The Origins of Phenotypic Heterogeneity in Cancer

Multifaceted roles of OCT4 in tumor microenvironment: biology and therapeutic implications

OCT4 (Octamer-binding transcription factor 4, encoded by the POU5F1 gene) is a master transcription factor for maintaining the self-renewal and pluripotency of pluripotent stem cells, as well as a pioneer factor regulating epigenetics-driven cell reprogramming and cell fate conversion. It is also detected in a variety of cancer tissues and particularly in a small subpopulation of cancer cells known as cancer stem cells (CSCs). Accumulating evidence has revealed that CSCs are a dynamic population, exhibiting shift between multipotency and differentiation states, or quiescence and proliferation states. Such cellular plasticity of CSCs is profoundly influenced by dynamic interplay between CSCs and the tumor microenvironment (TME). Here, we review recent evidence showing that OCT4 expressed in CSCs plays a multifaceted role in shaping the TME by interacting with the cellular TME components, including cancer-associated fibroblasts, tumor endothelial cells, tumor-infiltrating immune cells, as well as the non-cellular TME components, such as extracellular matrix (ECM), metabolites, soluble factors (e.g., growth factors, cytokines and chemokines), and intra-tumoral microbiota. Together, OCT4 regulates crucial processes encompassing ECM remodeling, epithelial-mesenchymal transition, metabolic reprogramming, angiogenesis, and immune responses. The complex and bidirectional interactions between OCT4-expressing CSCs and the TME create a supportive niche for tumor growth, invasion, and resistance to therapy. Better understanding OCT4's roles in such interactions can provide deeper insights into potential therapeutic strategies and targets for disrupting the supportive environment of tumors. The emerging therapies targeting OCT4 in CSCs might hold promise to resensitize therapeutic-resistant cancer cells, and to eradicate all cancer cells when combined with other therapies targeting the bulk of differentiated cancer cells as well as the TME.

Pre-existing stem cell heterogeneity dictates clonal responses to the acquisition of leukemic driver mutations

Cancer cells display wide phenotypic variation even across patients with the same mutations. Differences in the cell of origin provide a potential explanation, but traditional assays lack the resolution to distinguish clonally heterogeneous subsets of stem and progenitor cells. To address this challenge, we developed simultaneous tracking of recombinase activation and clonal kinetics (STRACK), a method to trace clonal dynamics and gene expression before and after the acquisition of cancer mutations. Using mouse models, we studied two leukemic mutations, Dnmt3a-R878H and Npm1c, and found that their effect was highly variable across different stem cell states. Specifically, a subset of differentiation-primed stem cells, which normally becomes outcompeted with time, expands with both mutations. Intriguingly, Npm1c mutations reversed the intrinsic bias of the clone of origin, with differentiation-primed stem cells giving rise to more primitive malignant states. Thus, we highlight the relevance of single-cell lineage tracing to unravel early events in cancer evolution and posit that different cellular histories carry distinct cancer phenotypic potential.

ALK-EML4 Fusion in Small Cell Lung Cancer: Clinical and Molecular Insights From a Rare Case

BACKGROUND

Small cell lung cancer (SCLC) is a rare and aggressive malignancy with a poor prognosis and limited therapeutic options. While anaplastic lymphoma kinase (ALK) rearrangements are commonly observed in non-small cell lung cancer (NSCLC), their occurrence in SCLC is exceedingly rare. This report presents a unique case of SCLC harboring the ALK-EML4 fusion gene, identified through next-generation sequencing (NGS), contributing to a deeper understanding of potential targeted therapies for SCLC patients.

CASE DESCRIPTION

A 35-year-old male with a 45-pack-year smoking history presented with hyponatremia, dyspnea, and weight loss. Imaging revealed a left hilar mass with mediastinal lymphadenopathy, and a CT-guided biopsy confirmed the diagnosis of SCLC. The patient underwent initial treatment with chemotherapy and radiation. Despite an initial positive response, disease progression occurred, leading to a change in treatment with dual immunotherapy and palliative reirradiation. Subsequent NGS testing identified an EML4-ALK fusion gene. The patient was readmitted later with worsening symptoms and new metastatic lesions. Due to disease progression, immunotherapy was discontinued, and chemotherapy with carboplatin and irinotecan, along with alectinib, was initiated. Follow-up imaging revealed a partial response in the primary tumor.

CONCLUSIONS

This case highlights the rare occurrence of ALK fusion in SCLC, which may offer insights into potential targeted treatments. The patient's progression despite standard therapies suggests the need for further investigation into ALK inhibitors for SCLC patients with such mutations. Clinicians should consider NGS testing as a valuable diagnostic tool for identifying genetic alterations in SCLC, which could guide personalized treatment strategies and improve outcomes.

Synthesis, Biological Activities, and Molecular Docking Studies of 15-Site Matrine Based Isatohydrazone Derivatives as Potential Anticancer Agents

To acquire matrine derivatives with enhanced anticancer activity, we designed and synthesized twenty-one 15-site matrine based isatohydrazone derivatives were designed and synthesized. The anti-proliferative activity of all the compounds against human cervical cancer cells (HeLa), human colon cancer cells (HCT116), and non-small cell lung cancer cells (A549) was examined by the MTT method. The majority of the compounds exhibited superior anticancer activity compared to matrine. Among them, compound 5a displayed the most potent anti-proliferative activity, with IC50 values of 9.02±0.33 μM (HeLa), 10.49±1.09 μM (HCT116), and 15.23±0.12 μM (A549), respectively. Compound 5a can also induce cell cycle arrest in the G0/G1 phase and trigger apoptosis, as well as inhibit cell clonal formation and migration. Molecular docking experiments have demonstrated that compound 5a can form hydrogen bonds and hydrophobic interactions with EGFR-related protein 7AEI.

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.

Prognostic Biomarkers in Breast Cancer via Multi-Omics Clustering Analysis

Breast cancer (BC) is a highly heterogeneous disease with diverse molecular subtypes, which complicates prognosis and treatment. In this study, we performed a multi-omics clustering analysis using the Cancer Integration via MultIkernel LeaRning (CIMLR) method on a large BC dataset from The Cancer Genome Atlas (TCGA) to identify key prognostic biomarkers. We identified three genes-LMO1, PRAME, and RSPO2-that were significantly associated with poor prognosis in both the TCGA dataset and an additional dataset comprising 146 metastatic BC patients. Patients' stratification based on the expression of these three genes revealed distinct subtypes with markedly different overall survival (OS) outcomes. Further validation using almost 2000 BC patients' data from the METABRIC dataset and RNA sequencing data from therapy-resistant cell lines confirmed the upregulation of LMO1 and PRAME, respectively, in patients with worse prognosis and in resistant cells, also suggesting their potential role in drug resistance. Our findings highlight LMO1 and PRAME as potential biomarkers for identifying high-risk BC patients and informing targeted treatment strategies. This study provides valuable insights into the multi-omics landscape of BC and underscores the importance of personalized therapeutic approaches based on molecular profiles.

Polymerase Chain Reaction Chips for Biomarker Discovery and Validation in Drug Development

Polymerase chain reaction (PCR) chips are advanced, microfluidic platforms that have revolutionized biomarker discovery and validation because of their high sensitivity, specificity, and throughput levels. These chips miniaturize traditional PCR processes for the speed and precision of nucleic acid biomarker detection relevant to advancing drug development. Biomarkers, which are useful in helping to explain disease mechanisms, patient stratification, and therapeutic monitoring, are hard to identify and validate due to the complexity of biological systems and the limitations of traditional techniques. The challenges to which PCR chips respond include high-throughput capabilities coupled with real-time quantitative analysis, enabling researchers to identify novel biomarkers with greater accuracy and reproducibility. More recent design improvements of PCR chips have further expanded their functionality to also include digital and multiplex PCR technologies. Digital PCR chips are ideal for quantifying rare biomarkers, which is essential in oncology and infectious disease research. In contrast, multiplex PCR chips enable simultaneous analysis of multiple targets, therefore simplifying biomarker validation. Furthermore, single-cell PCR chips have made it possible to detect biomarkers at unprecedented resolution, hence revealing heterogeneity within cell populations. PCR chips are transforming drug development, enabling target identification, patient stratification, and therapeutic efficacy assessment. They play a major role in the development of companion diagnostics and, therefore, pave the way for personalized medicine, ensuring that the right patient receives the right treatment. While this tremendously promising technology has exhibited many challenges regarding its scalability, integration with other omics technologies, and conformity with regulatory requirements, many still prevail. Future breakthroughs in chip manufacturing, the integration of artificial intelligence, and multi-omics applications will further expand PCR chip capabilities. PCR chips will not only be important for the acceleration of drug discovery and development but also in raising the bar in improving patient outcomes and, hence, global health care as these technologies continue to mature.

National yearly cost of breast cancer screening in the USA and projected cost of advocated guidelines: a simulation study with life table modelling

OBJECTIVE

The aim of this study was to estimate the total national direct cost of breast cancer screening from 2019 to 2022 and project the total national cost and average lifetime cost of screening per woman for three current guidelines.

DESIGN

We estimated the national cost of screening from 2019 to 2022, and per cancer detected in 2022, using real-world data on the number of mammograms performed per year. We also projected the national cost of screening using life table modelling for three guidelines: 2021/2023 American College of Radiology (ACR), 2023 American Cancer Society (ACS) and 2024 United States Preventative Services Task Force (USPSTF). The average lifetime cost to screen one woman until age 74 years with each guideline was also estimated. The Optum Labs Data Warehouse was used to estimate commercial and Medicare costs and recall rates. Sensitivity analyses were used to estimate uncertainty and determine which inputs had the largest impact on total national costs.

SETTING

This study was conducted for the USA.

PARTICIPANTS

Women eligible for breast cancer screening.

INTERVENTIONS

Digital mammograms (2D) or digital breast tomosynthesis (3D) and/or MRI.

PRIMARY OUTCOME MEASURE

Total national cost of screening calculated as the sum of screening and recall costs. Average lifetime cost of screening per woman until 74 years.

RESULTS

Nationally, screening cost approximately US$11 billion (B) per year from 2019 to 2022 with approximately 37% of eligible women screened each year. In 2022, screening cost US$55 471 per 3D-detected and US$44 000 per 2D-detected invasive or ductal carcinoma in situ case. Using target yearly participation rates of 54%-78% by age of women, the projected cost of screening was US$30B for ACR, US$18B for ACS and US$8B for USPSTF guidelines. The average lifetime cost to screen an average-risk woman was: US$13 416 for ACR, US$7946 for ACS and US$6931 for USPSTF. Participation rates, the proportion of women with a lifetime risk>20% and commercial MRI and 3D costs had the largest impact on total costs.

CONCLUSION

The cost of screening varies significantly by guideline (US$8B-US$30B) and was most influenced by participation rates, high-risk population proportions and technology costs. Future work can investigate whether risk-based screening strategies being tested in ongoing clinical trials can reduce national screening costs while improving outcomes.Cite Now.

Network-based transfer of pan-cancer immunotherapy responses to guide breast cancer prognosis

Breast cancer prognosis is complicated by tumor heterogeneity. Traditional methods focus on cancer-specific gene signatures, but cross-cancer strategies that provide deeper insights into tumor homogeneity are rarely used. Immunotherapy, particularly immune checkpoint inhibitors, results from variable responses across cancers, offering valuable prognostic insights. We introduced a network-based transfer (NBT) of pan-cancer immunotherapy responses to enhance breast cancer prognosis using node embedding and heat diffusion algorithms, identifying gene signatures netNE and netHD. Our results showed that netHD and netNE outperformed seven established breast cancer signatures in prognostic metrics, with netHD excelling. All nine gene signatures were grouped into three clusters, with netHD and netNE enriching the immune-related interferon-gamma pathway. Stratifying TCGA patients into two groups based on netHD revealed significant immunological differences and variations in 20 of 50 cancer hallmarks, emphasizing immune-related markers. This approach leverages pan-cancer insights to enhance breast cancer prognosis, facilitating insight transfer and improving tumor homogeneity understanding.Abstract graph of network-based insights translating pan-cancer immunotherapy responses to breast cancer prognosis. This abstract graph illustrates the conceptual framework for transferring immunotherapy response insights from pan-cancer studies to breast cancer prognosis. It highlights the integration of PPI networks to bridge genetic data and clinical phenotypes. The network-based method facilitates the identification of prognostic gene signatures in breast cancer by leveraging immunotherapy response information, providing a novel perspective on tumor homogeneity and its implications for clinical outcomes.

CaClust: linking genotype to transcriptional heterogeneity of follicular lymphoma using BCR and exomic variants

Tumours exhibit high genotypic and transcriptional heterogeneity. Both affect cancer progression and treatment, but have been predominantly studied separately in follicular lymphoma. To comprehensively investigate the evolution and genotype-to-phenotype maps in follicular lymphoma, we introduce CaClust, a probabilistic graphical model integrating deep whole exome, single-cell RNA and B-cell receptor sequencing data to infer clone genotypes, cell-to-clone mapping, and single-cell genotyping. CaClust outperforms a state-of-the-art model on simulated and patient data. In-depth analyses of single cells from four samples showcase effects of driver mutations, follicular lymphoma evolution, possible therapeutic targets, and single-cell genotyping that agrees with an independent targeted resequencing experiment.

Cellular landscape of adrenocortical carcinoma at single-nuclei resolution

Adrenocortical carcinoma (ACC) is a rare yet devastating tumour of the adrenal gland with a molecular pathology that remains incompletely understood. To gain novel insights into the cellular landscape of ACC, we generated single-nuclei RNA sequencing (snRNA-seq) data sets from twelve ACC tumour samples and analysed these alongside snRNA-seq data sets from normal adrenal glands (NAGs). We find the ACC tumour microenvironment to be relatively devoid of immune cells compared to NAG tissues, consistent with known high tumour purity values for ACC as an immunologically "cold" tumour. Our analysis identifies three separate groups of ACC samples that are characterised by different relative compositions of adrenocortical cell types. These include cell populations that are specifically enriched in the most clinically aggressive and hormonally active tumours, displaying hallmarks of reorganised cell mechanobiology and dysregulated steroidogenesis, respectively. We also identified and validated a population of mitotically active adrenocortical cells that strongly overexpress genes POLQ, DIAPH3 and EZH2 to support tumour expansion alongside an LGR4+ progenitor-like or cell-of-origin candidate for adrenocortical carcinogenesis. Trajectory inference suggests the fate adopted by malignant adrenocortical cells upon differentiation is associated with the copy number or allelic balance state of the imprinted DLK1/MEG3 genomic locus, which we verified by assessing bulk tumour DNA methylation status. In conclusion, our results therefore provide new insights into the clinical and cellular heterogeneity of ACC, revealing how genetic perturbations to healthy adrenocortical renewal and zonation provide a molecular basis for disease pathogenesis.

Enigmatic exosomal connection in lung cancer drug resistance

Lung cancer remains a significant global health concern with limited treatment options and poor prognosis, particularly in advanced stages. Small extracellular vesicles such as exosomes, secreted by cancer cells, play a pivotal role in mediating drug resistance in lung cancer. Exosomes have been found to facilitate intercellular communication by transferring various biomolecules between cancer cells and their microenvironment. Additionally, exosomes can transport signaling molecules promoting cancer cell survival and proliferation conferring resistance to chemotherapy. Moreover, exosomes can modulate the tumor microenvironment by inducing phenotypic changes hindering drug response. Understanding the role of exosomes in mediating drug resistance in lung cancer is crucial for developing novel therapeutic strategies and biomarkers to overcome treatment limitations. In this review, we summarize the current knowledge on conventional and emerging drug resistance mechanisms and the involvement of exosomes as well as exosome-mediated factors mediating drug resistance in lung cancer.

Research autopsy programmes in oncology: shared experience from 14 centres across the world

While there is a great clinical need to understand the biology of metastatic cancer in order to treat it more effectively, research is hampered by limited sample availability. Research autopsy programmes can crucially advance the field through synchronous, extensive, and high-volume sample collection. However, it remains an underused strategy in translational research. Via an extensive questionnaire, we collected information on the study design, enrolment strategy, study conduct, sample and data management, and challenges and opportunities of research autopsy programmes in oncology worldwide. Fourteen programmes participated in this study. Eight programmes operated 24 h/7 days, resulting in a lower median postmortem interval (time between death and start of the autopsy, 4 h) compared with those operating during working hours (9 h). Most programmes (n = 10) succeeded in collecting all samples within a median of 12 h after death. A large number of tumour sites were sampled during each autopsy (median 15.5 per patient). The median number of samples collected per patient was 58, including different processing methods for tumour samples but also non-tumour tissues and liquid biopsies. Unique biological insights derived from these samples included metastatic progression, treatment resistance, disease heterogeneity, tumour dormancy, interactions with the tumour micro-environment, and tumour representation in liquid biopsies. Tumour patient-derived xenograft (PDX) or organoid (PDO) models were additionally established, allowing for drug discovery and treatment sensitivity assays. Apart from the opportunities and achievements, we also present the challenges related with postmortem sample collections and strategies to overcome them, based on the shared experience of these 14 programmes. Through this work, we hope to increase the transparency of postmortem tissue donation, to encourage and aid the creation of new programmes, and to foster collaborations on these unique sample collections. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Heterogeneity generating capacity in tumorigenesis and cancer therapeutics

Cells of multicellular organisms generate heterogeneity in a controlled and transient fashion during embryogenesis, which can be reactivated in pathologies such as cancer. Although genomic heterogeneity is an important part of tumorigenesis, continuous generation of phenotypic heterogeneity is central for the adaptation of cancer cells to the challenges of tumorigenesis and response to therapy. Here I discuss the capacity of generating heterogeneity, hereafter called cell hetness, in cancer cells both as the activation of hetness oncogenes and inactivation of hetness tumor suppressor genes, which increase the generation of heterogeneity, ultimately producing an increase in adaptability and cell fitness. Transcriptomic high hetness states in therapy-tolerant cell states denote its importance in cancer resistance to therapy. The definition of the concept of hetness will allow the understanding of its origins, its control during embryogenesis, its loss of control in tumorigenesis and cancer therapeutics and its active targeting.

A partial epithelial-mesenchymal transition signature for highly aggressive colorectal cancer cells that survive under nutrient restriction

Partial epithelial-mesenchymal transition (p-EMT) has recently been identified as a hybrid state consisting of cells with both epithelial and mesenchymal characteristics and is associated with the migration, metastasis, and chemoresistance of cancer cells. Here, we describe the induction of p-EMT in starved colorectal cancer (CRC) cells and identify a p-EMT gene signature that can predict prognosis. Functional characterisation of starvation-induced p-EMT in HCT116, DLD1, and HT29 cells showed changes in proliferation, morphology, and drug sensitivity, supported by in vivo studies using the chorioallantoic membrane model. An EMT-specific quantitative polymerase chain reaction (qPCR) array was used to screen for deregulated genes, leading to the establishment of an in silico gene signature that was correlated with poor disease-free survival in CRC patients along with the CRC consensus molecular subtype CMS4. Among the significantly deregulated p-EMT genes, a triple-gene signature consisting of SERPINE1, SOX10, and epidermal growth factor receptor (EGFR) was identified. Starvation-induced p-EMT was characterised by increased migratory potential and chemoresistance, as well as E-cadherin processing and internalisation. Both gene signature and E-cadherin alterations could be reversed by the proteasomal inhibitor MG132. Spatially resolving EGFR expression with high-resolution immunofluorescence imaging identified a proliferation stop in starved CRC cells caused by EGFR internalisation. In conclusion, we have gained insight into a previously undiscovered EMT mechanism that may become relevant when tumour cells are under nutrient stress, as seen in early stages of metastasis. Targeting this process of tumour cell dissemination might help to prevent EMT and overcome drug resistance. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Increased heterogeneity in expression of genes associated with cancer progression and drug resistance

Fluctuations in the number of regulatory molecules and differences in timings of molecular events can generate variation in gene expression among genetically identical cells in the same environmental condition. This variation, termed as expression noise, can create differences in metabolic state and cellular functions, leading to phenotypic heterogeneity. Expression noise and phenotypic heterogeneity have been recognized as important contributors to intra-tumor heterogeneity, and have been associated with cancer growth, progression, and therapy resistance. However, how expression noise changes with cancer progression in actual cancer patients has remained poorly explored. Such an analysis, through identification of genes with increasing expression noise, can provide valuable insights into generation of intra-tumor heterogeneity, and could have important implications for understanding immune-suppression, drug tolerance and therapy resistance. In this work, we performed a genome-wide identification of changes in gene expression noise with cancer progression using single-cell RNA-seq data of lung adenocarcinoma patients at different stages of cancer. We identified 37 genes in epithelial cells that showed an increasing noise trend with cancer progression, many of which were also associated with cancer growth, EMT and therapy resistance. We found that expression of several of these genes was positively associated with expression of mitochondrial genes, suggesting an important role of mitochondria in generation of heterogeneity. In addition, we uncovered substantial differences in sample-specific noise profiles which could have implications for personalized prognosis and treatment.

The contribution of asymmetric cell division to phenotypic heterogeneity in cancer

Cells have evolved intricate mechanisms for dividing their contents in the most symmetric way during mitosis. However, a small proportion of cell divisions results in asymmetric segregation of cellular components, which leads to differences in the characteristics of daughter cells. Although the classical function of asymmetric cell division (ACD) in the regulation of pluripotency is the generation of one differentiated daughter cell and one self-renewing stem cell, recent evidence suggests that ACD plays a role in other physiological processes. In cancer, tumor heterogeneity can result from the asymmetric segregation of genetic material and other cellular components, resulting in cell-to-cell differences in fitness and response to therapy. Defining the contribution of ACD in generating differences in key features relevant to cancer biology is crucial to advancing our understanding of the causes of tumor heterogeneity and developing strategies to mitigate or counteract it. In this Review, we delve into the occurrence of asymmetric mitosis in cancer cells and consider how ACD contributes to the variability of several phenotypes. By synthesizing the current literature, we explore the molecular mechanisms underlying ACD, the implications of phenotypic heterogeneity in cancer, and the complex interplay between these two phenomena.

Cancer cell cycle heterogeneity as a critical determinant of therapeutic resistance

Intra-tumor heterogeneity is now arguably one of the most-studied topics in tumor biology, as it represents a major obstacle to effective cancer treatment. Since tumor cells are highly diverse at genetic, epigenetic, and phenotypic levels, intra-tumor heterogeneity can be assumed as an important contributing factor to the nullification of chemotherapeutic effects, and recurrence of the tumor. Based on the role of heterogeneous subpopulations of cancer cells with varying cell-cycle dynamics and behavior during cancer progression and treatment; herein, we aim to establish a comprehensive definition for adaptation of neoplastic cells against therapy. We discuss two parallel and yet distinct subpopulations of tumor cells that play pivotal roles in reducing the effects of chemotherapy: "resistant" and "tolerant" populations. Furthermore, this review also highlights the impact of the quiescent phase of the cell cycle as a survival mechanism for cancer cells. Beyond understanding the mechanisms underlying the quiescence, it provides an insightful perspective on cancer stem cells (CSCs) and their dual and intertwined functions based on their cell cycle state in response to treatment. Moreover, CSCs, epithelial-mesenchymal transformed cells, circulating tumor cells (CTCs), and disseminated tumor cells (DTCs), which are mostly in a quiescent state of the cell cycle are proved to have multiple biological links and can be implicated in our viewpoint of cell cycle heterogeneity in tumors. Overall, increasing our knowledge of cell cycle heterogeneity is a key to identifying new therapeutic solutions, and this emerging concept may provide us with new opportunities to prevent the dreadful cancer recurrence.

Mitochondrial response of glioma cells to temozolomide

Metabolic adaptations are central for carcinogenesis and response to therapy, but little is known about the contribution of mitochondrial dynamics to the response of glioma cells to the standard treatment with temozolomide (TMZ). Glioma cells responded to TMZ with mitochondrial mass increased and the production of round structures of dysfunctional mitochondria. At single-cell level, asymmetric mitosis contributed to the heterogeneity of mitochondrial levels. It affected the fitness of cells in control and treated condition, indicating that the mitochondrial levels are relevant for glioma cell fitness in the presence of TMZ.

Metabolic Footprinting-Based DNA-AuNP Encoders for Extracellular Metabolic Response Profiling

Metabolic footprinting as a convenient and non-invasive cell metabolomics strategy relies on monitoring the whole extracellular metabolic process. It covers nutrient consumption and metabolite secretion of in vitro cell culture, which is hindered by low universality owing to pre-treatment of the cell medium and special equipment. Here, we report the design and a variety of applicability, for quantifying extracellular metabolism, of fluorescently labeled single-stranded DNA (ssDNA)-AuNP encoders, whose multi-modal signal response is triggered by extracellular metabolites. We constructed metabolic response profiling of cells by detecting extracellular metabolites in different tumor cells and drug-induced extracellular metabolites. We further assessed the extracellular metabolism differences using a machine learning algorithm. This metabolic response profiling based on the DNA-AuNP encoder strategy is a powerful complement to metabolic footprinting, which significantly applies potential non-invasive identification of tumor cell heterogeneity.

The role of mitosis in generating fitness heterogeneity

Cancer cells have heterogeneous fitness, and this heterogeneity stems from genetic and epigenetic sources. Here, we sought to assess the contribution of asymmetric mitosis (AM) and time on the variability of fitness in sister cells. Around one quarter of sisters had differences in fitness, assessed as the intermitotic time (IMT), from 330 to 510 min. Phenotypes related to fitness, such as ERK activity (herein referring to ERK1 and ERK2, also known as MAPK3 and MAPK1, respectively), DNA damage and nuclear morphological phenotypes were also asymmetric at mitosis or turned asymmetric over the course of the cell cycle. The ERK activity of mother cell was found to influence the ERK activity and the IMT of the daughter cells, and cells with ERK asymmetry at mitosis produced more offspring with AMs, suggesting heritability of the AM phenotype for ERK activity. Our findings demonstrate how variabilities in sister cells can be generated, contributing to the phenotype heterogeneities in tumor cells.

Single-cell technologies: From research to application

In recent years, more and more single-cell technologies have been developed. A vast amount of single-cell omics data has been generated by large projects, such as the Human Cell Atlas, the Mouse Cell Atlas, the Mouse RNA Atlas, the Mouse ATAC Atlas, and the Plant Cell Atlas. Based on these single-cell big data, thousands of bioinformatics algorithms for quality control, clustering, cell-type annotation, developmental inference, cell-cell transition, cell-cell interaction, and spatial analysis are developed. With powerful experimental single-cell technology and state-of-the-art big data analysis methods based on artificial intelligence, the molecular landscape at the single-cell level can be revealed. With spatial transcriptomics and single-cell multi-omics, even the spatial dynamic multi-level regulatory mechanisms can be deciphered. Such single-cell technologies have many successful applications in oncology, assisted reproduction, embryonic development, and plant breeding. We not only review the experimental and bioinformatics methods for single-cell research, but also discuss their applications in various fields and forecast the future directions for single-cell technologies. We believe that spatial transcriptomics and single-cell multi-omics will become the next booming business for mechanism research and commercial industry.

The crossroads of adenosinergic pathway and epithelial-mesenchymal plasticity in cancer

Epithelial-mesenchymal transition (EMT) is a key mechanism related to tumor progression, invasion, metastasis, resistance to therapy and poor prognosis in several types of cancer. However, targeting EMT or partial-EMT, as well as the molecules involved in this process, has remained a challenge. Recently, the CD73 enzyme, which hydrolyzes AMP to produce adenosine (ADO), has been linked to the EMT process. This relationship is not only due to the production of the immunosuppressant ADO but also to its role as a receptor for extracellular matrix proteins, being involved in cell adhesion and migration. This article reviews the crosstalk between the adenosinergic pathway and the EMT program and the impact of this interrelation on cancer development and progression. An in silico analysis of RNAseq datasets showed that several tumor types have a significant correlation between an EMT score and NT5E (CD73) and ENTPD1 (CD39) expressions, with the strongest correlations in prostate adenocarcinoma. Furthermore, it is evident that the cooperation between EMT and adenosinergic pathway in tumor progression is context and tumor-dependent. The increased knowledge about this topic will help broaden the view to explore new treatments and therapies for different types of cancer.