Cellular heterogeneity and molecular evolution in cancer

Sensing, Imaging, and Therapeutic Strategies Endowing by Conjugate Polymers for Precision Medicine

Conjugated polymers (CPs) have promising applications in biomedical fields, such as disease monitoring, real-time imaging diagnosis, and disease treatment. As a promising luminescent material with tunable emission, high brightness and excellent stability, CPs are widely used as fluorescent probes in biological detection and imaging. Rational molecular design and structural optimization have broadened absorption/emission range of CPs, which are more conductive for disease diagnosis and precision therapy. This review provides a comprehensive overview of recent advances in the application of CPs, aiming to elucidate their structural and functional relationships. The fluorescence properties of CPs and the mechanism of detection signal amplification are first discussed, followed by an elucidation of their emerging applications in biological detection. Subsequently, CPs-based imaging systems and therapeutic strategies are illustrated systematically. Finally, recent advancements in utilizing CPs as electroactive materials for bioelectronic devices are also investigated. Moreover, the challenges and outlooks of CPs for precision medicine are discussed. Through this systematic review, it is hoped to highlight the frontier progress of CPs and promote new breakthroughs in fundamental research and clinical transformation.

Small cells - big issues: biological implications and preclinical advancements in small cell lung cancer

Current treatment guidelines refer to small cell lung cancer (SCLC), one of the deadliest human malignancies, as a homogeneous disease. Accordingly, SCLC therapy comprises chemoradiation with or without immunotherapy. Meanwhile, recent studies have made significant advances in subclassifying SCLC based on the elevated expression of the transcription factors ASCL1, NEUROD1, and POU2F3, as well as on certain inflammatory characteristics. The role of the transcription regulator YAP1 in defining a unique SCLC subset remains to be established. Although preclinical analyses have described numerous subtype-specific characteristics and vulnerabilities, the so far non-existing clinical subtype distinction may be a contributor to negative clinical trial outcomes. This comprehensive review aims to provide a framework for the development of novel personalized therapeutic approaches by compiling the most recent discoveries achieved by preclinical SCLC research. We highlight the challenges faced due to limited access to patient material as well as the advances accomplished by implementing state-of-the-art models and methodologies.

Multidimensional progressive single-cell sequencing reveals cell microenvironment composition and cancer heterogeneity in lung cancer

Despite substantial advances in cancer biology and treatment, the clinical outcomes of patients with lung cancer remain unsatisfactory. The tumor microenvironment (TME) is a potential target. Using single-cell RNA sequencing, we could distinguish eight distinct cell types in the lung cancer microenvironment, demonstrating substantial intratumoral heterogeneity in 19 different lung cancer tumor samples. Through the re-dimensional grouping of cancer-associated fibroblasts (CAFs), myeloid cells, epithelial cells, natural killer (NK) cells, and T cells, the difference in the TME of lung cancer was revealed. We discovered SFTPB, SFN, and KRT8 as possible predictive biomarkers for lung cancer by assessing the gene expression patterns in epithelial cells. Examining cell-to-cell communications showed a robust association between the quantity of matrix CAFs, epithelial cells, and macrophages in the thrombospondin signaling pathway. Additionally, we found that the amyloid precursor protein signaling pathway primarily originated from the matrix, and inflammatory cancer-associated endothelial and fibroblast cells showed a co-expression relationship with myeloid cells and B cells. Through cell-to-cell correlation analysis, we found positive regulation between NK cells, regulatory T cells, GZMB-CD8 T cells, and GZMK-CD8 T cells, which could play a role in developing immune TMEs. These findings support studies on cancer heterogeneity and add to our understanding of lung cancer's cellular microenvironment.

Measuring Drug Response with Single-Cell Growth Rate Quantification

Intratumoral heterogeneity is a substantial cause of drug resistance development during chemotherapy or other drug treatments for cancer. Therefore, monitoring and measuring cell exposure and response to drugs at the single-cell level are crucial. Previous research suggested that the single-cell growth rate can be used to investigate drug-cell interactions. However, currently established methods for quantifying single-cell growth are limited to isolated or monolayer cells. Here, we introduce a technique that accurately measures both 2D and 3D cell growth rates using label-free ratiometric stimulated Raman scattering (SRS) microscopy. We use deuterated amino acids, leucine, isoleucine, and valine, as tracers and measure the C-D SRS signal from deuterium-labeled proteins and the C-H SRS signal from unlabeled proteins simultaneously to determine the cell growth rate at the single-cell level. The technique offers single-cell level drug sensitivity measurement with a shorter turnaround time (within 12 h) than most traditional assays. The submicrometer resolution of the imaging technique allows us to examine the effects of chemotherapeutic drugs, including kinase inhibitors, mitotic inhibitors, and topoisomerase II inhibitors, on both the cell growth rate and morphology. The capability of quantifying 3D cell growth rates provides insight into a deeper understanding of the cell-drug interaction in the actual tumor environment.

Single-cell Analysis Reveals Inter- and Intratumour Heterogeneity in Metastatic Breast Cancer

Metastasis is the leading cause of cancer-related deaths of breast cancer patients. Some cancer cells in a tumour go through successive steps, referred to as the metastatic cascade, and give rise to metastases at a distant site. We know that the plasticity and heterogeneity of cancer cells play critical roles in metastasis but the precise underlying molecular mechanisms remain elusive. Here we aimed to identify molecular mechanisms of metastasis during colonization, one of the most important yet poorly understood steps of the cascade. We performed single-cell RNA-Seq (scRNA-Seq) on tumours and matched lung macrometastases of patient-derived xenografts of breast cancer. After correcting for confounding factors such as the cell cycle and the percentage of detected genes (PDG), we identified cells in three states in both tumours and metastases. Gene-set enrichment analysis revealed biological processes specific to proliferation and invasion in two states. Our findings suggest that these states are a balance between epithelial-to-mesenchymal (EMT) and mesenchymal-to-epithelial transitions (MET) traits that results in so-called partial EMT phenotypes. Analysis of the top differentially expressed genes (DEGs) between these cell states revealed a common set of partial EMT transcription factors (TFs) controlling gene expression, including ZNF750, OVOL2, TP63, TFAP2C and HEY2. Our data suggest that the TFs related to EMT delineate different cell states in tumours and metastases. The results highlight the marked interpatient heterogeneity of breast cancer but identify common features of single cells from five models of metastatic breast cancer.

Notch receptor/ligand diversity: contribution to colorectal cancer stem cell heterogeneity

Cancer cell heterogeneity is a key contributor to therapeutic failure and post-treatment recurrence. Targeting cell subpopulations responsible for chemoresistance and recurrence seems to be an attractive approach to improve treatment outcome in cancer patients. However, this remains challenging due to the complexity and incomplete characterization of tumor cell subpopulations. The heterogeneity of cells exhibiting stemness-related features, such as self-renewal and chemoresistance, fuels this complexity. Notch signaling is a known regulator of cancer stem cell (CSC) features in colorectal cancer (CRC), though the effects of its heterogenous signaling on CRC cell stemness are only just emerging. In this review, we discuss how Notch ligand-receptor specificity contributes to regulating stemness, self-renewal, chemoresistance and cancer stem cells heterogeneity in CRC.

Spherical nucleic acids-based nanoplatforms for tumor precision medicine and immunotherapy

Precise diagnosis and treatment of tumors currently still face considerable challenges due to the development of highly degreed heterogeneity in the dynamic evolution of tumors. With the rapid development of genomics, personalized diagnosis and treatment using specific genes may be a robust strategy to break through the bottleneck of traditional tumor treatment. Nevertheless, efficient in vivo gene delivery has been frequently hampered by the inherent defects of vectors and various biological barriers. Encouragingly, spherical nucleic acids (SNAs) with good modularity and programmability are excellent candidates capable of addressing traditional gene transfer-associated issues, which enables SNAs a precision nanoplatform with great potential for diverse biomedical applications. In this regard, there have been detailed reviews of SNA in drug delivery, gene regulation, and dermatology treatment. Still, to the best of our knowledge, there is no published systematic review summarizing the use of SNAs in oncology precision medicine and immunotherapy, which are considered new guidelines for oncology treatment. To this end, we summarized the notable advances in SNAs-based precision therapy and immunotherapy for tumors following a classification standard of different types of precise spatiotemporal control on active species by SNAs. Specifically, we focus on the structural diversity and programmability of SNAs. Finally, the challenges and possible solutions were discussed in the concluding remarks. This review will promote the rational design and development of SNAs for tumor-precise medicine and immunotherapy.

Tumour follower cells: A novel driver of leader cells in collective invasion (Review)

Collective cellular invasion in malignant tumours is typically characterized by the cooperative migration of multiple cells in close proximity to each other. Follower cells are led away from the tumour by specialized leader cells, and both cell populations play a crucial role in collective invasion. Follower cells form the main body of the migration system and depend on intercellular contact for migration, whereas leader cells indicate the direction for the entire cell population. Although collective invasion can occur in epithelial and non‑epithelial malignant neoplasms, such as medulloblastoma and rhabdomyosarcoma, the present review mainly provided an extensive analysis of epithelial tumours. In the present review, the cooperative mechanisms of contact inhibition locomotion between follower and leader cells, where follower cells coordinate and direct collective movement through physical (mechanical) and chemical (signalling) interactions, is summarised. In addition, the molecular mechanisms of follower cell invasion and metastasis during remodelling and degradation of the extracellular matrix and how chemotaxis and lateral inhibition mediate follower cell behaviour were analysed. It was also demonstrated that follower cells exhibit genetic and metabolic heterogeneity during invasion, unlike leader cells.

Combining stereotactic body radiotherapy with immunotherapy in stage IV non-small cell lung cancer

Immunotherapy has revolutionized the treatment of metastatic non-small cell lung cancer (NSCLC). Oligometastasis has been associated with better prognosis than widespread metastatic disease and may be curable by stereotactic body radiotherapy (SBRT). SBRT can stimulate immunogenic anti-tumor activity, which can be further augmented when combined with immunotherapy, such as immune checkpoint inhibitors (ICIs). Thus, its combination with immunotherapy was recognized as a promising treatment option, especially in the metastatic setting. However, the most optimal approach to combine SBRT with immunotherapy remains controversial with early clinical evidence emerging. Here, we review the current clinical evidence supporting the combination of SBRT with immunotherapy in the treatment of metastatic NSCLC. Also, we discuss the current controversies and areas for further exploration associated with this treatment strategy.

Clonal selection parallels between normal and cancer tissues

Clonal selection and drift drive both normal tissue and cancer development. However, the biological mechanisms and environmental conditions underpinning these processes remain to be elucidated. Clonal selection models are centered in Darwinian evolutionary theory, where some clones with the fittest features are selected and populate the tissue or tumor. We suggest that different subclasses of stem cells, each of which is responsible for a distinct feature of the selection process, share common features between normal and cancer conditions. While active stem cells populate the tissue, dormant cells account for tissue replenishment/regeneration in both normal and cancerous tissues. We also discuss potential mechanisms that drive clonal drift, their interactions with clonal selection, and their similarities during normal and cancer tissue development.

Light and electron microscopy continuum-resolution imaging of 3D cell cultures

3D cell cultures, in particular organoids, are emerging models in the investigation of healthy or diseased tissues. Understanding the complex cellular sociology in organoids requires integration of imaging modalities across spatial and temporal scales. We present a multi-scale imaging approach that traverses millimeter-scale live-cell light microscopy to nanometer-scale volume electron microscopy by performing 3D cell cultures in a single carrier that is amenable to all imaging steps. This allows for following organoids' growth, probing their morphology with fluorescent markers, identifying areas of interest, and analyzing their 3D ultrastructure. We demonstrate this workflow on mouse and human 3D cultures and use automated image segmentation to annotate and quantitatively analyze subcellular structures in patient-derived colorectal cancer organoids. Our analyses identify local organization of diffraction-limited cell junctions in compact and polarized epithelia. The continuum-resolution imaging pipeline is thus suited to fostering basic and translational organoid research by simultaneously exploiting the advantages of light and electron microscopy.

Prognostic subtypes of thyroid cancer was constructed based on single cell and bulk-RNA sequencing data and verified its authenticity

There has been an increase in the mortality rate of thyroid cancer (THCA), which is the most common endocrine malignancy. We identified six distinct cell types in the THAC microenvironment by analyzing single-cell RNA sequencing (Sc-RNAseq) data from 23 THCA tumor samples, indicating high intratumoral heterogeneity. Through re-dimensional clustering of immune subset cells, myeloid cells, cancer-associated fibroblasts, and thyroid cell subsets, we deeply reveal differences in the tumor microenvironment of thyroid cancer. Through an in-depth analysis of thyroid cell subsets, we identified the process of thyroid cell deterioration (normal, intermediate, malignant cells). Through cell-to-cell communication analysis, we found a strong link between thyroid cells and fibroblasts and B cells in the MIF signaling pathway. In addition, we found a strong correlation between thyroid cells and B cells, TampNK cells, and bone marrow cells. Finally, we developed a prognostic model based on differentially expressed genes in thyroid cells from single-cell analysis. Both in the training set and the testing set, it can effectively predict the survival of thyroid patients. In addition, we identified significant differences in the composition of immune cell subsets between high-risk and low-risk patients, which may be responsible for their different prognosis. Through in vitro experiments, we identify that knockdown of NPC2 can significantly promote thyroid cancer cell apoptosis, and NPC2 may be a potential therapeutic target for thyroid cancer. In this study, we developed a well-performing prognostic model based on Sc-RNAseq data, revealing the cellular microenvironment and tumor heterogeneity of thyroid cancer. This will help to provide more accurate personalized treatment for patients in clinical diagnosis.

Molecular Pathways Implicated in Radioresistance of Glioblastoma Multiforme: What Is the Role of Extracellular Vesicles?

Glioblastoma multiforme (GBM) is a primary brain tumor that is very aggressive, resistant to treatment, and characterized by a high degree of anaplasia and proliferation. Routine treatment includes ablative surgery, chemotherapy, and radiotherapy. However, GMB rapidly relapses and develops radioresistance. Here, we briefly review the mechanisms underpinning radioresistance and discuss research to stop it and install anti-tumor defenses. Factors that participate in radioresistance are varied and include stem cells, tumor heterogeneity, tumor microenvironment, hypoxia, metabolic reprogramming, the chaperone system, non-coding RNAs, DNA repair, and extracellular vesicles (EVs). We direct our attention toward EVs because they are emerging as promising candidates as diagnostic and prognostication tools and as the basis for developing nanodevices for delivering anti-cancer agents directly into the tumor mass. EVs are relatively easy to obtain and manipulate to endow them with the desired anti-cancer properties and to administer them using minimally invasive procedures. Thus, isolating EVs from a GBM patient, supplying them with the necessary anti-cancer agent and the capability of recognizing a specified tissue-cell target, and reinjecting them into the original donor appears, at this time, as a reachable objective of personalized medicine.

Acoustic-Based Theranostic Probes Activated by Tumor Microenvironment for Accurate Tumor Diagnosis and Assisted Tumor Therapy

Acoustic-based imaging techniques, including ultrasonography and photoacoustic imaging, are powerful noninvasive approaches for tumor imaging owing to sound transmission facilitation, deep tissue penetration, and high spatiotemporal resolution. Usually, imaging modes were classified into "always-on" mode and "activatable" mode. Conventional "always-on" acoustic-based probes often have difficulty distinguishing lesion regions of interest from surrounding healthy tissues due to poor target-to-background signal ratios. As compared, activatable probes have attracted attention with improved sensitivity, which can boost or amplify imaging signals only in response to specific biomolecular recognition or interactions. The tumor microenvironment (TME) exhibits abnormal physiological conditions that can be used to identify tumor sections from normal tissues. Various types of organic dyes and biomaterials can react with TME, leading to obvious changes in their optical properties. The TME also affects the self-assembly or aggregation state of nanoparticles, which can be used to design activatable imaging probes. Moreover, acoustic-based imaging probes and therapeutic agents can be coencapsulated into one nanocarrier to develop nanotheranostic probes, achieving tumor imaging and cooperative therapy. Satisfactorily, ultrasound waves not only accelerate the release of encapsulated therapeutic agents but also activate therapeutic agents to exert or enhance their therapeutic performance. Meanwhile, various photoacoustic probes can convert photon energy into heat under irradiation, achieving photoacoustic imaging and cooperative photothermal therapy. In this review, we focus on the recently developed TME-triggered ultrasound and photoacoustic theranostic probes for precise tumor imaging and targeted tumor therapy.

Automated cell-type classification combining dilated convolutional neural networks with label-free acoustic sensing

This study aimed to automatically classify live cells based on their cell type by analyzing the patterns of backscattered signals of cells with minimal effect on normal cell physiology and activity. Our previous studies have demonstrated that label-free acoustic sensing using high-frequency ultrasound at a high pulse repetition frequency (PRF) can capture and analyze a single object from a heterogeneous sample. However, eliminating possible errors in the manual setting and time-consuming processes when postprocessing integrated backscattering (IB) coefficients of backscattered signals is crucial. In this study, an automated cell-type classification system that combines a label-free acoustic sensing technique with deep learning-empowered artificial intelligence models is proposed. We applied an one-dimensional (1D) convolutional autoencoder to denoise the signals and conducted data augmentation based on Gaussian noise injection to enhance the robustness of the proposed classification system to noise. Subsequently, denoised backscattered signals were classified into specific cell types using convolutional neural network (CNN) models for three types of signal data representations, including 1D CNN models for waveform and frequency spectrum analysis and two-dimensional (2D) CNN models for spectrogram analysis. We evaluated the proposed system by classifying two types of cells (e.g., RBC and PNT1A) and two types of polystyrene microspheres by analyzing their backscattered signal patterns. We attempted to discover cell physical properties reflected on backscattered signals by controlling experimental variables, such as diameter and structure material. We further evaluated the effectiveness of the neural network models and efficacy of data representations by comparing their accuracy with that of baseline methods. Therefore, the proposed system can be used to classify reliably and precisely several cell types with different intrinsic physical properties for personalized cancer medicine development.

Cervical cancer heterogeneity: a constant battle against viruses and drugs

Cervical cancer is the first identified human papillomavirus (HPV) associated cancer and the most promising malignancy to be eliminated. However, the ever-changing virus subtypes and acquired multiple drug resistance continue to induce failure of tumor prevention and treatment. The exploration of cervical cancer heterogeneity is the crucial way to achieve effective prevention and precise treatment. Tumor heterogeneity exists in various aspects including the immune clearance of viruses, tumorigenesis, neoplasm recurrence, metastasis and drug resistance. Tumor development and drug resistance are often driven by potential gene amplification and deletion, not only somatic genomic alterations, but also copy number amplifications, histone modification and DNA methylation. Genomic rearrangements may occur by selection effects from chemotherapy or radiotherapy which exhibits genetic intra-tumor heterogeneity in advanced cervical cancers. The combined application of cervical cancer therapeutic vaccine and immune checkpoint inhibitors has become an effective strategy to address the heterogeneity of treatment. In this review, we will integrate classic and recently updated epidemiological data on vaccination rates, screening rates, incidence and mortality of cervical cancer patients worldwide aiming to understand the current situation of disease prevention and control and identify the direction of urgent efforts. Additionally, we will focus on the tumor environment to summarize the conditions of immune clearance and gene integration after different HPV infections and to explore the genomic factors of tumor heterogeneity. Finally, we will make a thorough inquiry into completed and ongoing phase III clinical trials in cervical cancer and summarize molecular mechanisms of drug resistance among chemotherapy, radiotherapy, biotherapy, and immunotherapy.

Zebrafish Avatars of rectal cancer patients validate the radiosensitive effect of metformin

Neoadjuvant chemoradiation (nCRT) followed by surgery represents the standard of care in patients with locally advanced rectal cancer. Increasing radiotherapy (RT) doses and chemotherapy cycles with 5FU have been associated with increased rates of complete response, however these strategies imply significant toxicity. In the last years, epidemiologic findings have demonstrated that metformin is associated with significantly higher rates of pathological complete response to nCRT. Also, pre-clinical studies using cell lines provide evidence for the radiosensitive effect of metformin. However, no studies have been performed using rectal cancer patient samples to test this radiosensitive effect of metformin and compared it to the standard 5FU. Here, we designed an experimental study to compare both radiosensitizers in the zebrafish xenograft model (zAvatar), using rectal cancer surgical specimens and diagnostic biopsies. Patient zAvatars confirmed that metformin has indeed a powerful in vivo radiosensitizer effect, similar to 5FU. Our work confirms that metformin constitutes a promising less toxic alternative to the standard 5FU, which could be game changing in elderly/frail patients to optimize tumor regression.

Multianalyte liquid biopsy to aid the diagnostic workup of breast cancer

Breast cancer (BC) affects 1 in every 8 women in the United States and is currently the most prevalent cancer worldwide. Precise staging at diagnosis and prognosis are essential components for the clinical management of BC patients. In this study, we set out to evaluate the feasibility of the high-definition single cell (HDSCA) liquid biopsy (LBx) platform to stratify late-stage BC, early-stage BC, and normal donors using peripheral blood samples. Utilizing 5 biomarkers, we identified rare circulating events with epithelial, mesenchymal, endothelial and hematological origin. We detected a higher level of CTCs in late-stage patients, compared to the early-stage and normal donors. Additionally, we observed more tumor-associated large extracellular vesicles (LEVs) in the early-stage, compared to late-stage and the normal donor groups. Overall, we were able to detect reproducible patterns in the enumeration of rare cells and LEVs of cancer vs. normal donors and early-stage vs. late-stage BC with high accuracy, allowing for robust stratification. Our findings illustrate the feasibility of the LBx assay to provide robust detection of rare circulating events in peripheral blood draws and to stratify late-stage BC, early-stage BC, and normal donor samples.

Ecdysoneless Overexpression Drives Mammary Tumorigenesis through Upregulation of C-MYC and Glucose Metabolism

Ecdysoneless (ECD) protein is essential for embryogenesis, cell-cycle progression, and cellular stress mitigation with an emerging role in mRNA biogenesis. We have previously shown that ECD protein as well as its mRNA are overexpressed in breast cancer and ECD overexpression predicts shorter survival in patients with breast cancer. However, the genetic evidence for an oncogenic role of ECD has not been established. Here, we generated transgenic mice with mammary epithelium-targeted overexpression of an inducible human ECD transgene (ECDTg). Significantly, ECDTg mice develop mammary hyperplasia, preneoplastic lesions, and heterogeneous tumors with occasional lung metastasis. ECDTg tumors exhibit epithelial to mesenchymal transition and cancer stem cell characteristics. Organoid cultures of ECDTg tumors showed ECD dependency for in vitro oncogenic phenotype and in vivo growth when implanted in mice. RNA sequencing (RNA-seq) analysis of ECDTg tumors showed a c-MYC signature, and alterations in ECD levels regulated c-MYC mRNA and protein levels as well as glucose metabolism. ECD knockdown-induced decrease in glucose uptake was rescued by overexpression of mouse ECD as well as c-MYC. Publicly available expression data analyses showed a significant correlation of ECD and c-MYC overexpression in breast cancer, and ECD and c-MYC coexpression exhibits worse survival in patients with breast cancer. Taken together, we establish a novel role of overexpressed ECD as an oncogenesis driver in the mouse mammary gland through upregulation of c-MYC-mediated glucose metabolism.

IMPLICATIONS

We demonstrate ECD overexpression in the mammary gland of mice led to the development of a tumor progression model through upregulation of c-MYC signaling and glucose metabolism.

In-Depth Mass Spectrometry-Based Proteomics of Formalin-Fixed, Paraffin-Embedded Tissues with a Spatial Resolution of 50-200 μm

Formalin-fixed, paraffin-embedded (FFPE) tissues are banked in large repositories to cost-effectively preserve valuable specimens for later study. With the rapid growth of spatial proteomics, FFPE tissues can serve as a more accessible alternative to more commonly used frozen tissues. However, extracting proteins from FFPE tissues is challenging due to cross-links formed between proteins and formaldehyde. Here, we have adapted the nanoPOTS sample processing workflow, which was previously applied to single cells and fresh-frozen tissues, to profile protein expression from FFPE tissues. Following the optimization of extraction solvents, times, and temperatures, we identified an average of 1312 and 3184 high-confidence master proteins from 10 μm thick FFPE-preserved mouse liver tissue squares having lateral dimensions of 50 and 200 μm, respectively. The observed proteome coverage for FFPE tissues was on average 88% of that achieved for similar fresh-frozen tissues. We also characterized the performance of our fully automated sample preparation and analysis workflow, termed autoPOTS, for FFPE spatial proteomics. This modified nanodroplet processing in one pot for trace samples (nanoPOTS) and fully automated processing in one pot for trace sample (autoPOTS) workflows provides the greatest coverage reported to date for high-resolution spatial proteomics applied to FFPE tissues. Data are available via ProteomeXchange with identifier PXD029729.

Polyploidy as an Adaptation against Loss of Heterozygosity in Cancer

Polyploidy is common in cancer cells and has implications for tumor progression and resistance to therapies, but it is unclear whether it is an adaptation of the tumor or the non-adaptive effect of genomic instability. I discuss the possibility that polyploidy reduces the deleterious effects of loss of heterozygosity, which arises as a consequence of mitotic recombination, and which in diploid cells leads instead to the rapid loss of complementation of recessive deleterious mutations. I use computational predictions of loss of heterozygosity to show that a population of diploid cells dividing by mitosis with recombination can be easily invaded by mutant polyploid cells or cells that divide by endomitosis, which reduces loss of complementation, or by mutant cells that occasionally fuse, which restores heterozygosity. A similar selective advantage of polyploidy has been shown for the evolution of different types of asexual reproduction in nature. This provides an adaptive explanation for cyclical ploidy, mitotic slippage and cell fusion in cancer cells.

Recent Advances in Single Cell Analysis by Electrochemiluminescence

Understanding biological mechanisms operating in cells is one of the major goals of biology. Since heterogeneity is the fundamental property of cellular systems, single cell measurements can provide more accurate information about the composition, dynamics, and regulatory circuits of cells than population-averaged assays. Electrochemiluminescence (ECL), the light emission triggered by electrochemical reactions, is an emerging approach for single cell analysis. Numerous analytes, ranging from small biomolecules such as glucose and cholesterol, proteins and nucleic acids to subcellular structures, have been determined in single cells by ECL, which yields new insights into cellular functions. This review aims to provide an overview of research progress on ECL principles and systems for single cell analysis in recent years. The ECL reaction mechanisms are briefly introduced, and then the advances and representative works in ECL single cell analysis are summarized. Finally, outlooks and challenges in this field are addressed.

Dynamic Co-Evolution of Cancer Cells and Cancer-Associated Fibroblasts: Role in Right- and Left-Sided Colon Cancer Progression and Its Clinical Relevance

Simple Summary

The versatile crosstalk between cancer cells and cancer-associated fibroblasts (CAFs) of the tumour microenvironment (TME) drives colorectal carcinogenesis and heterogeneity. Colorectal cancer (CRC) can be classified by the anatomical sites from which the cancer arises, either from the right or left colon. Although the cancer cell–CAF interaction is being widely studied, its role in the progression of cancer in the right and left colon and cancer heterogeneity are still yet to be elucidated. Further insight into the complex interaction between different cellular components in the cancer niche, their evolutionary process and their influence on cancer progression would propel the discovery of effective targeted CRC therapy.

Abstract

Cancer is a result of a dynamic evolutionary process. It is composed of cancer cells and the tumour microenvironment (TME). One of the major cellular constituents of TME, cancer-associated fibroblasts (CAFs) are known to interact with cancer cells and promote colorectal carcinogenesis. The accumulation of these activated fibroblasts is linked to poor diagnosis in colorectal cancer (CRC) patients and recurrence of the disease. However, the interplay between cancer cells and CAFs is yet to be described, especially in relation to the sidedness of colorectal carcinogenesis. CRC, which is the third most commonly diagnosed cancer globally, can be classified according to the anatomical region from which they originate: left-sided (LCRC) and right-sided CRC (RCR). Both cancers differ in many aspects, including in histology, evolution, and molecular signatures. Despite occurring at lower frequency, RCRC is often associated with worse diagnosis compared to LCRC. The differences in molecular profiles between RCRC and LCRC also influence the mode of treatment that can be used to specifically target these cancer entities. A better understanding of the cancer cell–CAF interplay and its association with RCRC and LRCR progression will provide better insight into potential translational aspects of targeted treatment for CRC.

Measuring competitive exclusion in non-small cell lung cancer

In this study, we experimentally measure the frequency-dependent interactions between a gefitinib-resistant non-small cell lung cancer population and its sensitive ancestor via the evolutionary game assay. We show that cost of resistance is insufficient to accurately predict competitive exclusion and that frequency-dependent growth rate measurements are required. Using frequency-dependent growth rate data, we then show that gefitinib treatment results in competitive exclusion of the ancestor, while the absence of treatment results in a likely, but not guaranteed, exclusion of the resistant strain. Then, using simulations, we demonstrate that incorporating ecological growth effects can influence the predicted extinction time. In addition, we show that higher drug concentrations may not lead to the optimal reduction in tumor burden. Together, these results highlight the potential importance of frequency-dependent growth rate data for understanding competing populations, both in the laboratory and as we translate adaptive therapy regimens to the clinic.

Cytoplasmic LMO2-LDB1 Complex Activates STAT3 Signaling through Interaction with gp130-JAK in Glioma Stem Cells

The oncogenic role of nuclear LIM domain only 2 (LMO2) as a transcriptional regulator is well established, but its function in the cytoplasm is largely unknown. Here, we identified LMO2 as a cytoplasmic activator for signal transducer and activator of transcription 3 (STAT3) signaling in glioma stem cells (GSCs) through biochemical and bioinformatics analyses. LMO2 increases STAT3 phosphorylation by interacting with glycoprotein 130 (gp130) and Janus kinases (JAKs). LMO2-driven activation of STAT3 signaling requires the LDB1 protein and leads to increased expression of an inhibitor of differentiation 1 (ID1), a master regulator of cancer stemness. Our findings indicate that the cytoplasmic LMO2-LDB1 complex plays a crucial role in the activation of the GSC signaling cascade via interaction with gp130 and JAK1/2. Thus, LMO2-LDB1 is a bona fide oncogenic protein complex that activates either the JAK-STAT signaling cascade in the cytoplasm or direct transcriptional regulation in the nucleus.

Evolutionary perspectives, heterogeneity and ovarian cancer: a complicated tale from past to present

Ovarian cancer is composed of a complex system of cells best described by features such as clonal evolution, spatial and temporal genetic heterogeneity, and development of drug resistance, thus making it the most lethal gynecologic cancer. Seminal work on cancer as an evolutionary process has a long history; however, recent cost-effective large-scale molecular profiling has started to provide novel insights coupled with the development of mathematical algorithms. In the current review, we have systematically searched for articles that focused on the clonal evolution of ovarian cancer to offer the whole landscape of research that has been done and highlight future research avenues given its characteristic features and connections to evolutionary biology.

Hepatocellular Carcinoma: The Role of MicroRNAs

Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related deaths worldwide. HCC is diagnosed in its advanced stage when limited treatment options are available. Substantial morphologic, genetic and epigenetic heterogeneity has been reported in HCC, which poses a challenge for the development of a targeted therapy. In this review, we discuss the role and involvement of several microRNAs (miRs) in the heterogeneity and metastasis of hepatocellular carcinoma with a special emphasis on their possible role as a diagnostic and prognostic tool in the risk prediction, early detection, and treatment of hepatocellular carcinoma.

Role of the Mosaic Cisternal Maturation Machinery in Glycan Synthesis and Oncogenesis

Tumorigenesis is associated with the deregulation of multiple processes, among which the glycosylation of lipids and proteins is one of the most extensively affected. However, in most cases, it remains unclear whether aberrant glycosylation is a cause, a link in the pathogenetic chain, or a mere consequence of tumorigenesis. In other cases, instead, studies have shown that aberrant glycans can promote oncogenesis. To comprehend how aberrant glycans are generated it is necessary to clarify the underlying mechanisms of glycan synthesis at the Golgi apparatus, which are still poorly understood. Important factors that determine the glycosylation potential of the Golgi apparatus are the levels and intra-Golgi localization of the glycosylation enzymes. These factors are regulated by the process of cisternal maturation which transports the cargoes through the Golgi apparatus while retaining the glycosylation enzymes in the organelle. This mechanism has till now been considered a single, house-keeping and constitutive function. Instead, we here propose that it is a mosaic of pathways, each controlling specific set of functionally related glycosylation enzymes. This changes the conception of cisternal maturation from a constitutive to a highly regulated function. In this new light, we discuss potential new groups oncogenes among the cisternal maturation machinery that can contribute to aberrant glycosylation observed in cancer cells. Further, we also discuss the prospects of novel anticancer treatments targeting the intra-Golgi trafficking process, particularly the cisternal maturation mechanism, to control/inhibit the production of pro-tumorigenic glycans.

Chemokines in Triple-Negative Breast Cancer Heterogeneity: New Challenges for Clinical Implications

Tumor heterogeneity is a hallmark of cancer and one of the primary causes of resistance to therapies. Triple-negative breast cancer (TNBC), which accounts for 15% to 20% of all breast cancers and is the most aggressive subtype, is very diverse, connected to metastatic potential and response to therapy. It is a very diverse disease at the molecular, pathologic, and clinical levels. TNBC is substantially more likely to recur and has a worse overall survival rate following diagnosis than other breast cancer subtypes. Chemokines, low molecular weight proteins that stimulate chemotaxis, have been shown to control the cues responsible for TNBC heterogeneity. In this review, we have focused on tumor heterogeneity and the role of chemokines in modulating tumor heterogeneity, since this is the most critical issue in treating TNBC. Additionally, we examined numerous cues mediated by chemokine networks that contribute to the heterogeneity of TNBC. Recent developments in our knowledge of the chemokine networks that regulate TNBC heterogeneity may pave the door for developing difficult-to-treat TNBC treatment options.

Single cell analysis of mechanical properties and EMT-related gene expression profiles in cancer fingers

Collective cell migration is associated with cancer metastasis. Cancer fingers are formed when groups of migrating cancer cells follow the leader cells in the front. Epithelial to mesenchymal transition (EMT) is a critical process of cancer metastasis. However, the role of EMT in cancer finger formation remains unclear. In this work, we investigated the EMT-associated mechanical properties and gene expression at single-cell levels in non-small lung cancer fingers. We found that leader cells were more elastic and less sticky than follower cells. Spatial EMT-related gene expression profiling in cancer fingers revealed cellular heterogeneity. Particularly, SNAIL and VIM were found to be two key genes that positively correlated with leader cell phenotypes and controlled cancer finger formation. Silencing either SNAIL or VIM, decreased cancer cell elasticity, cancer finger formation and migration, and increased adhesiveness. These findings indicated that SNAIL and VIM are two driver genes for cancer finger formation.

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Spatial mapping of EMT genes and mechanical properties of cancer finger at single cell level

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Cancer cell elasticity and adhesiveness are two physical biomarkers for leader cells

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SNAIL and VIM drive finger cell formation and are potential targets for therapy

LncRNAs, the Molecules Involved in Communications With Colorectal Cancer Stem Cells

Colorectal cancer stem cells (CRCSCs) can actively self-renew, as well as having multidirectional differentiation and tumor regeneration abilities. Because the high functional activities of CRCSCs are associated with low cure rates in patients with colorectal cancer, efforts have sought to determine the function and regulatory mechanisms of CRCSCs. To date, however, the potential regulatory mechanisms of CRCSCs remain incompletely understood. Many non-coding genes are involved in tumor invasion and spread through their regulation of CRCSCs, with long non-coding RNAs (lncRNAs) being important non-coding RNAs. LncRNAs may be involved in the colorectal cancer development and drug resistance through their regulation of CRCSCs. This review systematically evaluates the latest research on the ability of lncRNAs to regulate CRCSC signaling pathways and the involvement of these lncRNAs in colorectal cancer promotion and suppression. The regulatory network of lncRNAs in the CRCSC signaling pathway has been determined. Further analysis of the potential clinical applications of lncRNAs as novel clinical diagnostic and prognostic biomarkers and therapeutic targets for colorectal cancer may provide new ideas and protocols for the prevention and treatment of colorectal cancer.

Quantifying the cell morphology and predicting biological behavior of signet ring cell carcinoma using deep learning

Signet ring cell carcinoma (SRCC) is a malignant tumor of the digestive system. This tumor has long been considered to be poorly differentiated and highly invasive because it has a higher rate of metastasis than well-differentiated adenocarcinoma. But some studies in recent years have shown that the prognosis of some SRCC is more favorable than other poorly differentiated adenocarcinomas, which suggests that SRCC has different degrees of biological behavior. Therefore, we need to find a histological stratification that can predict the biological behavior of SRCC. Some studies indicate that the morphological status of cells can be linked to the invasiveness potential of cells, however, the traditional histopathological examination can not objectively define and evaluate them. Recent improvements in biomedical image analysis using deep learning (DL) based neural networks could be exploited to identify and analyze SRCC. In this study, we used DL to identify each cancer cell of SRCC in whole slide images (WSIs) and quantify their morphological characteristics and atypia. Our results show that the biological behavior of SRCC can be predicted by quantifying the morphology of cancer cells by DL. This technique could be used to predict the biological behavior and may change the stratified treatment of SRCC.

A mathematical model for phenotypic heterogeneity in breast cancer with implications for therapeutic strategies

Inevitably, almost all cancer patients develop resistance to targeted therapy. Intratumour heterogeneity is a major cause of drug resistance. Mathematical models that explain experiments quantitatively are useful in understanding the origin of intratumour heterogeneity, which then could be used to explore scenarios for efficacious therapy. Here, we develop a mathematical model to investigate intratumour heterogeneity in breast cancer by exploiting the observation that HER2+ and HER2- cells could divide symmetrically or asymmetrically. Our predictions for the evolution of cell fractions are in quantitative agreement with single-cell experiments. Remarkably, the colony size of HER2+ cells emerging from a single HER2- cell (or vice versa), which occurs in about four cell doublings, also agrees with experimental results, without tweaking any parameter in the model. The theory explains experimental data on the responses of breast tumours under different treatment protocols. We then used the model to predict that, not only the order of two drugs, but also the treatment period for each drug and the tumour cell plasticity could be manipulated to improve the treatment efficacy. Mathematical models, when integrated with data on patients, make possible exploration of a broad range of parameters readily, which might provide insights in devising effective therapies.

Quantification of tumor heterogeneity: from data acquisition to metric generation

Tumors are unique and complex ecosystems, in which heterogeneous cell subpopulations with variable molecular profiles, aggressiveness, and proliferation potential coexist and interact. Understanding how heterogeneity influences tumor progression has important clinical implications for improving diagnosis, prognosis, and treatment response prediction. Several recent innovations in data acquisition methods and computational metrics have enabled the quantification of spatiotemporal heterogeneity across different scales of tumor organization. Here, we summarize the most promising efforts from a common experimental and computational perspective, discussing their advantages, shortcomings, and challenges. With personalized medicine entering a new era of unprecedented opportunities, our vision is that of future workflows integrating across modalities, scales, and dimensions to capture intricate aspects of the tumor ecosystem and to open new avenues for improved patient care.

Active Targeted Nanoformulations via Folate Receptors: State of the Art and Future Perspectives

In normal tissues, the expression of folate receptors is low and limited to cells that are important for embryonic development or for folate reabsorption. However, in several pathological conditions some cells, such as cancer cells and activated macrophages, overexpress folate receptors (FRs). This overexpression makes them a potential therapeutic target in the treatment of cancer and inflammatory diseases to obtain a selective delivery of drugs at altered cells level, and thus to improve the therapeutic efficacy and decrease the systemic toxicity of the pharmacological treatments. Two strategies have been used to achieve this folate receptor targeting: (i) the use of ligands with high affinity to FRs (e.g., folic acid or anti-FRs monoclonal antibodies) linked to the therapeutic agents or (ii) the use of nanocarriers whose surface is decorated with these ligands and in which the drug is encapsulated. This manuscript analyzes the use of FRs as a target to develop new therapeutic tools in the treatment of cancer and inflammatory diseases with an emphasis on the nanoformulations that have been developed for both therapeutic and imaging purposes.

Single cell RNA sequencing techniques and applications in research of ovary development and related diseases

The ovary is a highly organized composite of germ cells and various types of somatic cells, whose communications dictate ovary development to generate functional oocytes. The differences between individual cells might have profound effects on ovary functions. Single cell RNA sequencing techniques are promising approaches to explore the cell type composition of organisms, the dynamics of transcriptomes, the regulatory network between genes and the signaling pathways between cell types at the single cell resolution. In this review, we provide an overview of the currently available single cell RNA sequencing techniques including Smart-seq2 and Drop-seq, as well as their applications in biological and clinical research to provide a better understanding on the molecular mechanisms underlying ovary development and associated diseases.

Traditional chinese medicine syndromes classification associates with tumor cell and microenvironment heterogeneity in colorectal cancer: a single cell RNA sequencing analysis

BACKGROUND

Colorectal cancer (CRC) is one of the common gastrointestinal malignancies, tumor heterogeneity is the main cause of refractory CRC. Syndrome differentiation is the premise of individualized treatment of traditional Chinese medicine (TCM), but TCM syndrome lacks objective identification in CRC. This study is to investigate the correlation and significance of tumor heterogeneity and TCM syndromes classification in CRC.

METHODS

In this study, we using scRNA-seq technology, investigate the significance of tumor heterogeneity in TCM syndromes classification on CRC.

RESULTS

The results showed that 662 cells isolated from 11 primary CRC tumors are divided into 14 different cell clusters, and each cell subtype and its genes have different functions and signal transduction pathways, indicating significant heterogeneity. CRC tumor cell clusters have different proportions in Excess, Deficiency and Deficiency-Excess syndromes, and have their own characteristic genes, gene co-expression networks, gene functional interpretations as well as monocle functional evolution. Moreover, there were significant differences between the high expressions of MUC2, REG4, COL1A2, POSTN, SDPR, GPX1, ELF3, KRT8, KRT18, KRT19, FN1, SERPINE1, TCF4 and ZEB1 genes in Excess and Deficiency syndrome classification in CRC (P < 0.01).

CONCLUSIONS

The Excess and Deficiency syndromes classification may be related to tumor heterogeneity and its microenvironment in CRC.

Tumor-propagating side population cells are a dynamic subpopulation in undifferentiated pleomorphic sarcoma

Sarcomas contain a subpopulation of tumor-propagating cells (TPCs) with enhanced tumor-initiating and self-renewal properties. However, it is unclear whether the TPC phenotype in sarcomas is stable or a dynamic cell state that can derive from non-TPCs. In this study, we utilized a mouse model of undifferentiated pleomorphic sarcoma (UPS) to trace the lineage relationship between sarcoma side population (SP) cells that are enriched for TPCs and non-SP cells. By cotransplanting SP and non-SP cells expressing different endogenous fluorescent reporters, we show that non-SP cells can give rise to SP cells with enhanced tumor-propagating potential in vivo. Lineage trajectory analysis using single-cell RNA sequencing from SP and non-SP cells supports the notion that non-SP cells can assume the SP cell phenotype de novo. To test the effect of eradicating SP cells on tumor growth and self-renewal, we generated mouse sarcomas in which the diphtheria toxin receptor is expressed in the SP cells and their progeny. Ablation of the SP population using diphtheria toxin did not impede tumor growth or self-renewal. Altogether, we show that the sarcoma SP represent a dynamic cell state and targeting TPCs alone is insufficient to eliminate tumor progression.

Quantification of EGFR-HER2 Heterodimers in HER2-Overexpressing Breast Cancer Cells Using Liquid-Phase Electron Microscopy

Currently, breast cancer patients are classified uniquely according to the expression level of hormone receptors, and human epidermal growth factor receptor 2 (HER2). This coarse classification is insufficient to capture the phenotypic complexity and heterogeneity of the disease. A methodology was developed for absolute quantification of receptor surface density ρ_R, and molecular interaction (dimerization), as well as the associated heterogeneities, of HER2 and its family member, the epidermal growth factor receptor (EGFR) in the plasma membrane of HER2 overexpressing breast cancer cells. Quantitative, correlative light microscopy (LM) and liquid-phase electron microscopy (LPEM) were combined with quantum dot (QD) labeling. Single-molecule position data of receptors were obtained from scanning transmission electron microscopy (STEM) images of intact cancer cells. Over 280,000 receptor positions were detected and statistically analyzed. An important finding was the subcellular heterogeneity in heterodimer shares with respect to plasma membrane regions with different dynamic properties. Deriving quantitative information about EGFR and HER2 ρ_R, as well as their dimer percentages, and the heterogeneities thereof, in single cancer cells, is potentially relevant for early identification of patients with HER2 overexpressing tumors comprising an enhanced share of EGFR dimers, likely increasing the risk for drug resistance, and thus requiring additional targeted therapeutic strategies.

Deciphering intratumoral heterogeneity using integrated clonal tracking and single-cell transcriptome analyses

Cellular heterogeneity is a major cause of treatment resistance in cancer. Despite recent advances in single-cell genomic and transcriptomic sequencing, it remains difficult to relate measured molecular profiles to the cellular activities underlying cancer. Here, we present an integrated experimental system that connects single cell gene expression to heterogeneous cancer cell growth, metastasis, and treatment response. Our system integrates single cell transcriptome profiling with DNA barcode based clonal tracking in patient-derived xenograft models. We show that leukemia cells exhibiting unique gene expression respond to different chemotherapies in distinct but consistent manners across multiple mice. In addition, we uncover a form of leukemia expansion that is spatially confined to the bone marrow of single anatomical sites and driven by cells with distinct gene expression. Our integrated experimental system can interrogate the molecular and cellular basis of the intratumoral heterogeneity underlying disease progression and treatment resistance.

DNA barcoding is a promising technology for the simultaneous analysis of genetic and phenotypic heterogeneity. Here, the authors combine DNA barcoding and single-cell RNA-seq to study heterogeneity, progression and response to therapy in B-cell acute lymphoblastic leukaemia patient-derived xenografts.

Subtypes of schizophrenia identified by multi-omic measures associated with dysregulated immune function

Epigenetic modifications are plausible molecular sources of phenotypic heterogeneity across schizophrenia patients. The current study investigated biological heterogeneity in schizophrenia using peripheral epigenetic profiles to delineate illness subtypes independent of their phenomenological manifestations. We applied epigenome-wide profiling with a DNA methylation array from blood samples of 63 schizophrenia patients and 59 healthy controls. Non-negative matrix factorization (NMF) and k-means clustering were performed to identify DNA methylation-related patient subtypes. The validity of the partition was tested by assessing the profile of the T cell receptor (TCR) repertoires. The uniqueness of the identified subtypes in relation to brain structural and clinical measures were evaluated. Two distinct patterns of DNA methylation profiles were identified in patients. One subtype (60.3% of patients) showed relatively limited changes in methylation levels and cell composition compared to controls, while a second subtype (39.7% of patients) exhibited widespread methylation level alterations among genes enriched in immune cell activity, as well as a higher proportion of neutrophils and lower proportion of lymphocytes. Differentiation of the two patient subtypes was validated by TCR repertoires, which paralleled the partition based on DNA methylation profiles. The subtype with widespread methylation modifications had higher symptom severity, performed worse on cognitive measures, and displayed greater reductions in fractional anisotropy of white matter tracts and evidence of gray matter thickening compared to the other subtype. Identification of a distinct subtype of schizophrenia with unique molecular, cerebral, and clinical features provide a novel parcellation of the schizophrenia syndrome with potential to guide development of individualized therapeutics.

Mechanism of activation and the rewired network: New drug design concepts

Precision oncology benefits from effective early phase drug discovery decisions. Recently, drugging inactive protein conformations has shown impressive successes, raising the cardinal questions of which targets can profit and what are the principles of the active/inactive protein pharmacology. Cancer driver mutations have been established to mimic the protein activation mechanism. We suggest that the decision whether to target an inactive (or active) conformation should largely rest on the protein mechanism of activation. We next discuss the recent identification of double (multiple) same-allele driver mutations and their impact on cell proliferation and suggest that like single driver mutations, double drivers also mimic the mechanism of activation. We further suggest that the structural perturbations of double (multiple) in cis mutations may reveal new surfaces/pockets for drug design. Finally, we underscore the preeminent role of the cellular network which is deregulated in cancer. Our structure-based review and outlook updates the traditional Mechanism of Action, informs decisions, and calls attention to the intrinsic activation mechanism of the target protein and the rewired tumor-specific network, ushering innovative considerations in precision medicine.

Double Recombinant Vaccinia Virus: A Candidate Drug against Human Glioblastoma

Glioblastoma is one of the most aggressive brain tumors. Given the poor prognosis of this disease, novel methods for glioblastoma treatment are needed. Virotherapy is one of the most actively developed approaches for cancer therapy today. VV-GMCSF-Lact is a recombinant vaccinia virus with deletions of the viral thymidine kinase and growth factor genes and insertions of the granulocyte–macrophage colony-stimulating factor and oncotoxic protein lactaptin genes. The virus has high cytotoxic activity against human cancer cells of various histogenesis and antitumor efficacy against breast cancer. In this work, we show VV-GMCSF-Lact to be a promising therapeutic agent for glioblastoma treatment. VV-GMCSF-Lact effectively decreases the viability of glioblastoma cells of both immortalized and patient-derived cultures in vitro, crosses the blood–brain barrier, selectively replicates into orthotopically transplanted human glioblastoma when intravenously injected, and inhibits glioblastoma xenograft and metastasis growth when injected intratumorally.

Microfluidics applications for high-throughput single cell sequencing

The inherent heterogeneity of individual cells in cell populations plays significant roles in disease development and progression, which is critical for disease diagnosis and treatment. Substantial evidences show that the majority of traditional gene profiling methods mask the difference of individual cells. Single cell sequencing can provide data to characterize the inherent heterogeneity of individual cells, and reveal complex and rare cell populations. Different microfluidic technologies have emerged for single cell researches and become the frontiers and hot topics over the past decade. In this review article, we introduce the processes of single cell sequencing, and review the principles of microfluidics for single cell analysis. Also, we discuss the common high-throughput single cell sequencing technologies along with their advantages and disadvantages. Lastly, microfluidics applications in single cell sequencing technology for the diagnosis of cancers and immune system diseases are briefly illustrated.

In situ functional cell phenotyping reveals microdomain networks in colorectal cancer recurrence

Tumors are dynamic ecosystems comprising localized niches (microdomains), possessing distinct compositions and spatial configurations of cancer and non-cancer cell populations. Microdomain-specific network signaling coevolves with a continuum of cell states and functional plasticity associated with disease progression and therapeutic responses. We present LEAPH, an unsupervised machine learning algorithm for identifying cell phenotypes, which applies recursive steps of probabilistic clustering and spatial regularization to derive functional phenotypes (FPs) along a continuum. Combining LEAPH with pointwise mutual information and network biology analyses enables the discovery of outcome-associated microdomains visualized as distinct spatial configurations of heterogeneous FPs. Utilization of an immunofluorescence-based (51 biomarkers) image dataset of colorectal carcinoma primary tumors (n = 213) revealed microdomain-specific network dysregulation supporting cancer stem cell maintenance and immunosuppression that associated selectively with the recurrence phenotype. LEAPH enables an explainable artificial intelligence platform providing insights into pathophysiological mechanisms and novel drug targets to inform personalized therapeutic strategies.

Single-cell DNA and RNA sequencing reveals the dynamics of intra-tumor heterogeneity in a colorectal cancer model

BACKGROUND

Intra-tumor heterogeneity (ITH) encompasses cellular differences in tumors and is related to clinical outcomes such as drug resistance. However, little is known about the dynamics of ITH, owing to the lack of time-series analysis at the single-cell level. Mouse models that recapitulate cancer development are useful for controlled serial time sampling.

RESULTS

We performed single-cell exome and transcriptome sequencing of 200 cells to investigate how ITH is generated in a mouse colorectal cancer model. In the model, a single normal intestinal cell is grown into organoids that mimic the intestinal crypt structure. Upon RNAi-mediated downregulation of a tumor suppressor gene APC, the transduced organoids were serially transplanted into mice to allow exposure to in vivo microenvironments, which play relevant roles in cancer development. The ITH of the transcriptome increased after the transplantation, while that of the exome decreased. Mutations generated during organoid culture did not greatly change at the bulk-cell level upon the transplantation. The RNA ITH increase was due to the emergence of new transcriptional subpopulations. In contrast to the initial cells expressing mesenchymal-marker genes, new subpopulations repressed these genes after the transplantation. Analyses of colorectal cancer data from The Cancer Genome Atlas revealed a high proportion of metastatic cases in human subjects with expression patterns similar to the new cell subpopulations in mouse. These results suggest that the birth of transcriptional subpopulations may be a key for adaptation to drastic micro-environmental changes when cancer cells have sufficient genetic alterations at later tumor stages.

CONCLUSIONS

This study revealed an evolutionary dynamics of single-cell RNA and DNA heterogeneity in tumor progression, giving insights into the mesenchymal-epithelial transformation of tumor cells at metastasis in colorectal cancer.

Proteomic Imaging of Vestibular Schwannomas and Normal Nerves. Histopathologic Correlations

OBJECTIVES

Proteomic analysis of vestibular schwannoma (VS), non-vestibular schwannoma (NVS), and normal nerve (NN) using mass spectrometry and imaging of matrix assisted laser desorption ionization-time of flight (MALDI-TOF).

METHODS

Retrospective, qualitative, and descriptive study on VS, NVS, and NN. Samples were provided by our Tumor Bank. They were analyzed histologically then sprayed by acid matrix. The laser beam of MALDI performed desorption-ionization of the sample. A mass spectrogram (MS) was drawn depending on time of flight of ionized peptides, and MALDI-imaging was obtained which is a summation color spectrum depending on sample's peptide content. The slice was reexamined histologically and results compared with MALDI-imaging.

RESULTS

Fifty schwannomas were sampled, of which 27 exploitable: 22 VS (17 Antoni type A and five type B) and five NVS (all Antoni type B). Eleven NN were analyzed. Among the 22 VS, near-total correlation between MALDI-imaging and pathology was found in two cases (9.1%), partial correlation in four (18.2%), and no correlation in 16 (72.7%); correlations were more frequent in VS of the Antoni type B. MS showed a peptide spike at 2,000 m/z in 7 (31.8%) and 5,000 m/z in 21 (95.5%). Among the five NVS, near-total correlation was found in three cases (60%), partial correlation in one (20%), and no correlation in one (20%). MS showed a peptide spike at 2,000 m/z in two (40%) and 5,000 m/z in all (100%). Among the 11 NN, near-total correlation was found in nine cases (81.8%), partial correlation in one (9.1%), and no correlation in one (9.1%). MS showed no peptide spike at 2,000 or 5,000 m/z. Behind homogeneous areas on histology, there was great heterogeneity on MALDI-imaging and MS, regarding VS and NVS, but not NN.

CONCLUSIONS

There was a lack of correlation between MALDI-imaging and pathology in VS (except Antoni type B) as compared with NVS and NN. The lack of correlation in VS of the type A as compared with type B VS and NVS could be attributed to the overexpression of degeneration-associated proteins/peptides in VS of the type B as well as NVS that are better correlated with histologic findings. The two peptide spikes detected in schwannoma and not in NN opens up the prospect of tumor biomarkers identifiable by sequencing. The proteomic polymorphism found in VS and NVS was absent on histology which is a new morphologic characteristic of schwannoma. Further studies should be performed in the future to confirm the benefit and usefulness of the MALDI in the analysis of VS and NVS.