DRUG-NEM: Optimizing drug combinations using single-cell perturbation response to account for intratumoral heterogeneity

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

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

Personalized tumor combination therapy optimization using the single-cell transcriptome

BACKGROUND

The precise characterization of individual tumors and immune microenvironments using transcriptome sequencing has provided a great opportunity for successful personalized cancer treatment. However, the cancer treatment response is often characterized by in vitro assays or bulk transcriptomes that neglect the heterogeneity of malignant tumors in vivo and the immune microenvironment, motivating the need to use single-cell transcriptomes for personalized cancer treatment.

METHODS

Here, we present comboSC, a computational proof-of-concept study to explore the feasibility of personalized cancer combination therapy optimization using single-cell transcriptomes. ComboSC provides a workable solution to stratify individual patient samples based on quantitative evaluation of their personalized immune microenvironment with single-cell RNA sequencing and maximize the translational potential of in vitro cellular response to unify the identification of synergistic drug/small molecule combinations or small molecules that can be paired with immune checkpoint inhibitors to boost immunotherapy from a large collection of small molecules and drugs, and finally prioritize them for personalized clinical use based on bipartition graph optimization.

RESULTS

We apply comboSC to publicly available 119 single-cell transcriptome data from a comprehensive set of 119 tumor samples from 15 cancer types and validate the predicted drug combination with literature evidence, mining clinical trial data, perturbation of patient-derived cell line data, and finally in-vivo samples.

CONCLUSIONS

Overall, comboSC provides a feasible and one-stop computational prototype and a proof-of-concept study to predict potential drug combinations for further experimental validation and clinical usage using the single-cell transcriptome, which will facilitate and accelerate personalized tumor treatment by reducing screening time from a large drug combination space and saving valuable treatment time for individual patients. A user-friendly web server of comboSC for both clinical and research users is available at www.combosc.top . The source code is also available on GitHub at https://github.com/bm2-lab/comboSC .

Advances in Mass Spectrometry-Based Single Cell Analysis

Technological developments and improvements in single-cell isolation and analytical platforms allow for advanced molecular profiling at the single-cell level, which reveals cell-to-cell variation within the admixture cells in complex biological or clinical systems. This helps to understand the cellular heterogeneity of normal or diseased tissues and organs. However, most studies focused on the analysis of nucleic acids (e.g., DNA and RNA) and mass spectrometry (MS)-based analysis for proteins and metabolites of a single cell lagged until recently. Undoubtedly, MS-based single-cell analysis will provide a deeper insight into cellular mechanisms related to health and disease. This review summarizes recent advances in MS-based single-cell analysis methods and their applications in biology and medicine.

Achieving a Deeper Understanding of Drug Metabolism and Responses Using Single-Cell Technologies

Recent advancements in single-cell technologies have enabled detection of RNA, proteins, metabolites, and xenobiotics in individual cells, and the application of these technologies has the potential to transform pharmacological research. Single-cell data has already resulted in the development of human and model species cell atlases, identifying different cell types within a tissue, further facilitating the characterization of tumor heterogeneity, and providing insight into treatment resistance. Research discussed in this review demonstrates that distinct cell populations express drug metabolizing enzymes to different extents, indicating there may be variability in drug metabolism not only between organs, but within tissue types. Additionally, we put forth the concept that single-cell analyses can be used to expose underlying variability in cellular response to drugs, providing a unique examination of drug efficacy, toxicity, and metabolism. We will outline several of these techniques: single-cell RNA-sequencing and mass cytometry to characterize and distinguish different cell types, single-cell proteomics to quantify drug metabolizing enzymes and characterize cellular responses to drug, capillary electrophoresis-ultrasensitive laser-induced fluorescence detection and single-probe single-cell mass spectrometry for detection of drugs, and others. Emerging single-cell technologies such as these can comprehensively characterize heterogeneity in both cell-type-specific drug metabolism and response to treatment, enhancing progress toward personalized and precision medicine. SIGNIFICANCE STATEMENT: Recent technological advances have enabled the analysis of gene expression and protein levels in single cells. These types of analyses are important to investigating mechanisms that cannot be elucidated on a bulk level, primarily due to the variability of cell populations within biological systems. Here, we summarize cell-type-specific drug metabolism and how pharmacologists can utilize single-cell approaches to obtain a comprehensive understanding of drug metabolism and cellular heterogeneity in response to drugs.

IL-1β derived from mixed-polarized macrophages activates fibroblasts and synergistically forms a cancer-promoting microenvironment

BACKGROUND

Remodeling the tumor microenvironment (TME) to benefit cancer cells is crucial for tumor progression. Although diffuse-type gastric cancer (DGC) preferentially interacts with the TME, the precise mechanism of the complicated network remains unknown. This study aimed to investigate the mutual activation mechanism underlying DGC progression.

METHODS

Mass cytometry analysis of co-cultured macrophages, noncancerous fibroblasts (NFs), and DGC cells was performed. RNA sequencing was applied to examine gene expression in fibroblasts. DGC cells were treated with cytokines to examine their effect on characteristic changes. The TCGA and Kumamoto University cohorts were used to evaluate the clinical relevance of the in vitro findings.

RESULTS

Cohort analysis revealed that DGC patients had a poor prognosis. The fibroblasts and macrophages interacted with DGC cells to form a cell cluster in the invasive front of DGC tissue. The original 3D triple co-culture system determined the promotional effects of nonmalignant cells on DGC invasive growth. We notably identified a mixed-polarized macrophage cell type with M1/M2 cell surface markers in a triple co-culture system. IL-1β from mixed-polarized macrophages induced the conversion of NFs to cancer-associated fibroblast-like (CAF-like) cells, promoting the malignant phenotype of DGC cells by inducing the secretion of IL-6, IL-24, and leukemia inhibitory factor (LIF). Moreover, IL-6 and colony stimulating factor 2 (GM-CSF) cooperated to maintain the stable state of mixed-polarized macrophages. Finally, we found that mixed-polarized macrophages were frequently detected in DGC tissues.

CONCLUSION

These findings demonstrated that mixed-polarized macrophages exist as a novel subtype through the reciprocal interaction between DGC cells and nonmalignant cells.

Single-cell mass spectrometry

Owing to recent advances in mass spectrometry (MS), tens to hundreds of proteins, lipids, and small molecules can be measured in single cells. The ability to characterize the molecular heterogeneity of individual cells is necessary to define the full assortment of cell subtypes and identify their function. We review single-cell MS including high-throughput, targeted, mass cytometry-based approaches and antibody-free methods for broad profiling of the proteome and metabolome of single cells. The advantages and disadvantages of different methods are discussed, as well as the challenges and opportunities for further improvements in single-cell MS. These methods is being used in biomedicine in several applications including revealing tumor heterogeneity and high-content drug screening.

Leveraging laboratory and clinical studies to design effective antibiotic combination therapy

Interest in antibiotic combination therapy is increasing due to antimicrobial resistance and a slowing antibiotic pipeline. However, aside from specific indications, combination therapy in the clinic is often not administered systematically; instead, it is used at the physician's discretion as a bet-hedging mechanism to increase the chances of appropriately targeting a pathogen(s) with an unknown antibiotic resistance profile. Some recent clinical trials have been unable to demonstrate superior efficacy of combination therapy over monotherapy. Other trials have shown a benefit of combination therapy in defined circumstances consistent with recent studies indicating that factors including species, strain, resistance profile, and microenvironment affect drug combination efficacy and drug interactions. In this review, we discuss how a careful study design that takes these factors into account, along with the different drug interaction and potency metrics for assessing combination performance, may provide the necessary insight to understand the best clinical use-cases for combination therapy.

High-throughput screening for drug discovery targeting the cancer cell-microenvironment interactions in hematological cancers

INTRODUCTION

The interactions between leukemic blasts and cells within the bone marrow environment affect oncogenesis, cancer stem cell survival, as well as drug resistance in hematological cancers. The importance of this interaction is increasingly being recognized as a potentially important target for future drug discoveries and developments. Recent innovations in the high throughput drug screening-related technologies, novel ex-vivo disease-models, and freely available machine-learning algorithms are advancing the drug discovery process by targeting earlier undruggable proteins, complex pathways, as well as physical interactions (e.g. leukemic cell-bone microenvironment interaction).

AREA COVERED

In this review, the authors discuss the recent methodological advancements and existing challenges to target specialized hematopoietic niches within the bone marrow during leukemia and suggest how such methods can be used to identify drugs targeting leukemic cell-bone microenvironment interactions.

EXPERT OPINION

The recent development in cell-cell communication scoring technology and culture conditions can speed up the drug discovery by targeting the cell-microenvironment interaction. However, to accelerate this process, collecting clinical-relevant patient tissues, developing culture model systems, and implementing computational algorithms, especially trained to predict drugs and their combination targeting the cancer cell-bone microenvironment interaction are needed.

LEARNING GENERAL TRANSFORMATIONS OF DATA FOR OUT-OF-SAMPLE EXTENSIONS

While generative models such as GANs have been successful at mapping from noise to specific distributions of data, or more generally from one distribution of data to another, they cannot isolate the transformation that is occurring and apply it to a new distribution not seen in training. Thus, they memorize the domain of the transformation, and cannot generalize the transformation out of sample. To address this, we propose a new neural network called a Neuron Transformation Network (NTNet) that isolates the signal representing the transformation itself from the other signals representing internal distribution variation. This signal can then be removed from a new dataset distributed differently from the original one trained on. We demonstrate the effectiveness of our NTNet on more than a dozen synthetic and biomedical single-cell RNA sequencing datasets, where the NTNet is able to learn the data transformation performed by genetic and drug perturbations on one sample of cells and successfully apply it to another sample of cells to predict treatment outcome.

The optimization of combinatorial drug therapies: Strategies and laboratorial platforms

Designing optimal combinatorial drug therapies is challenging, because the drug interactions depend not only on the drugs involved, but also on their doses. With recent advances, combinatorial drug therapy is closer than ever to clinical application. Herein, we summarize approaches and advances over the past decade for identifying and optimizing drug combination therapies, with innovations across research fields, covering physical laboratory platforms for combination screening to computational models and algorithms designed for synergism prediction and optimization. By comparing different types of approach, we detail a three-step workflow that could maximize the overall optimization efficiency, thus enabling the application of personalized optimization of combinatorial drug therapy.

Large-scale pharmacogenomic studies and drug response prediction for personalized cancer medicine

The response rate of most anti-cancer drugs is limited because of the high heterogeneity of cancer and the complex mechanism of drug action. Personalized treatment that stratifies patients into subgroups using molecular biomarkers is promising to improve clinical benefit. With the accumulation of preclinical models and advances in computational approaches of drug response prediction, pharmacogenomics has made great success over the last 20 years and is increasingly used in the clinical practice of personalized cancer medicine. In this article, we first summarize FDA-approved pharmacogenomic biomarkers and large-scale pharmacogenomic studies of preclinical cancer models such as patient-derived cell lines, organoids, and xenografts. Furthermore, we comprehensively review the recent developments of computational methods in drug response prediction, covering network, machine learning, and deep learning technologies and strategies to evaluate immunotherapy response. In the end, we discuss challenges and propose possible solutions for further improvement.

Therapeutic delivery of siRNA with polymeric carriers to down-regulate STAT5A expression in high-risk B-cell acute lymphoblastic leukemia (B-ALL)

Overexpression and persistent activation of STAT5 play an important role in the development and progression of acute lymphoblastic leukemia (ALL), the most common pediatric cancer. Small interfering RNA (siRNA)-mediated downregulation of STAT5 represents a promising therapeutic approach for ALL to overcome the limitations of current treatment modalities such as high relapse rates and poor prognosis. However, to effectively transport siRNA molecules to target cells, development of potent carriers is of utmost importance to surpass hurdles of delivery. In this study, we investigated the use of lipopolymers as non-viral delivery systems derived from low molecular weight polyethylenimines (PEI) substituted with lauric acid (Lau), linoleic acid (LA) and stearic acid (StA) to deliver siRNA molecules to ALL cell lines and primary samples. Among the lipid-substituted polymers explored, Lau- and LA-substituted PEI displayed excellent siRNA delivery to SUP-B15 and RS4;11 cells. STAT5A gene expression was downregulated (36-92%) in SUP-B15 and (32%) in RS4;11 cells using the polymeric delivery systems, which consequently reduced cell growth and inhibited the formation of colonies in ALL cells. With regard to ALL primary cells, siRNA-mediated STAT5A gene silencing was observed in four of eight patient cells using our leading polymeric delivery system, 1.2PEI-Lau8, accompanied by the significant reduction in colony formation in three of eight patients. In both BCR-ABL positive and negative groups, three of five patients demonstrated marked cell growth inhibition in both MTT and trypan blue exclusion assays using 1.2PEI-Lau8/siRNA complexes in comparison with their control siRNA groups. Three patient samples did not show any positive results with our delivery systems. Differential therapeutic responses to siRNA therapy observed in different patients could result from variable genetic profiles and patient-to-patient variability in delivery. This study supports the potential of siRNA therapy and the designed lipopolymers as a delivery system in ALL therapy.

Machine learning for perturbational single-cell omics

Cell biology is fundamentally limited in its ability to collect complete data on cellular phenotypes and the wide range of responses to perturbation. Areas such as computer vision and speech recognition have addressed this problem of characterizing unseen or unlabeled conditions with the combined advances of big data, deep learning, and computing resources in the past 5 years. Similarly, recent advances in machine learning approaches enabled by single-cell data start to address prediction tasks in perturbation response modeling. We first define objectives in learning perturbation response in single-cell omics; survey existing approaches, resources, and datasets (https://github.com/theislab/sc-pert); and discuss how a perturbation atlas can enable deep learning models to construct an informative perturbation latent space. We then examine future avenues toward more powerful and explainable modeling using deep neural networks, which enable the integration of disparate information sources and an understanding of heterogeneous, complex, and unseen systems.

Extending the lymphoblastoid cell line model for drug combination pharmacogenomics

Combination drug therapies have become an integral part of precision oncology, and while evidence of clinical effectiveness continues to grow, the underlying mechanisms supporting synergy are poorly understood. Immortalized human lymphoblastoid cell lines (LCLs) have been proven as a particularly useful, scalable and low-cost model in pharmacogenetics research, and are suitable for elucidating the molecular mechanisms of synergistic combination therapies. In this review, we cover the advantages of LCLs in synergy pharmacogenomics and consider recent studies providing initial evidence of the utility of LCLs in synergy research. We also discuss several opportunities for LCL-based systems to address gaps in the research through the expansion of testing regimens, assessment of new drug classes and higher-order combinations, and utilization of integrated omics technologies.

Leveraging Single-Cell Approaches in Cancer Precision Medicine

Cancer precision medicine aims to improve patient outcomes by tailoring treatment to the unique genomic background of a tumor. However, efforts to develop prognostic and drug response biomarkers largely rely on bulk 'omic' data, which fails to capture intratumor heterogeneity (ITH) and deconvolve signals from normal versus tumor cells. These shortcomings in measuring clinically relevant features are being addressed with single-cell technologies, which provide a fine-resolution map of the genetic and phenotypic heterogeneity in tumors and their microenvironment, as well as an improved understanding of the patterns of subclonal tumor populations. Here we present recent advances in the application of single-cell technologies, towards gaining a deeper understanding of ITH and evolution, and potential applications in developing personalized therapeutic strategies.

Inferring perturbation profiles of cancer samples

Motivation

Cancer is one of the most prevalent diseases in the world. Tumors arise due to important genes changing their activity, e.g. when inhibited or over-expressed. But these gene perturbations are difficult to observe directly. Molecular profiles of tumors can provide indirect evidence of gene perturbations. However, inferring perturbation profiles from molecular alterations is challenging due to error-prone molecular measurements and incomplete coverage of all possible molecular causes of gene perturbations.

Results

We have developed a novel mathematical method to analyze cancer driver genes and their patient-specific perturbation profiles. We combine genetic aberrations with gene expression data in a causal network derived across patients to infer unobserved perturbations. We show that our method can predict perturbations in simulations, CRISPR perturbation screens and breast cancer samples from The Cancer Genome Atlas.

Availability and implementation

The method is available as the R-package nempi at https://github.com/cbg-ethz/nempi and http://bioconductor.org/packages/nempi.

Supplementary information

Supplementary data are available at Bioinformatics online.

A numerical study on tumor-on-chip performance and its optimization for nanodrug-based combination therapy

Microfluidic devices, such as the tumor-on-a-chip (ToC), allow for the delivery of multiple drugs as desired for various therapies such as cancer treatment. Due to the complexity involved, visualizing, and gaining knowledge of the performance of such devices through experimentation alone is difficult if not impossible. In this paper, we performed a numerical simulation study on ToC performance, which focuses on the ability to combine multiple nanodrugs and optimized ToC performance. The numerical simulations of the chip performance were performed based on the typical chip design and operating parameters, as well as the established governing equations, boundary conditions, and fluid-structure interaction. The effect of cell injection time and position, inlet flow rate, number of inlets, medium viscosity, and cell concentration on the chip performance in terms of shear stress and cell distribution were examined. The results illustrate the profound effect of operation parameters, thus allowing for rigorously determining operational parameters to prevent spheroids ejection from microwells and to restrict the shear stresses within a physiological range. Also, the results show that triple-inlets can increase the uniformity of cell distribution in comparison with single or double inlets. Based on the simulation results, the architecture of the primary ToC was further optimized, resulting in a novel design that enables applying multiple, yet simultaneous, nanodrugs with optimal drug combination as desired for an individual patient. Furthermore, our simulations on the optimized chip showed a uniform cell distribution required for uniform-sized tumor spheroids generation, and complete medium exchange. Taken together, this study not only illustrates that numerical simulations are effective to visualize the ToCs performance, but also develops a novel ToC design optimized for nanodrug-based combination therapy.

Patient-tailored design for selective co-inhibition of leukemic cell subpopulations

The extensive drug resistance requires rational approaches to design personalized combinatorial treatments that exploit patient-specific therapeutic vulnerabilities to selectively target disease-driving cell subpopulations. To solve the combinatorial explosion challenge, we implemented an effective machine learning approach that prioritizes patient-customized drug combinations with a desired synergy-efficacy-toxicity balance by combining single-cell RNA sequencing with ex vivo single-agent testing in scarce patient-derived primary cells. When applied to two diagnostic and two refractory acute myeloid leukemia (AML) patient cases, each with a different genetic background, we accurately predicted patient-specific combinations that not only resulted in synergistic cancer cell co-inhibition but also were capable of targeting specific AML cell subpopulations that emerge in differing stages of disease pathogenesis or treatment regimens. Our functional precision oncology approach provides an unbiased means for systematic identification of personalized combinatorial regimens that selectively co-inhibit leukemic cells while avoiding inhibition of nonmalignant cells, thereby increasing their likelihood for clinical translation.

Progress and applications of mass cytometry in sketching immune landscapes

Recently emerged mass cytometry (cytometry by time-of-flight [CyTOF]) technology permits the identification and quantification of inherently diverse cellular systems, and the simultaneous measurement of functional attributes at the single-cell resolution. By virtue of its multiplex ability with limited need for compensation, CyTOF has led a critical role in immunological research fields. Here, we present an overview of CyTOF, including the introduction of CyTOF principle and advantages that make it a standalone tool in deciphering immune mysteries. We then discuss the functional assays, introduce the bioinformatics to interpret the data yield via CyTOF, and depict the emerging clinical and research applications of CyTOF technology in sketching immune landscape in a wide variety of diseases.

Single-Cell Analysis of Signaling Proteins Provides Insights into Proapoptotic Properties of Anticancer Drugs

Single-cell DNA analysis technology has provided unprecedented insights into many physiological and pathological processes. In contrast, technologies that allow protein analysis in single cells have lagged behind. Herein, a method called single-cell Plasmonic ImmunoSandwich Assay (scPISA) that is capable of measuring signaling proteins and protein complexes in single living cells is described. scPISA is straightforward, comprising specific in-cell extraction and ultrasensitive plasmonic detection. It is applied to evaluate the efficacy and kinetics of cytotoxic drugs. It reveals that different drugs exhibit distinct proapoptotic properties at the single-cell level. A set of new parameters is thus proposed for comprehensive and quantitative evaluation of the efficacy of anticancer drugs. It discloses that metformin can dramatically enhance the overall anticancer efficacy when combined with actinomycin D, although it itself is significantly less effective. Furthermore, scPISA reveals that survivin interacts with cytochrome C and caspase-3 in a dynamic fashion in single cells during continuous drug treatment. As compared with conventional assays, scPISA exhibits several significant advantages, such as ultrahigh sensitivity, single-cell resolution, fast speed, and so on. Therefore, this approach may provide a powerful tool for wide, important applications from basic research to clinical applications, particularly precision medicine.

Machine learning approaches to drug response prediction: challenges and recent progress

Cancer is a leading cause of death worldwide. Identifying the best treatment using computational models to personalize drug response prediction holds great promise to improve patient's chances of successful recovery. Unfortunately, the computational task of predicting drug response is very challenging, partially due to the limitations of the available data and partially due to algorithmic shortcomings. The recent advances in deep learning may open a new chapter in the search for computational drug response prediction models and ultimately result in more accurate tools for therapy response. This review provides an overview of the computational challenges and advances in drug response prediction, and focuses on comparing the machine learning techniques to be of utmost practical use for clinicians and machine learning non-experts. The incorporation of new data modalities such as single-cell profiling, along with techniques that rapidly find effective drug combinations will likely be instrumental in improving cancer care.

Charting the Fragmented Landscape of Drug Synergy

Even as the clinical impact of drug combinations continues to accelerate, no consensus on how to quantify drug synergy has emerged. Rather, surveying the landscape of drug synergy reveals the persistence of historical fissures regarding the appropriate domains of conflicting synergy models - fissures impacting all aspects of combination therapy discovery and deployment. Herein we chronicle the impact of these divisions on: (i) the design, interpretation, and reproducibility of high-throughput combination screens; (ii) the performance of algorithms to predict synergistic mixtures; and (iii) the search for higher-order synergistic interactions. Further progress in each of these subfields hinges on reaching a consensus regarding the long-standing rifts in the field.

Single-Cell Proteomic Profiling Identifies Combined AXL and JAK1 Inhibition as a Novel Therapeutic Strategy for Lung Cancer

Cytometry by time-of-flight (CyTOF) simultaneously measures multiple cellular proteins at the single-cell level and is used to assess intertumor and intratumor heterogeneity. This approach may be used to investigate the variability of individual tumor responses to treatments. Herein, we stratified lung tumor subpopulations based on AXL signaling as a potential targeting strategy. Integrative transcriptome analyses were used to investigate how TP-0903, an AXL kinase inhibitor, influences redundant oncogenic pathways in metastatic lung cancer cells. CyTOF profiling revealed that AXL inhibition suppressed SMAD4/TGFβ signaling and induced JAK1-STAT3 signaling to compensate for the loss of AXL. Interestingly, high JAK1-STAT3 was associated with increased levels of AXL in treatment-naïve tumors. Tumors with high AXL, TGFβ, and JAK1 signaling concomitantly displayed CD133-mediated cancer stemness and hybrid epithelial-to-mesenchymal transition features in advanced-stage patients, suggesting greater potential for distant dissemination. Diffusion pseudotime analysis revealed cell-fate trajectories among four different categories that were linked to clinicopathologic features for each patient. Patient-derived organoids (PDO) obtained from tumors with high AXL and JAK1 were sensitive to TP-0903 and ruxolitinib (JAK inhibitor) treatments, supporting the CyTOF findings. This study shows that single-cell proteomic profiling of treatment-naïve lung tumors, coupled with ex vivo testing of PDOs, identifies continuous AXL, TGFβ, and JAK1-STAT3 signal activation in select tumors that may be targeted by combined AXL-JAK1 inhibition. SIGNIFICANCE: Single-cell proteomic profiling of clinical samples may facilitate the optimal selection of novel drug targets, interpretation of early-phase clinical trial data, and development of predictive biomarkers valuable for patient stratification.

Single-cell analysis targeting the proteome

The existence of cellular heterogeneity and its central relevance to biological phenomena provides a strong rationale for a need for analytical methods that enable analysis at the single-cell level. Analysis of the genome and transcriptome is possible at the single-cell level, but the comprehensive interrogation of the proteome with this level of resolution remains challenging. Single-cell protein analysis tools are advancing rapidly, however, and providing insights into collections of proteins with great relevance to cell and disease biology. Here, we review single-cell protein analysis technologies and assess their advantages and limitations. The emerging technologies presented have the potential to reveal new insights into tumour heterogeneity and therapeutic resistance, elucidate mechanisms of immune response and immunotherapy, and accelerate drug discovery.

A comparison framework and guideline of clustering methods for mass cytometry data

Background

With the expanding applications of mass cytometry in medical research, a wide variety of clustering methods, both semi-supervised and unsupervised, have been developed for data analysis. Selecting the optimal clustering method can accelerate the identification of meaningful cell populations.

Result

To address this issue, we compared three classes of performance measures, “precision” as external evaluation, “coherence” as internal evaluation, and stability, of nine methods based on six independent benchmark datasets. Seven unsupervised methods (Accense, Xshift, PhenoGraph, FlowSOM, flowMeans, DEPECHE, and kmeans) and two semi-supervised methods (Automated Cell-type Discovery and Classification and linear discriminant analysis (LDA)) are tested on six mass cytometry datasets. We compute and compare all defined performance measures against random subsampling, varying sample sizes, and the number of clusters for each method. LDA reproduces the manual labels most precisely but does not rank top in internal evaluation. PhenoGraph and FlowSOM perform better than other unsupervised tools in precision, coherence, and stability. PhenoGraph and Xshift are more robust when detecting refined sub-clusters, whereas DEPECHE and FlowSOM tend to group similar clusters into meta-clusters. The performances of PhenoGraph, Xshift, and flowMeans are impacted by increased sample size, but FlowSOM is relatively stable as sample size increases.

Conclusion

All the evaluations including precision, coherence, stability, and clustering resolution should be taken into synthetic consideration when choosing an appropriate tool for cytometry data analysis. Thus, we provide decision guidelines based on these characteristics for the general reader to more easily choose the most suitable clustering tools.

Opportunities for improving cancer treatment using systems biology

Current cancer therapies target a limited set of tumor features, rather than considering the tumor as a whole. Systems biology aims to reveal therapeutic targets associated with a variety of facets in an individual's tumor, such as genetic heterogeneity and its evolution, cancer cell-autonomous phenotypes, and microenvironmental signaling. These disparate characteristics can be reconciled using mathematical modeling that incorporates concepts from ecology and evolution. This provides an opportunity to predict tumor growth and response to therapy, to tailor patient-specific approaches in real time or even prospectively. Importantly, as data regarding patient tumors is often available from only limited time points during treatment, systems-based approaches can address this limitation by interpolating longitudinal events within a principled framework. This review outlines areas in medicine that could benefit from systems biology approaches to deconvolve the complexity of cancer.

Systems biology approaches to measure and model phenotypic heterogeneity in cancer

The recent wide-spread adoption of single cell profiling technologies has revealed that individual cancers are not homogenous collections of deregulated cells, but instead are comprised of multiple genetically and phenotypically distinct cell subpopulations that exhibit a wide range of responses to extracellular signals and therapeutic insult. Such observations point to the urgent need to understand cancer as a complex, adaptive system. Cancer systems biology studies seek to develop the experimental and theoretical methods required to understand how biological components work together to determine how cancer cells function. Ultimately, such approaches will lead to improvements in how cancer is managed and treated. In this review, we discuss recent advances in cancer systems biology approaches to quantify, model, and elucidate mechanisms of heterogeneity.

In Silico Drug Prescription for Targeting Cancer Patient Heterogeneity and Prediction of Clinical Outcome

In silico drug prescription tools for precision cancer medicine can match molecular alterations with tailored candidate treatments. These methodologies require large and well-annotated datasets to systematically evaluate their performance, but this is currently constrained by the lack of complete patient clinicopathological data. Moreover, in silico drug prescription performance could be improved by integrating additional tumour information layers like intra-tumour heterogeneity (ITH) which has been related to drug response and tumour progression. PanDrugs is an in silico drug prescription method which prioritizes anticancer drugs combining both biological and clinical evidence. We have systematically evaluated PanDrugs in the Genomic Data Commons repository (GDC). Our results showed that PanDrugs is able to establish an a priori stratification of cancer patients treated with Epidermal Growth Factor Receptor (EGFR) inhibitors. Patients labelled as responders according to PanDrugs predictions showed a significantly increased overall survival (OS) compared to non-responders. PanDrugs was also able to suggest alternative tailored treatments for non-responder patients. Additionally, PanDrugs usefulness was assessed considering spatial and temporal ITH in cancer patients and showed that ITH can be approached therapeutically proposing drugs or combinations potentially capable of targeting the clonal diversity. In summary, this study is a proof of concept where PanDrugs predictions have been correlated to OS and can be useful to manage ITH in patients while increasing therapeutic options and demonstrating its clinical utility.

Clinical Trials and Therapeutic Rationale for Drug Repurposing in Schizophrenia

There is a paucity of efficacious novel drugs to address high rates of treatment resistance and refractory symptoms in schizophrenia. The identification of novel therapeutic indications for approved drugs-drug repurposing-has the potential to expedite clinical trials and reduce the costly risk of failure which currently limits central nervous system drug discovery efforts. In the present Review we discuss the historical role of drug repurposing in schizophrenia drug discovery and review the main classes of repurposing candidates currently in clinical trials for schizophrenia in terms of their therapeutic rationale, mechanisms of action, and preliminary results from clinical trials. Subsequently we outline the challenges and limitations which face the clinical repurposing pipeline and how novel technologies might serve to address these.

The FGFR1 V561M Gatekeeper Mutation Drives AZD4547 Resistance through STAT3 Activation and EMT

FGFR1 has been implicated in numerous cancer types including squamous cell lung cancer, a subset of non-small cell lung cancer with a dismal 5-year survival rate. Small-molecule inhibitors targeting FGFR1 are currently in clinical trials, with AZD4547 being one of the furthest along; however, the development of drug resistance is a major challenge for targeted therapies. A prevalent mechanism of drug resistance in kinases occurs through mutation of the gatekeeper residue, V561M in FGFR1; however, mechanisms underlying V561M resistance to AZD4547 are not fully understood. Here, the cellular consequences of the V561M gatekeeper mutation were characterized, and it was found that although AZD4547 maintains nanomolar affinity for V561M FGFR1, based on in vitro binding assays, cells expressing V561M demonstrate dramatic resistance to AZD4547 driven by increased STAT3 activation downstream of V561M FGFR1. The data reveal that the V561M mutation biases cells toward a more mesenchymal phenotype, including increased levels of proliferation, migration, invasion, and anchorage-independent growth, which was confirmed using CyTOF, a novel single-cell analysis tool. Using shRNA knockdown, loss of STAT3 restored sensitivity of cancer cells expressing V561M FGFR1 to AZD4547. Thus, the data demonstrate that combination therapies including FGFR and STAT3 may overcome V561M FGFR1-driven drug resistance in the clinic. IMPLICATIONS: The V561M FGFR1 gatekeeper mutation leads to devastating drug resistance through activation of STAT3 and the epithelial-mesenchymal transition; this study demonstrates that FGFR1 inhibitor sensitivity can be restored upon STAT3 knockdown.

Microfluidic and Paper-Based Devices for Disease Detection and Diagnostic Research

Recent developments in microfluidic devices, nanoparticle chemistry, fluorescent microscopy, and biochemical techniques such as genetic identification and antibody capture have provided easier and more sensitive platforms for detecting and diagnosing diseases as well as providing new fundamental insight into disease progression. These advancements have led to the development of new technology and assays capable of easy and early detection of pathogenicity as well as the enhancement of the drug discovery and development pipeline. While some studies have focused on treatment, many of these technologies have found initial success in laboratories as a precursor for clinical applications. This review highlights the current and future progress of microfluidic techniques geared toward the timely and inexpensive diagnosis of disease including technologies aimed at high-throughput single cell analysis for drug development. It also summarizes novel microfluidic approaches to characterize fundamental cellular behavior and heterogeneity.