Single-Cell Transcriptomics in Cancer Immunobiology: The Future of Precision Oncology

Personalized analysis of human cancer multi-omics for precision oncology

Multi-omics technologies, encompassing genomics, proteomics, and transcriptomics, provide profound insights into cancer biology. A fundamental computational approach for analyzing multi-omics data is differential analysis, which identifies molecular distinctions between cancerous and normal tissues. Traditional methods, however, often fail to address the distinct heterogeneity of individual tumors, thereby neglecting crucial patient-specific molecular traits. This shortcoming underscores the necessity for tailored differential analysis algorithms, which focus on particular patient variations. Such approaches offer a more nuanced understanding of cancer biology and are instrumental in pinpointing personalized therapeutic strategies. In this review, we summarize the principles of current individualized techniques. We also review their efficacy in analyzing cancer multi-omics data and discuss their potential applications in clinical practice.

Application of Single-Cell Sequencing Technology in Research on Colorectal Cancer

Colorectal cancer (CRC) is the third most prevalent and second most lethal cancer globally, with gene mutations and tumor metastasis contributing to its poor prognosis. Single-cell sequencing technology enables high-throughput analysis of the genome, transcriptome, and epigenetic landscapes at the single-cell level. It offers significant insights into analyzing the tumor immune microenvironment, detecting tumor heterogeneity, exploring metastasis mechanisms, and monitoring circulating tumor cells (CTCs). This article provides a brief overview of the technical procedure and data processing involved in single-cell sequencing. It also reviews the current applications of single-cell sequencing in CRC research, aiming to enhance the understanding of intratumoral heterogeneity, CRC development, CTCs, and novel drug targets. By exploring the diverse molecular and clinicopathological characteristics of tumor heterogeneity using single-cell sequencing, valuable insights can be gained into early diagnosis, therapy, and prognosis of CRC. Thus, this review serves as a valuable resource for identifying prognostic markers, discovering new therapeutic targets, and advancing personalized therapy in CRC.

Harnessing the potential of CAR-T cell therapy: progress, challenges, and future directions in hematological and solid tumor treatments

Traditional cancer treatments use nonspecific drugs and monoclonal antibodies to target tumor cells. Chimeric antigen receptor (CAR)-T cell therapy, however, leverages the immune system's T-cells to recognize and attack tumor cells. T-cells are isolated from patients and modified to target tumor-associated antigens. CAR-T therapy has achieved FDA approval for treating blood cancers like B-cell acute lymphoblastic leukemia, large B-cell lymphoma, and multiple myeloma by targeting CD-19 and B-cell maturation antigens. Bi-specific chimeric antigen receptors may contribute to mitigating tumor antigen escape, but their efficacy could be limited in cases where certain tumor cells do not express the targeted antigens. Despite success in blood cancers, CAR-T technology faces challenges in solid tumors, including lack of reliable tumor-associated antigens, hypoxic cores, immunosuppressive tumor environments, enhanced reactive oxygen species, and decreased T-cell infiltration. To overcome these challenges, current research aims to identify reliable tumor-associated antigens and develop cost-effective, tumor microenvironment-specific CAR-T cells. This review covers the evolution of CAR-T therapy against various tumors, including hematological and solid tumors, highlights challenges faced by CAR-T cell therapy, and suggests strategies to overcome these obstacles, such as utilizing single-cell RNA sequencing and artificial intelligence to optimize clinical-grade CAR-T cells.

Single-Cell Analysis in Immuno-Oncology

The complexity of the cellular and non-cellular milieu surrounding human tumors plays a decisive role in the course and outcome of disease. The high variability in the distribution of the immune and non-immune compartments within the tumor microenvironments (TME) among different patients governs the mode of their response or resistance to current immunotherapeutic approaches. Through deciphering this diversity, one can tailor patients' management to meet an individual's needs. Single-cell (sc) omics technologies have given a great boost towards this direction. This review gathers recent data about how multi-omics profiling, including the utilization of single-cell RNA sequencing (scRNA-seq), assay for transposase-accessible chromatin with sequencing (scATAC-seq), T-cell receptor sequencing (scTCR-seq), mass, tissue-based, or microfluidics cytometry, and related bioinformatics tools, contributes to the high-throughput assessment of a large number of analytes at single-cell resolution. Unravelling the exact TCR clonotype of the infiltrating T cells or pinpointing the classical or novel immune checkpoints across various cell subsets of the TME provide a boost to our comprehension of adaptive immune responses, their antigen specificity and dynamics, and grant suggestions for possible therapeutic targets. Future steps are expected to merge high-dimensional data with tissue localization data, which can serve the investigation of novel multi-modal biomarkers for the selection and/or monitoring of the optimal treatment from the current anti-cancer immunotherapeutic armamentarium.

A Method for Rapid, Quantitative Evaluation of Particle Sorting in Microfluidics Using Basic Cytometry Equipment

This paper describes, in detail, a method that uses flow cytometry to quantitatively characterise the performance of continuous-flow microfluidic devices designed to separate particles. Whilst simple, this approach overcomes many of the issues with the current commonly utilised methods (high-speed fluorescent imaging, or cell counting via either a hemocytometer or a cell counter), as it can accurately assess device performance even in complex, high concentration mixtures in a way that was previously not possible. Uniquely, this approach takes advantage of pulse processing in flow cytometry to allow quantitation of cell separation efficiencies and resulting sample purities on both single cells as well as cell clusters (such as circulating tumour cell (CTC) clusters). Furthermore, it can readily be combined with cell surface phenotyping to measure separation efficiencies and purities in complex cell mixtures. This method will facilitate the rapid development of a raft of continuous flow microfluidic devices, will be helpful in testing novel separation devices for biologically relevant clusters of cells such as CTC clusters, and will provide a quantitative assessment of device performance in complex samples, which was previously impossible.

Pan-cancer classification of single cells in the tumour microenvironment

Single-cell RNA sequencing can reveal valuable insights into cellular heterogeneity within tumour microenvironments (TMEs), paving the way for a deep understanding of cellular mechanisms contributing to cancer. However, high heterogeneity among the same cancer types and low transcriptomic variation in immune cell subsets present challenges for accurate, high-resolution confirmation of cells' identities. Here we present scATOMIC; a modular annotation tool for malignant and non-malignant cells. We trained scATOMIC on >300,000 cancer, immune, and stromal cells defining a pan-cancer reference across 19 common cancers and employ a hierarchical approach, outperforming current classification methods. We extensively confirm scATOMIC's accuracy on 225 tumour biopsies encompassing >350,000 cancer and a variety of TME cells. Lastly, we demonstrate scATOMIC's practical significance to accurately subset breast cancers into clinically relevant subtypes and predict tumours' primary origin across metastatic cancers. Our approach represents a broadly applicable strategy to analyse multicellular cancer TMEs.

Advances in Microfluidics for Single Red Blood Cell Analysis

The utilizations of microfluidic chips for single RBC (red blood cell) studies have attracted great interests in recent years to filter, trap, analyze, and release single erythrocytes for various applications. Researchers in this field have highlighted the vast potential in developing micro devices for industrial and academia usages, including lab-on-a-chip and organ-on-a-chip systems. This article critically reviews the current state-of-the-art and recent advances of microfluidics for single RBC analyses, including integrated sensors and microfluidic platforms for microscopic/tomographic/spectroscopic single RBC analyses, trapping arrays (including bifurcating channels), dielectrophoretic and agglutination/aggregation studies, as well as clinical implications covering cancer, sepsis, prenatal, and Sickle Cell diseases. Microfluidics based RBC microarrays, sorting/counting and trapping techniques (including acoustic, dielectrophoretic, hydrodynamic, magnetic, and optical techniques) are also reviewed. Lastly, organs on chips, multi-organ chips, and drug discovery involving single RBC are described. The limitations and drawbacks of each technology are addressed and future prospects are discussed.

Deciphering colorectal cancer immune microenvironment transcriptional landscape on single cell resolution – A role for immunotherapy

Single cell RNA sequencing (scRNA-seq) is a novel high-throughput technique that enables the investigation of a single cell’s entire transcriptome. It elucidates intricate cellular networks and generates indices that will eventually enable the development of more targeted and personalized medications. The importance of scRNA-seq has been highlighted in complex biological systems such as cancer and the immune system, which exhibit significant cellular heterogeneity. Colorectal cancer (CRC) is the third most common type of cancer and the second leading cause of cancer-related death globally. Chemotherapy continues to be used to treat these patients. However, 5-FU has been utilized in chemotherapy regimens with oxaliplatin and irinotecan since the 1960s and is still used today. Additionally, chemotherapy-resistant metastatic CRCs with poor prognoses have been treated with immunotherapy employing monoclonal antibodies, immune checkpoint inhibitors, adoptive cell therapy and cancer vaccines. Personalized immunotherapy employing tumor-specific neoantigens allows for treating each patient as a distinct group. Sequencing and multi-omics approaches have helped us identify patients more precisely in the last decade. The introduction of modern methods and neoantigen-based immunotherapy may usher in a new era in treating CRC. The unmet goal is to better understand the cellular and molecular mechanisms that contribute to CRC pathogenesis and resistance to treatment, identify novel therapeutic targets, and make more stratified and informed treatment decisions using single cell approaches. This review summarizes current scRNA-seq utilization in CRC research, examining its potential utility in the development of precision immunotherapy for CRC.

A Novel Attention-Mechanism Based Cox Survival Model by Exploiting Pan-Cancer Empirical Genomic Information

Cancer prognosis is an essential goal for early diagnosis, biomarker selection, and medical therapy. In the past decade, deep learning has successfully solved a variety of biomedical problems. However, due to the high dimensional limitation of human cancer transcriptome data and the small number of training samples, there is still no mature deep learning-based survival analysis model that can completely solve problems in the training process like overfitting and accurate prognosis. Given these problems, we introduced a novel framework called SAVAE-Cox for survival analysis of high-dimensional transcriptome data. This model adopts a novel attention mechanism and takes full advantage of the adversarial transfer learning strategy. We trained the model on 16 types of TCGA cancer RNA-seq data sets. Experiments show that our module outperformed state-of-the-art survival analysis models such as the Cox proportional hazard model (Cox-ph), Cox-lasso, Cox-ridge, Cox-nnet, and VAECox on the concordance index. In addition, we carry out some feature analysis experiments. Based on the experimental results, we concluded that our model is helpful for revealing cancer-related genes and biological functions.

Capturing tumour heterogeneity in pre- and post-chemotherapy colorectal cancer ascites-derived cells using single-cell RNA-sequencing

Malignant ascites is an abnormal accumulation of fluid within the peritoneal cavity, caused by metastasis of several types of cancers, including colorectal cancer (CRC). Cancer cells in ascites reflect poor prognosis and serve as a good specimen to study tumour heterogeneity, as they represent a collection of multiple metastatic sites in the peritoneum. In the present study, we have employed single-cell RNA-sequencing (scRNA-seq) to explore and characterise ascites-derived cells from a CRC patient. The samples were prepared using mechanical and enzymatic dissociations, and obtained before and after a chemotherapy treatment. Unbiased clustering of 19,653 cells from four samples reveals 14 subclusters with unique transcriptomic patterns in four major cell types: epithelial cells, myeloid cells, fibroblasts, and lymphocytes. Interestingly, the percentages of cells recovered from different cell types appeared to be influenced by the preparation protocols, with more than 90% reduction in the number of myeloid cells recovered by enzymatic preparation. Analysis of epithelial cell subpopulations unveiled only three out of eleven subpopulations with clear contraction after the treatment, suggesting that the majority of the heterogeneous ascites-derived cells were resistant to the treatment, potentially reflecting the poor treatment outcome observed in the patient. Overall, our study showcases highly heterogeneous cancer subpopulations at single-cell resolution, which respond differently to a particular chemotherapy treatment. All in all, this work highlights the potential benefit of single-cell analyses in planning appropriate treatments and real-time monitoring of therapeutic response in cancer patients through routinely discarded ascites samples.

Immunotherapy for Hepatocellular Carcinoma: New Prospects for the Cancer Therapy

Hepatocellular carcinoma (HCC) is the fourth leading cause of cancer-related death worldwide. HCC patients may benefit from liver transplantation, hepatic resection, radiofrequency ablation, transcatheter arterial chemoembolization, and targeted therapies. The increased infiltration of immunosuppressive immune cells and the elevated expression of immunosuppressive factors in the HCC microenvironment are the main culprits of the immunosuppressive nature of the HCC milieu. The immunosuppressive tumor microenvironment can substantially attenuate antitumoral immune responses and facilitate the immune evasion of tumoral cells. Immunotherapy is an innovative treatment method that has been promising in treating HCC. Immune checkpoint inhibitors (ICIs), adoptive cell transfer (ACT), and cell-based (primarily dendritic cells) and non-cell-based vaccines are the most common immunotherapeutic approaches for HCC treatment. However, these therapeutic approaches have not generally induced robust antitumoral responses in clinical settings. To answer to this, growing evidence has characterized immune cell populations and delineated intercellular cross-talk using single-cell RNA sequencing (scRNA-seq) technologies. This review aims to discuss the various types of tumor-infiltrating immune cells and highlight their roles in HCC development. Besides, we discuss the recent advances in immunotherapeutic approaches for treating HCC, e.g., ICIs, dendritic cell (DC)-based vaccines, non-cell-based vaccines, oncolytic viruses (OVs), and ACT. Finally, we discuss the potentiality of scRNA-seq to improve the response rate of HCC patients to immunotherapeutic approaches.

The Dual Role of Innate Lymphoid and Natural Killer Cells in Cancer. from Phenotype to Single-Cell Transcriptomics, Functions and Clinical Uses

Simple Summary

Innate lymphoid cells (ILCs), a family of innate immune cells including natural killers (NKs), play a multitude of roles in first-line cancer control, in escape from immunity and in cancer progression. In this review, we summarize preclinical and clinical data on ILCs and NK cells concerning their phenotype, function and clinical applications in cellular therapy trials. We also describe how single-cell transcriptome sequencing has been used and forecast how it will be used to better understand ILC and NK involvement in cancer control and progression as well as their therapeutic potential.

Abstract

The role of innate lymphoid cells (ILCs), including natural killer (NK) cells, is pivotal in inflammatory modulation and cancer. Natural killer cell activity and count have been demonstrated to be regulated by the expression of activating and inhibitory receptors together with and as a consequence of different stimuli. The great majority of NK cell populations have an anti-tumor activity due to their cytotoxicity, and for this reason have been used for cellular therapies in cancer patients. On the other hand, the recently classified helper ILCs are fundamentally involved in inflammation and they can be either helpful or harmful in cancer development and progression. Tissue niche seems to play an important role in modulating ILC function and conversion, as observed at the transcriptional level. In the past, these cell populations have been classified by the presence of specific cellular receptor markers; more recently, due to the advent of single-cell RNA sequencing (scRNA-seq), it has been possible to also explore them at the transcriptomic level. In this article we review studies on ILC (and NK cell) classification, function and their involvement in cancer. We also summarize the potential application of NK cells in cancer therapy and give an overview of the most recent studies involving ILCs and NKs at scRNA-seq, focusing on cancer. Finally, we provide a resource for those who wish to start single-cell transcriptomic analysis on the context of these innate lymphoid cell populations.

Single-Cell RNA Sequencing of Retina:New Looks for Gene Marker and Old Diseases

The retina is composed of 11 types of cells, including neurons, glial cells and vascular bed cells. It contains five types of neurons, each with specific physiological, morphological, and molecular definitions. Currently, single-cell RNA sequencing (sRNA-seq) is emerging as one of the most powerful tools to reveal the complexity of the retina. The continuous discovery of retina-related gene targets plays an important role in helping us understand the nature of diseases. The revelation of new cell subpopulations can focus the occurrence and development of diseases on specific biological activities of specific cells. In addition, sRNA-seq performs high-throughput sequencing analysis of epigenetics, transcriptome and genome at the single-cell level, with the advantages of high-throughput and high-resolution. In this paper, we systematically review the development history of sRNA-seq technology, and summarize the new subtypes of retinal cells and some specific gene markers discovered by this technology. The progress in the diagnosis of retinal related diseases is also discussed.

Comprehensive description of the current breast cancer microenvironment advancements via single-cell analysis

Breast cancer is a heterogeneous disease with a complex microenvironment consisting of tumor cells, immune cells, fibroblasts and vascular cells. These cancer-associated cells shape the tumor microenvironment (TME) and influence the progression of breast cancer and the therapeutic responses in patients. The exact composition of the intra-tumoral cells is mixed as the highly heterogeneous and dynamic nature of the TME. Recent advances in single-cell technologies such as single-cell DNA sequencing (scDNA-seq), single-cell RNA sequencing (scRNA-seq) and mass cytometry have provided new insights into the phenotypic and functional diversity of tumor-infiltrating cells in breast cancer. In this review, we have outlined the recent progress in single-cell characterization of breast tumor ecosystems, and summarized the phenotypic diversity of intra-tumoral cells and their potential prognostic relevance.

Single-cell transcriptomics reveals involution mimicry during the specification of the basal breast cancer subtype

Basal breast cancer is associated with younger age, early relapse, and a high mortality rate. Here, we use unbiased droplet-based single-cell RNA sequencing (RNA-seq) to elucidate the cellular basis of tumor progression during the specification of the basal breast cancer subtype from the luminal progenitor population in the MMTV-PyMT (mouse mammary tumor virus-polyoma middle tumor-antigen) mammary tumor model. We find that basal-like cancer cells resemble the alveolar lineage that is specified upon pregnancy and encompass the acquisition of an aberrant post-lactation developmental program of involution that triggers remodeling of the tumor microenvironment and metastatic dissemination. This involution mimicry is characterized by a highly interactive multicellular network, with involution cancer-associated fibroblasts playing a pivotal role in extracellular matrix remodeling and immunosuppression. Our results may partially explain the increased risk and poor prognosis of breast cancer associated with childbirth.

Improved survival analysis by learning shared genomic information from pan-cancer data

Motivation

Recent advances in deep learning have offered solutions to many biomedical tasks. However, there remains a challenge in applying deep learning to survival analysis using human cancer transcriptome data. As the number of genes, the input variables of survival model, is larger than the amount of available cancer patient samples, deep-learning models are prone to overfitting. To address the issue, we introduce a new deep-learning architecture called VAECox. VAECox uses transfer learning and fine tuning.

Results

We pre-trained a variational autoencoder on all RNA-seq data in 20 TCGA datasets and transferred the trained weights to our survival prediction model. Then we fine-tuned the transferred weights during training the survival model on each dataset. Results show that our model outperformed other previous models such as Cox Proportional Hazard with LASSO and ridge penalty and Cox-nnet on the 7 of 10 TCGA datasets in terms of C-index. The results signify that the transferred information obtained from entire cancer transcriptome data helped our survival prediction model reduce overfitting and show robust performance in unseen cancer patient samples.

Availability and implementation

Our implementation of VAECox is available at https://github.com/dmis-lab/VAECox.

Supplementary information

Supplementary data are available at Bioinformatics online.

Bioinformatic Approaches to Validation and Functional Analysis of 3D Lung Cancer Models

3D models of cancer have the potential to improve basic, translational, and clinical studies. Patient-derived xenografts, spheroids, and organoids are broad categories of 3D models of cancer, and to date, these 3D models of cancer have been established for a variety of cancer types. In lung cancer, for example, 3D models offer a promising new avenue to gain novel insights into lung tumor biology and improve outcomes for patients afflicted with the number one cancer killer worldwide. However, the adoption and utility of these 3D models of cancer vary, and demonstrating the fidelity of these models is a critical first step before seeking meaningful applications. Here, we review use cases of current 3D lung cancer models and bioinformatic approaches to assessing model fidelity. Bioinformatics approaches play a key role in both validating 3D lung cancer models and high dimensional functional analyses to support downstream applications.

SCCLRR: A Robust Computational Method for Accurate Clustering Single Cell RNA-Seq Data

Single-cell RNA transcriptome data present a tremendous opportunity for studying the cellular heterogeneity. Identifying subpopulations based on scRNA-seq data is a hot topic in recent years, although many researchers have been focused on designing elegant computational methods for identifying new cell types; however, the performance of these methods is still unsatisfactory due to the high dimensionality, sparsity and noise of scRNA-seq data. In this study, we propose a new cell type detection method by learning a robust and accurate similarity matrix, named SCCLRR. The method simultaneously captures both global and local intrinsic properties of data based on a low rank representation (LRR) framework mathematical model. The integrated normalized Euclidean distance and cosine similarity are used to balance the intrinsic linear and nonlinear manifold of data in the local regularization term. To solve the non-convex optimization model, we present an iterative optimization procedure using the alternating direction method of multipliers (ADMM) algorithm. We evaluate the performance of the SCCLRR method on nine real scRNA-seq datasets and compare it with seven state-of-the-art methods. The simulation results show that the SCCLRR outperforms other methods and is robust and effective for clustering scRNA-seq data. (The code of SCCLRR is free available for academic https://github.com/wzhangwhu/SCCLRR).

The current landscape of single-cell transcriptomics for cancer immunotherapy

Immunotherapies such as immune checkpoint blockade and adoptive cell transfer have revolutionized cancer treatment, but further progress is hindered by our limited understanding of tumor resistance mechanisms. Emerging technologies now enable the study of tumors at the single-cell level, providing unprecedented high-resolution insights into the genetic makeup of the tumor microenvironment and immune system that bulk genomics cannot fully capture. Here, we highlight the recent key findings of the use of single-cell RNA sequencing to deconvolute heterogeneous tumors and immune populations during immunotherapy. Single-cell RNA sequencing has identified new crucial factors and cellular subpopulations that either promote tumor progression or leave tumors vulnerable to immunotherapy. We anticipate that the strategic use of single-cell analytics will promote the development of the next generation of successful, rationally designed immunotherapeutics.

Single-Cell RNA Sequencing Analysis: A Step-by-Step Overview

Thanks to innovative sample-preparation and sequencing technologies, gene expression in individual cells can now be measured for thousands of cells in a single experiment. Since its introduction, single-cell RNA sequencing (scRNA-seq) approaches have revolutionized the genomics field as they created unprecedented opportunities for resolving cell heterogeneity by exploring gene expression profiles at a single-cell resolution. However, the rapidly evolving field of scRNA-seq invoked the emergence of various analytics approaches aimed to maximize the full potential of this novel strategy. Unlike population-based RNA sequencing approaches, scRNA seq necessitates comprehensive computational tools to address high data complexity and keep up with the emerging single-cell associated challenges. Despite the vast number of analytical methods, a universal standardization is lacking. While this reflects the fields' immaturity, it may also encumber a newcomer to blend in.In this review, we aim to bridge over the abovementioned hurdle and propose four ready-to-use pipelines for scRNA-seq analysis easily accessible by a newcomer, that could fit various biological data types. Here we provide an overview of the currently available single-cell technologies for cell isolation and library preparation and a step by step guide that covers the entire canonical analytic workflow to analyse scRNA-seq data including read mapping, quality controls, gene expression quantification, normalization, feature selection, dimensionality reduction, and cell clustering useful for trajectory inference and differential expression. Such workflow guidelines will escort novices as well as expert users in the analysis of complex scRNA-seq datasets, thus further expanding the research potential of single-cell approaches in basic science, and envisaging its future implementation as best practice in the field.

RNA Sequencing of Early-Stage Gastric Adenocarcinoma Reveals Multiple Activated Pathways and Novel Long Non-Coding RNAs in Patient Tissue Samples

Background

Gastric cancer is among the most common cancers worldwide that currently lacks effective diagnostic biomarkers and therapeutic targets. Next-generation RNA sequencing is a powerful tool that allows rapid and accurate transcriptome-wide profiling to detect differentially expressed transcripts involved in normal biological and pathological processes. Given the function of this technique, it has the potential to identify new molecular targets for the early diagnosis of disease, particularly in gastric adenocarcinoma.

Methods

In this study, whole-transcriptome analysis was performed with RNA sequencing on tumoral and non-tumoral tissue samples from patients with early-stage gastric cancer. Gene ontology and pathway enrichment analysis were used to determine the main function of the specific genes and pathways present in tissue samples.

Results

Analysis of the differentially expressed genes revealed 5 upregulated and 234 downregulated genes in gastric cancer tissues. Pathway enrichment analysis revealed significantly dysregulated signalling pathways, including those involved in gastric acid secretion, drug metabolism and transporters, molecular toxicology, O-linked glycosylation of mucins, immunotoxicity, metabolism of xenobiotics by cytochrome P450, and glycosylation. We also found novel downregulated non-coding RNAs present in gastric cancer tissues, including GATA6 antisense RNA 1, antisense to LYZ, antisense P4HB, overlapping ACER2, long intergenic non-protein coding RNA 2688 (LINC02688) and uncharacterized LOC25845 (PP7080).

Conclusion

The transcriptomic data found in this study illustrates the power of RNA-sequencing in discovering novel genes ‎and tumorigenic pathways involved in human carcinogenesis. The anomalies present in these genes may serve as promising tools for the development of accurate diagnostic biomarkers for the detection of early-stage gastric cancer.

SITC cancer immunotherapy resource document: a compass in the land of biomarker discovery

Since the publication of the Society for Immunotherapy of Cancer's (SITC) original cancer immunotherapy biomarkers resource document, there have been remarkable breakthroughs in cancer immunotherapy, in particular the development and approval of immune checkpoint inhibitors, engineered cellular therapies, and tumor vaccines to unleash antitumor immune activity. The most notable feature of these breakthroughs is the achievement of durable clinical responses in some patients, enabling long-term survival. These durable responses have been noted in tumor types that were not previously considered immunotherapy-sensitive, suggesting that all patients with cancer may have the potential to benefit from immunotherapy. However, a persistent challenge in the field is the fact that only a minority of patients respond to immunotherapy, especially those therapies that rely on endogenous immune activation such as checkpoint inhibitors and vaccination due to the complex and heterogeneous immune escape mechanisms which can develop in each patient. Therefore, the development of robust biomarkers for each immunotherapy strategy, enabling rational patient selection and the design of precise combination therapies, is key for the continued success and improvement of immunotherapy. In this document, we summarize and update established biomarkers, guidelines, and regulatory considerations for clinical immune biomarker development, discuss well-known and novel technologies for biomarker discovery and validation, and provide tools and resources that can be used by the biomarker research community to facilitate the continued development of immuno-oncology and aid in the goal of durable responses in all patients.

Unfolding innate mechanisms in the cancer microenvironment: The emerging role of the mesenchyme

Innate mechanisms in the tumor stroma play a crucial role both in the initial rejection of tumors and in cancer promotion. Here, we provide a concise overview of the innate system in cancer and recent advances in the field, including the activation and functions of innate immune cells and the emerging innate properties and modulatory roles of the fibroblastic mesenchyme. Novel insights into the diverse identities and functions of the innate immune and mesenchymal cells in the microenvironment of tumors should lead to improved anticancer therapies.

Developmental scRNAseq Trajectories in Gene- and Cell-State Space-The Flatworm Example

Single-cell RNA sequencing has become a standard technique to characterize tissue development. Hereby, cross-sectional snapshots of the diversity of cell transcriptomes were transformed into (pseudo-) longitudinal trajectories of cell differentiation using computational methods, which are based on similarity measures distinguishing cell phenotypes. Cell development is driven by alterations of transcriptional programs e.g., by differentiation from stem cells into various tissues or by adapting to micro-environmental requirements. We here complement developmental trajectories in cell-state space by trajectories in gene-state space to more clearly address this latter aspect. Such trajectories can be generated using self-organizing maps machine learning. The method transforms multidimensional gene expression patterns into two dimensional data landscapes, which resemble the metaphoric Waddington epigenetic landscape. Trajectories in this landscape visualize transcriptional programs passed by cells along their developmental paths from stem cells to differentiated tissues. In addition, we generated developmental "vector fields" using RNA-velocities to forecast changes of RNA abundance in the expression landscapes. We applied the method to tissue development of planarian as an illustrative example. Gene-state space trajectories complement our data portrayal approach by (pseudo-)temporal information about changing transcriptional programs of the cells. Future applications can be seen in the fields of tissue and cell differentiation, ageing and tumor progression and also, using other data types such as genome, methylome, and also clinical and epidemiological phenotype data.

Defining the Skin Cellular Community Using Single-Cell Genomics to Advance Precision Medicine

Single-cell genomics has revolutionized biological science, enabling high-resolution analysis of human tissues. The ability to demonstrate the role and function of distinct cell types comprising human tissues paves the way for a new understanding of cellular pathways, interactions, and future research directions. The skin, easily accessible and possessing a diverse and complex role in defending us both physically and immunologically from the outside world, lends itself ideally to single-cell genomics analysis. Here, we outline the benefits of single-cell RNA sequencing while also highlighting the challenges in achieving a meaningful result from its use. Key milestones relating to the study of skin in this way are introduced, covering both healthy and diseased states, and we discuss the potential promise of single-cell RNA sequencing to result in tangible medical advances, with a particular focus on precision medicine.

An Integrated Dielectrophoresis-Trapping and Nanowell Transfer Approach to Enable Double-Sub-Poisson Single-Cell RNA Sequencing

Current technologies for high-throughput single-cell RNA sequencing (scRNA-seq) are based upon stochastic pairing of cells and barcoded beads in nanoliter droplets or wells. They are limited by the mathematical principle of the Poisson statistics such that the utilization of either cells or beads or both is no more than ∼33%. Despite the versatile design of microfluidics or microwells for high-yield loading of beads that beats the Poisson limit, subsequent encapsulation of single cells is still determined by stochastic pairing, representing a fundamental limitation in the field of single-cell sequencing. Here, we present dTNT-seq, an integrated dielectrophoresis (DEP)-trapping-nanowell-transfer (dTNT) approach to perform cell trapping and bead loading both in a sub-Poisson manner to facilitate scRNA-seq. A larger-sized 50 μm microwell array was prealigned precisely on top of the 20 μm DEP nanowell array such that single cells trapped by DEP can be readily transferred into the underneath larger wells by flipping the device, followed by subsequent hydrodynamic bead loading and coisolation with transferred single cells. Using a dTNT device composed of 3600 electroactive DEP-nanowell units, we demonstrated a single-cell trapping rate of 91.84%, a transfer efficiency of 82%, and a routine bead loading rate of >99%, which breaks the Poisson limit for the capture of both cells and beads, thus called double-sub-Poisson distribution, prior to encapsulating them in nanoliter wells for cellular mRNA barcoding. This approach was applied to human (HEK) and mouse (3T3) cells. Comparison with a non-DEP-based method through gene expression clustering and regulatory pathway analysis demonstrates consistent patterns and negligible alternation of cellular transcriptional states by DEP. We envision the dTNT-seq device can be modified for studying cell-cell interactions and enable other applications requiring active manipulation of single cells prior to transcriptome sequencing.

Identification of non-cancer cells from cancer transcriptomic data

Interactions between cancer cells and non-cancer cells composing the tumour microenvironment play a primary role in determining cancer progression and shaping the response to therapy. The qualitative and quantitative characterisation of the different cell populations in the tumour microenvironment is therefore crucial to understand its role in cancer. In recent years, many experimental and computational approaches have been developed to identify the cell populations composing heterogeneous tissue samples, such as cancer. In this review, we describe the state-of-the-art approaches for the quantification of non-cancer cells from bulk and single-cell cancer transcriptomic data, with a focus on immune cells. We illustrate the main features of these approaches and highlight their applications for the analysis of the tumour microenvironment in solid cancers. We also discuss techniques that are complementary and alternative to RNA sequencing, particularly focusing on approaches that can provide spatial information on the distribution of the cells within the tumour in addition to their qualitative and quantitative measurements. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.

Single Cell Transcriptome in Colorectal Cancer-Current Updates on Its Application in Metastasis, Chemoresistance and the Roles of Circulating Tumor Cells

Colorectal cancer (CRC) is among the most common cancer worldwide, a challenge for research, and a model for studying the molecular mechanisms involved in its development. Previously, bulk transcriptomics analyses were utilized to classify CRC based on its distinct molecular and clinicopathological features for prognosis and diagnosis of patients. The introduction of single-cell transcriptomics completely turned the table by enabling the examination of the expression levels of individual cancer cell within a single tumor. In this review, we highlighted the importance of these single-cell transcriptomics analyses as well as suggesting circulating tumor cells (CTCs) as the main focus of single-cell RNA sequencing. Characterization of these cells might reveal the intratumoral heterogeneity present in CRC while providing critical insights into cancer metastasis. To summarize, we believed the analysis of gene expression patterns of CTC from CRC at single-cell resolution holds the potential to provide key information for identification of prognostic and diagnostic markers as well as the development of precise and personalized cancer treatment.

Myeloid-Derived Suppressor Cells as a Therapeutic Target for Cancer

The emergence of immunotherapy has been an astounding breakthrough in cancer treatments. In particular, immune checkpoint inhibitors, targeting PD-1 and CTLA-4, have shown remarkable therapeutic outcomes. However, response rates from immunotherapy have been reported to be varied, with some having pronounced success and others with minimal to no clinical benefit. An important aspect associated with this discrepancy in patient response is the immune-suppressive effects elicited by the tumour microenvironment (TME). Immune suppression plays a pivotal role in regulating cancer progression, metastasis, and reducing immunotherapy success. Most notably, myeloid-derived suppressor cells (MDSC), a heterogeneous population of immature myeloid cells, have potent mechanisms to inhibit T-cell and NK-cell activity to promote tumour growth, development of the pre-metastatic niche, and contribute to resistance to immunotherapy. Accumulating research indicates that MDSC can be a therapeutic target to alleviate their pro-tumourigenic functions and immunosuppressive activities to bolster the efficacy of checkpoint inhibitors. In this review, we provide an overview of the general immunotherapeutic approaches and discuss the characterisation, expansion, and activities of MDSCs with the current treatments used to target them either as a single therapeutic target or synergistically in combination with immunotherapy.

Tracking intratumoral heterogeneity in glioblastoma via regularized classification of single-cell RNA-Seq data

Background

Understanding cellular and molecular heterogeneity in glioblastoma (GBM), the most common and aggressive primary brain malignancy, is a crucial step towards the development of effective therapies. Besides the inter-patient variability, the presence of multiple cell populations within tumors calls for the need to develop modeling strategies able to extract the molecular signatures driving tumor evolution and treatment failure. With the advances in single-cell RNA Sequencing (scRNA-Seq), tumors can now be dissected at the cell level, unveiling information from their life history to their clinical implications.

Results

We propose a classification setting based on GBM scRNA-Seq data, through sparse logistic regression, where different cell populations (neoplastic and normal cells) are taken as classes. The goal is to identify gene features discriminating between the classes, but also those shared by different neoplastic clones. The latter will be approached via the network-based twiner regularizer to identify gene signatures shared by neoplastic cells from the tumor core and infiltrating neoplastic cells originated from the tumor periphery, as putative disease biomarkers to target multiple neoplastic clones. Our analysis is supported by the literature through the identification of several known molecular players in GBM. Moreover, the relevance of the selected genes was confirmed by their significance in the survival outcomes in bulk GBM RNA-Seq data, as well as their association with several Gene Ontology (GO) biological process terms.

Conclusions

We presented a methodology intended to identify genes discriminating between GBM clones, but also those playing a similar role in different GBM neoplastic clones (including migrating cells), therefore potential targets for therapy research. Our results contribute to a deeper understanding on the genetic features behind GBM, by disclosing novel therapeutic directions accounting for GBM heterogeneity.

High-Throughput Transcriptome Profiling in Drug and Biomarker Discovery

The development of new drugs is multidisciplinary and systematic work. High-throughput techniques based on “-omics” have driven the discovery of biomarkers in diseases and therapeutic targets of drugs. A transcriptome is the complete set of all RNAs transcribed by certain tissues or cells at a specific stage of development or physiological condition. Transcriptome research can demonstrate gene functions and structures from the whole level and reveal the molecular mechanism of specific biological processes in diseases. Currently, gene expression microarray and high-throughput RNA-sequencing have been widely used in biological, medical, clinical, and drug research. The former has been applied in drug screening and biomarker detection of drugs due to its high throughput, fast detection speed, simple analysis, and relatively low price. With the further development of detection technology and the improvement of analytical methods, the detection flux of RNA-seq is much higher but the price is lower, hence it has powerful advantages in detecting biomarkers and drug discovery. Compared with the traditional RNA-seq, scRNA-seq has higher accuracy and efficiency, especially the single-cell level of gene expression pattern analysis can provide more information for drug and biomarker discovery. Therefore, (sc)RNA-seq has broader application prospects, especially in the field of drug discovery. In this overview, we will review the application of these technologies in drug, especially in natural drug and biomarker discovery and development. Emerging applications of scRNA-seq and the third generation RNA-sequencing tools are also discussed.

Tumor Functional Heterogeneity Unraveled by scRNA-seq Technologies

Effective cancer treatment has been precluded by the presence of various forms of intratumoral complexity that drive treatment resistance and metastasis. Recent single-cell sequencing technologies are significantly facilitating the characterization of tumor internal architecture during disease progression. New applications and advances occurring at a fast pace predict an imminent broad application of these technologies in many research areas. As occurred with next-generation sequencing (NGS) technologies, once applied to clinical samples across tumor types, single-cell sequencing technologies could trigger an exponential increase in knowledge of the molecular pathways involved in cancer progression and contribute to the improvement of cancer treatment.

Immune cells within the tumor microenvironment: Biological functions and roles in cancer immunotherapy

The immune cells within the tumor microenvironment (TME) play important roles in tumorigenesis. It has been known that these tumor associated immune cells may possess tumor-antagonizing or tumor-promoting functions. Although the tumor-antagonizing immune cells within TME tend to target and kill the cancer cells in the early stage of tumorigenesis, the cancer cells seems to eventually escape from immune surveillance and even inhibit the cytotoxic function of tumor-antagonizing immune cells through a variety of mechanisms. The immune evasion capability, as a new hallmark of cancer, accidently provides opportunities for new strategies of cancer therapy, namely harnessing the immune cells to battle the cancer cells. Recently, the administrations of immune checkpoint modulators (represented by anti-CTLA4 and anti-PD antibodies) and adoptive immune cells (represented by CAR-T) have exhibited unexpected antitumor effect in multiple types of cancer, bringing a new era for cancer therapy. Here, we review the biological functions of immune cells within TME and their roles in cancer immunotherapy, and discuss the perspectives of the basic studies for improving the effectiveness of the clinical use.

Genomics-Guided Immunotherapy for Precision Medicine in Cancer

Next-generation sequencing (NGS) data have been central to the development of targeted therapy and immunotherapy for precision oncology. In targeted therapy, drugs directly attack cancer, by altering the expression of critical cancer genes identified with cancer genome profiling. Immunotherapy drugs indirectly attack cancer, by inducing the immune system to attack and treat cancer. Harnessing genomic data for deployment and development of immunotherapy comprises the field of immunogenomics. The discovery of a link between cancer cells escaping immune destruction and cancer progression, led to extensive research into this mechanism and drug development. In the past few years, FDA has granted accelerated approval to several immunotherapy cancer treatment drugs, pembrolizumab, nivolumab, and atezolizumab, belonging to the class of checkpoint inhibitors. Utilization of pretreatment genomic cancer screening to identify patients most likely to respond to immunotherapy and to customize immunotherapy for a given patient, promises to improve cancer treatment outcomes. Recent advances in molecular profiling, high-throughput sequencing, and computational efficiency has made immunogenomics the major tenet of precision medicine in cancer treatment. This review provides a brief overview on the state of art of immunogenomics in precision cancer medicine.

Tumor-Infiltrating Leukocyte Composition and Prognostic Power in Hepatitis B- and Hepatitis C-Related Hepatocellular Carcinomas

Background: Tumor-infiltrating leukocytes (TILs) are immune cells surrounding tumor cells, and several studies have shown that TILs are potential survival predictors in different cancers. However, few studies have dissected the differences between hepatitis B- and hepatitis C-related hepatocellular carcinoma (HBV−HCC and HCV−HCC). Therefore, we aimed to determine whether the abundance and composition of TILs are potential predictors for survival outcomes in HCC and which TILs are the most significant predictors. Methods: Two bioinformatics algorithms, ESTIMATE and CIBERSORT, were utilized to analyze the gene expression profiles from 6 datasets, from which the abundance of corresponding TILs was inferred. The ESTIMATE algorithm examined the overall abundance of TILs, whereas the CIBERSORT algorithm reported the relative abundance of 22 different TILs. Both HBV−HCC and HCV−HCC were analyzed. Results: The results indicated that the total abundance of TILs was higher in non-tumor tissue regardless of the HCC type. Alternatively, the specific TILs associated with overall survival (OS) and recurrence-free survival (RFS) varied between subtypes. For example, in HBV−HCC, plasma cells (hazard ratio [HR] = 1.05; 95% CI 1.00–1.10; p = 0.034) and activated dendritic cells (HR = 1.08; 95% CI 1.01–1.17; p = 0.03) were significantly associated with OS, whereas in HCV−HCC, monocytes (HR = 1.21) were significantly associated with OS. Furthermore, for RFS, CD8+ T cells (HR = 0.98) and M0 macrophages (HR = 1.02) were potential biomarkers in HBV−HCC, whereas neutrophils (HR = 1.01) were an independent predictor in HCV−HCC. Lastly, in both HBV−HCC and HCV−HCC, CD8+ T cells (HR = 0.97) and activated dendritic cells (HR = 1.09) had a significant association with OS, while γ delta T cells (HR = 1.04), monocytes (HR = 1.05), M0 macrophages (HR = 1.04), M1 macrophages (HR = 1.02), and activated dendritic cells (HR = 1.15) were highly associated with RFS. Conclusions: These findings demonstrated that TILs are potential survival predictors in HCC and different kinds of TILs are observed according to the virus type. Therefore, further investigations are warranted to elucidate the role of TILs in HCC, which may improve immunotherapy outcomes.

Droplet-based single cell RNAseq tools: a practical guide

Droplet based scRNA-seq systems such as Drop-seq, inDrop and Chromium 10X have been the catalyst for the wide adoption of high-throughput scRNA-seq technologies in the research laboratory. In order to understand the capabilities of these systems to deeply interrogate biology; here we provide a practical guide through all the steps involved in a typical scRNA-seq experiment. Through comparing and contrasting these three main droplet based systems (and their derivatives), we provide an overview of all critical considerations in obtaining high quality and biologically relevant data. We also discuss the limitations of these systems and how they fit into the emerging field of Genomic Cytometry.

Immune checkpoint modulators in cancer immunotherapy: Recent advances and combination rationales

As a new hallmark of cancer, immune surveillance evading plays a critical role in carcinogenesis. Through modulating the immune checkpoints, immune cells in tumor microenvironment can be harnessed to battle cancer cells. In recent years, the administration of anti-CTLA or/and anti-PD-1/L1 antibody has exhibited unexpected antitumor effect in multiple types of cancer, motivating the researchers to find more potential immune checkpoints as clinical targets. A wealth of clinical trials have been done to evaluate the safety and efficacy of monotherapy or combination therapy with immune checkpoint modulators. However, there still exist problems such as low response rate and adverse events in the clinical, which in turn leads us to the basic study. The better understanding of the crosstalk between the immune cells and the cancer cells within the microenvironment may inspire us new ideas for cancer treatment. In this review, we mainly summarize the recent advances in application of immune checkpoint modulators and the combination rationales, and discuss the problems existing in the precision therapy with immune checkpoint modulators.