Reproducible copy number variation patterns among single circulating tumor cells of lung cancer patients

Single-Cell Sequencing: Genomic and Transcriptomic Approaches in Cancer Cell Biology

This article reviews the impact of single-cell sequencing (SCS) on cancer biology research. SCS has revolutionized our understanding of cancer and tumor heterogeneity, clonal evolution, and the complex interplay between cancer cells and tumor microenvironment. SCS provides high-resolution profiling of individual cells in genomic, transcriptomic, and epigenomic landscapes, facilitating the detection of rare mutations, the characterization of cellular diversity, and the integration of molecular data with phenotypic traits. The integration of SCS with multi-omics has provided a multidimensional view of cellular states and regulatory mechanisms in cancer, uncovering novel regulatory mechanisms and therapeutic targets. Advances in computational tools, artificial intelligence (AI), and machine learning have been crucial in interpreting the vast amounts of data generated, leading to the identification of new biomarkers and the development of predictive models for patient stratification. Furthermore, there have been emerging technologies such as spatial transcriptomics and in situ sequencing, which promise to further enhance our understanding of tumor microenvironment organization and cellular interactions. As SCS and its related technologies continue to advance, they are expected to drive significant advances in personalized cancer diagnostics, prognosis, and therapy, ultimately improving patient outcomes in the era of precision oncology.

Single-cell omics: experimental workflow, data analyses and applications

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

Mutational differences between primary cancer tissue and circulating tumor cells in early-stage non-small cell lung cancer

Background

Early-stage non-small cell lung cancer (NSCLC) has a high recurrence rate despite proper management, including curative surgery. Circulating tumor cells (CTCs) are believed to play a key role in the distant metastasis of lung cancer. Immunofluorescence imaging studies of CTCs have revealed that they are associated with the prognosis of NSCLC. However, the mutational profiling of CTCs from early-stage NSCLC has not been extensively explored. We hypothesized that CTCs could be detected by mutational analysis using panel sequencing and would have distinct mutations associated with distant metastasis compared to those of primary cancer tissue in early-stage NSCLC. Thus, this study examined the DNA from CTCs using targeted panel sequencing to identify mutations and compared them with mutations found in primary cancer tissue in patients with resectable early-stage NSCLC.

Methods

Overall, 45 patients with resectable NSCLC were prospectively enrolled from September to December 2023. Matched whole blood samples and primary cancer tissues were collected during curative surgery. Then, 405-gene targeted panel sequencing was performed on DNA from primary cancer tissues and CTCs.

Results

In this study, 37 patients (82%) had adenocarcinoma, and 30 (67%) were classified as having pathologic stage 1 disease. Mutated genes were detected in all (100%) and 31 patients (69%) for primary cancer tissue and CTCs from panel sequencing, respectively. The partial concordance rate of mutations between primary cancer tissue and CTCs was 17.8%, with the top 10 mutated genes differing significantly. Among primary cancer tissue samples, mutated genes differed by stage and histologic type; these findings were not observed in CTCs. CTCs predominantly displayed mutations in tumor suppressor genes, whereas primary cancer tissues exhibited mutations in both oncogenes and tumor suppressor genes.

Conclusions

CTCs exhibited unique mutations, showing low concordance with mutations found in primary cancer tissue. CTCs may possess specific mutations independent from those of primary cancer tissue in early-stage NSCLC.

Mugen-UMAP: UMAP visualization and clustering of mutated genes in single-cell DNA sequencing data

BACKGROUND

The application of Uniform Manifold Approximation and Projection (UMAP) for dimensionality reduction and visualization has revolutionized the analysis of single-cell RNA expression and population genetics. However, its potential in single-cell DNA sequencing data analysis, particularly for visualizing gene mutation information, has not been fully explored.

RESULTS

We introduce Mugen-UMAP, a novel Python-based program that extends UMAP's utility to single-cell DNA sequencing data. This innovative tool provides a comprehensive pipeline for processing gene annotation files of single-cell somatic single-nucleotide variants and metadata to the visualization of UMAP projections for identifying clusters, along with various statistical analyses. Employing Mugen-UMAP, we analyzed whole-exome sequencing data from 365 single-cell samples across 12 non-small cell lung cancer (NSCLC) patients, revealing distinct clusters associated with histological subtypes of NSCLC. Moreover, to demonstrate the general utility of Mugen-UMAP, we applied the program to 9 additional single-cell WES datasets from various cancer types, uncovering interesting patterns of cell clusters that warrant further investigation. In summary, Mugen-UMAP provides a quick and effective visualization method to uncover cell cluster patterns based on the gene mutation information from single-cell DNA sequencing data.

CONCLUSIONS

The application of Mugen-UMAP demonstrates its capacity to provide valuable insights into the visualization and interpretation of single-cell DNA sequencing data. Mugen-UMAP can be found at https://github.com/tengchn/Mugen-UMAP.

Advances in predictive biomarkers associated with immunotherapy in extensive-stage small cell lung cancer

Small cell lung cancer (SCLC) is a highly malignant and poor-prognosis cancer, with most cases diagnosed at the extensive stage (ES). Amidst a landscape marked by limited progress in treatment modalities for ES-SCLC over the past few decades, the integration of immune checkpoint inhibitors (ICIs) with platinum-based chemotherapy has provided a milestone approach for improving prognosis, emerging as the new standard for initial therapy in ES-SCLC. However, only a minority of SCLC patients can benefit from ICIs, which frequently come with varying degrees of immune-related adverse events (irAEs). Therefore, it is crucial to investigate predictive biomarkers to screen potential beneficiaries of ICIs, mitigate the risk of side effects, and improve treatment precision. This review summarized potential biomarkers for predicting ICI response in ES-SCLC, with a primary focus on markers sourced from tumor tissue or peripheral blood samples. The former mainly included PD-L1 expression, tumor mutational burden (TMB), along with cellular or molecular components related to the tumor microenvironment (TME) and antigen presentation machinery (APM), molecular subtypes of SCLC, and inflammatory gene expression profiles. Circulating biomarkers predominantly comprised circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), cytokines, plasma autoantibodies, inflammation-related parameters, and blood TMB. We synthesized and analyzed the research progress of these potential markers. Notably, investigations into PD-L1 expression and TMB have been the most extensive, exhibiting preliminary predictive efficacy in salvage immunotherapy; however, consistent conclusions have yet to be reached across studies. Additionally, novel predictive markers developed based on TME composition, APM, transcriptomic and genomic features provide promising tools for precision immunotherapy. Circulating biomarkers offer the advantages of convenience, non-invasiveness, and a comprehensive reflection of tumor molecular characteristics. They may serve as alternative options for predicting immunotherapy efficacy in SCLC. However, there is a scarcity of studies, and the significant heterogeneity in research findings warrants attention.

Specific Deletions of Chromosomes 3p, 5q, 13q, and 21q among Patients with G2 Grade of Non-Small Cell Lung Cancer

Non-small cell lung cancer (NSCLC) leads as a primary cause of cancer-related premature mortality in Western populations. This study leverages cutting-edge gene-expression-profiling technologies to perform an in-depth molecular characterization of NSCLC specimens, with the objective of uncovering tumor-specific genomic alterations. By employing DNA microarray analysis, our research aims to refine the classification of NSCLC for early detection, guide molecular-targeted treatment approaches, enhance prognostication, and broaden the scientific understanding of the disease's biology. We identified widespread genomic abnormalities in our samples, including the recurrent loss of chromosomal regions 3p, 5q, 13q, and 21q and the gain of 12p. Furthermore, utilizing Metascape for bioinformatic analysis revealed critical biological pathways disrupted in NSCLC, offering promising leads for novel therapeutic interventions.

Clinical significance of genomic sequencing of circulating tumour cells (CTCs) in cancer

Circulating tumour cell (CTC), a rare subpopulations of tumour cells, plays a significant role in cancer metastasis and recurrence. The current review focuses on information of CTCs detection, enrichment, genome sequencing and investigating on clinical significance of CTCs sequencing in monitoring progress of patients with cancer. Tissue biopsy is not always favorable for monitoring cancer recurrence and metastases and can be difficult and risky for the patient's condition. On the other hand, enrichment and characterization of CTC could be effective in these circumstances. Accordingly, a number of detection (physical, immunological etc.), isolation (laser capture microdissection, DEPArray Di Electro phenetic array, fluorescence-activated cell sorting etc.) and enrichment platforms (Cellsearch, MagSwepeer, CTC-Chip etc.) have been developed. In addition, technologies for phenotypic characterization and genomic profiling (Tang's method, Smart-seq, Cel-seq etc.) at single-cell level has being established followed by genomic amplification. Importantly, numerous preclinical and clinical studies showed effective prognostic and predictive implications of molecular characterization of CTCs. CTC's sequencing has been successfully used as an effective tool to monitor genomic variations in the primary and metastatic tumours, thereby could predict the therapy resistance, recurrence of tumours. The genes expression profiles, stratification of cancers as well as identify the cancer cells with potential to undergo epithelial-mesenchymal transition (EMT) could also be identified. In addition, detection of mutational variants of CTCs by genome sequencing infer the therapeutic outcome and patient's survival. Therefore, CTC sequencing has potential to be used as a liquid biopsy tool for management of patients with cancers in clinical settings.

Adjuvant Novel Nanocarrier-Based Targeted Therapy for Lung Cancer

Lung cancer has the lowest survival rate due to its late-stage diagnosis, poor prognosis, and intra-tumoral heterogeneity. These factors decrease the effectiveness of treatment. They release chemokines and cytokines from the tumor microenvironment (TME). To improve the effectiveness of treatment, researchers emphasize personalized adjuvant therapies along with conventional ones. Targeted chemotherapeutic drug delivery systems and specific pathway-blocking agents using nanocarriers are a few of them. This study explored the nanocarrier roles and strategies to improve the treatment profile's effectiveness by striving for TME. A biofunctionalized nanocarrier stimulates biosystem interaction, cellular uptake, immune system escape, and vascular changes for penetration into the TME. Inorganic metal compounds scavenge reactive oxygen species (ROS) through their photothermal effect. Stroma, hypoxia, pH, and immunity-modulating agents conjugated or modified nanocarriers co-administered with pathway-blocking or condition-modulating agents can regulate extracellular matrix (ECM), Cancer-associated fibroblasts (CAF),Tyro3, Axl, and Mertk receptors (TAM) regulation, regulatory T-cell (Treg) inhibition, and myeloid-derived suppressor cells (MDSC) inhibition. Again, biomimetic conjugation or the surface modification of nanocarriers using ligands can enhance active targeting efficacy by bypassing the TME. A carrier system with biofunctionalized inorganic metal compounds and organic compound complex-loaded drugs is convenient for NSCLC-targeted therapy.

Liquid biopsy in T-cell lymphoma: biomarker detection techniques and clinical application

T-cell lymphoma is a highly invasive tumor with significant heterogeneity. Invasive tissue biopsy is the gold standard for acquiring molecular data and categorizing lymphoma patients into genetic subtypes. However, surgical intervention is unfeasible for patients who are critically ill, have unresectable tumors, or demonstrate low compliance, making tissue biopsies inaccessible to these patients. A critical need for a minimally invasive approach in T-cell lymphoma is evident, particularly in the areas of early diagnosis, prognostic monitoring, treatment response, and drug resistance. Therefore, the clinical application of liquid biopsy techniques has gained significant attention in T-cell lymphoma. Moreover, liquid biopsy requires fewer samples, exhibits good reproducibility, and enables real-time monitoring at molecular levels, thereby facilitating personalized health care. In this review, we provide a comprehensive overview of the current liquid biopsy biomarkers used for T-cell lymphoma, focusing on circulating cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), Epstein-Barr virus (EBV) DNA, antibodies, and cytokines. Additionally, we discuss their clinical application, detection methodologies, ongoing clinical trials, and the challenges faced in the field of liquid biopsy.

Single-cell low-pass whole genome sequencing accurately detects circulating tumor cells for liquid biopsy-based multi-cancer diagnosis

Accurate detection of circulating tumor cells (CTCs) in blood and non-blood body fluids enables generation of deterministic cancer diagnosis and represent a less invasive and safer liquid biopsy approach. Although genomic alternations have been widely used in circulating tumor DNA (ctDNA) analysis, studies on cell-based genomic alternations profiling for CTC detection are rare due to major technical limitations in single-cell whole genome sequencing (WGS) including low throughput, low accuracy and high cost. We report a single-cell low-pass WGS-based protocol (scMet-Seq) for sensitive and accurate CTC detection by combining a metabolic function-associated marker Hexokinase 2 (HK2) and a Tn5 transposome-based WGS method with improved cell fixation strategy. To explore the clinical use, scMet-Seq has been investigated with blood and non-blood body fluids in diagnosing metastatic diseases, including ascites-based diagnosis of malignant ascites (MA) and blood-based diagnosis of metastatic small-cell lung cancer (SCLC). ScMet-Seq shows high diagnostic sensitivity (MA: 79% in >10 cancer types; metastatic SCLC: 90%) and ~100% of diagnostic specificity and positive predictive value, superior to clinical cytology that exhibits diagnostic sensitivity of 52% in MA diagnosis and could not generate blood-based diagnosis. ScMet-Seq represents a liquid biopsy approach for deterministic cancer diagnosis in different types of cancers and body fluids.

Detecting radio- and chemoresistant cells in 3D cancer co-cultures using chromatin biomarkers

The heterogenous treatment response of tumor cells limits the effectiveness of cancer therapy. While this heterogeneity has been linked to cell-to-cell variability within the complex tumor microenvironment, a quantitative biomarker that identifies and characterizes treatment-resistant cell populations is still missing. Herein, we use chromatin organization as a cost-efficient readout of the cells' states to identify subpopulations that exhibit distinct responses to radiotherapy. To this end, we developed a 3D co-culture model of cancer spheroids and patient-derived fibroblasts treated with radiotherapy. Using the model we identified treatment-resistant cells that bypassed DNA damage checkpoints and exhibited an aggressive growth phenotype. Importantly, these cells featured more condensed chromatin which primed them for treatment evasion, as inhibiting chromatin condensation and DNA damage repair mechanisms improved the efficacy of not only radio- but also chemotherapy. Collectively, our work shows the potential of using chromatin organization to cost-effectively study the heterogeneous treatment susceptibility of cells and guide therapeutic design.

Application of single-cell sequencing to the research of tumor microenvironment

Single-cell sequencing is a technique for detecting and analyzing genomes, transcriptomes, and epigenomes at the single-cell level, which can detect cellular heterogeneity lost in conventional sequencing hybrid samples, and it has revolutionized our understanding of the genetic heterogeneity and complexity of tumor progression. Moreover, the tumor microenvironment (TME) plays a crucial role in the formation, development and response to treatment of tumors. The application of single-cell sequencing has ushered in a new age for the TME analysis, revealing not only the blueprint of the pan-cancer immune microenvironment, but also the heterogeneity and differentiation routes of immune cells, as well as predicting tumor prognosis. Thus, the combination of single-cell sequencing and the TME analysis provides a unique opportunity to unravel the molecular mechanisms underlying tumor development and progression. In this review, we summarize the recent advances in single-cell sequencing and the TME analysis, highlighting their potential applications in cancer research and clinical translation.

Single-cell phylogenies reveal changes in the evolutionary rate within cancer and healthy tissues

Cell lineages accumulate somatic mutations during organismal development, potentially leading to pathological states. The rate of somatic evolution within a cell population can vary due to multiple factors, including selection, a change in the mutation rate, or differences in the microenvironment. Here, we developed a statistical test called the Poisson Tree (PT) test to detect varying evolutionary rates among cell lineages, leveraging the phylogenetic signal of single-cell DNA sequencing (scDNA-seq) data. We applied the PT test to 24 healthy and cancer samples, rejecting a constant evolutionary rate in 11 out of 15 cancer and five out of nine healthy scDNA-seq datasets. In six cancer datasets, we identified subclonal mutations in known driver genes that could explain the rate accelerations of particular cancer lineages. Our findings demonstrate the efficacy of scDNA-seq for studying somatic evolution and suggest that cell lineages often evolve at different rates within cancer and healthy tissues.

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

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

Integrative analysis of DNA methylomes reveals novel cell-free biomarkers in lung adenocarcinoma

Lung cancer is a leading cause of cancer-related deaths worldwide, with a low 5-year survival rate due in part to a lack of clinically useful biomarkers. Recent studies have identified DNA methylation changes as potential cancer biomarkers. The present study identified cancer-specific CpG methylation changes by comparing genome-wide methylation data of cfDNA from lung adenocarcinomas (LUAD) patients and healthy donors in the discovery cohort. A total of 725 cell-free CpGs associated with LUAD risk were identified. Then XGBoost algorithm was performed to identify seven CpGs associated with LUAD risk. In the training phase, the 7-CpGs methylation panel was established to classify two different prognostic subgroups and showed a significant association with overall survival (OS) in LUAD patients. We found that the methylation of cg02261780 was negatively correlated with the expression of its representing gene GNA11. The methylation and expression of GNA11 were significantly associated with LAUD prognosis. Based on bisulfite PCR, the methylation levels of five CpGs (cg02261780, cg09595050, cg20193802, cg15309457, and cg05726109) were further validated in tumor tissues and matched non-malignant tissues from 20 LUAD patients. Finally, validation of the seven CpGs with RRBS data of cfDNA methylation was conducted and further proved the reliability of the 7-CpGs methylation panel. In conclusion, our study identified seven novel methylation markers from cfDNA methylation data which may contribute to better prognosis for LUAD patients.

Single-cell omics: a new perspective for early detection of pancreatic cancer?

Pancreatic cancer is one of the most lethal cancers, mostly due to late diagnosis and limited treatment options. Early detection of pancreatic cancer in high-risk populations bears the potential to greatly improve outcomes, but current screening approaches remain of limited value despite recent technological advances. This review explores the possible advantages of liquid biopsies for this application, particularly focusing on circulating tumour cells (CTCs) and their subsequent single-cell omics analysis. Originating from both primary and metastatic tumour sites, CTCs provide important information for diagnosis, prognosis and tailoring of treatment strategies. Notably, CTCs have even been detected in the blood of subjects with pancreatic precursor lesions, suggesting their suitability as a non-invasive tool for the early detection of malignant transformation in the pancreas. As intact cells, CTCs offer comprehensive genomic, transcriptomic, epigenetic and proteomic information that can be explored using rapidly developing techniques for analysing individual cells at the molecular level. Studying CTCs during serial sampling and at single-cell resolution will help to dissect tumour heterogeneity for individual patients and among different patients, providing new insights into cancer evolution during disease progression and in response to treatment. Using CTCs for non-invasive tracking of cancer features, including stemness, metastatic potential and expression of immune targets, provides important and readily accessible molecular insights. Finally, the emerging technology of ex vivo culturing of CTCs could create new opportunities to study the functionality of individual cancers at any stage and develop personalised and more effective treatment approaches for this lethal disease.

Liquid Biopsy in Lung Cancer: Biomarkers for the Management of Recurrence and Metastasis

Liquid biopsies have emerged as a promising tool for the detection of metastases as well as local and regional recurrence in lung cancer. Liquid biopsy tests involve analyzing a patient's blood, urine, or other body fluids for the detection of biomarkers, including circulating tumor cells or tumor-derived DNA/RNA that have been shed into the bloodstream. Studies have shown that liquid biopsies can detect lung cancer metastases with high accuracy and sensitivity, even before they are visible on imaging scans. Such tests are valuable for early intervention and personalized treatment, aiming to improve patient outcomes. Liquid biopsies are also minimally invasive compared to traditional tissue biopsies, which require the removal of a sample of the tumor for further analysis. This makes liquid biopsies a more convenient and less risky option for patients, particularly those who are not good candidates for invasive procedures due to other medical conditions. While liquid biopsies for lung cancer metastases and relapse are still being developed and validated, they hold great promise for improving the detection and treatment of this deadly disease. Herein, we summarize available and novel approaches to liquid biopsy tests for lung cancer metastases and recurrence detection and describe their applications in clinical practice.

Sequence-Based Platforms for Discovering Biomarkers in Liquid Biopsy of Non-Small-Cell Lung Cancer

Lung cancer detection and monitoring are hampered by a lack of sensitive biomarkers, which results in diagnosis at late stages and difficulty in tracking response to treatment. Recent developments have established liquid biopsies as promising non-invasive methods for detecting biomarkers in lung cancer patients. With concurrent advances in high-throughput sequencing technologies and bioinformatics tools, new approaches for biomarker discovery have emerged. In this article, we survey established and emerging biomarker discovery methods using nucleic acid materials derived from bodily fluids in the context of lung cancer. We introduce nucleic acid biomarkers extracted from liquid biopsies and outline biological sources and methods of isolation. We discuss next-generation sequencing (NGS) platforms commonly used to identify novel biomarkers and describe how these have been applied to liquid biopsy. We highlight emerging biomarker discovery methods, including applications of long-read sequencing, fragmentomics, whole-genome amplification methods for single-cell analysis, and whole-genome methylation assays. Finally, we discuss advanced bioinformatics tools, describing methods for processing NGS data, as well as recently developed software tailored for liquid biopsy biomarker detection, which holds promise for early diagnosis of lung cancer.

Treatment of advanced non-small cell lung cancer with driver mutations: current applications and future directions

With the improved understanding of driver mutations in non-small cell lung cancer (NSCLC), expanding the targeted therapeutic options improved the survival and safety. However, responses to these agents are commonly temporary and incomplete. Moreover, even patients with the same oncogenic driver gene can respond diversely to the same agent. Furthermore, the therapeutic role of immune-checkpoint inhibitors (ICIs) in oncogene-driven NSCLC remains unclear. Therefore, this review aimed to classify the management of NSCLC with driver mutations based on the gene subtype, concomitant mutation, and dynamic alternation. Then, we provide an overview of the resistant mechanism of target therapy occurring in targeted alternations ("target-dependent resistance") and in the parallel and downstream pathways ("target-independent resistance"). Thirdly, we discuss the effectiveness of ICIs for NSCLC with driver mutations and the combined therapeutic approaches that might reverse the immunosuppressive tumor immune microenvironment. Finally, we listed the emerging treatment strategies for the new oncogenic alternations, and proposed the perspective of NSCLC with driver mutations. This review will guide clinicians to design tailored treatments for NSCLC with driver mutations.

Computational Methods for Single-cell DNA Methylome Analysis

Dissecting intercellular epigenetic differences is key to understanding tissue heterogeneity. Recent advances in single-cell DNA methylome profiling have presented opportunities to resolve this heterogeneity at the maximum resolution. While these advances enable us to explore frontiers of chromatin biology and better understand cell lineage relationships, they pose new challenges in data processing and interpretation. This review surveys the current state of computational tools developed for single-cell DNA methylome data analysis. We discuss critical components of single-cell DNA methylome data analysis, including data preprocessing, quality control, imputation, dimensionality reduction, cell clustering, supervised cell annotation, cell lineage reconstruction, gene activity scoring, and integration with transcriptome data. We also highlight unique aspects of single-cell DNA methylome data analysis and discuss how techniques common to other single-cell omics data analyses can be adapted to analyze DNA methylomes. Finally, we discuss existing challenges and opportunities for future development.

Biology, vulnerabilities and clinical applications of circulating tumour cells

In recent years, exceptional technological advances have enabled the identification and interrogation of rare circulating tumour cells (CTCs) from blood samples of patients, leading to new fields of research and fostering the promise for paradigm-changing, liquid biopsy-based clinical applications. Analysis of CTCs has revealed distinct biological phenotypes, including the presence of CTC clusters and the interaction between CTCs and immune or stromal cells, impacting metastasis formation and providing new insights into cancer vulnerabilities. Here we review the progress made in understanding biological features of CTCs and provide insight into exploiting these developments to design future clinical tools for improving the diagnosis and treatment of cancer.

Incorporating cell hierarchy to decipher the functional diversity of single cells

Cells possess functional diversity hierarchically. However, most single-cell analyses neglect the nested structures while detecting and visualizing the functional diversity. Here, we incorporate cell hierarchy to study functional diversity at subpopulation, club (i.e., sub-subpopulation), and cell layers. Accordingly, we implement a package, SEAT, to construct cell hierarchies utilizing structure entropy by minimizing the global uncertainty in cell-cell graphs. With cell hierarchies, SEAT deciphers functional diversity in 36 datasets covering scRNA, scDNA, scATAC, and scRNA-scATAC multiome. First, SEAT finds optimal cell subpopulations with high clustering accuracy. It identifies cell types or fates from omics profiles and boosts accuracy from 0.34 to 1. Second, SEAT detects insightful functional diversity among cell clubs. The hierarchy of breast cancer cells reveals that the specific tumor cell club drives AREG-EGFT signaling. We identify a dense co-accessibility network of cis-regulatory elements specified by one cell club in GM12878. Third, the cell order from the hierarchy infers periodic pseudo-time of cells, improving accuracy from 0.79 to 0.89. Moreover, we incorporate cell hierarchy layers as prior knowledge to refine nonlinear dimension reduction, enabling us to visualize hierarchical cell layouts in low-dimensional space.

Current Status of Circulating Tumor Cells in Head and Neck Squamous Cell Carcinoma: A Review

OBJECTIVE

Circulating tumor cells (CTCs) are found in the blood of patients with cancer, including head and neck squamous cell carcinomas (HNSCCs). The aim is to review the most up-to-date status of CTCs for applications in patients with HNSCC.

DATA SOURCES

English articles in PubMed.

REVIEW METHODS

All the studies on CTCs in HNSCCs in the literature were reviewed.

CONCLUSIONS

There is emerging information on the diagnostic and prognostic value of CTCs in HNSCCs. Evidence also highlights the advantages of various downstream analysis approaches over circulating tumor DNA (ctDNA), such as single-CTC analysis, ex vivo, and in vivo expansion of CTCs. Multiple phenotypic surface markers (cytokeratins, EpCAM, vimentin, etc.), used for CTCs characterization using different immunoassays, could predict disease progression as well as patients' response to treatment efficacy. Immune checkpoint inhibitors' status in CTCs could also provide better insight into treatment. Clonal expansion of CTCs and single-cell analysis of CTCs are the most emerging fields nowadays which may offer an understanding of the mechanism of tumor evolution as well as therapeutic efficacy. Although several clinical trials are ongoing, limitations still exist in the detection and characterization of CTCs. Due to the lack of a gold standard protocol, the sensitivity and specificity of CTC enumeration methods vary.

IMPLICATIONS FOR PRACTICE

Prospective clinical trials are still needed before CTCs can be employed as diagnostic and prognostic markers in the clinical management of patients with HNSCC.

The diagnostic significance of cerebrospinal fluid cytology and circulating tumor DNA in meningeal carcinomatosis

Objective

The objective of this research is to investigate the clinical application value of cerebrospinal fluid (CSF) cytology and circulating tumor DNA (ctDNA) in lung adenocarcinoma (LUAD) meningeal metastasis-meningeal carcinomatosis (MC), and to further explore the possible molecular mechanisms and drug treatment targets of LUAD meningeal metastasis by next-generation sequencing (NGS).

Methods

We retrospectively analyzed LUAD with MC in 52 patients. CSF cytology was carried out using the slide centrifugation precipitation method and May-Grüwald-Giemsa (MGG) staining. Tumor tissue, plasma and CSF ctDNA of some MC patients were detected by NGS.

Results

Of the 52 MC patients, 46 (88.46%) were positive for CSF cytology and 34 (65.38%) were positive for imaging, with statistically significant differences in diagnostic positivity (P < 0.05). In 32 of these patients, CSF cytology, cerebrospinal fluid ctDNA, plasma ctDNA and MRI examination were performed simultaneously, and the positive rates were 84.38, 100, 56.25, and 62.50% respectively, the difference was statistically significant (P < 0.001). Analysis of the NGS profiles of tumor tissues, plasma and CSF of 12 MC patients: the mutated gene with the highest detection rate was epidermal growth factor receptor (EGFR) and the detection rate were 100, 58.33, and 100% respectively in tumor tissues, plasma and CSF, and there were 6 cases of concordance between plasma and tissue EGFR mutation sites, with a concordance rate of 50.00%, and 12 cases of concordance between CSF and tissue EGFR mutation sites, with a concordance rate of 100%. In addition, mutations not found in tissue or plasma were detected in CSF: FH mutation, SETD2 mutation, WT1 mutation, CDKN2A mutation, CDKN2B mutation, and multiple copy number variants (CNV), with the most detected being CDKN2A mutation and MET amplification.

Conclusion

CSF cytology is more sensitive than traditional imaging in the diagnosis of meningeal carcinomatosis and has significant advantages in the early screening and diagnosis of MC patients. CSF ctDNA can be used as a complementary diagnostic method to negative results of CSF cytology and MRI, and CSF ctDNA can be used as an important method for liquid biopsy of patients with MC, which has important clinical significance in revealing the possible molecular mechanisms and drug treatment targets of meningeal metastasis of LUAD.

Single-Cell DNA Methylation Analysis in Cancer

Morphological, transcriptomic, and genomic defects are well-explored parameters of cancer biology. In more recent years, the impact of epigenetic influences, such as DNA methylation, is becoming more appreciated. Aberrant DNA methylation has been implicated in many types of cancers, influencing cell type, state, transcriptional regulation, and genomic stability to name a few. Traditionally, large populations of cells from the tissue of interest are coalesced for analysis, producing averaged methylome data. Considering the inherent heterogeneity of cancer, analysing populations of cells as a whole denies the ability to discover novel aberrant methylation patterns, identify subpopulations, and trace cell lineages. Due to recent advancements in technology, it is now possible to obtain methylome data from single cells. This has both research and clinical implications, ranging from the identification of biomarkers to improved diagnostic tools. As with all emerging technologies, distinct experimental, bioinformatic, and practical challenges present themselves. This review begins with exploring the potential impact of single-cell sequencing on understanding cancer biology and how it could eventually benefit a clinical setting. Following this, the techniques and experimental approaches which made this technology possible are explored. Finally, the present challenges currently associated with single-cell DNA methylation sequencing are described.

Shallow WGS of individual CTCs identifies actionable targets for informing treatment decisions in metastatic breast cancer

BACKGROUND

We report copy-number profiling by low-pass WGS (LP-WGS) in individual circulating tumour cells (CTCs) for guiding treatment in patients with metastatic breast cancer (MBC), comparing CTC results with mutations detected in circulating tumour DNA (ctDNA) in the same blood samples.

METHODS

Across 10 patients with MBC who were progressing at the time of blood sampling and that had >20 CTCs detected by CellSearch®, 63 single cells (50 CTCs and 13 WBCs) and 16 cell pools (8 CTC pools and 8 WBC pools) were recovered from peripheral blood by CellSearch®/DEPArray™ and sequenced with Ampli1 LowPass technology (Menarini Silicon Biosystems). Copy-number aberrations were identified using the MSBiosuite software platform, and results were compared with mutations detected in matched plasma cfDNA analysed by targeted next-generation sequencing using the Oncomine™ Breast cfDNA Assay (Thermo Fisher).

RESULTS

LP-WGS data demonstrated copy-number gains/losses in individual CTCs in regions including FGFR1, JAK2 and CDK6 in five patients, ERBB2 amplification in two HER2-negative patients and BRCA loss in two patients. Seven of eight matched plasmas also had mutations in ctDNA in PIK3CA, TP53, ESR1 and KRAS genes with mutant allele frequencies (MAF) ranging from 0.05 to 33.11%. Combining results from paired CTCs and ctDNA, clinically actionable targets were identified in all ten patients.

CONCLUSION

This combined analysis of CTCs and ctDNA may offer a new approach for monitoring of disease progression and to direct therapy in patients with advanced MBC, at a time when they are coming towards the end of other treatment options.

Noninvasive detection and interpretation of gastrointestinal diseases by collaborative serum metabolite and magnetically controlled capsule endoscopy

Gastrointestinal diseases are complex diseases that occur in the gastrointestinal tract. Common gastrointestinal diseases include chronic gastritis, peptic ulcers, inflammatory bowel disease, and gastrointestinal tumors. These diseases may manifest a long course, difficult treatment, and repeated attacks.

Gastroscopy and mucosal biopsy are the gold standard methods for diagnosing gastric and duodenal diseases, but they are invasive procedures and carry risks due to the necessity of sedation and anesthesia. Recently, several new approaches have been developed, including serological examination and magnetically controlled capsule endoscopy (MGCE). However, serological markers lack lesion information, while MGCE images lack molecular information.

This study proposes combining these two technologies in a collaborative noninvasive diagnostic scheme as an alternative to the standard procedures. We introduce an interpretable framework for the clinical diagnosis of gastrointestinal diseases. Based on collected blood samples and MGCE records of patients with gastrointestinal diseases and comparisons with normal individuals, we selected serum metabolite signatures by bioinformatic analysis, captured image embedding signatures by convolutional neural networks, and inferred the location-specific associations between these signatures.

Our study successfully identified five key metabolite signatures with functional relevance to gastrointestinal disease. The combined signatures achieved discrimination AUC of 0.88. Meanwhile, the image embedding signatures showed different levels of validation and testing accuracy ranging from 0.7 to 0.9 according to different locations in the gastrointestinal tract as explained by their specific associations with metabolite signatures. Overall, our work provides a new collaborative noninvasive identification pipeline and candidate metabolite biomarkers for image auxiliary diagnosis. This method should be valuable for the noninvasive detection and interpretation of gastrointestinal and other complex diseases.

Development and Validation of a DNA Methylation-related Classifier of Circulating Tumour Cells to Predict Prognosis and to provide a therapeutic strategy in Lung Adenocarcinoma

Background: A significant factor influencing the prognosis of lung adenocarcinoma (LUAD) is tumor metastasis. Studies have shown that abnormal DNA methylation in circulating tumor cells (CTCs) is associated with tumour metastasis. Based on the genes expressed in CTCs that play an important role in DNA methylation, we hope to build a risk model to predict prognosis and provide a therapeutic strategy in LUAD.

Methods: The CTC sequencing data for LUAD were obtained from GSE74639, which contains 10 CTC samples and 6 primary tumour samples. To carefully assess the clinical value, functional status, involvement of the tumor microenvironment (TME) based on the risk model, and genetic variants based on based on data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO), a reliable risk model was successfully built.

Results: Three differentially methylated genes (DMGs) of CTCs for LUAD, including mitochondrial ribosomal protein L51 (MRPL51), STE20-like kinase (SLK), and protein regulator of cytokinesis 1(PRC1), were effectively used to construct a risk model. Both the training and validation cohorts' stability and accuracy of the risk model were evaluated. Each patient in the TCGA-LUAD cohort received a risk score, and based on the median score, they were divided into high- and low-risk groups. The tumors in the high-risk group in this study were classified as "cold" and immunosuppressed, which may be linked to a poor prognosis. The tumors in the low-risk group, however, were deemed "hot" and had immune hyperfunction linked to a positive prognosis. Additionally, patients in the low-risk group showed greater sensitivity to immunotherapy than those in the high-risk group.

Conclusions: Based on DMGs of CTCs from LUAD, we successfully developed a predictive risk model and discovered differences in biological function, TME, genetic variation, and clinical outcomes between those at high and low risk group.

[Research Progress on the Application of Liquid Biopsy in the Diagnosis and Treatment of Small Cell Lung Cancer]

Small cell lung cancer (SCLC) is a malignant tumor with strong invasiveness and high mortality. It has the characteristics of easy metastasis, fast growth, high degree of malignancy and strong invasiveness. The prognosis of patients is generally poor. The current clinical diagnosis of SCLC is mainly based on tissue biopsy, which is invasive, long cycle time and high cost. In recent years, liquid biopsy has been gradually applied because of its non-invasive, comprehensive and real-time characteristics that traditional tissue biopsy does not have. The main detection objects of liquid biopsy include circulating tumor DNA (ctDNA), circulating tumor cells (CTCs) and exosomes in peripheral blood. The application of liquid biopsy in the clinical treatment of SCLC will help clinicians to improve the detailed diagnosis of SCLC patients, as well as the timely control and response to the treatment response of patients.
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Single-Circulating Tumor Cell Whole Genome Amplification to Unravel Cancer Heterogeneity and Actionable Biomarkers

The field of single-cell analysis has advanced rapidly in the last decade and is providing new insights into the characterization of intercellular genetic heterogeneity and complexity, especially in human cancer. In this regard, analyzing single circulating tumor cells (CTCs) is becoming particularly attractive due to the easy access to CTCs from simple blood samples called “liquid biopsies”. Analysis of multiple single CTCs has the potential to allow the identification and characterization of cancer heterogeneity to guide best therapy and predict therapeutic response. However, single-CTC analysis is restricted by the low amounts of DNA in a single cell genome. Whole genome amplification (WGA) techniques have emerged as a key step, enabling single-cell downstream molecular analysis. Here, we provide an overview of recent advances in WGA and their applications in the genetic analysis of single CTCs, along with prospective views towards clinical applications. First, we focus on the technical challenges of isolating and recovering single CTCs and then explore different WGA methodologies and recent developments which have been utilized to amplify single cell genomes for further downstream analysis. Lastly, we list a portfolio of CTC studies which employ WGA and single-cell analysis for genetic heterogeneity and biomarker detection.

Interrogating breast cancer heterogeneity using single and pooled circulating tumor cell analysis

Single cell technologies allow the interrogation of tumor heterogeneity, providing insights into tumor evolution and treatment resistance. To better understand whether circulating tumor cells (CTCs) could complement metastatic biopsies for tumor genomic profiling, we characterized 11 single CTCs and 10 pooled CTC samples at the mutational and copy number aberration (CNA) levels, and compared these results with matched synchronous tumor biopsies from 3 metastatic breast cancer patients with triple-negative (TNBC), HER2-positive and estrogen receptor-positive (ER+) tumors. Similar CNA profiles and the same patient-specific driver mutations were found in bulk tissue and CTCs for the HER2-positive and TNBC tumors, whereas different CNA profiles and driver mutations were identified for the ER+ tumor, which presented two distinct clones in CTCs defined by mutations in ESR1 Y537N and TP53, respectively. Furthermore, de novo mutational signatures derived from CTCs described patient-specific biological processes. These data suggest that tumor tissue and CTCs provide complementary clinically relevant information to map tumor heterogeneity and tumor evolution.

Whole exome analysis reveals the genomic profiling related to chemo-resistance in Chinese population with limited-disease small cell lung cancer

PURPOSE

The mechanism of chemo-resistance in small cell lung cancer (SCLC) is unclear. This study aims to explore the resistance-related genomic profiles of residual tumors after neo-adjuvant chemotherapy (NAC) in SCLC through the whole-exome sequencing (WES).

EXPERIMENTAL DESIGN

A total of 416 limited diseases (LD) SCLC patients underwent surgery were retrospectively analyzed, of which 40 patients received NAC. Then we selected 29 patients undergoing NAC (n = 19) and chemotherapy naïve (CTN, n = 10) to perform WES sequence with formalin-fixed paraffin-embedded samples including tumor and paired para-tumor.

RESULTS

In total, single nucleotide variation and mutation rate were similar between NAC and CTN groups. The mutation signatures were significantly discrepant between NAC and CTN groups, as well as among patients with partial response (PR), stable disease (SD), and progressive disease. There were more copy number variation deletions in NAC group compared with CTN group. The inactivation of TP53 and RB1 were the most significantly events in both NAC and CTN groups. RB1 nonsense mutations were recurrent in NAC group (9/19 vs. 0/9, 47.4% vs. 0%) with favorable survival, while the frame-shift deletions were frequent in CTN group (3/9 vs. 3/19, 33.3% vs.15.8%). Integrated function enrichment revealed that the frequently mutant genes were involved in cell cycle, metabolic reprogramming, and oncogenic signaling pathways in NAC group, such as BTG2 pathway, glycolysis in senescence and P53 pathway. A total of 27 genes presented frequently mutant in NAC group and might played a positive role in drug resistance. Multiple genes including BRINP3, MYH6, ST18, and PCHD15, which were associated with prognosis, occurred mutant frequently in PR and SD groups.

CONCLUSION

Residual tumors after neo-adjuvant therapy exhibited different mutation signature spectrum. Multiple genes including RB1 nonsense mutations, BRINP3, MYH6, ST18, and PCHD15 were with frequent mutation in residual tumors, which might participate chemo-resistance and influenced the prognosis in patients with limited disease SCLC.

HBV genome-enriched single cell sequencing revealed heterogeneity in HBV-driven hepatocellular carcinoma (HCC)

BACKGROUND

Hepatitis B virus (HBV) related hepatocellular carcinoma (HCC) is heterogeneous and frequently contains multifocal tumors, but how the multifocal tumors relate to each other in terms of HBV integration and other genomic patterns is not clear.

METHODS

To interrogate heterogeneity of HBV-HCC, we developed a HBV genome enriched single cell sequencing (HGE-scSeq) procedure and a computational method to identify HBV integration sites and infer DNA copy number variations (CNVs).

RESULTS

We performed HGE-scSeq on 269 cells from four tumor sites and two tumor thrombi of a HBV-HCC patient. HBV integrations were identified in 142 out of 269 (53%) cells sequenced, and were enriched in two HBV integration hotspots chr1:34,397,059 (CSMD2) and chr8:118,557,327 (MED30/EXT1). There were also 162 rare integration sites. HBV integration sites were enriched in DNA fragile sites and sequences around HBV integration sites were enriched for microhomologous sequences between human and HBV genomes. CNVs were inferred for each individual cell and cells were grouped into four clonal groups based on their CNVs. Cells in different clonal groups had different degrees of HBV integration heterogeneity. All of 269 cells carried chromosome 1q amplification, a recurrent feature of HCC tumors, suggesting that 1q amplification occurred before HBV integration events in this case study. Further, we performed simulation studies to demonstrate that the sequential events (HBV infecting transformed cells) could result in the observed phenotype with biologically reasonable parameters.

CONCLUSION

Our HGE-scSeq data reveals high heterogeneity of HCC tumor cells in terms of both HBV integrations and CNVs. There were two HBV integration hotspots across cells, and cells from multiple tumor sites shared some HBV integration and CNV patterns.

Somatic variant calling from single-cell DNA sequencing data

Single-cell sequencing has gained popularity in recent years. Despite its numerous applications, single-cell DNA sequencing data is highly error-prone due to technical biases arising from uneven sequencing coverage, allelic dropout, and amplification error. With these artifacts, the identification of somatic genomic variants becomes a challenging task, and over the years, several methods have been developed explicitly for this type of data. Single-cell variant callers implement distinct strategies, make different use of the data, and typically result in many discordant calls when applied to real data. Here, we review current approaches for single-cell variant calling, emphasizing single nucleotide variants. We highlight their potential benefits and shortcomings to help users choose a suitable tool for their data at hand.

Decoding Lung Cancer at Single-Cell Level

Lung cancer is the leading cause of cancer death due to its high degree of malignancy, rapid growth, and early metastasis. Recent studies have found that lung cancer has a high degree of heterogeneity which is characterized by the mixture of different tumor cell types. However, the driving genetic/epigenetic mechanism of lung cancer heterogeneity, how different types of cells interact, and the relationship between heterogeneity and drug resistance have been poorly understood. Single-cell technology can decompose high throughput sequencing information into each cell and provide single-cell information in high resolution. By using single-cell analysis, researchers can not only fully understand the molecular characteristics of different cell types in the same tissue, but also define completely new cell types. Thus, single-cell analysis has been widely utilized in systems biology, drug discovery, disease diagnosis and precision medicine. We review recent exploration of the mechanism of heterogeneity, tumor microenvironment and drug resistance in lung cancer by using single-cell analysis. We propose that the recent findings may pave new ways for the treatment strategies of lung cancer.

Single-cell sequencing: a promising approach for uncovering the mechanisms of tumor metastasis

Single-cell sequencing (SCS) is an emerging high-throughput technology that can be used to study the genomics, transcriptomics, and epigenetics at a single cell level. SCS is widely used in the diagnosis and treatment of various diseases, including cancer. Over the years, SCS has gradually become an effective clinical tool for the exploration of tumor metastasis mechanisms and the development of treatment strategies. Currently, SCS can be used not only to analyze metastasis-related malignant biological characteristics, such as tumor heterogeneity, drug resistance, and microenvironment, but also to construct metastasis-related cell maps for predicting and monitoring the dynamics of metastasis. SCS is also used to identify therapeutic targets related to metastasis as it provides insights into the distribution of tumor cell subsets and gene expression differences between primary and metastatic tumors. Additionally, SCS techniques in combination with artificial intelligence (AI) are used in liquid biopsy to identify circulating tumor cells (CTCs), thereby providing a novel strategy for treating tumor metastasis. In this review, we summarize the potential applications of SCS in the field of tumor metastasis and discuss the prospects and limitations of SCS to provide a theoretical basis for finding therapeutic targets and mechanisms of metastasis.

Nanotechnology Tools Enabling Biological Discovery

Over the years, the engineering aspect of nanotechnology has been significantly exploited. Medical intervention strategies have been developed by leveraging existing molecular biology knowledge and combining it with nanotechnology tools to improve outcomes. However, little attention has been paid to harnessing the strengths of nanotechnology as a biological discovery tool. Fundamental understanding of controlling dynamic biological processes at the subcellular level is key to developing personalized therapeutic and diagnostic interventions. Single-cell analyses using intravital microscopy, expansion microscopy, and microfluidic-based platforms have been helping to better understand cell heterogeneity in healthy and diseased cells, a major challenge in oncology. Also, single-cell analysis has revealed critical signaling pathways and biological intracellular components with key biological functions. The physical manipulation enabled by nanotools can allow real-time monitoring of biological changes at a single-cell level by sampling intracellular fluid from the same cell. The formation of intercellular highways by nanotube-like structures has important clinical implications such as metastasis development. The integration of nanomaterials into optical and molecular imaging techniques has rendered valuable morphological, structural, and biological information. Nanoscale imaging unravels mechanisms of temporality by enabling the visualization of nanoscale dynamics never observed or measured between individual cells with standard biological techniques. The exceptional sensitivity of nanozymes, artificial enzymes, make them perfect components of the next-generation mobile diagnostics devices. Here, we highlight these impactful cancer-related biological discoveries enabled by nanotechnology and producing a paradigm shift in cancer research and oncology.

Single-Cell Sequencing and Its Applications in Liver Cancer

As one of the most lethal cancers, primary liver cancer (PLC) has high tumor heterogeneity, including the heterogeneity between cancer cells. Traditional methods which have been used to identify tumor heterogeneity for a long time are based on large mixed cell samples, and the research results usually show average level of the cell population, ignoring the heterogeneity between cancer cells. In recent years, single-cell sequencing has been increasingly applied to the studies of PLCs. It can detect the heterogeneity between cancer cells, distinguish each cell subgroup in the tumor microenvironment (TME), and also reveal the clonal characteristics of cancer cells, contributing to understand the evolution of tumor. Here, we introduce the process of single-cell sequencing, review the applications of single-cell sequencing in the heterogeneity of cancer cells, TMEs, oncogenesis, and metastatic mechanisms of liver cancer, and discuss some of the current challenges in the field.

EGFR signaling pathway as therapeutic target in human cancers

Epidermal Growth Factor Receptor (EGFR) enacts major roles in the maintenance of epithelial tissues. However, when EGFR signaling is altered, it becomes the grand orchestrator of epithelial transformation, and hence one of the most world-wide studied tyrosine kinase receptors involved in neoplasia, in several tissues. In the last decades, EGFR-targeted therapies shaped the new era of precision-oncology. Despite major advances, the dream of converting solid tumors into a chronic disease is still unfulfilled, and long-term remission eludes us. Studies investigating the function of this protein in solid malignancies have revealed numerous ways how tumor cells dysregulate EGFR function. Starting from preclinical models (cell lines, organoids, murine models) and validating in clinical specimens, EGFR-related oncogenic pathways, mechanisms of resistance, and novel avenues to inhibit tumor growth and metastatic spread enriching the therapeutic portfolios, were identified. Focusing on non-small cell lung cancer (NSCLC), where EGFR mutations are major players in the adenocarcinoma subtype, we will go over the most relevant discoveries that led us to understand EGFR and beyond, and highlight how they revolutionized cancer treatment by expanding the therapeutic arsenal at our disposal.

Isolation and Genomic Analysis of Single Circulating Tumor Cell Using Human Telomerase Reverse Transcriptase and Desmoglein-2

As epithelial cells in the circulation are considered to originate from the tumor, the epithelial cell adhesion molecule has been commonly used as a standard marker for circulating tumor cells (CTCs) isolation. However, it seems to disappear after the epithelial-mesenchymal transition that most cancer cells undergo for intravasation. Thus, more advanced techniques for CTC detection are needed to better understand the clinical significance of CTCs. A cancer cell-specifically-infecting or replicating virus that codes a fluorescent monitor gene can be a solution to efficiently detect CTCs. Thus, the authors designed an adenovirus to bind to desmoglein-2, which is highly expressed in most cancer cells. A cancer-specific human telomerase reverse transcriptase promoter is inserted to control a viral E1 region. The adenovirus is utilized to compare the number of CTCs from renal cell carcinoma and prostate cancer patients before and after surgery. The isolated two or three CTCs are eligible for whole genome sequencing. The genomic analysis proves the difference of variants between primary tumors and CTCs. Taken together, it is a fast and exact serial method for CTC isolation and the enriched genome sequencing may be used to determine the prognosis and as a point-of-care system for patients with cancer.

Nanomaterial-Based Immunocapture Platforms for the Recognition, Isolation, and Detection of Circulating Tumor Cells

Circulating tumor cells (CTCs) are a type of cancer cells that circulate in the peripheral blood after breaking away from solid tumors and are essential for the establishment of distant metastasis. Up to 90% of cancer-related deaths are caused by metastatic cancer. As a new type of liquid biopsy, detecting and analyzing CTCs will provide insightful information for cancer diagnosis, especially the in-time disease status, which would avoid some flaws and limitations of invasive tissue biopsy. However, due to the extremely low levels of CTCs among a large number of hematologic cells, choosing immunocapture platforms for CTC detection and isolation will achieve good performance with high purity, selectivity, and viability. These properties are directly associated with precise downstream analysis of CTC profiling. Recently, inspired by the nanoscale interactions of cells in the tissue microenvironment, platforms based on nanomaterials have been widely explored to efficiently enrich and sensitively detect CTCs. In this review, various immunocapture platforms based on different nanomaterials for efficient isolation and sensitive detection of CTCs are outlined and discussed. First, the design principles of immunoaffinity nanomaterials are introduced in detail. Second, the immunocapture and release of platforms based on nanomaterials ranging from nanoparticles, nanostructured substrates, and immunoaffinity microfluidic chips are summarized. Third, recent advances in single-cell release and analysis of CTCs are introduced. Finally, some perspectives and challenges are provided in future trends of CTC studies.

Biosensors for circulating tumor cells (CTCs)-biomarker detection in lung and prostate cancer: Trends and prospects

Cancer is one of the leading cause of death worldwide. Lung cancer (LCa) and prostate cancer (PCa) are the two most common ones particularly among men with about 20% of aggressive metastatic form leading to shorter overall survival. In recent years, circulating tumor cells (CTCs) have been investigated extensively for their role in metastatic progression and their involvement in reduced overall survival and treatment responses. Analysis of these cells and their associated biomarkers as "liquid biopsy" can provide valuable real-time information regarding the disease state and can be a potential avenue for early-stage detection and possible selection of personalized treatments. This review focuses on the role of CTCs and their associated biomarkers in lung and prostate cancer, as well as the shortcomings of conventional methods for their isolation and analysis. To overcome these drawbacks, biosensors are an elegant alternative because they are capable of providing valuable multiplexed information in real-time and analyzing biomarkers at lower concentrations. A comparative analysis of different transducing elements specific for the analysis of cancer cell and cancer biomarkers have been compiled in this review.

Sensitivity to copy number variation analysis in single cell genomics

While previous studies have given some guidance, the sensitivity of copy number calling in single-cell genomics is still not comprehensive. We studied the impact of sequencing depth and other factors on single-cell copy number analysis. Sequencing Data from 26 datasets were retrieved, and 2946 single cells passed the filter. Thirty-eight single cells were independently downscaled to evaluate copy number variation (CNV) detection sensitivity at different bin sizes. The sensitivity of whole genome amplification (WGA) approaches and cell types to CNV calling were evaluated using downsampling of 101 and 70 cells. Cluster analysis based on t-SNE was executed to evaluate CNV calling performance. Our results suggest 0.75× sequencing depth with moderate resolution (250 kb bin size) may be a practical guideline considering both sequencing cost and performance of copy number calling, which can be appropriately optimized based on amplification approach, cell type, and sample complexity.

Relevance of Circulating Tumor Cells as Predictive Markers for Cancer Incidence and Relapse

Shedding of cancer cells from the primary site or undetectable bone marrow region into the circulatory system, resulting in clinically overt metastasis or dissemination, is the hallmark of unfavorable invasive cancers. The shed cells remain in circulation until they extravasate to form a secondary metastatic lesion or undergo anoikis. The circulating tumor cells (CTCs) found as single cells or clusters carry a plethora of information, are acknowledged as potential biomarkers for predicting cancer prognosis and cancer progression, and are supposed to play key roles in determining tailored therapies for advanced diseases. With the advent of novel technologies that allow the precise isolation of CTCs, more and more clinical trials are focusing on the prognostic and predictive potential of CTCs. In this review, we summarize the role of CTCs as a predictive marker for cancer incidence, relapse, and response to therapy.

Circulating tumor cells: biology and clinical significance

Circulating tumor cells (CTCs) are tumor cells that have sloughed off the primary tumor and extravasate into and circulate in the blood. Understanding of the metastatic cascade of CTCs has tremendous potential for the identification of targets against cancer metastasis. Detecting these very rare CTCs among the massive blood cells is challenging. However, emerging technologies for CTCs detection have profoundly contributed to deepening investigation into the biology of CTCs and have facilitated their clinical application. Current technologies for the detection of CTCs are summarized herein, together with their advantages and disadvantages. The detection of CTCs is usually dependent on molecular markers, with the epithelial cell adhesion molecule being the most widely used, although molecular markers vary between different types of cancer. Properties associated with epithelial-to-mesenchymal transition and stemness have been identified in CTCs, indicating their increased metastatic capacity. Only a small proportion of CTCs can survive and eventually initiate metastases, suggesting that an interaction and modulation between CTCs and the hostile blood microenvironment is essential for CTC metastasis. Single-cell sequencing of CTCs has been extensively investigated, and has enabled researchers to reveal the genome and transcriptome of CTCs. Herein, we also review the clinical applications of CTCs, especially for monitoring response to cancer treatment and in evaluating prognosis. Hence, CTCs have and will continue to contribute to providing significant insights into metastatic processes and will open new avenues for useful clinical applications.

The Single-Cell Sequencing: A Dazzling Light Shining on the Dark Corner of Cancer

Tumorigenesis refers to the process of clonal dysplasia that occurs due to the collapse of normal growth regulation in cells caused by the action of various carcinogenic factors. These “successful” tumor cells pass on the genetic templates to their generations in evolutionary terms, but they also constantly adapt to ever-changing host environments. A unique peculiarity known as intratumor heterogeneity (ITH) is extensively involved in tumor development, metastasis, chemoresistance, and immune escape. An understanding of ITH is urgently required to identify the diversity and complexity of the tumor microenvironment (TME), but achieving this understanding has been a challenge. Single-cell sequencing (SCS) is a powerful tool that can gauge the distribution of genomic sequences in a single cell and the genetic variability among tumor cells, which can improve the understanding of ITH. SCS provides fundamental ideas about existing diversity in specific TMEs, thus improving cancer diagnosis and prognosis prediction, as well as improving the monitoring of therapeutic response. Herein, we will discuss advances in SCS and review SCS application in tumors based on current evidence.

Progress and application of circulating tumor cells in non-small cell lung cancer

Non-small cell lung cancer (NSCLC) has the highest morbidity and mortality worldwide among malignant tumors. NSCLC is a great threat to health and well-being. Biopsy is the gold standard to diagnose lung cancer, but traditional biopsy methods cannot fully reflect the true condition of tumors. There is growing evidence that a single-point biopsy fails to reveal the complete landscape of the tumor due to intratumor heterogeneity, but it is impractical to complete multiple biopsies that are separated both spatially and temporally. Liquid biopsy heralds that a new era is coming. Circulating tumor cells (CTCs) are tumor cells that circulate in the peripheral blood after being shed from primary or metastatic tumors. CTCs constitute a considerable portion of a liquid biopsy, which contributes to the diagnosis, assessment of prognosis, and therapy of NSCLC. Herein, this review discusses the technologies for detection and enrichment of CTCs as well as clinical applications involving CTCs.

Applications of Single-Cell DNA Sequencing

Over the past decade, genomic analyses of single cells-the fundamental units of life-have become possible. Single-cell DNA sequencing has shed light on biological questions that were previously inaccessible across diverse fields of research, including somatic mutagenesis, organismal development, genome function, and microbiology. Single-cell DNA sequencing also promises significant future biomedical and clinical impact, spanning oncology, fertility, and beyond. While single-cell approaches that profile RNA and protein have greatly expanded our understanding of cellular diversity, many fundamental questions in biology and important biomedical applications require analysis of the DNA of single cells. Here, we review the applications and biological questions for which single-cell DNA sequencing is uniquely suited or required. We include a discussion of the fields that will be impacted by single-cell DNA sequencing as the technology continues to advance.

Using single-cell sequencing technology to detect circulating tumor cells in solid tumors

Circulating tumor cells are tumor cells with high vitality and high metastatic potential that invade and shed into the peripheral blood from primary solid tumors or metastatic foci. Due to the heterogeneity of tumors, it is difficult for high-throughput sequencing analysis of tumor tissues to find the genomic characteristics of low-abundance tumor stem cells. Single-cell sequencing of circulating tumor cells avoids interference from tumor heterogeneity by comparing the differences between single-cell genomes, transcriptomes, and epigenetic groups among circulating tumor cells, primary and metastatic tumors, and metastatic lymph nodes in patients' peripheral blood, providing a new perspective for understanding the biological process of tumors. This article describes the identification, biological characteristics, and single-cell genome-wide variation in circulating tumor cells and summarizes the application of single-cell sequencing technology to tumor typing, metastasis analysis, progression detection, and adjuvant therapy.