Applying high-dimensional single-cell technologies to the analysis of cancer immunotherapy

The evolving tumor microenvironment: From cancer initiation to metastatic outgrowth

Cancers represent complex ecosystems comprising tumor cells and a multitude of non-cancerous cells, embedded in an altered extracellular matrix. The tumor microenvironment (TME) includes diverse immune cell types, cancer-associated fibroblasts, endothelial cells, pericytes, and various additional tissue-resident cell types. These host cells were once considered bystanders of tumorigenesis but are now known to play critical roles in the pathogenesis of cancer. The cellular composition and functional state of the TME can differ extensively depending on the organ in which the tumor arises, the intrinsic features of cancer cells, the tumor stage, and patient characteristics. Here, we review the importance of the TME in each stage of cancer progression, from tumor initiation, progression, invasion, and intravasation to metastatic dissemination and outgrowth. Understanding the complex interplay between tumor cell-intrinsic, cell-extrinsic, and systemic mediators of disease progression is critical for the rational development of effective anti-cancer treatments.

Combinatorial targeting of immune checkpoints and epigenetic regulators for hepatocellular carcinoma therapy

Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related mortality worldwide. The five-year survival rate of patients with unresectable HCC is about 12%. The liver tumor microenvironment (TME) is immune tolerant and heavily infiltrated with immunosuppressive cells. Immune checkpoint inhibitors (ICIs), in some cases, can reverse tumor cell immune evasion and enhance antitumor immunity. Rapidly evolving ICIs have expanded systemic treatment options in advanced HCC; however, single-agent ICIs achieve a limited 15-20% objective response rate in advanced HCC. Therefore, other combinatorial approaches that amplify the efficacy of ICIs or suppress other tumor-promoting pathways may enhance clinical outcomes. Epigenetic alterations (e.g., changes in chromatin states and non-genetic DNA modifications) have been shown to drive HCC tumor growth and progression as well as their response to ICIs. Recent studies have combined ICIs and epigenetic inhibitors in preclinical and clinical settings to contain several cancers, including HCC. In this review, we outline current ICI treatments for HCC, the mechanism behind their successes and failures, and how ICIs can be combined with distinct epigenetic inhibitors to increase the durability of ICIs and potentially treat "immune-cold" HCC.

Targeting galectin-driven regulatory circuits in cancer and fibrosis

Galectins are a family of endogenous glycan-binding proteins that have crucial roles in a broad range of physiological and pathological processes. As a group, these proteins use both extracellular and intracellular mechanisms as well as glycan-dependent and independent pathways to reprogramme the fate and function of numerous cell types. Given their multifunctional roles in both tissue fibrosis and cancer, galectins have been identified as potential therapeutic targets for these disorders. Here, we focus on the therapeutic relevance of galectins, particularly galectin 1 (GAL1), GAL3 and GAL9 to tumour progression and fibrotic diseases. We consider an array of galectin-targeted strategies, including small-molecule carbohydrate inhibitors, natural polysaccharides and their derivatives, peptides, peptidomimetics and biological agents (notably, neutralizing monoclonal antibodies and truncated galectins) and discuss their mechanisms of action, selectivity and therapeutic potential in preclinical models of fibrosis and cancer. We also review the results of clinical trials that aim to evaluate the efficacy of galectin inhibitors in patients with idiopathic pulmonary fibrosis, nonalcoholic steatohepatitis and cancer. The rapid pace of glycobiology research, combined with the acute need for drugs to alleviate fibrotic inflammation and overcome resistance to anticancer therapies, will accelerate the translation of anti-galectin therapeutics into clinical practice.

Single-cell dissection of cellular and molecular features underlying human cervical squamous cell carcinoma initiation and progression

Cervical squamous cell carcinoma (CESC) is a prototypical human cancer with well-characterized pathological stages of initiation and progression. However, high-resolution knowledge of the transcriptional programs underlying each stage of CESC is lacking, and important questions remain. We performed single-cell RNA sequencing of 76,911 individual cells from 13 samples of human cervical tissues at various stages of malignancy, illuminating the transcriptional tumorigenic trajectory of cervical epithelial cells and revealing key factors involved in CESC initiation and progression. In addition, we found significant correlations between the abundance of specific myeloid, lymphoid, and endothelial cell populations and the progression of CESC, which were also associated with patients' prognosis. Last, we demonstrated the tumor-promoting function of matrix cancer-associated fibroblasts via the NRG1-ERBB3 pathway in CESC. This study provides a valuable resource and deeper insights into CESC initiation and progression, which is helpful in refining CESC diagnosis and for the design of optimal treatment strategies.

RNA cytometry of single-cells using semi-permeable microcapsules

Analytical tools for gene expression profiling of individual cells are critical for studying complex biological systems. However, the techniques enabling rapid measurements of gene expression on thousands of single-cells are lacking. Here, we report a high-throughput RNA cytometry for digital profiling of single-cells isolated in liquid droplets enveloped by a thin semi-permeable membrane (microcapsules). Due to the selective permeability of the membrane, the desirable enzymes and reagents can be loaded, or replaced, in the microcapsule at any given step by simply changing the reaction buffer in which the microcapsules are dispersed. Therefore, complex molecular biology workflows can be readily adapted to conduct nucleic acid analysis on encapsulated mammalian cells, or other biological species. The microcapsules support sequential multi-step enzymatic reactions and remain intact under different biochemical conditions, freezing, thawing, and thermocycling. Combining microcapsules with conventional FACS provides a high-throughput approach for conducting RNA cytometry of individual cells based on their digital gene expression signature.

The ectonucleotidase CD39 identifies tumor-reactive CD8+ T cells predictive of immune checkpoint blockade efficacy in human lung cancer

Improved identification of anti-tumor T cells is needed to advance cancer immunotherapies. CD39 expression is a promising surrogate of tumor-reactive CD8+ T cells. Here, we comprehensively profiled CD39 expression in human lung cancer. CD39 expression enriched for CD8+ T cells with features of exhaustion, tumor reactivity, and clonal expansion. Flow cytometry of 440 lung cancer biospecimens revealed weak association between CD39+ CD8+ T cells and tumoral features, such as programmed death-ligand 1 (PD-L1), tumor mutation burden, and driver mutations. Immune checkpoint blockade (ICB), but not cytotoxic chemotherapy, increased intratumoral CD39+ CD8+ T cells. Higher baseline frequency of CD39+ CD8+ T cells conferred improved clinical outcomes from ICB therapy. Furthermore, a gene signature of CD39+ CD8+ T cells predicted benefit from ICB, but not chemotherapy, in a phase III clinical trial of non-small cell lung cancer. These findings highlight CD39 as a proxy of tumor-reactive CD8+ T cells in human lung cancer.

Are PD-1 inhibitors effective for recurrent/metastatic nasopharyngeal carcinoma? Meta-analysis and systematic review

Objective: For metastatic/recurrent nasopharyngeal carcinoma (NPC) patients, a programmed cell death protein 1 (PD-1) is a controversial option. This meta-analysis aimed to investigate the efficacy and safety of PD-1 inhibitors in patients with metastatic/recurrent NPC. Methods: Electronic databases such as PubMed, Embase, Cochrane library, and Web of Science were manually searched until 1 July 2022, and Stata 15.0 was used to analyze the data. Result: A total of 10 studies were included, of which three were randomized controlled trials with data, and seven were single-arm studies. For randomized controlled trial (RCT) study, ORR [OR = 1.11, 95% CI (.49, 2.52); p = .812], OS [1-year OR = 1.26, 95% CI (.76, 2.08); p = .367], [2-year OR = 1.04, 95% CI (.39, 2.71); p = .928] in patients with metastatic/recurrent NPC were consistent with PD-1 inhibitor therapy and conventional chemotherapy. However, PD-1 inhibitor had higher 1-year PFS than conventional chemotherapy [OR = 2.16, 95% CI (1.26, 3.70); p = .005]. For single-arm studies, after PD-1 inhibitor therapy, the ORR of patients with recurrent/metastatic NPC reached [ES = 37%, 95 CI (17%-56%)], 1-year OS [ES = 61%, 95% CI (46%-76%)], 2-year [ES = 16%, 95% CI (6%-26%)], and 1-year PFS [ES = 16%,95% CI (12%-20%)]. Conclusion: The efficacy of PD-1 inhibitor monotherapy in patients with metastatic/recurrent nasopharyngeal carcinoma was not significantly different from that of conventional chemotherapy; however, due to the limitations of the included studies, further phase III RCTs are required to corroborate our conclusion. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022342400; Identifier: CRD42022342400.

Tumor Cell-Intrinsic SETD2 Deficiency Reprograms Neutrophils to Foster Immune Escape in Pancreatic Tumorigenesis

Genetic and epigenetic alterations play central roles in shaping the immunosuppressive tumor microenvironment (TME) to evade immune surveillance. The previous study shows that SETD2-H3K36me3 loss promotes KRAS-induced pancreatic tumorigenesis. However, little is known about its role in remodeling the TME and immune evasion. Here, it is shown that SETD2 deficiency can reprogram neutrophils to an immunosuppressive phenotype, thereby promoting immune escape during pancreatic tumor progression. By comprehensive profiling of the intratumoral immune cells, neutrophils are identified as the subset with the most significant changes upon Setd2 loss. Setd2-deficient pancreatic tumor cells directly enhance neutrophil recruitment and reprogramming, thereby inhibiting the cytotoxicity of CD8⁺ T cells to foster tumor progression. Mechanistically, it is revealed that Setd2-H3K36me3 loss leads to ectopic gain of H3K27me3 to downregulate Cxadr expression, which boosts the PI3K-AKT pathway and excessive expression of CXCL1 and GM-CSF, thereby promoting neutrophil recruitment and reprogramming toward an immunosuppressive phenotype. The study provides mechanistic insights into how tumor cell-intrinsic Setd2 deficiency strengthens the immune escape during pancreatic tumorigenesis, which may offer potential therapeutic implications for pancreatic cancer patients with SETD2 deficiency.

DNALI1 is a prognosis-related biomarker and correlates with immune infiltrates in low grade glioma

BACKGROUND

Dynein axonemal light intermediate chain 1 (DNALI1) is a component of axonemal dyneins and its role in cancer progression is not known.

OBJECTIVE

The influence of DNALI1 expression on the prognosis of low-grade gliomas (LGG) and the possible mechanisms of DNALI1 in promoting the progression of LGG was investigated by applying multiple bioinformatics analyses using datasets from TCGA, GTEx, CPTAC, and CGGA.

METHODS

The expression of DNALI1 in different tumor tissues including LGG was investigated. GO functional annotation, KEGG pathway analysis, and GSEA enrichment analysis were performed. The correlation between DNALI1 and prognosis, tumor microenvironment (TME) and immune checkpoints in LGG were assessed.

RESULTS

DNALI1 is mainly expressed in malignant cells in the TME of LGG and positively correlated with the development of LGG. DNALI1 expression is negatively correlated with isocitrate dehydrogenase (IDH) mutations and 1p/19q co-deletion. High DNALI1 expression is associated with poor prognosis in LGG. DNALI1 may promote LGG progression through multiple immune-related pathways. The expression of DNALI1 is positively correlated with the infiltration of certain types of immune cells and the expression of some immune checkpoints.

CONCLUSIONS

DNALI1 is a potential prognostic marker for LGG, and high expression of DNALI1 may play an important role in maintaining the immunosuppressive microenvironment of LGG.

Artificial intelligence-based multi-omics analysis fuels cancer precision medicine

With biotechnological advancements, innovative omics technologies are constantly emerging that have enabled researchers to access multi-layer information from the genome, epigenome, transcriptome, proteome, metabolome, and more. A wealth of omics technologies, including bulk and single-cell omics approaches, have empowered to characterize different molecular layers at unprecedented scale and resolution, providing a holistic view of tumor behavior. Multi-omics analysis allows systematic interrogation of various molecular information at each biological layer while posing tricky challenges regarding how to extract valuable insights from the exponentially increasing amount of multi-omics data. Therefore, efficient algorithms are needed to reduce the dimensionality of the data while simultaneously dissecting the mysteries behind the complex biological processes of cancer. Artificial intelligence has demonstrated the ability to analyze complementary multi-modal data streams within the oncology realm. The coincident development of multi-omics technologies and artificial intelligence algorithms has fuelled the development of cancer precision medicine. Here, we present state-of-the-art omics technologies and outline a roadmap of multi-omics integration analysis using an artificial intelligence strategy. The advances made using artificial intelligence-based multi-omics approaches are described, especially concerning early cancer screening, diagnosis, response assessment, and prognosis prediction. Finally, we discuss the challenges faced in multi-omics analysis, along with tentative future trends in this field. With the increasing application of artificial intelligence in multi-omics analysis, we anticipate a shifting paradigm in precision medicine becoming driven by artificial intelligence-based multi-omics technologies.

Deep transfer learning enables lesion tracing of circulating tumor cells

Liquid biopsy offers great promise for noninvasive cancer diagnostics, while the lack of adequate target characterization and analysis hinders its wide application. Single-cell RNA sequencing (scRNA-seq) is a powerful technology for cell characterization. Integrating scRNA-seq into a CTC-focused liquid biopsy study can perhaps classify CTCs by their original lesions. However, the lack of CTC scRNA-seq data accumulation and prior knowledge hinders further development. Therefore, we design CTC-Tracer, a transfer learning-based algorithm, to correct the distributional shift between primary cancer cells and CTCs to transfer lesion labels from the primary cancer cell atlas to CTCs. The robustness and accuracy of CTC-Tracer are validated by 8 individual standard datasets. We apply CTC-Tracer on a complex dataset consisting of RNA-seq profiles of single CTCs, CTC clusters from a BRCA patient, and two xenografts, and demonstrate that CTC-Tracer has potential in knowledge transfer between different types of RNA-seq data of lesions and CTCs.

A roadmap for translational cancer glycoimmunology at single cell resolution

Cancer cells can evade immune responses by exploiting inhibitory immune checkpoints. Immune checkpoint inhibitor (ICI) therapies based on anti-CTLA-4 and anti-PD-1/PD-L1 antibodies have been extensively explored over the recent years to unleash otherwise compromised anti-cancer immune responses. However, it is also well established that immune suppression is a multifactorial process involving an intricate crosstalk between cancer cells and the immune systems. The cancer glycome is emerging as a relevant source of immune checkpoints governing immunosuppressive behaviour in immune cells, paving an avenue for novel immunotherapeutic options. This review addresses the current state-of-the-art concerning the role played by glycans controlling innate and adaptive immune responses, while shedding light on available experimental models for glycoimmunology. We also emphasize the tremendous progress observed in the development of humanized models for immunology, the paramount contribution of advances in high-throughput single-cell analysis in this context, and the importance of including predictive machine learning algorithms in translational research. This may constitute an important roadmap for glycoimmunology, supporting careful adoption of models foreseeing clinical translation of fundamental glycobiology knowledge towards next generation immunotherapies.

Research progress of single-cell transcriptome sequencing in autoimmune diseases and autoinflammatory disease: A review

Autoimmunity refers to the phenomenon that the body's immune system produces antibodies or sensitized lymphocytes to its own tissues to cause an immune response. Immune disorders caused by autoimmunity can mediate autoimmune diseases. Autoimmune diseases have complicated pathogenesis due to the many types of cells involved, and the mechanism is still unclear. The emergence of single-cell research technology can solve the problem that ordinary transcriptome technology cannot be accurate to cell type. It provides unbiased results through independent analysis of cells in tissues and provides more mRNA information for identifying cell subpopulations, which provides a novel approach to study disruption of immune tolerance and disturbance of pro-inflammatory pathways on a cellular basis. It may fundamentally change the understanding of molecular pathways in the pathogenesis of autoimmune diseases and develop targeted drugs. Single-cell transcriptome sequencing (scRNA-seq) has been widely applied in autoimmune diseases, which provides a powerful tool for demonstrating the cellular heterogeneity of tissues involved in various immune inflammations, identifying pathogenic cell populations, and revealing the mechanism of disease occurrence and development. This review describes the principles of scRNA-seq, introduces common sequencing platforms and practical procedures, and focuses on the progress of scRNA-seq in 41 autoimmune diseases, which include 9 systemic autoimmune diseases and autoinflammatory diseases (rheumatoid arthritis, systemic lupus erythematosus, etc.) and 32 organ-specific autoimmune diseases (5 Skin diseases, 3 Nervous system diseases, 4 Eye diseases, 2 Respiratory system diseases, 2 Circulatory system diseases, 6 Liver, Gallbladder and Pancreas diseases, 2 Gastrointestinal system diseases, 3 Muscle, Bones and joint diseases, 3 Urinary system diseases, 2 Reproductive system diseases). This review also prospects the molecular mechanism targets of autoimmune diseases from the multi-molecular level and multi-dimensional analysis combined with single-cell multi-omics sequencing technology (such as scRNA-seq, Single cell ATAC-seq and single cell immune group library sequencing), which provides a reference for further exploring the pathogenesis and marker screening of autoimmune diseases and autoimmune inflammatory diseases in the future.

Genomic profiling for clinical decision making in lymphoid neoplasms

With the introduction of large-scale molecular profiling methods and high-throughput sequencing technologies, the genomic features of most lymphoid neoplasms have been characterized at an unprecedented scale. Although the principles for the classification and diagnosis of these disorders, founded on a multidimensional definition of disease entities, have been consolidated over the past 25 years, novel genomic data have markedly enhanced our understanding of lymphomagenesis and enriched the description of disease entities at the molecular level. Yet, the current diagnosis of lymphoid tumors is largely based on morphological assessment and immunophenotyping, with only few entities being defined by genomic criteria. This paper, which accompanies the International Consensus Classification of mature lymphoid neoplasms, will address how established assays and newly developed technologies for molecular testing already complement clinical diagnoses and provide a novel lens on disease classification. More specifically, their contributions to diagnosis refinement, risk stratification, and therapy prediction will be considered for the main categories of lymphoid neoplasms. The potential of whole-genome sequencing, circulating tumor DNA analyses, single-cell analyses, and epigenetic profiling will be discussed because these will likely become important future tools for implementing precision medicine approaches in clinical decision making for patients with lymphoid malignancies.

Assessment of spatial transcriptomics for oncology discovery

Tumor heterogeneity is a major challenge for oncology drug discovery and development. Understanding of the spatial tumor landscape is key to identifying new targets and impactful model systems. Here, we test the utility of spatial transcriptomics (ST) for oncology discovery by profiling 40 tissue sections and 80,024 capture spots across a diverse set of tissue types, sample formats, and RNA capture chemistries. We verify the accuracy and fidelity of ST by leveraging matched pathology analysis, which provides a ground truth for tissue section composition. We then use spatial data to demonstrate the capture of key tumor depth features, identifying hypoxia, necrosis, vasculature, and extracellular matrix variation. We also leverage spatial context to identify relative cell-type locations showing the anti-correlation of tumor and immune cells in syngeneic cancer models. Lastly, we demonstrate target identification approaches in clinical pancreatic adenocarcinoma samples, highlighting tumor intrinsic biomarkers and paracrine signaling.

Single cell sequencing revealed the mechanism of PD-1 resistance affected by the expression profile of peripheral blood immune cells in ESCC

BACKGROUND

Esophageal squamous carcinoma (ESCC) is a highly lethal malignancy with poor prognosis. The effect of transcriptome characteristics of patient immune microenvironment (TME) on the efficacy of immunosuppressive agents is still poorly understood.

METHODS

Here we extracted and isolated immune cells from peripheral blood of patients with PD-1 monoclonal antibody sensitivity and resistance, and conducted deep single-cell RNA sequencing to describe the baseline landscape of the composition, lineage, and functional status of infiltrating immune cells in peripheral blood of patients with esophageal cancer.

RESULTS

The transcriptome characteristics of immune cells were comprehensively analyzed, and the dynamic changes of cell percentage, heterogeneity of cell subtypes and interactions between cells were explained. Co-expression and pedigree tracking based on T-cell antigen receptors revealed a significant proportion of highly migratory intertissue-effector T cells. GO and KEGG enrichment pathway Analysis of CD8+ effect-T cells ESCC_S group and ESCC_D1,2 group, found that in the up-regulated enrichment pathway, ESCC_S group enriched more PD-L1 and PD-1 checkpoint pathways expressed in tumors (JUN/NFKBIA/FOS/KRAS/IFNG), which also exist in T cell receptor signaling pathways. MT2A, MT1X and MT1E were differentially expressed in ESCC patients with PD-1 monoclonal antibody resistance, which may be related to the resistance of PD-1 mMAB.

CONCLUSIONS

This study has an in-depth understanding of the influence of peripheral immune cell infiltration on the sensitivity of monoclonal antibody PD-1 in patients with esophageal cancer, which is helpful to promote the immunotherapy of patients with esophageal cancer.

Spatial transcriptomics technology in cancer research

In recent years, spatial transcriptomics (ST) technologies have developed rapidly and have been widely used in constructing spatial tissue atlases and characterizing spatiotemporal heterogeneity of cancers. Currently, ST has been used to profile spatial heterogeneity in multiple cancer types. Besides, ST is a benefit for identifying and comprehensively understanding special spatial areas such as tumor interface and tertiary lymphoid structures (TLSs), which exhibit unique tumor microenvironments (TMEs). Therefore, ST has also shown great potential to improve pathological diagnosis and identify novel prognostic factors in cancer. This review presents recent advances and prospects of applications on cancer research based on ST technologies as well as the challenges.

Spatial maps of T cell receptors and transcriptomes reveal distinct immune niches and interactions in the adaptive immune response

T cells mediate antigen-specific immune responses to disease through the specificity and diversity of their clonotypic T cell receptors (TCRs). Determining the spatial distributions of T cell clonotypes in tissues is essential to understanding T cell behavior, but spatial sequencing methods remain unable to profile the TCR repertoire. Here, we developed Slide-TCR-seq, a 10-μm-resolution method, to sequence whole transcriptomes and TCRs within intact tissues. We confirmed the ability of Slide-TCR-seq to map the characteristic locations of T cells and their receptors in mouse spleen. In human lymphoid germinal centers, we identified spatially distinct TCR repertoires. Profiling T cells in renal cell carcinoma and melanoma specimens revealed heterogeneous immune responses: T cell states and infiltration differed intra- and inter-clonally, and adjacent tumor and immune cells exhibited distinct gene expression. Altogether, our method yields insights into the spatial relationships between clonality, neighboring cell types, and gene expression that drive T cell responses.

Signaling pathways and targeted therapies in lung squamous cell carcinoma: mechanisms and clinical trials

Lung cancer is the leading cause of cancer-related death across the world. Unlike lung adenocarcinoma, patients with lung squamous cell carcinoma (LSCC) have not benefitted from targeted therapies. Although immunotherapy has significantly improved cancer patients' outcomes, the relatively low response rate and severe adverse events hinder the clinical application of this promising treatment in LSCC. Therefore, it is of vital importance to have a better understanding of the mechanisms underlying the pathogenesis of LSCC as well as the inner connection among different signaling pathways, which will surely provide opportunities for more effective therapeutic interventions for LSCC. In this review, new insights were given about classical signaling pathways which have been proved in other cancer types but not in LSCC, including PI3K signaling pathway, VEGF/VEGFR signaling, and CDK4/6 pathway. Other signaling pathways which may have therapeutic potentials in LSCC were also discussed, including the FGFR1 pathway, EGFR pathway, and KEAP1/NRF2 pathway. Next, chromosome 3q, which harbors two key squamous differentiation markers SOX2 and TP63 is discussed as well as its related potential therapeutic targets. We also provided some progress of LSCC in epigenetic therapies and immune checkpoints blockade (ICB) therapies. Subsequently, we outlined some combination strategies of ICB therapies and other targeted therapies. Finally, prospects and challenges were given related to the exploration and application of novel therapeutic strategies for LSCC.

Integrating microarray-based spatial transcriptomics and single-cell RNA-sequencing reveals tissue architecture in esophageal squamous cell carcinoma

Background

The tumor microenvironment (TME) serves as an important factor in tumorigenesis and metastasis. Although distinct cell subsets can be identified via single-cell RNA sequencing (scRNA-seq), the spatial composition of cells within the TME is difficult to characterise.

Methods

Tissue samples were collected from three patients with esophageal squamous cell carcinoma (ESCC), and scRNA-seq was performed to identify distinct cell subsets. In addition, a microarray-based spatial transcriptomics (ST) method was used to characterise the spatial landscape of expression data via an array of spots. Using multimodal intersection analysis (MIA) to integrate scRNA-seq and ST, the exact cellular components of the tumor and stromal regions were annotated.

Findings

The subpopulations of seven stromal cells were identified within the TME of ESCC, and the architecture of scRNA-seq-determined subsets was mapped in cancer and stromal regions. The distribution of various stromal cells and their subpopulations was heterogeneous. Compared with immune cells, non-immune stromal cells were significantly enriched in the TME. Most subsets of epithelial cells were enriched in the cancer regions, whereas inflammatory cancer-associated fibroblasts were correlated with the stromal regions. Furthermore, TME features were different between metastatic and non-metastatic samples and between the primary and metastatic sites of the metastatic sample.

Interpretation

This study revealed the spatial landscape of various cell subsets within the TME and the potential cross-talk among diverse cells, which provides novel insights into cancer intervention.

Funding

A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.

Associating resistance to immune checkpoint inhibitors with immunological escape in colorectal cancer

Colorectal cancer is a common malignant tumor that ranks third in incidence and second in mortality worldwide, and surgery in conjunction with chemotherapy and radiotherapy remains the most common treatment option. As a result of radiotherapy’s severe side effects and dismal survival rates, it is anticipated that more alternatives may emerge. Immunotherapy, a breakthrough treatment, has made significant strides in colorectal cancer over the past few years, overcoming specialized therapy, which has more selectivity and a higher survival prognosis than chemoradiotherapy. Among these, immune checkpoint inhibitor therapy has emerged as the primary immunotherapy for colorectal cancer nowadays. Nonetheless, as the use of immune checkpoint inhibitor has expanded, resistance has arisen inevitably. Immune escape is the primary cause of non-response and resistance to immune checkpoint inhibitors. That is the development of primary and secondary drug resistance. In this article, we cover the immune therapy-related colorectal cancer staging, the specific immune checkpoint inhibitors treatment mechanism, and the tumor microenvironment and immune escape routes of immunosuppressive cells that may be associated with immune checkpoint inhibitors resistance reversal. The objective is to provide better therapeutic concepts for clinical results and to increase the number of individuals who can benefit from colorectal cancer immunotherapy.

Identification of Human Global, Tissue and Within-Tissue Cell-Specific Stably Expressed Genes at Single-Cell Resolution

Stably Expressed Genes (SEGs) are a set of genes with invariant expression. Identification of SEGs, especially among both healthy and diseased tissues, is of clinical relevance to enable more accurate data integration, gene expression comparison and biomarker detection. However, it remains unclear how many global SEGs there are, whether there are development-, tissue- or cell-specific SEGs, and whether diseases can influence their expression. In this research, we systematically investigate human SEGs at single-cell level and observe their development-, tissue- and cell-specificity, and expression stability under various diseased states. A hierarchical strategy is proposed to identify a list of 408 spatial-temporal SEGs. Development-specific SEGs are also identified, with adult tissue-specific SEGs enriched with the function of immune processes and fetal tissue-specific SEGs enriched in RNA splicing activities. Cells of the same type within different tissues tend to show similar SEG composition profiles. Diseases or stresses do not show influence on the expression stableness of SEGs in various tissues. In addition to serving as markers and internal references for data normalization and integration, we examine another possible application of SEGs, i.e., being applied for cell decomposition. The deconvolution model could accurately predict the fractions of major immune cells in multiple independent testing datasets of peripheral blood samples. The study provides a reliable list of human SEGs at the single-cell level, facilitates the understanding on the property of SEGs, and extends their possible applications.

Patient-derived cancer models: Valuable platforms for anticancer drug testing

Molecularly targeted treatments and immunotherapy are cornerstones in oncology, with demonstrated efficacy across different tumor types. Nevertheless, the overwhelming majority metastatic disease is incurable due to the onset of drug resistance. Preclinical models including genetically engineered mouse models, patient-derived xenografts and two- and three-dimensional cell cultures have emerged as a useful resource to study mechanisms of cancer progression and predict efficacy of anticancer drugs. However, variables including tumor heterogeneity and the complexities of the microenvironment can impair the faithfulness of these platforms. Here, we will discuss advantages and limitations of these preclinical models, their applicability for drug testing and in co-clinical trials and potential strategies to increase their reliability in predicting responsiveness to anticancer medications.

The Role of Pathology-Based Methods in Qualitative and Quantitative Approaches to Cancer Immunotherapy

Simple Summary

Immunotherapy has become the filar of modern oncological treatment, and programmed death-ligand 1 expression is one of the primary immune markers assessed by pathologists. However, there are still some issues concerning the evaluation of the marker and limited information about the interaction between the tumour and associated immune cells. Recent studies have focused on cancer immunology to try to understand the complex tumour microenvironment, and multiplex imaging methods are more widely used for this purpose. The presented article aims to provide an overall review of a different multiplex in situ method using spectral imaging, supported by automated image-acquisition and software-assisted marker visualisation and interpretation. Multiplex imaging methods could improve the current understanding of complex tumour-microenvironment immunology and could probably help to better match patients to appropriate treatment regimens.

Abstract

Immune checkpoint inhibitors, including those concerning programmed cell death 1 (PD-1) and its ligand (PD-L1), have revolutionised the cancer therapy approach in the past decade. However, not all patients benefit from immunotherapy equally. The prediction of patient response to this type of therapy is mainly based on conventional immunohistochemistry, which is limited by intraobserver variability, semiquantitative assessment, or single-marker-per-slide evaluation. Multiplex imaging techniques and digital image analysis are powerful tools that could overcome some issues concerning tumour-microenvironment studies. This novel approach to biomarker assessment offers a better understanding of the complicated interactions between tumour cells and their environment. Multiplex labelling enables the detection of multiple markers simultaneously and the exploration of their spatial organisation. Evaluating a variety of immune cell phenotypes and differentiating their subpopulations is possible while preserving tissue histology in most cases. Multiplexing supported by digital pathology could allow pathologists to visualise and understand every cell in a single tissue slide and provide meaning in a complex tumour-microenvironment contexture. This review aims to provide an overview of the different multiplex imaging methods and their application in PD-L1 biomarker assessment. Moreover, we discuss digital imaging techniques, with a focus on slide scanners and software.

T-Cell Receptor Repertoire Sequencing and Its Applications: Focus on Infectious Diseases and Cancer

The immune system is a dynamic feature of each individual and a footprint of our unique internal and external exposures. Indeed, the type and level of exposure to physical and biological agents shape the development and behavior of this complex and diffuse system. Many pathological conditions depend on how our immune system responds or does not respond to a pathogen or a disease or on how the regulation of immunity is altered by the disease itself. T-cells are important players in adaptive immunity and, together with B-cells, define specificity and monitor the internal and external signals that our organism perceives through its specific receptors, TCRs and BCRs, respectively. Today, high-throughput sequencing (HTS) applied to the TCR repertoire has opened a window of opportunity to disclose T-cell repertoire development and behavior down to the clonal level. Although TCR repertoire sequencing is easily accessible today, it is important to deeply understand the available technologies for choosing the best fit for the specific experimental needs and questions. Here, we provide an updated overview of TCR repertoire sequencing strategies, providers and applications to infectious diseases and cancer to guide researchers’ choice through the multitude of available options. The possibility of extending the TCR repertoire to HLA characterization will be of pivotal importance in the near future to understand how specific HLA genes shape T-cell responses in different pathological contexts and will add a level of comprehension that was unthinkable just a few years ago.

The Multi-Dimensional Biomarker Landscape in Cancer Immunotherapy

The field of immuno-oncology is now at the forefront of cancer care and is rapidly evolving. The immune checkpoint blockade has been demonstrated to restore antitumor responses in several cancer types. However, durable responses can be observed only in a subset of patients, highlighting the importance of investigating the tumor microenvironment (TME) and cellular heterogeneity to define the phenotypes that contribute to resistance as opposed to those that confer susceptibility to immune surveillance and immunotherapy. In this review, we summarize how some of the most widely used conventional technologies and biomarkers may be useful for the purpose of predicting immunotherapy outcomes in patients, and discuss their shortcomings. We also provide an overview of how emerging single-cell spatial omics may be applied to further advance our understanding of the interactions within the TME, and how these technologies help to deliver important new insights into biomarker discovery to improve the prediction of patient response.

Reshaping the systemic tumor immune environment (STIE) and tumor immune microenvironment (TIME) to enhance immunotherapy efficacy in solid tumors

The development of combination immunotherapy based on the mediation of regulatory mechanisms of the tumor immune microenvironment (TIME) is promising. However, a deep understanding of tumor immunology must involve the systemic tumor immune environment (STIE) which was merely illustrated previously. Here, we aim to review recent advances in single-cell transcriptomics and spatial transcriptomics for the studies of STIE, TIME, and their interactions, which may reveal heterogeneity in immunotherapy responses as well as the dynamic changes essential for the treatment effect. We review the evidence from preclinical and clinical studies related to TIME, STIE, and their significance on overall survival, through different immunomodulatory pathways, such as metabolic and neuro-immunological pathways. We also evaluate the significance of the STIE, TIME, and their interactions as well as changes after local radiotherapy and systemic immunotherapy or combined immunotherapy. We focus our review on the evidence of lung cancer, hepatocellular carcinoma, and nasopharyngeal carcinoma, aiming to reshape STIE and TIME to enhance immunotherapy efficacy.

Cell-cell communication inference and analysis in the tumour microenvironments from single-cell transcriptomics: data resources and computational strategies

Carcinomas are complex ecosystems composed of cancer, stromal and immune cells. Communication between these cells and their microenvironments induces cancer progression and causes therapy resistance. In order to improve the treatment of cancers, it is essential to quantify crosstalk between and within various cell types in a tumour microenvironment. Focusing on the coordinated expression patterns of ligands and cognate receptors, cell-cell communication can be inferred through ligand-receptor interactions (LRIs). In this manuscript, we carry out the following work: (i) introduce pipeline for ligand-receptor-mediated intercellular communication estimation from single-cell transcriptomics and list a few available LRI-related databases and visualization tools; (ii) demonstrate seven classical intercellular communication scoring strategies, highlight four types of representative intercellular communication inference methods, including network-based approaches, machine learning-based approaches, spatial information-based approaches and other approaches; (iii) summarize the evaluation and validation avenues for intercellular communication inference and analyze the advantages and limitations for the above four types of cell-cell communication methods; (iv) comment several major challenges while provide further research directions for intercellular communication analysis in the tumour microenvironments. We anticipate that this work helps to better understand intercellular crosstalk and to further develop powerful cell-cell communication estimation tools for tumor-targeted therapy.

Next-generation sequencing: unraveling genetic mechanisms that shape cancer immunotherapy efficacy

Immunity is governed by fundamental genetic processes. These processes shape the nature of immune cells and set the rules that dictate the myriad complex cellular interactions that power immune systems. Everything from the generation of T cell receptors and antibodies, control of epitope presentation, and recognition of pathogens by the immunoediting of cancer cells is, in large part, made possible by core genetic mechanisms and the cellular machinery that they encode. In the last decade, next-generation sequencing has been used to dissect the complexities of cancer immunity with potent effect. Sequencing of exomes and genomes has begun to reveal how the immune system recognizes “foreign” entities and distinguishes self from non-self, especially in the setting of cancer. High-throughput analyses of transcriptomes have revealed deep insights into how the tumor microenvironment affects immunotherapy efficacy. In this Review, we discuss how high-throughput sequencing has added to our understanding of how immune systems interact with cancer cells and how cancer immunotherapies work.

Restructuring of Lamina-Associated Domains in Senescence and Cancer

Induction of cellular senescence or cancer is associated with a reshaping of the nuclear envelope and a broad reorganization of heterochromatin. At the periphery of mammalian nuclei, heterochromatin is stabilized at the nuclear lamina via lamina-associated domains (LADs). Alterations in the composition of the nuclear lamina during senescence lead to a loss of peripheral heterochromatin, repositioning of LADs, and changes in epigenetic states of LADs. Cancer initiation and progression are also accompanied by a massive reprogramming of the epigenome, particularly in domains coinciding with LADs. Here, we review recent knowledge on alterations in chromatin organization and in the epigenome that affect LADs and related genomic domains in senescence and cancer.

Immunotherapy discovery on tumor organoid-on-a-chip platforms that recapitulate the tumor microenvironment

Cancer immunotherapy has achieved remarkable success over the past decade by modulating patients' own immune systems and unleashing pre-existing immunity. However, only a minority of cancer patients across different cancer types are able to benefit from immunotherapy treatment; moreover, among those small portions of patients with response, intrinsic and acquired resistance remains a persistent challenge. Because the tumor microenvironment (TME) is well recognized to play a critical role in tumor initiation, progression, metastasis, and the suppression of the immune system and responses to immunotherapy, understanding the interactions between the TME and the immune system is a pivotal step in developing novel and efficient cancer immunotherapies. With unique features such as low reagent consumption, dynamic and precise fluid control, versatile structures and function designs, and 3D cell co-culture, microfluidic tumor organoid-on-a-chip platforms that recapitulate key factors of the TME and the immune contexture have emerged as innovative reliable tools to investigate how tumors regulate their TME to counteract antitumor immunity and the mechanism of tumor resistance to immunotherapy. In this comprehensive review, we focus on recent advances in tumor organoid-on-a-chip platforms for studying the interaction between the TME and the immune system. We first review different factors of the TME that recent microfluidic in vitro systems reproduce to generate advanced tools to imitate the crosstalk between the TME and the immune system. Then, we discuss their applications in the assessment of different immunotherapies' efficacy using tumor organoid-on-a-chip platforms. Finally, we present an overview and the outlook of engineered microfluidic platforms in investigating the interactions between cancer and immune systems, and the adoption of patient-on-a-chip models in clinical applications toward personalized immunotherapy.

Breaking the Immune Complexity of the Tumor Microenvironment Using Single-Cell Technologies

Tumors are not a simple aggregate of transformed cells but rather a complicated ecosystem containing various components, including infiltrating immune cells, tumor-related stromal cells, endothelial cells, soluble factors, and extracellular matrix proteins. Profiling the immune contexture of this intricate framework is now mandatory to develop more effective cancer therapies and precise immunotherapeutic approaches by identifying exact targets or predictive biomarkers, respectively. Conventional technologies are limited in reaching this goal because they lack high resolution. Recent developments in single-cell technologies, such as single-cell RNA transcriptomics, mass cytometry, and multiparameter immunofluorescence, have revolutionized the cancer immunology field, capturing the heterogeneity of tumor-infiltrating immune cells and the dynamic complexity of tenets that regulate cell networks in the tumor microenvironment. In this review, we describe some of the current single-cell technologies and computational techniques applied for immune-profiling the cancer landscape and discuss future directions of how integrating multi-omics data can guide a new "precision oncology" advancement.

Enhancement of antitumor immunotherapy using mitochondria-targeted cancer cell membrane-biomimetic MOF-mediated sonodynamic therapy and checkpoint blockade immunotherapy

Immunotherapeutic interventions represent a promising approach to treating cancer, with strategies such as immune checkpoint blockade (ICB), immunogenic sonodynamic therapy (SDT), and immune adjuvant T cell delivery having exhibited clinical promise. In this report, we describe the use of cancer cell membrane-coated triphenylphosphonium (TPP) decorated nano-metal-organic framework (nMOF) constructs [Zr-TCPP(TPP)/R837@M] that were used to generate homologous, mitochondria-targeted platforms with a high rate of sonosensitizer loading. This construct was utilized to simultaneously promote tumor antigen presentation via enhancing SDT while synergistically promoting dendritic cell (DC) maturation through the delivery of the Toll-like receptor agonist R837. In vitro, these functionalized nMOFs were readily internalized by homologous tumor cells in which they were efficiently targeted to the mitochondria, promoting DC activation through the induction of immunogenic cell death (ICD) following ultrasound exposure. Moreover, this nanoplatform was able to achieve in vivo synergy with anti-CTLA-4 ICB to reverse immunosuppression tumor microenvironment (TME), thus achieving more robust antitumor efficacy capable of suppressing metastatic disease progression and facilitating the development of durable antitumor memory responses. Together, these results highlight a promising approach to achieving enhanced SDT activity while overcoming an immunosuppressive TME, thereby achieving more robust antitumor immunity.

Genetic Characteristics of Resectable Colorectal Cancer with Pulmonary Metastasis

The lung is the most common extra-abdominal metastasis site of colorectal cancer (CRC). This study aimed to investigate the genetic variation of pulmonary metastases (PM) and primary tumors in resectable CRC. The clinical data of 410 patients with PM after CRC surgery and 33 paraffin-embedded tissue samples from January 2012 to July 2019 in our hospital were collected retrospectively. Next, 450-panel gene detection technologies based on next-generation sequencing (NGS) were used to analyze the changes in the gene map and the overall variation in cancer-related genes in PM and primary tumors. After quality control, 19 samples were included in the final gene analysis. The results showed that APC (89.5%), TP53 (89.5%), and KRAS (53%) were the most common mutations in PM and primary tumors, but the gene amplification variation was enriched in primary tumors (4.6% vs. 11.4%). KRAS G12D was the most common site variation of the KRAS gene in both PM and primary tumors of CRC. There was no hotspot mutation in the TP53 locus in CRC, and the TP53 mutation in the PM was consistent with that in the primary lesion. The microsatellite instability (MSI) levels of 10 patients were MSS. The mean tumor mutation burden (TMB) of the primary tumor (5.3 muts·Mb^-1) was slightly higher than that of metastasis (5.0 muts·Mb^-1). In our institution, the genetic characteristics of resectable PM from CRC may be highly consistent with those of the primary tumor.

Afatinib and Pembrolizumab for Recurrent or Metastatic Head and Neck Squamous Cell Carcinoma (ALPHA Study): A Phase II Study with Biomarker Analysis

PURPOSE

EGFR pathway inhibition may promote anti-programmed cell death protein 1 (PD-1) responses in preclinical models, but how EGFR inhibition affects tumor antigen presentation during anti-PD-1 monotherapy in humans remain unknown. We hypothesized that afatinib, an irreversible EGFR tyrosine kinase inhibitor, would improve outcomes in patients treated with pembrolizumab for recurrent or metastatic head and neck squamous cell carcinoma (HNSCC) by promoting antigen presentation and immune activation in the tumor microenvironment.

PATIENTS AND METHODS

The ALPHA study (NCT03695510) was a single-arm, Phase II study with Simon's 2-stage design. Afatinib and pembrolizumab were administered to patients with platinum-refractory, recurrent, or metastatic HNSCC. The primary endpoint was the objective response rate (ORR). The study applied gene expression analysis using a NanoString PanCancer Immune Profiling Panel and next-generation sequencing using FoundationOne CDx.

RESULTS

From January 2019 to March 2020, the study enrolled 29 eligible patients. Common treatment-related adverse events were skin rash (75.9%), diarrhea (58.6%), and paronychia (44.8%). Twelve patients (41.4%) had an objective partial response to treatment. The median progression-free survival was 4.1 months, and the median overall survival was 8.9 months. In a paired tissue analysis, afatinib-pembrolizumab were found to upregulate genes involved in antigen presentation, immune activation, and natural killer cell-mediated cytotoxicity. Unaltered methylthioadenosine phosphorylase and EGFR amplification may predict the clinical response to the therapy.

CONCLUSIONS

Afatinib may augment pembrolizumab therapy and improve the ORR in patients with HNSCC. Bioinformatics analysis suggested the enhancement of antigen presentation machinery in the tumor microenvironment.

A mathematical model for treatment using chemo-immunotherapy

In this study, we investigated a mathematical model for chemoimmunotherapy (a combination of chemotherapy and immunotherapy) for brain cancer. In most cases, the standard protocol for cancer treatment is fixed in terms of treatment time intervals and dosages. We offer a wide range of non-fixed protocols, which essentially vary in terms of time intervals and dosages (i.e., personalised medicine). The functions that describe this treatment are explicit and analytical. Hence, the parameters of the function can be easily changed and a new protocol can be obtained. We compared different protocols and obtained an optimal solution. In addition, we applied the singular perturbed vector field (SPVF) method to determine the hierarchy of the system of equations, which enabled us to identify the equilibrium points of the mathematical model and investigate their stability.

Application of High-Throughput Imaging Mass Cytometry Hyperion in Cancer Research

Imaging mass cytometry (IMC) enables the in situ analysis of in-depth-phenotyped cells in their native microenvironment within the preserved architecture of a single tissue section. To date, it permits the simultaneous analysis of up to 50 different protein- markers targeted by metal-conjugated antibodies. The application of IMC in the field of cancer research may notably help 1) to define biomarkers of prognostic and theragnostic significance for current and future treatments against well-established and novel therapeutic targets and 2) to improve our understanding of cancer progression and its resistance mechanisms to immune system and how to overcome them. In the present article, we not only provide a literature review on the use of the IMC in cancer-dedicated studies but we also present the IMC method and discuss its advantages and limitations among methods dedicated to deciphering the complexity of cancer tissue.

Understanding initiation and progression of hepatocellular carcinoma through single cell sequencing

Unsatisfied clinical outcome drives to better understand hepatic carcinogenesis, microenvironment and escape of immune surveillance in hepatocellular carcinoma (HCC). Single cell RNA sequencing (scRNA-Seq) has generated enormous data to pinpoint pathophysiologic alterations in tumor microenvironment (TME) or trace lineage development in cancer stem cells (CSCs), circulating tumor cells (CTCs), and subsets of immune cells, such as exhausting T cells, tumor-associated macrophages (TAMs), dendritic cells or other lineages. New insights have significantly advanced current understanding in progression, poor responses to molecular-targeted therapeutics or immune checkpoint inhibitors, metastasis in both basic research and clinical practice. The present review intends to cover a basic workflow of the scRNA-seq technology, existing limitations and improvement areas. Moreover, in-depth understanding in TME, exhausting T cells, CSCs, CTCs, tumor-associated macrophages, dendritic cells in HCC facilitates implementation of personalized and precise therapy in an era of availability with an array of systemic regimens.

Single-Cell RNA Sequencing Revealed a 3-Gene Panel Predicted the Diagnosis and Prognosis of Thyroid Papillary Carcinoma and Associated With Tumor Immune Microenvironment

OBJECTIVE

The objective of this research was to screen prognostic related genes of thyroid papillary carcinoma (PTC) by single-cell RNA sequencing (scRNA-seq), to construct the diagnostic and prognostic models based on The Cancer Genome Atlas Thyroid Cancer (TCGA-THCA) data, and to evaluate the association between tumor immune microenvironment and the prognostic model.

METHOD

The differentially expressed genes (DEGs) and tumor evolution were analyzed by scRNA-seq based on public databases. The potential regulatory networks of DEGs related to prognosis were analyzed by multi-omics data in the THCA. Logistic regression and Cox proportional hazards regression were utilized to construct the diagnosis and prognostic model of PTC. The performance of the diagnostic model was verified by bulk RNA sequencing (RNA-seq) of our cohort. The tumor immune microenvironment associated with the prognostic model was evaluated using multi-omics data. In addition, qRT-PCR was performed on tumor tissues and adjacent normal tissues of 20 patients to verify the expression levels of DEGs.

RESULTS

The DEGs screened by scRNA-seq can distinguish between tumor and healthy samples. DEGs play different roles in the evolution from normal epithelial cells to malignant cells. Three DEGs ((FN1, CLU, and ANXA1)) related to prognosis were filtered, which may be regulated by DNA methylation, RNA methylation (m6A) and upstream transcription factors. The area under curve (AUC) of the diagnostic model based on 3-gene in the validation of our RNA-seq was 1. In the prognostic model based on 3-gene, the overall survival (OS) of high-risk patients was shorter. Combined with the clinical information of patients, a nomogram was constructed by using tumor size (pT) and risk score to quantify the prognostic risk. The age and tumor size of high-risk patients in the prognostic model were greater. In addition, the increase of tumor mutation burden (TMB) and diversity of T cell receptor (TCR), and the decrease of CD8+ T cells in high-risk group suggest the existence of immunosuppressive microenvironment.

CONCLUSION

We applied the scRNA-seq pipeline to focus on epithelial cells in PTC, simulated the process of tumor evolution, and revealed a prognostic prediction model based on 3 genes, which is related to tumor immune microenvironment.

From Basic Science to Clinical Translation in Kidney Cancer: A Report from the Second Kidney Cancer Research Summit

The second Kidney Cancer Research Summit was held virtually in October 2020. The meeting gathered worldwide experts in the field of kidney cancer, including basic, translational, and clinical scientists as well as patient advocates. Novel studies were presented, addressing areas of unmet need related to different topics. These include novel metabolic targets, promising immunotherapeutic regimens, predictive genomic and transcriptomic biomarkers, and variant histologies of renal cell carcinoma (RCC). With the development of pioneering technologies, and an unprecedented commitment to kidney cancer research, the field has tremendously evolved. This perspective aims to summarize the different sessions of the conference, outline major advances in the understanding of RCC and discuss current challenges faced by the field.

Transcriptome profiling of PBMCs and formalin-fixed autopsy tissues from COVID-19 patients

Here we present an optimized protocol for transcriptome profiling of COVID-19 patient samples, including peripheral blood mononuclear cells (PBMCs) and formalin-fixed paraffin-embedded tissue samples obtained from the lung, liver, heart, kidney, and spleen, with the matched controls. We describe RNA extraction and subsequent transcriptome analysis using NanoString technology of the patient samples. The protocol provides information about sample preparation, RNA extraction, and NanoString profiling and analysis. It can be also applied to differentiated Th17 and Treg subsets or formalin-fixed colon tissue samples.

For complete details on the use and execution of this protocol, please refer to Wang et al. (2021).

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We present a protocol for RNA isolation and transcriptome analysis using NanoString

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The protocol is applicable to PBMC and formalin fixed paraffin-embedded tissue samples

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We inform about sample preparation, RNA extraction and NanoString profiling

Novel Potential Biomarkers for Retinopathy of Prematurity

Retinopathy of prematurity (ROP) is the main risk factor for vision-threatening disease in premature infants with low birth weight. An accumulating number of independent studies have focused on ROP pathogenesis and have demonstrated that laser photocoagulation therapy and/or anti-VEGF treatment are effective. However, early diagnosis of ROP is still critical. At present, the main method of ROP screening is based on binocular indirect ophthalmoscopy. However, the judgment of whether ROP occurs and whether treatment is necessary depends largely on ophthalmologists with a great deal of experience. Therefore, it is essential to develop a simple, accurate and effective diagnostic method. This review describes recent findings on novel biomarkers for the prediction, diagnosis and prognosis of ROP patients. The novel biomarkers were separated into the following categories: metabolites, cytokines and growth factors, non-coding RNAs, iconography, gut microbiota, oxidative stress biomarkers, and others. Biomarkers with high sensitivity and specificity are urgently needed for the clinical applications of ROP. In addition, using non-invasive or minimally invasive methods to obtain samples is also important. Our review provides an overview of potential biomarkers of ROP.

T-Cell Subsets as Potential Biomarkers for Hepatobiliary Cancers and Selection of Immunotherapy Regimens as a Treatment Strategy

Patients with advanced hepatocellular or biliary cancers have a dismal prognosis with limited efficacy from standard systemic therapies. The benefit of precision medicine has so far been limited to a subset of biliary cancers, including FGFR rearrangements; hotspot mutations in IDH1/2, BRAF, and BRCA1/2; and other rare alterations. In contrast, hepatocellular carcinoma, an inflammation-driven cancer with an immune-infiltrated microenvironment, provides a promising opportunity for immunotherapy, compared with the highly desmoplastic immune desert or excluded stromal microenvironment in biliary cancers. The immune contexture in hepatobiliary cancers is mostly immunosuppressive, protumorigenic, and exhausted, which together with low tumor mutation burden and decreased neoantigens provides challenges for immunotherapy. A better understanding of the spatiotemporal profile of T cells within the tumor microenvironment and the dynamic interplay of immune modulators in the context of standard or experimental therapies is crucial to define additional markers of response and design evidence-based combinatorial regimens. This review considers recent literature in this area and highlights promising leads and emerging trends.

The oncogenic role of treacle ribosome biogenesis factor 1 (TCOF1) in human tumors: a pan-cancer analysis

Treacle ribosome biogenesis factor 1 (TCOF1) plays a crucial role in multiple processes, including ribosome biogenesis, DNA damage response (DDR), mitotic regulation, and telomere integrity. However, its role in cancers remains unclear. We aimed to visualize the expression, prognostic, and mutational landscapes of TCOF1 across cancers and to explore its association with immune infiltration. In this work, we integrated information from TCGA and GEO to explore the differential expression and prognostic value of TCOF1. Then, the mutational profiles of TCOF1 in cancers were investigated. We further determined the correlation between TCOF1 and immune cell infiltration levels. Additionally, we determined correlations among certain immune checkpoints, microsatellite instability, tumor mutational burden (TMB), and TCOF1. Potential pathways of TCOF1 in tumorigenesis were analyzed as well. In general, tumor tissue had a higher expression level of TCOF1 than normal tissue. The prognostic value of TCOF1 was multifaceted, depending on type of cancer. TCOF1 was correlated with tumor purity, CD8+ T cells, CD4+ T cells, B cells, neutrophils, macrophages, and dendritic cells (DCs) in 6, 14, 16, 12, 20, 13, and 17 cancer types, respectively. TCOF1 might act on ATPase activity, microtubule binding, tubulin binding, and catalytic activity (on DNA), and participate in tumorigenesis through “cell cycle” and “cellular-senescence” pathways. TCOF1 could affect pan-cancer prognosis and was correlated with immune cell infiltration. “Cell cycle” and “cellular-senescence” pathways were involved in the functional mechanisms of TCOF1, a finding that awaits further experimental validation.

Harnessing big data to characterize immune-related adverse events

Immune-checkpoint inhibitors (ICIs) have transformed patient care in oncology but are associated with a unique spectrum of organ-specific inflammatory toxicities known as immune-related adverse events (irAEs). Given the expanding use of ICIs, an increasing number of patients with cancer experience irAEs, including severe irAEs. Proper diagnosis and management of irAEs are important to optimize the quality of life and long-term outcomes of patients receiving ICIs; however, owing to the substantial heterogeneity within irAEs, and despite multicentre initiatives, performing clinical studies of these toxicities with a sufficient cohort size is challenging. Pioneering studies from the past few years have demonstrated that aggregate clinical data, real-world data (such as data on pharmacovigilance or from electronic health records) and multi-omics data are alternative tools well suited to investigating the underlying mechanisms and clinical presentations of irAEs. In this Perspective, we summarize the advantages and shortcomings of different sources of 'big data' for the study of irAEs and highlight progress made using such data to identify biomarkers of irAE risk, evaluate associations between irAEs and therapeutic efficacy, and characterize the effects of demographic and anthropometric factors on irAE risk. Harnessing big data will accelerate research on irAEs and provide key insights that will improve the clinical management of patients receiving ICIs.

Classification of pyroptosis patterns and construction of a novel prognostic model for prostate cancer based on bulk and single-cell RNA sequencing

BACKGROUND

Emerging evidence suggests an important role for pyroptosis in tumorigenesis and recurrence, but it remains to be elucidated in prostate cancer (PCa). Considering the low accuracy of common clinical predictors of PCa recurrence, we aimed to develop a novel pyroptosis-related signature to predict the prognosis of PCa patients based on integrative analyses of bulk and single-cell RNA sequencing (RNA-seq) profiling.

METHODS

The RNA-seq data of PCa patients was downloaded from several online databases. PCa patients were stratified into two Classes by unsupervised clustering. A novel signature was constructed by Cox and the Least Absolute Shrinkage and Selection Operator (LASSO) regression. The Kaplan-Meier curve was employed to evaluate the prognostic value of this signature and the single sample Gene Set Enrichment Analysis (ssGSEA) algorithm was used to analysis tumor-infiltrating immune cells. At single-cell level, we also classified the malignant cells into two Classes and constructed cell developmental trajectories and cell-cell interaction networks. Furthermore, RT-qPCR and immunofluorescence were used to validate the expression of core pyroptosis-related genes.

RESULTS

Twelve prognostic pyroptosis-related genes were identified and used to classify PCa patients into two prognostic Classes. We constructed a signature that identified PCa patients with different risks of recurrence and the risk score was proven to be an independent predictor of the recurrence free survival (RFS). Patients in the high-risk group had a significantly lower RFS (P<0.001). The expression of various immune cells differed between the two Classes. At the single-cell level, we classified the malignant cells into two Classes and described the heterogeneity. In addition, we observed that malignant cells may shift from Class1 to Class2 and thus have a worse prognosis.

CONCLUSION

We have constructed a robust pyroptosis-related signature to predict the RFS of PCa patients and described the heterogeneity of prostate cancer cells in terms of pyroptosis.