Highly variable cancer subpopulations that exhibit enhanced transcriptome variability and metastatic fitness. Nat Commun. 2016;7:11246. [PMID: 27138336 PMCID: PMC4857405 DOI: 10.1038/ncomms11246]

Lineage Tracing Reveals Clone-Specific Responses to Doxorubicin in Triple-Negative Breast Cancer

Triple-negative breast cancer, characterized by aggressive growth and high intratumor heterogeneity, presents a significant clinical challenge. Here, we use a lineage-tracing system, ClonMapper, which couples heritable clonal identifying tags with single-cell RNA-sequencing (scRNA-seq), to better elucidate the response to doxorubicin in a model of TNBC. We demonstrate that, while there is a dose-dependent reduction in overall clonal diversity, there is no pre-existing resistance signature among surviving clones. Separately, we found the existence of two transcriptomically distinct clonal subpopulations that remain through the course of treatment. Among clones persisting across multiple samples we identified divergent phenotypes, suggesting a response to treament independent of clonal identity. Finally, a subset of clones harbor novel changes in expression following treatment. The clone and sample specific responses to treatment identified herein highlight the need for better personalized treatment strategies to overcome tumor heterogeneity.

Microfluidics for morpholomics and spatial omics applications

Creative designs, precise fluidic manipulation, and automation have supported the development of microfluidics for single-cell applications. Together with the advancements in detection technologies and artificial intelligence (AI), microfluidic-assisted platforms have been increasingly used for new modalities of single-cell investigations and in spatial omics applications. This review explores the use of microfluidic technologies for morpholomics and spatial omics with a focus on single-cell and tissue characterization. We emphasize how various fluid dynamic principles and unique design integrations enable highly precise fluid manipulation, enhancing sample handling in morpholomics. Additionally, we examine the use of microfluidics-assisted spatial barcoding with micrometer resolutions for the spatial profiling of tissue specimens. Finally, we discuss how microfluidics can serve as a bridge for integrating multiple unique fields in omics research and outline key challenges that these technologies may face in practical translation.

Epigenomic heterogeneity as a source of tumour evolution

In the past decade, remarkable progress in cancer medicine has been achieved by the development of treatments that target DNA sequence variants. However, a purely genetic approach to treatment selection is hampered by the fact that diverse cell states can emerge from the same genotype. In multicellular organisms, cell-state heterogeneity is driven by epigenetic processes that regulate DNA-based functions such as transcription; disruption of these processes is a hallmark of cancer that enables the emergence of defective cell states. Advances in single-cell technologies have unlocked our ability to quantify the epigenomic heterogeneity of tumours and understand its mechanisms, thereby transforming our appreciation of how epigenomic changes drive cancer evolution. This Review explores the idea that epigenomic heterogeneity and plasticity act as a reservoir of cell states and therefore as a source of tumour evolution. Best practices to quantify epigenomic heterogeneity and explore its various causes and consequences are discussed, including epigenomic reprogramming, stochastic changes and lasting memory. The design of new therapeutic approaches to restrict epigenomic heterogeneity, with the long-term objective of limiting cancer development and progression, is also addressed.

Unlocking Nature's Potential: Ferritin as a Universal Nanocarrier for Amplified Cancer Therapy Testing via 3D Microtissues

In the existing development of extensive drug screening models, 3D cell cultures outshine conventional 2D monolayer cells by closely imitating the in vivo tumor microenvironment. This makes 3D culture a more physiologically relevant and convenient system in the regime of preclinical drug testing. In the nanomedicinal world, nanoconjugates as nanocarriers are largely hunted due to their capability of precisely binding to target cells and distributing essential dosages of therapeutic drugs with enhanced safety profiles. Thus, for boosted drug availability, the evolution from conventional drug treatment to combination therapies and last switching to drug carriers has gained significant progression in cancer cure. In contrast to conventional engineered nanoparticles, herein, we successfully designed biomolecule (ferritin)-based drug nanoconjugates effective both as a single drug (valproic acid-VPA) and twin-drug (valproic acid/doxorubicin-Dox) carriers, which dramatically enhance the proficiency of the tumor therapeutic modality. To question the reported adjuvant drug property of VPA, we progressed utilizing at first VPA alone as an effective yet exclusive tumor therapy when delivered via some carrier molecule, in particular protein. Subsequently, we paralleled this comprehensive investigation output to compare and test the coloading strategy of drugs and observe the synergistic and/or additive behavior of VPA in conjugation with other anticancer agents (Dox) while given via a carrier molecule. To approach this, VPA and/or Dox molecules were encapsulated into the ferritin (F) cavity using a thermosensitive synthesis method by maintaining the temperature at 60 °C. The successful encapsulation of drugs in the protein nanocage was confirmed through various characterization techniques. The F-VPA/F-VPA-Dox nanoconjugates exhibited similar morphology and structural characteristics to the hollow ferritin cage and showed significant cytotoxicity than the naked drugs when tested on physiologically relevant 3D spheroid models. Precisely, our first designed carrier nanoconjugate, i.e., F-VPA, offered more than a 3-fold increased intratumoral drug concentration than free VPA and significantly suppressed tumor growth after a single-dose treatment. However, our second modeled carrier nanoconjugate, viz. F-VPA-Dox, revealed an extended median survival period and lesser toxicity when administered at a much more effective dose (∼3-5 μM), in 3D tumor spheroid models of various cancer cell lines. All in all, importantly, ferritin nanoconjugates exhibited an enhanced tumor inhibition rate with a single-dose treatment, which further confirms the benefits of the active targeting property of these nanocarriers. Moreover, these nanocarriers also offer to deliver a significant dose of the therapeutic drug into tumor cells, alongside tremendous biocompatibility and safety profiles in numerous tumor 3D spheroid models.

Mechanistic in silico explorations of the immunogenic and synergistic effects of radiotherapy and immunotherapy: a critical review

Immunotherapy is a rapidly evolving field, with many models attempting to describe its impact on the immune system, especially when paired with radiotherapy. Tumor response to this combination involves a complex spatiotemporal dynamic which makes either clinical or pre-clinical in vivo investigation across the resulting extensive solution space extremely difficult. In this review, several in silico models of the interaction between radiotherapy, immunotherapy, and the patient's immune system are examined. The study included only mathematical models published in English that investigated the effects of radiotherapy on the immune system, or the effect of immuno-radiotherapy with immune checkpoint inhibitors. The findings indicate that treatment efficacy was predicted to improve when both radiotherapy and immunotherapy were administered, compared to radiotherapy or immunotherapy alone. However, the models do not agree on the optimal schedule and fractionation of radiotherapy and immunotherapy. This corresponds to relevant clinical trials, which report an improved treatment efficacy with combination therapy, however, the optimal scheduling varies between clinical trials. This discrepancy between the models can be attributed to the variation in model approach and the specific cancer types modeled, making the determination of the optimum general treatment schedule and model challenging. Further research needs to be conducted with similar data sets to evaluate the best model and treatment schedule for a specific cancer type and stage.

Altruistic feeding and cell-cell signaling during bacterial differentiation actively enhance phenotypic heterogeneity

Starvation triggers bacterial spore formation, a committed differentiation program that transforms a vegetative cell into a dormant spore. Cells in a population enter sporulation nonuniformly to secure against the possibility that favorable growth conditions, which put sporulation-committed cells at a disadvantage, may resume. This heterogeneous behavior is initiated by a passive mechanism: stochastic activation of a master transcriptional regulator. Here, we identify a cell-cell communication pathway containing the proteins ShfA (YabQ) and ShfP (YvnB) that actively promotes phenotypic heterogeneity, wherein Bacillus subtilis cells that start sporulating early use a calcineurin-like phosphoesterase to release glycerol, which simultaneously acts as a signaling molecule and a nutrient to delay nonsporulating cells from entering sporulation. This produced a more diverse population that was better poised to exploit a sudden influx of nutrients compared to those generating heterogeneity via stochastic gene expression alone. Although conflict systems are prevalent among microbes, genetically encoded cooperative behavior in unicellular organisms can evidently also boost inclusive fitness.

Self-organizing ovarian somatic organoids preserve cellular heterogeneity and reveal cellular contributions to ovarian aging

Ovarian somatic cells are essential for reproductive function, but no existing ex vivo models recapitulate the cellular heterogeneity or interactions within this compartment. We engineered a novel ovarian somatic organoid model by culturing a stroma-enriched fraction of mouse ovaries in scaffold-free agarose micromolds. Ovarian somatic organoids self-organized, maintained diverse cell populations, produced extracellular matrix, and secreted hormones. Organoids generated from reproductively old mice exhibited reduced aggregation and growth compared to young counterparts, as well as differences in cellular composition. Interestingly, matrix fibroblasts from old mice demonstrated upregulation of pathways associated with the actin cytoskeleton and downregulation of cell adhesion pathways, indicative of increased cellular stiffness which may impair organoid aggregation. Cellular morphology, which is regulated by the cytoskeleton, significantly changed with age and in response to actin depolymerization. Moreover, actin depolymerization rescued age-associated organoid aggregation deficiency. Overall, ovarian somatic organoids have advanced fundamental knowledge of cellular contributions to ovarian aging.

Stain-Free Approach to Determine and Monitor Cell Heath Using Supervised and Unsupervised Image-Based Deep Learning

Cell-based medicinal products (CBMPs) are a growing class of therapeutics that promise new treatments for complex and rare diseases. Given the inherent complexity of the whole human cells comprising CBMPs, there is a need for robust and fast analytical methods for characterization, process monitoring, and quality control (QC) testing during their manufacture. Existing techniques to evaluate and monitor cell quality typically constitute labor-intensive, expensive, and highly specific staining assays. In this work, we combine image-based deep learning with flow imaging microscopy (FIM) to predict cell health metrics using cellular morphology "fingerprints" extracted from images of unstained Jurkat cells (immortalized human T-lymphocyte cells). A supervised (i.e., algorithm trained with human-generated labels for images) fingerprinting algorithm, trained on images of unstained healthy and dead cells, provides a robust stain-free, non-invasive, and non-destructive method for determining cell viability. Results from the stain-free method are in good agreement with traditional stain-based cytometric viability measurements. Additionally, when trained with images of healthy cells, dead cells and cells undergoing chemically induced apoptosis, the supervised fingerprinting algorithm is able to distinguish between the three cell states, and the results are independent of specific treatments or signaling pathways. We then show that an unsupervised variational autoencoder (VAE) algorithm trained on the same images, but without human-generated labels, is able to distinguish between samples of healthy, dead and apoptotic cells along with cellular debris based on learned morphological features and without human input. With this, we demonstrate that VAEs are a powerful exploratory technique that can be used as a process monitoring analytical tool.

Multicellular immune ecotypes within solid tumors predict real-world therapeutic benefits with immune checkpoint inhibitors

Background

Cancer initiation, progression, and immune evasion depend on the tumor microenvironment (TME). Thus, understanding the TME immune architecture is essential for understanding tumor metastasis and therapy response. This study aimed to create an immune cell states (CSs) atlas using bulk RNA-seq data enriched by eco-type analyses to resolve the complex immune architectures in the TME.

Methods

We employed EcoTyper, a machine-learning (ML) framework, to study the real-world prognostic significance of immune CSs and multicellular ecosystems, utilizing molecular data from 1,610 patients with multiple malignancies who underwent immune checkpoint inhibitor (ICI) therapy within the ORIEN Avatar cohort, a well-annotated real-world dataset.

Results

Our analysis revealed consistent ICI-specific prognostic TME carcinoma ecotypes (CEs) (including CE1, CE9, CE10) across our pan-cancer dataset, where CE1 being more lymphocyte-deficient and CE10 being more proinflammatory. Also, the analysis of specific immune CSs across different cancers showed consistent CD8+ and CD4+ T cell CS distribution patterns. Furthermore, survival analysis of the ORIEN ICI cohort demonstrated that ecotype CE9 is associated with the most favorable survival outcomes, while CE2 is linked to the least favorable outcomes. Notably, the melanoma-specific prognostic EcoTyper model confirmed that lower predicted risk scores are associated with improved survival and better response to immunotherapy. Finally, de novo discovery of ecotypes in the ORIEN ICI dataset identified Ecotype E3 as significantly associated with poorer survival outcomes.

Conclusion

Our findings offer important insights into refining the patient selection process for immunotherapy in real-world practice and guiding the creation of novel therapeutic strategies to target specific ecotypes within the TME.

Living in a noisy world-origins of gene expression noise and its impact on cellular decision-making

The expression level of a gene can vary between genetically identical cells under the same environmental condition-a phenomenon referred to as gene expression noise. Several studies have now elucidated a central role of transcription factors in the generation of expression noise. Transcription factors, as the key components of gene regulatory networks, drive many important cellular decisions in response to cellular and environmental signals. Therefore, a very relevant question is how expression noise impacts gene regulation and influences cellular decision-making. In this Review, we summarize the current understanding of the molecular origins of expression noise, highlighting the role of transcription factors in this process, and discuss the ways in which noise can influence cellular decision-making. As advances in single-cell technologies open new avenues for studying expression noise as well as gene regulatory circuits, a better understanding of the influence of noise on cellular decisions will have important implications for many biological processes.

Exploring novel protein-based biomarkers for advancing breast cancer diagnosis: A review

This review provides a contemporary examination of the evolving landscape of breast cancer (BC) diagnosis, focusing on the pivotal role of novel protein-based biomarkers. The overview begins by elucidating the multifaceted nature of BC, exploring its prevalence, subtypes, and clinical complexities. A critical emphasis is placed on the transformative impact of proteomics, dissecting the proteome to unravel the molecular intricacies of BC. Navigating through various sources of samples crucial for biomarker investigations, the review underscores the significance of robust sample processing methods and their validation in ensuring reliable outcomes. The central theme of the review revolves around the identification and evaluation of novel protein-based biomarkers. Cutting-edge discoveries are summarised, shedding light on emerging biomarkers poised for clinical application. Nevertheless, the review candidly addresses the challenges inherent in biomarker discovery, including issues of standardisation, reproducibility, and the complex heterogeneity of BC. The future direction section envisions innovative strategies and technologies to overcome existing challenges. In conclusion, the review summarises the current state of BC biomarker research, offering insights into the intricacies of proteomic investigations. As precision medicine gains momentum, the integration of novel protein-based biomarkers emerges as a promising avenue for enhancing the accuracy and efficacy of BC diagnosis. This review serves as a compass for researchers and clinicians navigating the evolving landscape of BC biomarker discovery, guiding them toward transformative advancements in diagnostic precision and personalised patient care.

Clinical Correlation of Transcription Factor SOX3 in Cancer: Unveiling Its Role in Tumorigenesis

Members of the SOX (SRY-related HMG box) family of transcription factors are crucial for embryonic development and cell fate determination. This review investigates the role of SOX3 in cancer, as aberrations in SOX3 expression have been implicated in several cancers, including osteosarcoma, breast, esophageal, endometrial, ovarian, gastric, hepatocellular carcinomas, glioblastoma, and leukemia. These dysregulations modulate key cancer outcomes such as apoptosis, epithelial-mesenchymal transition (EMT), invasion, migration, cell cycle, and proliferation, contributing to cancer development. SOX3 exhibits varied expression patterns correlated with clinicopathological parameters in diverse tumor types. This review aims to elucidate the nuanced role of SOX3 in tumorigenesis, correlating its expression with clinical and pathological characteristics in cancer patients and cellular modelsBy providing a comprehensive exploration of SOX3 involvement in cancer, this review underscores the multifaceted role of SOX3 across distinct tumor types. The complexity uncovered in SOX3 function emphasizes the need for further research to unravel its full potential in cancer therapeutics.

Cis-regulatory control of transcriptional timing and noise in response to estrogen

Cis-regulatory elements control transcription levels, temporal dynamics, and cell-cell variation or transcriptional noise. However, the combination of regulatory features that control these different attributes is not fully understood. Here, we used single-cell RNA-seq during an estrogen treatment time course and machine learning to identify predictors of expression timing and noise. We found that genes with multiple active enhancers exhibit faster temporal responses. We verified this finding by showing that manipulation of enhancer activity changes the temporal response of estrogen target genes. Analysis of transcriptional noise uncovered a relationship between promoter and enhancer activity, with active promoters associated with low noise and active enhancers linked to high noise. Finally, we observed that co-expression across single cells is an emergent property associated with chromatin looping, timing, and noise. Overall, our results indicate a fundamental tradeoff between a gene's ability to quickly respond to incoming signals and maintain low variation across cells.

Altruistic feeding and cell-cell signaling during bacterial differentiation actively enhance phenotypic heterogeneity

Starvation triggers bacterial spore formation, a committed differentiation program that transforms a vegetative cell into a dormant spore. Cells in a population enter sporulation non-uniformly to secure against the possibility that favorable growth conditions, which puts sporulation-committed cells at a disadvantage, may resume. This heterogeneous behavior is initiated by a passive mechanism: stochastic activation of a master transcriptional regulator. Here, we identify a cell-cell communication pathway that actively promotes phenotypic heterogeneity, wherein Bacillus subtilis cells that start sporulating early utilize a calcineurin-like phosphoesterase to release glycerol, which simultaneously acts as a signaling molecule and a nutrient to delay non-sporulating cells from entering sporulation. This produced a more diverse population that was better poised to exploit a sudden influx of nutrients compared to those generating heterogeneity via stochastic gene expression alone. Although conflict systems are prevalent among microbes, genetically encoded cooperative behavior in unicellular organisms can evidently also boost inclusive fitness.

Cell morphology best predicts tumorigenicity and metastasis in vivo across multiple TNBC cell lines of different metastatic potential

BACKGROUND

Metastasis is the leading cause of death in breast cancer patients. For metastasis to occur, tumor cells must invade locally, intravasate, and colonize distant tissues and organs, all steps that require tumor cell migration. The majority of studies on invasion and metastasis rely on human breast cancer cell lines. While it is known that these cells have different properties and abilities for growth and metastasis, the in vitro morphological, proliferative, migratory, and invasive behavior of these cell lines and their correlation to in vivo behavior is poorly understood. Thus, we sought to classify each cell line as poorly or highly metastatic by characterizing tumor growth and metastasis in a murine model of six commonly used human triple-negative breast cancer xenografts, as well as determine which in vitro assays commonly used to study cell motility best predict in vivo metastasis.

METHODS

We evaluated the liver and lung metastasis of human TNBC cell lines MDA-MB-231, MDA-MB-468, BT549, Hs578T, BT20, and SUM159 in immunocompromised mice. We characterized each cell line's cell morphology, proliferation, and motility in 2D and 3D to determine the variation in these parameters between cell lines.

RESULTS

We identified MDA-MB-231, MDA-MB-468, and BT549 cells as highly tumorigenic and metastatic, Hs578T as poorly tumorigenic and metastatic, BT20 as intermediate tumorigenic with poor metastasis to the lungs but highly metastatic to the livers, and SUM159 as intermediate tumorigenic but poorly metastatic to the lungs and livers. We showed that metrics that characterize cell morphology are the most predictive of tumor growth and metastatic potential to the lungs and liver. Further, we found that no single in vitro motility assay in 2D or 3D significantly correlated with metastasis in vivo.

CONCLUSIONS

Our results provide an important resource for the TNBC research community, identifying the metastatic potential of 6 commonly used cell lines. Our findings also support the use of cell morphological analysis to investigate the metastatic potential and emphasize the need for multiple in vitro motility metrics using multiple cell lines to represent the heterogeneity of metastasis in vivo.

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.

Cis-regulatory control of transcriptional timing and noise in response to estrogen

Cis-regulatory elements control transcription levels, temporal dynamics, and cell-cell variation or transcriptional noise. However, the combination of regulatory features that control these different attributes is not fully understood. Here, we used single cell RNA-seq during an estrogen treatment time course and machine learning to identify predictors of expression timing and noise. We find that genes with multiple active enhancers exhibit faster temporal responses. We verified this finding by showing that manipulation of enhancer activity changes the temporal response of estrogen target genes. Analysis of transcriptional noise uncovered a relationship between promoter and enhancer activity, with active promoters associated with low noise and active enhancers linked to high noise. Finally, we observed that co-expression across single cells is an emergent property associated with chromatin looping, timing, and noise. Overall, our results indicate a fundamental tradeoff between a gene's ability to quickly respond to incoming signals and maintain low variation across cells.

Single molecule MATAC-seq reveals key determinants of DNA replication origin efficiency

Stochastic origin activation gives rise to significant cell-to-cell variability in the pattern of genome replication. The molecular basis for heterogeneity in efficiency and timing of individual origins is a long-standing question. Here, we developed Methylation Accessibility of TArgeted Chromatin domain Sequencing (MATAC-Seq) to determine single-molecule chromatin accessibility of four specific genomic loci. MATAC-Seq relies on preferential modification of accessible DNA by methyltransferases combined with Nanopore-Sequencing for direct readout of methylated DNA-bases. Applying MATAC-Seq to selected early-efficient and late-inefficient yeast replication origins revealed large heterogeneity of chromatin states. Disruption of INO80 or ISW2 chromatin remodeling complexes leads to changes at individual nucleosomal positions that correlate with changes in their replication efficiency. We found a chromatin state with an accessible nucleosome-free region in combination with well-positioned +1 and +2 nucleosomes as a strong predictor for efficient origin activation. Thus, MATAC-Seq identifies the large spectrum of alternative chromatin states that co-exist on a given locus previously masked in population-based experiments and provides a mechanistic basis for origin activation heterogeneity during eukaryotic DNA replication. Consequently, our single-molecule chromatin accessibility assay will be ideal to define single-molecule heterogeneity across many fundamental biological processes such as transcription, replication, or DNA repair in vitro and ex vivo.

TGF-β, EMT, and resistance to anti-cancer treatment

Transforming growth factor-β (TGF-β) signaling regulates cell-specific programs involved in embryonic development, wound-healing, and immune homeostasis. Yet, during tumor progression, these TGF-β-mediated programs are altered, leading to epithelial cell plasticity and a reprogramming of epithelial cells into mesenchymal lineages through epithelial-to-mesenchymal transition (EMT), a critical developmental program in morphogenesis and organogenesis. These changes, in turn, lead to enhanced carcinoma cell invasion, metastasis, immune cell differentiation, immune evasion, and chemotherapy resistance. Here, we discuss EMT as one of the critical programs associated with carcinoma cell plasticity and the influence exerted by TGF-β on carcinoma status and function. We further explore the composition of carcinoma and other cell populations within the tumor microenvironment, and consider the relevant outcomes related to the programs associated with cancer treatment resistance.

Data-driven structural analysis of small cell lung cancer transcription factor network suggests potential subtype regulators and transition pathways

Small cell lung cancer (SCLC) is an aggressive disease and challenging to treat due to its mixture of transcriptional subtypes and subtype transitions. Transcription factor (TF) networks have been the focus of studies to identify SCLC subtype regulators via systems approaches. Yet, their structures, which can provide clues on subtype drivers and transitions, are barely investigated. Here, we analyze the structure of an SCLC TF network by using graph theory concepts and identify its structurally important components responsible for complex signal processing, called hubs. We show that the hubs of the network are regulators of different SCLC subtypes by analyzing first the unbiased network structure and then integrating RNA-seq data as weights assigned to each interaction. Data-driven analysis emphasizes MYC as a hub, consistent with recent reports. Furthermore, we hypothesize that the pathways connecting functionally distinct hubs may control subtype transitions and test this hypothesis via network simulations on a candidate pathway and observe subtype transition. Overall, structural analyses of complex networks can identify their functionally important components and pathways driving the network dynamics. Such analyses can be an initial step for generating hypotheses and can guide the discovery of target pathways whose perturbation may change the network dynamics phenotypically.

COSMOS: a platform for real-time morphology-based, label-free cell sorting using deep learning

Cells are the singular building blocks of life, and a comprehensive understanding of morphology, among other properties, is crucial to the assessment of underlying heterogeneity. We developed Computational Sorting and Mapping of Single Cells (COSMOS), a platform based on Artificial Intelligence (AI) and microfluidics to characterize and sort single cells based on real-time deep learning interpretation of high-resolution brightfield images. Supervised deep learning models were applied to characterize and sort cell lines and dissociated primary tissue based on high-dimensional embedding vectors of morphology without the need for biomarker labels and stains/dyes. We demonstrate COSMOS capabilities with multiple human cell lines and tissue samples. These early results suggest that our neural networks embedding space can capture and recapitulate deep visual characteristics and can be used to efficiently purify unlabeled viable cells with desired morphological traits. Our approach resolves a technical gap in the ability to perform real-time deep learning assessment and sorting of cells based on high-resolution brightfield images.

Artificial Intelligence-Driven Morphology-Based Enrichment of Malignant Cells from Body Fluid

Cell morphology is a fundamental feature used to evaluate patient specimens in pathologic analysis. However, traditional cytopathology analysis of patient effusion samples is limited by low tumor cell abundance coupled with the high background of nonmalignant cells, restricting the ability of downstream molecular and functional analyses to identify actionable therapeutic targets. We applied the Deepcell platform that combines microfluidic sorting, brightfield imaging, and real-time deep learning interpretations based on multidimensional morphology to enrich carcinoma cells from malignant effusions without cell staining or labels. Carcinoma cell enrichment was validated with whole genome sequencing and targeted mutation analysis, which showed a higher sensitivity for detection of tumor fractions and critical somatic variant mutations that were initially at low levels or undetectable in presort patient samples. Our study demonstrates the feasibility and added value of supplementing traditional morphology-based cytology with deep learning, multidimensional morphology analysis, and microfluidic sorting.

Implications of tumour heterogeneity on cancer evolution and therapy resistance: lessons from breast cancer

Tumour heterogeneity is pervasive amongst many cancers and leads to disease progression, and therapy resistance. In this review, using breast cancer as an exemplar, we focus on the recent advances in understanding the interplay between tumour cells and their microenvironment using single cell sequencing and digital spatial profiling technologies. Further, we discuss the utility of lineage tracing methodologies in pre-clinical models of breast cancer, and how these are being used to unravel new therapeutic vulnerabilities and reveal biomarkers of breast cancer progression. © 2023 The Pathological Society of Great Britain and Ireland.

Review of optical reporters of radiation effects in vivo: tools to quantify improvements in radiation delivery technique

Significance

Radiation damage studies are used to optimize radiotherapy treatment techniques. Although biological indicators of damage are the best assays of effect, they are highly variable due to biological heterogeneity. The free radical radiochemistry can be assayed with optical reporters, allowing for high precision titration of techniques.

Aim

We examine the optical reporters of radiochemistry to highlight those with the best potential for translational use in vivo, as surrogates for biological damage assays, to inform on mechanisms.

Approach

A survey of the radical chemistry effects from reactive oxygen species (ROS) and oxygen itself was completed to link to DNA or biological damage. Optical reporters of ROS include fluorescent, phosphorescent, and bioluminescent molecules that have a variety of activation pathways, and each was reviewed for its in vivo translation potential.

Results

There are molecular reporters of ROS having potential to report within living systems, including derivatives of luminol, 2'7'-dichlorofluorescein diacetate, Amplex Red, and fluorescein. None have unique specificity to singular ROS species. Macromolecular engineered reporters unique to specific ROS are emerging. The ability to directly measure oxygen via reporters, such as Oxyphor and protoporphyrin IX, is an opportunity to quantify the consumption of oxygen during ROS generation, and this translates from in vitro to in vivo use. Emerging techniques, such as ion particle beams, spatial fractionation, and ultra-high dose rate FLASH radiotherapy, provide the motivation for these studies.

Conclusions

In vivo optical reporters of radiochemistry are quantitatively useful for comparing radiotherapy techniques, although their use comes at the cost of the unknown connection to the mechanisms of radiobiological damage. Still their lower measurement uncertainty, compared with biological response assay, makes them an invaluable tool. Linkage to DNA damage and biological damage is needed, and measures such as oxygen consumption serve as useful surrogate measures that translate to in vivo use.

Nonmonotone invasion landscape by noise-aware control of metastasis activator levels

A major pharmacological assumption is that lowering disease-promoting protein levels is generally beneficial. For example, inhibiting metastasis activator BACH1 is proposed to decrease cancer metastases. Testing such assumptions requires approaches to measure disease phenotypes while precisely adjusting disease-promoting protein levels. Here we developed a two-step strategy to integrate protein-level tuning, noise-aware synthetic gene circuits into a well-defined human genomic safe harbor locus. Unexpectedly, engineered MDA-MB-231 metastatic human breast cancer cells become more, then less and then more invasive as we tune BACH1 levels up, irrespective of the native BACH1. BACH1 expression shifts in invading cells, and expression of BACH1's transcriptional targets confirm BACH1's nonmonotone phenotypic and regulatory effects. Thus, chemical inhibition of BACH1 could have unwanted effects on invasion. Additionally, BACH1's expression variability aids invasion at high BACH1 expression. Overall, precisely engineered, noise-aware protein-level control is necessary and important to unravel disease effects of genes to improve clinical drug efficacy.

Impact of variability in cell cycle periodicity on cell population dynamics

The cell cycle consists of a series of orchestrated events controlled by molecular sensing and feedback networks that ultimately drive the duplication of total DNA and the subsequent division of a single parent cell into two daughter cells. The ability to block the cell cycle and synchronize cells within the same phase has helped understand factors that control cell cycle progression and the properties of each individual phase. Intriguingly, when cells are released from a synchronized state, they do not maintain synchronized cell division and rapidly become asynchronous. The rate and factors that control cellular desynchronization remain largely unknown. In this study, using a combination of experiments and simulations, we investigate the desynchronization properties in cervical cancer cells (HeLa) starting from the G1/S boundary following double-thymidine block. Propidium iodide (PI) DNA staining was used to perform flow cytometry cell cycle analysis at regular 8 hour intervals, and a custom auto-similarity function to assess the desynchronization and quantify the convergence to an asynchronous state. In parallel, we developed a single-cell phenomenological model the returns the DNA amount across the cell cycle stages and fitted the parameters using experimental data. Simulations of population of cells reveal that the cell cycle desynchronization rate is primarily sensitive to the variability of cell cycle duration within a population. To validate the model prediction, we introduced lipopolysaccharide (LPS) to increase cell cycle noise. Indeed, we observed an increase in cell cycle variability under LPS stimulation in HeLa cells, accompanied with an enhanced rate of cell cycle desynchronization. Our results show that the desynchronization rate of artificially synchronized in-phase cell populations can be used a proxy of the degree of variance in cell cycle periodicity, an underexplored axis in cell cycle research.

Cell morphology best predicts tumorigenicity and metastasis in vivo across multiple TNBC cell lines of different metastatic potential

Background

Metastasis is the leading cause of death in breast cancer patients. For metastasis to occur, tumor cells must invade locally, intravasate, and colonize distant tissues and organs, all steps that require tumor cell migration. The majority of studies on invasion and metastasis rely on human breast cancer cell lines. While it is known that these cells have different properties and abilities for growth and metastasis, the in vitro morphological, proliferative, migratory, and invasive behavior of these cell lines and their correlation to in vivo behavior is poorly understood. Thus, we sought to classify each cell line as poorly or highly metastatic by characterizing tumor growth and metastasis in a murine model of six commonly used human triple-negative breast cancer xenografts, as well as determine which in vitro assays commonly used to study cell motility best predict in vivo metastasis.

Methods

We evaluated the liver and lung metastasis of human TNBC cell lines MDA-MB-231, MDA-MB-468, BT549, Hs578T, BT20, and SUM159 in immunocompromised mice. We characterized each cell line's cell morphology, proliferation, and motility in 2D and 3D to determine the variation in these parameters between cell lines.

Results

We identified MDA-MB-231, MDA-MB-468, and BT549 cells as highly tumorigenic and metastatic, Hs578T as poorly tumorigenic and metastatic, BT20 as intermediate tumorigenic with poor metastasis to the lungs but highly metastatic to the livers, and SUM159 as intermediate tumorigenic but poorly metastatic to the lungs and livers. We showed that metrics that characterize cell morphology are the most predictive of tumor growth and metastatic potential to the lungs and liver. Further, we found that no single in vitro motility assay in 2D or 3D significantly correlated with metastasis in vivo.

Conclusions

Our results provide an important resource for the TNBC research community, identifying the metastatic potential of 6 commonly used cell lines. Our findings also support the use of cell morphological analysis to investigate the metastatic potential and emphasize the need for multiple in vitro motility metrics using multiple cell lines to represent the heterogeneity of metastasis in vivo.

Data-driven structural analysis of Small Cell Lung Cancer transcription factor network suggests potential subtype regulators and transition pathways

Small Cell Lung Cancer (SCLC) is an aggressive disease and challenging to treat due to its mixture of transcriptional subtypes and subtype transitions. Transcription factor (TF) networks have been the focus of studies to identify SCLC subtype regulators via systems approaches. Yet, their structures, which can provide clues on subtype drivers and transitions, are barely investigated. Here, we analyze the structure of an SCLC TF network by using graph theory concepts and identify its structurally important components responsible for complex signal processing, called hubs. We show that the hubs of the network are regulators of different SCLC subtypes by analyzing first the unbiased network structure and then integrating RNA-seq data as weights assigned to each interaction. Data-driven analysis emphasizes MYC as a hub, consistent with recent reports. Furthermore, we hypothesize that the pathways connecting functionally distinct hubs may control subtype transitions and test this hypothesis via network simulations on a candidate pathway and observe subtype transition. Overall, structural analyses of complex networks can identify their functionally important components and pathways driving the network dynamics. Such analyses can be an initial step for generating hypotheses and can guide the discovery of target pathways whose perturbation may change the network dynamics phenotypically.

A Comprehensive Overview of RNA Deconvolution Methods and Their Application

Tumors are surrounded by a variety of tumor microenvironmental cells. Profiling individual cells within the tumor tissues is crucial to characterize the tumor microenvironment and its therapeutic implications. Since single-cell technologies are still not cost-effective, scientists have developed many statistical deconvolution methods to delineate cellular characteristics from bulk transcriptome data. Here, we present an overview of 20 deconvolution techniques, including cutting-edge techniques recently established. We categorized deconvolution techniques by three primary criteria: characteristics of methodology, use of prior knowledge of cell types and outcome of the methods. We highlighted the advantage of the recent deconvolution tools that are based on probabilistic models. Moreover, we illustrated two scenarios of the common application of deconvolution methods to study tumor microenvironments. This comprehensive review will serve as a guideline for the researchers to select the appropriate method for their application of deconvolution.

Chronic Exposure to Low Levels of Parabens Increases Mammary Cancer Growth and Metastasis in Mice

Methylparaben (MP) and propylparaben (PP) are commonly used as food, cosmetic, and drug preservatives. These parabens are detected in the majority of US women and children, bind and activate estrogen receptors (ER), and stimulate mammary tumor cell growth and invasion in vitro. Hemizygous B6.FVB-Tg (MMTV-PyVT)634Mul/LellJ female mice (n = 20/treatment) were exposed to MP or PP at levels within the US Food and Drug Administration's "human acceptable daily intake." These paraben-exposed mice had increased mammary tumor volume compared with control mice (P < 0.001) and a 28% and 91% increase in the number of pulmonary metastases per week compared with the control mice, respectively (P < 0.0001). MP and PP caused differential expression of 288 and 412 mammary tumor genes, respectively (false discovery rate < 0.05), a subset of which has been associated with human breast cancer metastasis. Molecular docking and luciferase reporter studies affirmed that MP and PP bound and activated human ER, and RNA-sequencing revealed increased ER expression in mammary tumors among paraben-exposed mice. However, ER signaling was not enriched in mammary tumors. Instead, both parabens strongly impaired tumor RNA metabolism (eg, ribosome, spliceosome), as evident from enriched KEGG pathway analysis of differential mammary tumor gene expression common to both paraben treatments (MP, P < 0.001; PP, P < 0.01). Indeed, mammary tumors from PP-exposed mice had an increased retention of introns (P < 0.05). Our data suggest that parabens cause substantial mammary cancer metastasis in mice as a function of their increasing alkyl chain length and highlight the emerging role of aberrant spliceosome activity in breast cancer metastasis.

Biophysical informatics reveals distinctive phenotypic signatures and functional diversity of single-cell lineages

MOTIVATION

In this work, we present an analytical method for quantifying both single-cell morphologies and cell network topologies of tumor cell populations and use it to predict 3D cell behavior.

RESULTS

We utilized a supervised deep learning approach to perform instance segmentation on label-free live cell images across a wide range of cell densities. We measured cell shape properties and characterized network topologies for 136 single-cell clones derived from the YUMM1.7 and YUMMER1.7 mouse melanoma cell lines. Using an unsupervised clustering algorithm, we identified six distinct morphological subclasses. We further observed differences in tumor growth and invasion dynamics across subclasses in an in vitro 3D spheroid model. Compared to existing methods for quantifying 2D or 3D phenotype, our analytical method requires less time, needs no specialized equipment and is capable of much higher throughput, making it ideal for applications such as high-throughput drug screening and clinical diagnosis.

AVAILABILITY AND IMPLEMENTATION

https://github.com/trevor-chan/Melanoma_NetworkMorphology.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The complex nature of heterogeneity and its roles in breast cancer biology and therapeutic responsiveness

Heterogeneity is a complex feature of cells and tissues with many interacting components. Depending on the nature of the research context, interacting features of cellular, drug response, genetic, molecular, spatial, temporal, and vascular heterogeneity may be present. We describe the various forms of heterogeneity with examples of their interactions and how they play a role in affecting cellular phenotype and drug responses in breast cancer. While cellular heterogeneity may be the most widely described and invoked, many forms of heterogeneity are evident within the tumor microenvironment and affect responses to the endocrine and cytotoxic drugs widely used in standard clinical care. Drug response heterogeneity is a critical determinant of clinical response and curative potential and also is multifaceted when encountered. The interactive nature of some forms of heterogeneity is readily apparent. For example, the process of metastasis has the properties of both temporal and spatial heterogeneity within the host, whereas each individual metastatic deposit may exhibit cellular, genetic, molecular, and vascular heterogeneity. This review describes the many forms of heterogeneity, their integrated activities, and offers some insights into how heterogeneity may be understood and studied in the future.

Single-Cell DNA Methylation Analysis in Cancer

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

Transcription factor binding process is the primary driver of noise in gene expression

Noise in expression of individual genes gives rise to variations in activity of cellular pathways and generates heterogeneity in cellular phenotypes. Phenotypic heterogeneity has important implications for antibiotic persistence, mutation penetrance, cancer growth and therapy resistance. Specific molecular features such as the presence of the TATA box sequence and the promoter nucleosome occupancy have been associated with noise. However, the relative importance of these features in noise regulation is unclear and how well these features can predict noise has not yet been assessed. Here through an integrated statistical model of gene expression noise in yeast we found that the number of regulating transcription factors (TFs) of a gene was a key predictor of noise, whereas presence of the TATA box and the promoter nucleosome occupancy had poor predictive power. With an increase in the number of regulatory TFs, there was a rise in the number of cooperatively binding TFs. In addition, an increased number of regulatory TFs meant more overlaps in TF binding sites, resulting in competition between TFs for binding to the same region of the promoter. Through modeling of TF binding to promoter and application of stochastic simulations, we demonstrated that competition and cooperation among TFs could increase noise. Thus, our work uncovers a process of noise regulation that arises out of the dynamics of gene regulation and is not dependent on any specific transcription factor or specific promoter sequence.

Single-cell transcriptomics reveals distinct cell response between acute and chronic pulmonary infection of Pseudomonas aeruginosa

Knowledge of the changes in the immune microenvironment during pulmonary bacterial acute and chronic infections is limited. The dissection of immune system may provide a basis for effective therapeutic strategies against bacterial infection. Here, we describe a single immune cell atlas of mouse lungs after acute and chronic Pseudomonas aeruginosa infection using single-cell transcriptomics, multiplex immunohistochemistry, and flow cytometry. Our single-cell transcriptomic analysis revealed large-scale comprehensive changes in immune cell composition and high variation in cell-cell interactions after acute and chronic P. aeruginosa infection. Bacterial infection reprograms the genetic architecture of immune cell populations. We identified specific immune cell types, including Cxcl2+ B cells and interstitial macrophages, in response to acute and chronic infection, such as their proportions, distribution, and functional status. Importantly, the patterns of immune cell response are drastically different between acute and chronic infection models. The distinct molecular signatures highlight the importance of the highly dynamic cell-cell interaction process in different pathological conditions, which has not been completely revealed previously. These findings provide a comprehensive and unbiased immune cellular landscape for respiratory pathogenesis in mice, enabling further understanding of immunologic mechanisms in infection and inflammatory diseases.

Two isoleucyl tRNAs that decode synonymous codons divergently regulate breast cancer metastatic growth by controlling translation of proliferation-regulating genes

The human genome contains 61 codons encoding 20 amino acids. Synonymous codons representing a given amino acid are decoded by a set of transfer RNAs (tRNAs) called isoacceptors. We report the surprising observation that two isoacceptor tRNAs that decode synonymous codons become modulated in opposing directions during breast cancer progression. Specifically, tRNAIleUAU became upregulated, whereas tRNAIleGAU became repressed as breast cancer cells attained enhanced metastatic capacity. Functionally, tRNAIleUAU promoted and tRNAIleGAU suppressed metastatic colonization in mouse xenograft models. These tRNAs mediated opposing effects on codon-dependent translation of growth-promoting genes, consistent with genomic enrichment or depletion of their cognate codons in mitotic genes. Our findings uncover a specific isoacceptor tRNA pair that act in opposition, divergently impacting growth-regulating genes and a disease phenotype. Degeneracy of the genetic code can thus be biologically exploited by human cancer cells via tRNA isoacceptor shifts that causally facilitate the transition toward a growth-promoting state.

Quantitative cell imaging approaches to metastatic state profiling

Genetic heterogeneity of metastatic dissemination has proven challenging to identify exploitable markers of metastasis; this bottom-up approach has caused a stalemate between advances in metastasis and the late stage of the disease. Advancements in quantitative cellular imaging have allowed the detection of morphological phenotype changes specific to metastasis, the morphological changes connected to the underlying complex signaling pathways, and a robust readout of metastatic cell state. This review focuses on the recent machine and deep learning developments to gain detailed information about the metastatic cell state using light microscopy. We describe the latest studies using quantitative cell imaging approaches to identify cell appearance-based metastatic patterns. We discuss how quantitative cancer biologists can use these frameworks to work backward toward exploitable hidden drivers in the metastatic cascade and pioneering new Frontier drug discoveries specific for metastasis.

Evolutionary Origins of Metabolic Reprogramming in Cancer

Metabolic changes that facilitate tumor growth are one of the hallmarks of cancer. These changes are not specific to tumors but also take place during the physiological growth of tissues. Indeed, the cellular and tissue mechanisms present in the tumor have their physiological counterpart in the repair of tissue lesions and wound healing. These molecular mechanisms have been acquired during metazoan evolution, first to eliminate the infection of the tissue injury, then to enter an effective regenerative phase. Cancer itself could be considered a phenomenon of antagonistic pleiotropy of the genes involved in effective tissue repair. Cancer and tissue repair are complex traits that share many intermediate phenotypes at the molecular, cellular, and tissue levels, and all of these are integrated within a Systems Biology structure. Complex traits are influenced by a multitude of common genes, each with a weak effect. This polygenic component of complex traits is mainly unknown and so makes up part of the missing heritability. Here, we try to integrate these different perspectives from the point of view of the metabolic changes observed in cancer.

Self-supervised classification of subcellular morphometric phenotypes reveals extracellular matrix-specific morphological responses

Cell morphology is profoundly influenced by cellular interactions with microenvironmental factors such as the extracellular matrix (ECM). Upon adhesion to specific ECM, various cell types are known to exhibit different but distinctive morphologies, suggesting that ECM-dependent cell morphological responses may harbour rich information on cellular signalling states. However, the inherent morphological complexity of cellular and subcellular structures has posed an ongoing challenge for automated quantitative analysis. Since multi-channel fluorescence microscopy provides robust molecular specificity important for the biological interpretations of observed cellular architecture, here we develop a deep learning-based analysis pipeline for the classification of cell morphometric phenotypes from multi-channel fluorescence micrographs, termed SE-RNN (residual neural network with squeeze-and-excite blocks). We demonstrate SERNN-based classification of distinct morphological signatures observed when fibroblasts or epithelial cells are presented with different ECM. Our results underscore how cell shapes are non-random and established the framework for classifying cell shapes into distinct morphological signature in a cell-type and ECM-specific manner.

Delineation of proteome changes driven by cell size and growth rate

Increasing cell size drives changes to the proteome, which affects cell physiology. As cell size increases, some proteins become more concentrated while others are diluted. As a result, the state of the cell changes continuously with increasing size. In addition to these proteomic changes, large cells have a lower growth rate (protein synthesis rate per unit volume). That both the cell's proteome and growth rate change with cell size suggests they may be interdependent. To test this, we used quantitative mass spectrometry to measure how the proteome changes in response to the mTOR inhibitor rapamycin, which decreases the cellular growth rate and has only a minimal effect on cell size. We found that large cell size and mTOR inhibition, both of which lower the growth rate of a cell, remodel the proteome in similar ways. This suggests that many of the effects of cell size are mediated by the size-dependent slowdown of the cellular growth rate. For example, the previously reported size-dependent expression of some senescence markers could reflect a cell's declining growth rate rather than its size per se. In contrast, histones and other chromatin components are diluted in large cells independently of the growth rate, likely so that they remain in proportion with the genome. Finally, size-dependent changes to the cell's growth rate and proteome composition are still apparent in cells continually exposed to a saturating dose of rapamycin, which indicates that cell size can affect the proteome independently of mTORC1 signaling. Taken together, our results clarify the dependencies between cell size, growth, mTOR activity, and the proteome remodeling that ultimately controls many aspects of cell physiology.

The cell cycle inhibitor RB is diluted in G1 and contributes to controlling cell size in the mouse liver

Every type of cell in an animal maintains a specific size, which likely contributes to its ability to perform its physiological functions. While some cell size control mechanisms are beginning to be elucidated through studies of cultured cells, it is unclear if and how such mechanisms control cell size in an animal. For example, it was recently shown that RB, the retinoblastoma protein, was diluted by cell growth in G1 to promote size-dependence of the G1/S transition. However, it remains unclear to what extent the RB-dilution mechanism controls cell size in an animal. We therefore examined the contribution of RB-dilution to cell size control in the mouse liver. Consistent with the RB-dilution model, genetic perturbations decreasing RB protein concentrations through inducible shRNA expression or through liver-specific Rb1 knockout reduced hepatocyte size, while perturbations increasing RB protein concentrations in an Fah -/- mouse model increased hepatocyte size. Moreover, RB concentration reflects cell size in G1 as it is lower in larger G1 hepatocytes. In contrast, concentrations of the cell cycle activators Cyclin D1 and E2f1 were relatively constant. Lastly, loss of Rb1 weakened cell size control, i.e., reduced the inverse correlation between how much cells grew in G1 and how large they were at birth. Taken together, our results show that an RB-dilution mechanism contributes to cell size control in the mouse liver by linking cell growth to the G1/S transition.

Predictive Biomarkers for Response and Toxicity of Induction Chemotherapy in Head and Neck Cancers

This review focuses on the molecular biology of head and neck squamous cell carcinomas and presents current and emerging biomarkers of the response of patients to induction chemotherapy. The usefulness of genes, proteins, and parameters from diagnostic clinical imaging as well as other clinicopathological parameters is thoroughly discussed. The role of induction chemotherapy before radiotherapy or before chemo-radiotherapy is still debated, as the data on its efficacy are somehow confusing. Despite the constant improvement of treatment protocols and the introduction of new cytostatics, there is still no consensus regarding the use of induction chemotherapy in the treatment of head and neck cancer, with the possible exception of larynx preservation. Such difficulties indicate that potential future treatment strategies should be personalized. Personalized medicine, in which individual tumor genetics drive the selection of targeted therapies and treatment plans for each patient, has recently emerged as the next generation of cancer therapy. Early prediction of treatment outcome or its toxicity may be highly beneficial for those who are at risk of the development of severe toxicities or treatment failure—a different treatment strategy may be applied to these patients, sparing them unnecessary pain. The literature search was carried out in the PubMed and ScienceDirect databases as well as in the selected conference proceedings repositories. Of the 265 articles and abstracts found, only 30 met the following inclusion criteria: human studies, analyzing prediction of induction chemotherapy outcome or toxicity based on the pretreatment (or after the first cycle, if more cycles of induction were administered) data, published after the year 2015. The studies regarding metastatic and recurrent cancers as well as the prognosis of overall survival or the outcome of consecutive treatment were not taken into consideration. As revealed from the systematic inspection of the papers, there are over 100 independent parameters analyzed for their suitability as prognostic markers in HNSCC patients undergoing induction chemotherapy. Some of them are promising, but usually they lack important features such as high specificity and sensitivity, low cost, high positive predictive value, clinical relevance, short turnaround time, etc. Subsequent studies are necessary to confirm the usability of the biomarkers for personal medicine.

The Hippo pathway drives the cellular response to hydrostatic pressure

Cells need to rapidly and precisely react to multiple mechanical and chemical stimuli in order to ensure precise context-dependent responses. This requires dynamic cellular signalling events that ensure homeostasis and plasticity when needed. A less well-understood process is cellular response to elevated interstitial fluid pressure, where the cell senses and responds to changes in extracellular hydrostatic pressure. Here, using quantitative label-free digital holographic imaging, combined with genome editing, biochemical assays and confocal imaging, we analyse the temporal cellular response to hydrostatic pressure. Upon elevated cyclic hydrostatic pressure, the cell responds by rapid, dramatic and reversible changes in cellular volume. We show that YAP and TAZ, the co-transcriptional regulators of the Hippo signalling pathway, control cell volume and that cells without YAP and TAZ have lower plasma membrane tension. We present direct evidence that YAP/TAZ drive the cellular response to hydrostatic pressure, a process that is at least partly mediated via clathrin-dependent endocytosis. Additionally, upon elevated oscillating hydrostatic pressure, YAP/TAZ are activated and induce TEAD-mediated transcription and expression of cellular components involved in dynamic regulation of cell volume and extracellular matrix. This cellular response confers a feedback loop that allows the cell to robustly respond to changes in interstitial fluid pressure.

Characterization of gene cluster heterogeneity in single-cell transcriptomic data within and across cancer types

Despite the remarkable progress in probing tumor transcriptomic heterogeneity by single-cell RNA sequencing (sc-RNAseq) data, several gaps exist in prior studies. Tumor heterogeneity is frequently mentioned but not quantified. Clustering analyses typically target cells rather than genes, and differential levels of transcriptomic heterogeneity of gene clusters are not characterized. Relations between gene clusters inferred from multiple datasets remain less explored. We provided a series of quantitative methods to analyze cancer sc-RNAseq data. First, we proposed two quantitative measures to assess intra-tumoral heterogeneity/homogeneity. Second, we established a hierarchy of gene clusters from sc-RNAseq data, devised an algorithm to reduce the gene cluster hierarchy to a compact structure, and characterized the gene clusters with functional enrichment and heterogeneity. Third, we developed an algorithm to align the gene cluster hierarchies from multiple datasets to a small number of meta gene clusters. By applying these methods to nine cancer sc-RNAseq datasets, we discovered that cancer cell transcriptomes were more homogeneous within tumors than the accompanying normal cells. Furthermore, many gene clusters from the nine datasets were aligned to two large meta gene clusters, which had high and low heterogeneity and were enriched with distinct functions. Finally, we found the homogeneous meta gene cluster retained stronger expression coherence and associations with survival times in bulk level RNAseq data than the heterogeneous meta gene cluster, yet the combinatorial expression patterns of breast cancer subtypes in bulk level data were not preserved in single-cell data. The inference outcomes derived from nine cancer sc-RNAseq datasets provide insights about the contributing factors for transcriptomic heterogeneity of cancer cells and complex relations between bulk level and single-cell RNAseq data. They demonstrate the utility of our methods to enable a comprehensive characterization of co-expressed gene clusters in a wide range of sc-RNAseq data in cancers and beyond.

Summary: We propose quantitative methods to analyze cancer sc-RNAseq data: measures of intra-tumoral heterogeneity, characterization of a hierarchy of gene clusters, and alignment of gene cluster hierarchies from multiple datasets.

Label-Free Vibrational and Quantitative Phase Microscopy Reveals Remarkable Pathogen-Induced Morphomolecular Divergence in Tumor-Derived Cells

Delineating the molecular and morphological changes that cancer cells undergo in response to extracellular stimuli is crucial for identifying factors that promote tumor progression. Label-free optical imaging offers a potentially promising route for retrieving such single-cell information by generating detailed visualization of the morphology and determining alterations in biomolecular composition. The potential of such nonperturbative morphomolecular microscopy for analyzing microbiota-cancer cell interactions has been surprisingly underappreciated, despite the growing evidence of the critical role of dysbiosis in malignant transformations. Here, using a model system of breast cancer cells, we show that label-free Raman microspectroscopy and quantitative phase microscopy can detect biomolecular and morphological changes in single cells exposed to Bacteroides fragilis toxin (BFT), a toxin secreted by enterotoxigenicB. fragilis. Remarkably, using machine learning to elucidate subtle, but consistent, cellular differences, we found that the morphomolecular differences between BFT-exposed and control breast cancer cells became more accentuated after in vivo passage, corroborating our findings that a short-term BFT exposure imparts a long-term effect on cancer cells and promotes a more invasive phenotype. Complementing more classical labeling techniques, our label-free platform offers a global detection approach with measurements representative of the overall cellular phenotype, paving the way for further investigations into the multifaceted interactions between the cancer cell and the microbiota.

Intrinsic transcriptional heterogeneity in neuroblastoma guides mechanistic and therapeutic insights

Cell state is controlled by master transcription factors (mTFs) that determine the cellular gene expression program. Cancer cells acquire dysregulated gene expression programs by mutational and non-mutational processes. Intratumoral heterogeneity can result from cells displaying distinct mTF-regulated cell states, which co-exist within the tumor. One archetypal tumor associated with transcriptionally regulated heterogeneity is high-risk neuroblastoma (NB). Patients with NB have poor overall survival despite intensive therapies, and relapsed patients are commonly refractory to treatment. The cellular populations that comprise NB are marked by different cohorts of mTFs and differential sensitivity to conventional therapies. Recent studies have highlighted mechanisms by which NB cells dynamically shift the cell state with treatment, revealing new opportunities to control the cellular response to treatment by manipulating cell-state-defining transcriptional programs. Here, we review recent advances in understanding transcriptionally defined cancer heterogeneity. We offer challenges to the field to encourage translation of basic science into clinical benefit.

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

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

The Continuing Search for Causality between Epithelial-to-Mesenchymal Transition and the Metastatic Fitness of Carcinoma Cells

The epithelial-to-mesenchymal transition (EMT) is an epithelial plasticity program that is associated with embryonic development and organogenesis, and which resurfaces to a certain extent following epithelial injury caused by inflammation, fibrosis, and carcinoma progression. Carcinoma cell EMT plasticity programs superimposed on inherent genetic defects have been implicated as important for metastatic dissemination and secondary tumor formation. A careful review of data-driven facts suggests that a causal relationship between any degree of EMT program and metastasis continues to be elusive, and the steps of metastasis likely involve other mechanisms influenced by the carcinoma cell genotype and the tumor microenvironment.