of gemcitabine plus oxaliplatin, in combination with lenvatinib and anti-PD1 antibody for intrahepatic cholangiocarcinoma. We find that high CD8 GZMB+ and CD8 proliferating proportions and a low Macro CD5L+ proportion predict good response to the therapy. In patients with a poor response, the CD8 GZMB+ and CD8 proliferating proportions are increased, but the CD8 GZMK+ proportion is decreased after the therapy. Transition of CD8 proliferating and CD8 GZMB+ to CD8 GZMK+ facilitates good response to the therapy, while Macro CD5L+-CD8 GZMB+ crosstalk impairs the response by increasing CTLA4 in CD8 GZMB+. Anti-CTLA4 antibody reverses resistance of the therapy in intrahepatic cholangiocarcinoma. Our data provide a resource for predicting response of the combination therapy and highlight the importance of CD8+T-cell status conversion and exhaustion induced by Macro CD5L+ in influencing the response, suggesting future avenues for cancer treatment optimization.

SingPro: a knowledge base providing single-cell proteomic data

Single-cell proteomics (SCP) has emerged as a powerful tool for detecting cellular heterogeneity, offering unprecedented insights into biological mechanisms that are masked in bulk cell populations. With the rapid advancements in AI-based time trajectory analysis and cell subpopulation identification, there exists a pressing need for a database that not only provides SCP raw data but also explicitly describes experimental details and protein expression profiles. However, no such database has been available yet. In this study, a database, entitled 'SingPro', specializing in single-cell proteomics was thus developed. It was unique in (a) systematically providing the SCP raw data for both mass spectrometry-based and flow cytometry-based studies and (b) explicitly describing experimental detail for SCP study and expression profile of any studied protein. Anticipating a robust interest from the research community, this database is poised to become an invaluable repository for OMICs-based biomedical studies. Access to SingPro is unrestricted and does not mandate a login at: http://idrblab.org/singpro/.

Systems immunology spanning tumors, lymph nodes, and periphery

The immune system defines a complex network of tissues and cell types that orchestrate responses across the body in a dynamic manner. The local and systemic interactions between immune and cancer cells contribute to disease progression. Lymphocytes are activated in lymph nodes, traffic through the periphery, and impact cancer progression through their interactions with tumor cells. As a result, therapeutic response and resistance are mediated across tissues, and a comprehensive understanding of lymphocyte dynamics requires a systems-level approach. In this review, we highlight experimental and computational methods that can leverage the study of leukocyte trafficking through an immunomics lens and reveal how adaptive immunity shapes cancer.

Inferring cellular and molecular processes in single-cell data with non-negative matrix factorization using Python, R and GenePattern Notebook implementations of CoGAPS

Non-negative matrix factorization (NMF) is an unsupervised learning method well suited to high-throughput biology. However, inferring biological processes from an NMF result still requires additional post hoc statistics and annotation for interpretation of learned features. Here, we introduce a suite of computational tools that implement NMF and provide methods for accurate and clear biological interpretation and analysis. A generalized discussion of NMF covering its benefits, limitations and open questions is followed by four procedures for the Bayesian NMF algorithm Coordinated Gene Activity across Pattern Subsets (CoGAPS). Each procedure will demonstrate NMF analysis to quantify cell state transitions in a public domain single-cell RNA-sequencing dataset. The first demonstrates PyCoGAPS, our new Python implementation that enhances runtime for large datasets, and the second allows its deployment in Docker. The third procedure steps through the same single-cell NMF analysis using our R CoGAPS interface. The fourth introduces a beginner-friendly CoGAPS platform using GenePattern Notebook, aimed at users with a working conceptual knowledge of data analysis but without a basic proficiency in the R or Python programming language. We also constructed a user-facing website to serve as a central repository for information and instructional materials about CoGAPS and its application programming interfaces. The expected timing to setup the packages and conduct a test run is around 15 min, and an additional 30 min to conduct analyses on a precomputed result. The expected runtime on the user's desired dataset can vary from hours to days depending on factors such as dataset size or input parameters.

Computational single cell oncology: state of the art

Single cell computational analysis has emerged as a powerful tool in the field of oncology, enabling researchers to decipher the complex cellular heterogeneity that characterizes cancer. By leveraging computational algorithms and bioinformatics approaches, this methodology provides insights into the underlying genetic, epigenetic and transcriptomic variations among individual cancer cells. In this paper, we present a comprehensive overview of single cell computational analysis in oncology, discussing the key computational techniques employed for data processing, analysis, and interpretation. We explore the challenges associated with single cell data, including data quality control, normalization, dimensionality reduction, clustering, and trajectory inference. Furthermore, we highlight the applications of single cell computational analysis, including the identification of novel cell states, the characterization of tumor subtypes, the discovery of biomarkers, and the prediction of therapy response. Finally, we address the future directions and potential advancements in the field, including the development of machine learning and deep learning approaches for single cell analysis. Overall, this paper aims to provide a roadmap for researchers interested in leveraging computational methods to unlock the full potential of single cell analysis in understanding cancer biology with the goal of advancing precision oncology. For this purpose, we also include a notebook that instructs on how to apply the recommended tools in the Preprocessing and Quality Control section.

Clinical and Molecular Features of Long-term Response to Immune Checkpoint Inhibitors in Patients with Advanced Non-Small Cell Lung Cancer

PURPOSE

We sought to identify features of patients with advanced non-small cell lung cancer (NSCLC) who achieve long-term response (LTR) to immune checkpoint inhibitors (ICI), and how these might differ from features predictive of short-term response (STR).

EXPERIMENTAL DESIGN

We performed a multicenter retrospective analysis of patients with advanced NSCLC treated with ICIs between 2011 and 2022. LTR and STR were defined as response ≥ 24 months and response < 12 months, respectively. Tumor programmed death ligand 1 (PD-L1) expression, tumor mutational burden (TMB), next-generation sequencing (NGS), and whole-exome sequencing (WES) data were analyzed to identify characteristics enriched in patients achieving LTR compared with STR and non-LTR.

RESULTS

Among 3,118 patients, 8% achieved LTR and 7% achieved STR, with 5-year overall survival (OS) of 81% and 18% among LTR and STR patients, respectively. High TMB (≥50th percentile) enriched for LTR compared with STR (P = 0.001) and non-LTR (P < 0.001). Whereas PD-L1 ≥ 50% enriched for LTR compared with non-LTR (P < 0.001), PD-L1 ≥ 50% did not enrich for LTR compared with STR (P = 0.181). Nonsquamous histology (P = 0.040) and increasing depth of response [median best overall response (BOR) -65% vs. -46%, P < 0.001] also associated with LTR compared with STR; no individual genomic alterations were uniquely enriched among LTR patients.

CONCLUSIONS

Among patients with advanced NSCLC treated with ICIs, distinct features including high TMB, nonsquamous histology, and depth of radiographic improvement distinguish patients poised to achieve LTR compared with initial response followed by progression, whereas high PD-L1 does not.

Diagnostic and Prognostic Value of Protein Post-translational Modifications in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a common malignant tumor with high incidence and cancer mortality worldwide. Post-translational modifications (PTMs) of proteins have a great impact on protein function. Almost all proteins can undergo PTMs, including phosphorylation, acetylation, methylation, glycosylation, ubiquitination, and so on. Many studies have shown that PTMs are related to the occurrence and development of cancers. The findings provide novel therapeutic targets for cancers, such as glypican-3 and mucin-1. Other clinical implications are also found in the studies of PTMs. Diagnostic or prognostic value, and response to therapy have been identified. In HCC, it has been shown that glycosylated alpha-fetoprotein (AFP) has a higher detection rate for early liver cancer than conventional AFP. In this review, we mainly focused on the diagnostic and prognostic value of PTM, in order to provide new insights into the clinical implication of PTM in HCC.

Single-cell dissection of tumor microenvironmental response and resistance to cancer therapy

Cancer treatment strategies have evolved significantly over the years, with chemotherapy, targeted therapy, and immunotherapy as major pillars. Each modality leads to unique treatment outcomes by interacting with the tumor microenvironment (TME), which imposes a fundamental selective pressure on cancer progression. The advent of single-cell profiling technologies has revolutionized our understanding of the intricate and heterogeneous nature of the TME at an unprecedented resolution. This review delves into the commonalities and differential manifestations of how cancer therapies reshape the microenvironment in diverse cancer types. We highlight how groundbreaking immune checkpoint blockade (ICB) strategies alone or in combination with tumor-targeting treatments are endowed with comprehensive mechanistic insights when decoded at the single-cell level, aiming to drive forward future research directions on personalized treatments.

Follicular lymphoma microenvironment: insights provided by single-cell analysis

Follicular lymphoma (FL) is the most frequent indolent lymphoma and is characterized by the abundant infiltration of tumor microenvironment (TME) cells. The activity of TME cells reportedly plays an important role in the biology of FL. TME cells that reside within neoplastic follicles, such as T-follicular helper cells and follicular dendritic cells, have been shown to aid in FL development and progression through interactions with malignant B cells, whereas regulatory T cells have unexpectedly shown an apparently favorable prognostic impact in FL. Unfortunately, the understanding of the FL TME, particularly regarding minor cell subsets, has been hampered by unknown cell heterogeneity. As with other solid and hematologic cancers, novel single-cell analysis technologies have recently been applied to FL research and have uncovered previously unrecognized heterogeneities, not only in malignant B cells but also in TME cells. These reports have greatly increased the resolution of our understanding of the FL TME and, at the same time, raised questions about newly identified TME cells. This review provides an overview of the unique aspects of FL TME cells with a clinical viewpoint and highlights recent discoveries from single-cell analysis, while also suggesting potential future directions.

Spatial transcriptomics analysis of neoadjuvant cabozantinib and nivolumab in advanced hepatocellular carcinoma identifies independent mechanisms of resistance and recurrence

BACKGROUND

Novel immunotherapy combination therapies have improved outcomes for patients with hepatocellular carcinoma (HCC), but responses are limited to a subset of patients. Little is known about the inter- and intra-tumor heterogeneity in cellular signaling networks within the HCC tumor microenvironment (TME) that underlie responses to modern systemic therapy.

METHODS

We applied spatial transcriptomics (ST) profiling to characterize the tumor microenvironment in HCC resection specimens from a prospective clinical trial of neoadjuvant cabozantinib, a multi-tyrosine kinase inhibitor that primarily blocks VEGF, and nivolumab, a PD-1 inhibitor in which 5 out of 15 patients were found to have a pathologic response at the time of resection.

RESULTS

ST profiling demonstrated that the TME of responding tumors was enriched for immune cells and cancer-associated fibroblasts (CAF) with pro-inflammatory signaling relative to the non-responders. The enriched cancer-immune interactions in responding tumors are characterized by activation of the PAX5 module, a known regulator of B cell maturation, which colocalized with spots with increased B cell marker expression suggesting strong activity of these cells. HCC-CAF interactions were also enriched in the responding tumors and were associated with extracellular matrix (ECM) remodeling as there was high activation of FOS and JUN in CAFs adjacent to the tumor. The ECM remodeling is consistent with proliferative fibrosis in association with immune-mediated tumor regression. Among the patients with major pathologic responses, a single patient experienced early HCC recurrence. ST analysis of this clinical outlier demonstrated marked tumor heterogeneity, with a distinctive immune-poor tumor region that resembles the non-responding TME across patients and was characterized by HCC-CAF interactions and expression of cancer stem cell markers, potentially mediating early tumor immune escape and recurrence in this patient.

CONCLUSIONS

These data show that responses to modern systemic therapy in HCC are associated with distinctive molecular and cellular landscapes and provide new targets to enhance and prolong responses to systemic therapy in HCC.

Targeting PPAR-gamma counteracts tumour adaptation to immune-checkpoint blockade in hepatocellular carcinoma

OBJECTIVE

Therapy-induced tumour microenvironment (TME) remodelling poses a major hurdle for cancer cure. As the majority of patients with hepatocellular carcinoma (HCC) exhibits primary or acquired resistance to antiprogrammed cell death (ligand)-1 (anti-PD-[L]1) therapies, we aimed to investigate the mechanisms underlying tumour adaptation to immune-checkpoint targeting.

DESIGN

Two immunotherapy-resistant HCC models were generated by serial orthotopic implantation of HCC cells through anti-PD-L1-treated syngeneic, immunocompetent mice and interrogated by single-cell RNA sequencing (scRNA-seq), genomic and immune profiling. Key signalling pathway was investigated by lentiviral-mediated knockdown and pharmacological inhibition, and further verified by scRNA-seq analysis of HCC tumour biopsies from a phase II trial of pembrolizumab (NCT03419481).

RESULTS

Anti-PD-L1-resistant tumours grew >10-fold larger than parental tumours in immunocompetent but not immunocompromised mice without overt genetic changes, which were accompanied by intratumoral accumulation of myeloid-derived suppressor cells (MDSC), cytotoxic to exhausted CD8+ T cell conversion and exclusion. Mechanistically, tumour cell-intrinsic upregulation of peroxisome proliferator-activated receptor-gamma (PPARγ) transcriptionally activated vascular endothelial growth factor-A (VEGF-A) production to drive MDSC expansion and CD8+ T cell dysfunction. A selective PPARγ antagonist triggered an immune suppressive-to-stimulatory TME conversion and resensitised tumours to anti-PD-L1 therapy in orthotopic and spontaneous HCC models. Importantly, 40% (6/15) of patients with HCC resistant to pembrolizumab exhibited tumorous PPARγ induction. Moreover, higher baseline PPARγ expression was associated with poorer survival of anti-PD-(L)1-treated patients in multiple cancer types.

CONCLUSION

We uncover an adaptive transcriptional programme by which tumour cells evade immune-checkpoint targeting via PPARγ/VEGF-A-mediated TME immunosuppression, thus providing a strategy for counteracting immunotherapeutic resistance in HCC.

How single-cell techniques help us look into lung cancer heterogeneity and immunotherapy

Lung cancer patients tend to have strong intratumoral and intertumoral heterogeneity and complex tumor microenvironment, which are major contributors to the efficacy of and drug resistance to immunotherapy. From a new perspective, single-cell techniques offer an innovative way to look at the intricate cellular interactions between tumors and the immune system and help us gain insights into lung cancer and its response to immunotherapy. This article reviews the application of single-cell techniques in lung cancer, with focuses directed on the heterogeneity of lung cancer and the efficacy of immunotherapy. This review provides both theoretical and experimental information for the future development of immunotherapy and personalized treatment for the management of lung cancer.

GTM-decon: guided-topic modeling of single-cell transcriptomes enables sub-cell-type and disease-subtype deconvolution of bulk transcriptomes

Cell-type composition is an important indicator of health. We present Guided Topic Model for deconvolution (GTM-decon) to automatically infer cell-type-specific gene topic distributions from single-cell RNA-seq data for deconvolving bulk transcriptomes. GTM-decon performs competitively on deconvolving simulated and real bulk data compared with the state-of-the-art methods. Moreover, as demonstrated in deconvolving disease transcriptomes, GTM-decon can infer multiple cell-type-specific gene topic distributions per cell type, which captures sub-cell-type variations. GTM-decon can also use phenotype labels from single-cell or bulk data to infer phenotype-specific gene distributions. In a nested-guided design, GTM-decon identified cell-type-specific differentially expressed genes from bulk breast cancer transcriptomes.

Cancer-on-chip: a 3D model for the study of the tumor microenvironment

The approval of anticancer therapeutic strategies is still slowed down by the lack of models able to faithfully reproduce in vivo cancer physiology. On one hand, the conventional in vitro models fail to recapitulate the organ and tissue structures, the fluid flows, and the mechanical stimuli characterizing the human body compartments. On the other hand, in vivo animal models cannot reproduce the typical human tumor microenvironment, essential to study cancer behavior and progression. This study reviews the cancer-on-chips as one of the most promising tools to model and investigate the tumor microenvironment and metastasis. We also described how cancer-on-chip devices have been developed and implemented to study the most common primary cancers and their metastatic sites. Pros and cons of this technology are then discussed highlighting the future challenges to close the gap between the pre-clinical and clinical studies and accelerate the approval of new anticancer therapies in humans.

A Review of Single-Cell RNA-Seq Annotation, Integration, and Cell-Cell Communication

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for investigating cellular biology at an unprecedented resolution, enabling the characterization of cellular heterogeneity, identification of rare but significant cell types, and exploration of cell-cell communications and interactions. Its broad applications span both basic and clinical research domains. In this comprehensive review, we survey the current landscape of scRNA-seq analysis methods and tools, focusing on count modeling, cell-type annotation, data integration, including spatial transcriptomics, and the inference of cell-cell communication. We review the challenges encountered in scRNA-seq analysis, including issues of sparsity or low expression, reliability of cell annotation, and assumptions in data integration, and discuss the potential impact of suboptimal clustering and differential expression analysis tools on downstream analyses, particularly in identifying cell subpopulations. Finally, we discuss recent advancements and future directions for enhancing scRNA-seq analysis. Specifically, we highlight the development of novel tools for annotating single-cell data, integrating and interpreting multimodal datasets covering transcriptomics, epigenomics, and proteomics, and inferring cellular communication networks. By elucidating the latest progress and innovation, we provide a comprehensive overview of the rapidly advancing field of scRNA-seq analysis.

Oncogenic Proteomics Approaches for Translational Research and HIV-Associated Malignancy Mechanisms

Recent advances in the field of proteomics have allowed extensive insights into the molecular regulations of the cell proteome. Specifically, this allows researchers to dissect a multitude of signaling arrays while targeting for the discovery of novel protein signatures. These approaches based on data mining are becoming increasingly powerful for identifying both potential disease mechanisms as well as indicators for disease progression and overall survival predictive and prognostic molecular markers for cancer. Furthermore, mass spectrometry (MS) integrations satisfy the ongoing demand for in-depth biomarker validation. For the purpose of this review, we will highlight the current developments based on MS sensitivity, to place quantitative proteomics into clinical settings and provide a perspective to integrate proteomics data for future applications in cancer precision medicine. We will also discuss malignancies associated with oncogenic viruses such as Acquire Immunodeficiency Syndrome (AIDS) and suggest novel mechanisms behind this phenomenon. Human Immunodeficiency Virus type-1 (HIV-1) proteins are known to be oncogenic per se, to induce oxidative and endoplasmic reticulum stresses, and to be released from the infected or expressing cells. HIV-1 proteins can act alone or in collaboration with other known oncoproteins, which cause the bulk of malignancies in people living with HIV-1 on ART.

Spatial metabolomics reveals glycogen as an actionable target for pulmonary fibrosis

Matrix assisted laser desorption/ionization imaging has greatly improved our understanding of spatial biology, however a robust bioinformatic pipeline for data analysis is lacking. Here, we demonstrate the application of high-dimensionality reduction/spatial clustering and histopathological annotation of matrix assisted laser desorption/ionization imaging datasets to assess tissue metabolic heterogeneity in human lung diseases. Using metabolic features identified from this pipeline, we hypothesize that metabolic channeling between glycogen and N-linked glycans is a critical metabolic process favoring pulmonary fibrosis progression. To test our hypothesis, we induced pulmonary fibrosis in two different mouse models with lysosomal glycogen utilization deficiency. Both mouse models displayed blunted N-linked glycan levels and nearly 90% reduction in endpoint fibrosis when compared to WT animals. Collectively, we provide conclusive evidence that lysosomal utilization of glycogen is required for pulmonary fibrosis progression. In summary, our study provides a roadmap to leverage spatial metabolomics to understand foundational biology in pulmonary diseases.

Converging on a Cure: The Roads to Predictive Immunotherapy

SUMMARY

Convergence science teams integrating clinical, biological, engineering, and computational expertise are inventing new forecast systems to monitor and predict evolutionary changes in tumor and immune interactions during early cancer progression and therapeutic response. The resulting methods should inform a new predictive medicine paradigm to select adaptive immunotherapeutic regimens personalized to patients' tumors at a given time during their cancer progression for durable patient response.

Uncovering the spatial landscape of molecular interactions within the tumor microenvironment through latent spaces

Recent advances in spatial transcriptomics (STs) enable gene expression measurements from a tissue sample while retaining its spatial context. This technology enables unprecedented in situ resolution of the regulatory pathways that underlie the heterogeneity in the tumor as well as the tumor microenvironment (TME). The direct characterization of cellular co-localization with spatial technologies facilities quantification of the molecular changes resulting from direct cell-cell interaction, as it occurs in tumor-immune interactions. We present SpaceMarkers, a bioinformatics algorithm to infer molecular changes from cell-cell interactions from latent space analysis of ST data. We apply this approach to infer the molecular changes from tumor-immune interactions in Visium spatial transcriptomics data of metastasis, invasive and precursor lesions, and immunotherapy treatment. Further transfer learning in matched scRNA-seq data enabled further quantification of the specific cell types in which SpaceMarkers are enriched. Altogether, SpaceMarkers can identify the location and context-specific molecular interactions within the TME from ST data.

Novel insights into Hodgkin lymphoma biology by single-cell analysis

The emergence and rapid development of single-cell technologies mark a paradigm shift in cancer research. Various technology implementations represent powerful tools to understand cellular heterogeneity, identify minor cell populations that were previously hard to detect and define, and make inferences about cell-to-cell interactions at single-cell resolution. Applied to lymphoma, recent advances in single-cell RNA sequencing have broadened opportunities to delineate previously underappreciated heterogeneity of malignant cell differentiation states and presumed cell of origin, and to describe the composition and cellular subsets in the ecosystem of the tumor microenvironment (TME). Clinical deployment of an expanding armamentarium of immunotherapy options that rely on targets and immune cell interactions in the TME emphasizes the requirement for a deeper understanding of immune biology in lymphoma. In particular, classic Hodgkin lymphoma (CHL) can serve as a study paradigm because of its unique TME, featuring infrequent tumor cells among numerous nonmalignant immune cells with significant interpatient and intrapatient variability. Synergistic to advances in single-cell sequencing, multiplexed imaging techniques have added a new dimension to describing cellular cross talk in various lymphoma entities. Here, we comprehensively review recent progress using novel single-cell technologies with an emphasis on the TME biology of CHL as an application field. The described technologies, which are applicable to peripheral blood, fresh tissues, and formalin-fixed samples, hold the promise to accelerate biomarker discovery for novel immunotherapeutic approaches and to serve as future assay platforms for biomarker-informed treatment selection, including immunotherapies.

Contribution of adaptive immunity to human COPD and experimental models of emphysema

The pathophysiology of chronic obstructive pulmonary disease (COPD) and the undisputed role of innate immune cells in this condition have dominated the field in the basic research arena for many years. Recently, however, compelling data suggesting that adaptive immune cells may also contribute to the progressive nature of lung destruction associated with COPD in smokers have gained considerable attention. The histopathological changes in the lungs of smokers can be limited to the large or small airways, but alveolar loss leading to emphysema, which occurs in some individuals, remains its most significant and irreversible outcome. Critically, however, the question of why emphysema progresses in a subset of former smokers remained a mystery for many years. The recognition of activated and organized tertiary T- and B-lymphoid aggregates in emphysematous lungs provided the first clue that adaptive immune cells may play a crucial role in COPD pathophysiology. Based on these findings from human translational studies, experimental animal models of emphysema were used to determine the mechanisms through which smoke exposure initiates and orchestrates adaptive autoreactive inflammation in the lungs. These models have revealed that T helper (Th)1 and Th17 subsets promote a positive feedback loop that activates innate immune cells, confirming their role in emphysema pathogenesis. Results from genetic studies and immune-based discoveries have further provided strong evidence for autoimmunity induction in smokers with emphysema. These new findings offer a novel opportunity to explore the mechanisms underlying the inflammatory landscape in the COPD lung and offer insights for development of precision-based treatment to halt lung destruction.

Multiomics Empowers Predictive Pancreatic Cancer Immunotherapy

Advances in cancer immunotherapy, particularly immune checkpoint inhibitors, have dramatically improved the prognosis for patients with metastatic melanoma and other previously incurable cancers. However, patients with pancreatic ductal adenocarcinoma (PDAC) generally do not respond to these therapies. PDAC is exceptionally difficult to treat because of its often late stage at diagnosis, modest mutation burden, and notoriously complex and immunosuppressive tumor microenvironment. Simultaneously interrogating features of cancer, immune, and other cellular components of the PDAC tumor microenvironment is therefore crucial for identifying biomarkers of immunotherapeutic resistance and response. Notably, single-cell and multiomics technologies, along with the analytical tools for interpreting corresponding data, are facilitating discoveries of the systems-level cellular and molecular interactions contributing to the overall resistance of PDAC to immunotherapy. Thus, in this review, we will explore how multiomics and single-cell analyses provide the unprecedented opportunity to identify biomarkers of resistance and response to successfully sensitize PDAC to immunotherapy.

Advancing CAR T cell therapy through the use of multidimensional omics data

Despite the notable success of chimeric antigen receptor (CAR) T cell therapies in the treatment of certain haematological malignancies, challenges remain in optimizing CAR designs and cell products, improving response rates, extending the durability of remissions, reducing toxicity and broadening the utility of this therapeutic modality to other cancer types. Data from multidimensional omics analyses, including genomics, epigenomics, transcriptomics, T cell receptor-repertoire profiling, proteomics, metabolomics and/or microbiomics, provide unique opportunities to dissect the complex and dynamic multifactorial phenotypes, processes and responses of CAR T cells as well as to discover novel tumour targets and pathways of resistance. In this Review, we summarize the multidimensional cellular and molecular profiling technologies that have been used to advance our mechanistic understanding of CAR T cell therapies. In addition, we discuss current applications and potential strategies leveraging multi-omics data to identify optimal target antigens and other molecular features that could be exploited to enhance the antitumour activity and minimize the toxicity of CAR T cell therapy. Indeed, fully utilizing multi-omics data will provide new insights into the biology of CAR T cell therapy, further accelerate the development of products with improved efficacy and safety profiles, and enable clinicians to better predict and monitor patient responses.

RevGel-seq: instrument-free single-cell RNA sequencing using a reversible hydrogel for cell-specific barcoding

Progress in sample preparation for scRNA-seq is reported based on RevGel-seq, a reversible-hydrogel technology optimized for samples of fresh cells. Complexes of one cell paired with one barcoded bead are stabilized by a chemical linker and dispersed in a hydrogel in the liquid state. Upon gelation on ice the complexes are immobilized and physically separated without requiring nanowells or droplets. Cell lysis is triggered by detergent diffusion, and RNA molecules are captured on the adjacent barcoded beads for further processing with reverse transcription and preparation for cDNA sequencing. As a proof of concept, analysis of PBMC using RevGel-seq achieves results similar to microfluidic-based technologies when using the same original sample and the same data analysis software. In addition, a clinically relevant application of RevGel-seq is presented for pancreatic islet cells. Furthermore, characterizations carried out on cardiomyocytes demonstrate that the hydrogel technology readily accommodates very large cells. Standard analyses are in the 10,000-input cell range with the current gelation device, in order to satisfy common requirements for single-cell research. A convenient stopping point after two hours has been established by freezing at the cell lysis step, with full preservation of gene expression profiles. Overall, our results show that RevGel-seq represents an accessible and efficient instrument-free alternative, enabling flexibility in terms of experimental design and timing of sample processing, while providing broad coverage of cell types.

Role of Th17 cells, Treg cells, and Th17/Treg imbalance in immune homeostasis disorders in patients with chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide, following strokes and cardiovascular diseases. Chronic lung inflammation is believed to play a role in the development of COPD. In addition, accumulating evidence shows that the immune system plays a crucial role in the pathogenesis of COPD. Significant advancements have been made in research on the pathogenesis of immune diseases and chronic inflammation in recent years, and T helper 17 (Th17) cells and regulatory T (Treg) cells have been found to play a crucial role in the autoimmune response. Th17 cells are a proinflammatory subpopulation that causes autoimmune disease and tissue damage. Treg cells, on the other hand, have a negative effect but can contribute to the occurrence of the same disease when their antagonism fails. This review mainly summarizes the biological characteristics of Th17 cells and Treg cells, their roles in chronic inflammatory diseases of COPD, and the role of the Th17/Treg ratio in the onset, development, and outcome of inflammatory disorders, as well as recent advancements in immunomodulatory treatment targeting Th17/Treg cells in COPD.

Mechanisms driving the immunoregulatory function of cancer cells

Tumours display an astonishing variation in the spatial distribution, composition and activation state of immune cells, which impacts their progression and response to immunotherapy. Shedding light on the mechanisms that govern the diversity and function of immune cells in the tumour microenvironment will pave the way for the development of more tailored immunomodulatory strategies for the benefit of patients with cancer. Cancer cells, by virtue of their paracrine and juxtacrine communication mechanisms, are key contributors to intertumour heterogeneity in immune contextures. In this Review, we discuss how cancer cell-intrinsic features, including (epi)genetic aberrations, signalling pathway deregulation and altered metabolism, play a key role in orchestrating the composition and functional state of the immune landscape, and influence the therapeutic benefit of immunomodulatory strategies. Moreover, we highlight how targeting cancer cell-intrinsic parameters or their downstream immunoregulatory pathways is a viable strategy to manipulate the tumour immune milieu in favour of antitumour immunity.

Correlative Fluorescence and Raman Microscopy to Define Mitotic Stages at the Single-Cell Level: Opportunities and Limitations in the AI Era

Nowadays, morphology and molecular analyses at the single-cell level have a fundamental role in understanding biology better. These methods are utilized for cell phenotyping and in-depth studies of cellular processes, such as mitosis. Fluorescence microscopy and optical spectroscopy techniques, including Raman micro-spectroscopy, allow researchers to examine biological samples at the single-cell level in a non-destructive manner. Fluorescence microscopy can give detailed morphological information about the localization of stained molecules, while Raman microscopy can produce label-free images at the subcellular level; thus, it can reveal the spatial distribution of molecular fingerprints, even in live samples. Accordingly, the combination of correlative fluorescence and Raman microscopy (CFRM) offers a unique approach for studying cellular stages at the single-cell level. However, subcellular spectral maps are complex and challenging to interpret. Artificial intelligence (AI) may serve as a valuable solution to characterize the molecular backgrounds of phenotypes and biological processes by finding the characteristic patterns in spectral maps. The major contributions of the manuscript are: (I) it gives a comprehensive review of the literature focusing on AI techniques in Raman-based cellular phenotyping; (II) via the presentation of a case study, a new neural network-based approach is described, and the opportunities and limitations of AI, specifically deep learning, are discussed regarding the analysis of Raman spectroscopy data to classify mitotic cellular stages based on their spectral maps.

Transcriptional vulnerabilities of striatal neurons in human and rodent models of Huntington's disease

Striatal projection neurons (SPNs), which progressively degenerate in human patients with Huntington's disease (HD), are classified along two axes: the canonical direct-indirect pathway division and the striosome-matrix compartmentation. It is well established that the indirect-pathway SPNs are susceptible to neurodegeneration and transcriptomic disturbances, but less is known about how the striosome-matrix axis is compromised in HD in relation to the canonical axis. Here we show, using single-nucleus RNA-sequencing data from male Grade 1 HD patient post-mortem brain samples and male zQ175 and R6/2 mouse models, that the two axes are multiplexed and differentially compromised in HD. In human HD, striosomal indirect-pathway SPNs are the most depleted SPN population. In mouse HD models, the transcriptomic distinctiveness of striosome-matrix SPNs is diminished more than that of direct-indirect pathway SPNs. Furthermore, the loss of striosome-matrix distinction is more prominent within indirect-pathway SPNs. These results open the possibility that the canonical direct-indirect pathway and striosome-matrix compartments are differentially compromised in late and early stages of disease progression, respectively, differentially contributing to the symptoms, thus calling for distinct therapeutic strategies.

Spatial transcriptomics analysis of neoadjuvant cabozantinib and nivolumab in advanced hepatocellular carcinoma identifies independent mechanisms of resistance and recurrence

Novel immunotherapy combination therapies have improved outcomes for patients with hepatocellular carcinoma (HCC), but responses are limited to a subset of patients and recurrence can also occur. Little is known about the inter- and intra-tumor heterogeneity in cellular signaling networks within the HCC tumor microenvironment (TME) that underlie responses to modern systemic therapy. We applied spatial transcriptomics (ST) profiling to characterize the tumor microenvironment in HCC resection specimens from a clinical trial of neoadjuvant cabozantinib, a multi-tyrosine kinase inhibitor that primarily blocks VEGF, and nivolumab, a PD-1 inhibitor in which 5 out of 15 patients were found to have a pathologic response. ST profiling demonstrated that the TME of responding tumors was enriched for immune cells and cancer associated fibroblasts (CAF) with pro-inflammatory signaling relative to the non-responders. The enriched cancer-immune interactions in responding tumors are characterized by activation of the PAX5 module, a known regulator of B cell maturation, which colocalized with spots with increased B cell markers expression suggesting strong activity of these cells. Cancer-CAF interactions were also enriched in the responding tumors and were associated with extracellular matrix (ECM) remodeling as there was high activation of FOS and JUN in CAFs adjacent to tumor. The ECM remodeling is consistent with proliferative fibrosis in association with immune-mediated tumor regression. Among the patients with major pathologic response, a single patient experienced early HCC recurrence. ST analysis of this clinical outlier demonstrated marked tumor heterogeneity, with a distinctive immune-poor tumor region that resembles the non-responding TME across patients and was characterized by cancer-CAF interactions and expression of cancer stem cell markers, potentially mediating early tumor immune escape and recurrence in this patient. These data show that responses to modern systemic therapy in HCC are associated with distinctive molecular and cellular landscapes and provide new targets to enhance and prolong responses to systemic therapy in HCC.

An anoikis-related gene signature predicts prognosis and reveals immune infiltration in hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is a global health burden with poor prognosis. Anoikis, a novel programmed cell death, has a close interaction with metastasis and progression of cancer. In this study, we aimed to construct a novel bioinformatics model for evaluating the prognosis of HCC based on anoikis-related gene signatures as well as exploring the potential mechanisms.

Materials and methods

We downloaded the RNA expression profiles and clinical data of liver hepatocellular carcinoma from TCGA database, ICGC database and GEO database. DEG analysis was performed using TCGA and verified in the GEO database. The anoikis-related risk score was developed via univariate Cox regression, LASSO Cox regression and multivariate Cox regression, which was then used to categorize patients into high- and low-risk groups. Then GO and KEGG enrichment analyses were performed to investigate the function between the two groups. CIBERSORT was used for determining the fractions of 22 immune cell types, while the ssGSEA analyses was used to estimate the differential immune cell infiltrations and related pathways. The "pRRophetic" R package was applied to predict the sensitivity of administering chemotherapeutic and targeted drugs.

Results

A total of 49 anoikis-related DEGs in HCC were detected and 3 genes (EZH2, KIF18A and NQO1) were selected out to build a prognostic model. Furthermore, GO and KEGG functional enrichment analyses indicated that the difference in overall survival between risk groups was closely related to cell cycle pathway. Notably, further analyses found the frequency of tumor mutations, immune infiltration level and expression of immune checkpoints were significantly different between the two risk groups, and the results of the immunotherapy cohort showed that patients in the high-risk group have a better immune response. Additionally, the high-risk group was found to have higher sensitivity to 5-fluorouracil, doxorubicin and gemcitabine.

Conclusion

The novel signature of 3 anoikis-related genes (EZH2, KIF18A and NQO1) can predict the prognosis of patients with HCC, and provide a revealing insight into personalized treatments in HCC.

Practical Considerations for Complex Tissue Dissociation for Single-Cell Transcriptomics

Single-cell and single-nucleus RNA sequencing have revolutionized biomedical research, allowing analysis of complex tissues, identification of novel cell types, and mapping of development as well as disease states. Successful application of this technology critically relies on the dissociation of solid organs and tissues into high-quality single-cell (or nuclei) suspensions.In this chapter, we examine several key aspects of the tissue handling workflow that need to be considered when establishing an efficient tissue processing protocol for single-cell RNA sequencing (scRNA-seq). These include tissue collection, transport, and storage, as well as the choice of the dissociation conditions. We emphasize the importance of the tissue quality check and discuss the advantages (and potential limitations) of tissue cryopreservation. We provide practical tips and considerations on each of the steps of the processing workflow, and comment on how to maximize cell viability and integrity, which are critical for obtaining high-quality single-cell transcriptomic data.

Integrative immune transcriptomic classification improves patient selection for precision immunotherapy in advanced gastro-oesophageal adenocarcinoma

BACKGROUND

Advanced gastro-oesophageal cancer (GEA) treatment has been improved by the introduction of immune checkpoint inhibitors (CPIs), yet identifying predictive biomarkers remains a priority, particularly in patients with a combined positive score (CPS) < 5, where the benefit is less clear. Our study assesses certain immune microenvironment features related to sensitivity or resistance to CPIs with the aim of implementing a personalised approach across CPS < 5 GEA.

DESIGN

Through integrative transcriptomic and clinicopathological analyses, we studied in both a retrospective and a prospective cohort, the immune tumour microenvironment features. We analysed the cell types composing the immune infiltrate highlighting their functional activity.

RESULTS

This integrative study allowed the identification of four different groups across our patients. Among them, we identified a cluster whose tumours expressed the most gene signatures related to immunomodulatory pathways and immunotherapy response. These tumours presented an enriched immune infiltrate showing high immune function activity that could potentially achieve the best benefit from CPIs. Finally, our findings were proven in an external CPI-exposed population, where the use of our transcriptomic results combined with CPS helped better identify those patients who could benefit from immunotherapy than using CPS alone (p = 0.043).

CONCLUSIONS

This transcriptomic classification could improve precision immunotherapy for GEA.

Tumor-intrinsic SIRPA promotes sensitivity to checkpoint inhibition immunotherapy in melanoma

Checkpoint inhibition immunotherapy has revolutionized cancer treatment, but many patients show resistance. Here we perform integrative transcriptomic and proteomic analyses on emerging immuno-oncology targets across multiple clinical cohorts of melanoma under anti-PD-1 treatment, on both bulk and single-cell levels. We reveal a surprising role of tumor-intrinsic SIRPA in enhancing antitumor immunity, in contrast to its well-established role as a major inhibitory immune modulator in macrophages. The loss of SIRPA expression is a marker of melanoma dedifferentiation, a key phenotype linked to immunotherapy efficacy. Inhibition of SIRPA in melanoma cells abrogates tumor killing by activated CD8+ T cells in a co-culture system. Mice bearing SIRPA-deficient melanoma tumors show no response to anti-PD-L1 treatment, whereas melanoma-specific SIRPA overexpression significantly enhances immunotherapy response. Mechanistically, SIRPA is regulated by its pseudogene, SIRPAP1. Our results suggest a complicated role of SIRPA in the tumor ecosystem, highlighting cell-type-dependent antagonistic effects of the same target on immunotherapy.

Multi-omic analyses of changes in the tumor microenvironment of pancreatic adenocarcinoma following neoadjuvant treatment with anti-PD-1 therapy

Successful pancreatic ductal adenocarcinoma (PDAC) immunotherapy necessitates optimization and maintenance of activated effector T cells (Teff). We prospectively collected and applied multi-omic analyses to paired pre- and post-treatment PDAC specimens collected in a platform neoadjuvant study of granulocyte-macrophage colony-stimulating factor-secreting allogeneic PDAC vaccine (GVAX) vaccine ± nivolumab (anti-programmed cell death protein 1 [PD-1]) to uncover sensitivity and resistance mechanisms. We show that GVAX-induced tertiary lymphoid aggregates become immune-regulatory sites in response to GVAX + nivolumab. Higher densities of tumor-associated neutrophils (TANs) following GVAX + nivolumab portend poorer overall survival (OS). Increased T cells expressing CD137 associated with cytotoxic Teff signatures and correlated with increased OS. Bulk and single-cell RNA sequencing found that nivolumab alters CD4+ T cell chemotaxis signaling in association with CD11b+ neutrophil degranulation, and CD8+ T cell expression of CD137 was required for optimal T cell activation. These findings provide insights into PD-1-regulated immune pathways in PDAC that should inform more effective therapeutic combinations that include TAN regulators and T cell activators.

Integrated T cell cytometry metrics for immune-monitoring applications in immunotherapy clinical trials

Mass cytometry, or cytometry by TOF (CyTOF), provides a robust means of determining protein-level measurements of more than 40 markers simultaneously. While the functional states of immune cells occur along continuous phenotypic transitions, cytometric studies surveying cell phenotypes often rely on static metrics, such as discrete cell-type abundances, based on canonical markers and/or restrictive gating strategies. To overcome this limitation, we applied single-cell trajectory inference and nonnegative matrix factorization methods to CyTOF data to trace the dynamics of T cell states. In the setting of cancer immunotherapy, we showed that patient-specific summaries of continuous phenotypic shifts in T cells could be inferred from peripheral blood-derived CyTOF mass cytometry data. We further illustrated that transfer learning enabled these T cell continuous metrics to be used to estimate patient-specific cell states in new sample cohorts from a reference patient data set. Our work establishes the utility of continuous metrics for CyTOF analysis as tools for translational discovery.

Transcriptional Heterogeneity of Cellular Senescence in Cancer

Cellular senescence plays a paradoxical role in tumorigenesis through the expression of diverse senescence-associated (SA) secretory phenotypes (SASPs). The heterogeneity of SA gene expression in cancer cells not only promotes cancer stemness but also protects these cells from chemotherapy. Despite the potential correlation between cancer and SA biomarkers, many transcriptional changes across distinct cell populations remain largely unknown. During the past decade, single-cell RNA sequencing (scRNA-seq) technologies have emerged as powerful experimental and analytical tools to dissect such diverse senescence-derived transcriptional changes. Here, we review the recent sequencing efforts that successfully characterized scRNA-seq data obtained from diverse cancer cells and elucidated the role of senescent cells in tumor malignancy. We further highlight the functional implications of SA genes expressed specifically in cancer and stromal cell populations in the tumor microenvironment. Translational research leveraging scRNA-seq profiling of SA genes will facilitate the identification of novel expression patterns underlying cancer susceptibility, providing new therapeutic opportunities in the era of precision medicine.

Versican enrichment predicts poor prognosis and response to adjuvant therapy and immunotherapy in gastric cancer

Background

Increasing evidence has revealed an important role of versican (VCAN) on various aspects of cancer progression. Here, we assessed the impact of VCAN expression on prognosis and the response to adjuvant therapy and immunotherapy in patients with gastric cancer (GC).

Methods

Four independent cohorts containing 1353 patients with GC, were utilized to investigate the effect of VCAN expression on prognosis and response to adjuvant therapy in GC. Two cohorts treated with immune checkpoint blockades were included to assess the predict value of VCAN expression on response to immunotherapy. Moreover, the bulk RNA-seq and single-cell RNA-seq data were analyzed to illustrate the role of VCAN in tumor microenvironment. Clinical outcomes of patient subgroups were compared by Kaplan-Meier curves with the log-rank test.

Result

High VCAN expression was associated with poor prognosis for patients with GC. Compared with patients with high VCAN expression, patients with low VCAN expression benefited more from adjuvant chemotherapy and adjuvant chemoradiotherapy. Moreover, patients with high VCAN expression tended to be resistant to immunotherapy, and VCAN could serve as a promising indicator for predicting the response to immunotherapy. VCANhigh tumors showed a specific microenvironment with more cancer associated fibroblasts infiltration and significant enrichment of stromal relevant signaling pathways.

Conclusion

VCAN could predict the response to adjuvant chemotherapy, adjuvant chemoradiotherapy and immunotherapy in GC, and designing new medicine target to VCAN might be an effective way to improve the efficacy of several treatment options for GC.