Single-Cell Phosphoproteomics Resolves Adaptive Signaling Dynamics and Informs Targeted Combination Therapy in Glioblastoma

Identification of drug-resistant individual cells within tumors by semi-supervised transfer learning from bulk to single-cell transcriptome

The presence of pre-existing or acquired drug-resistant cells within the tumor often leads to tumor relapse and metastasis. Single-cell RNA sequencing (scRNA-seq) enables elucidation of the subtle differences in drug responsiveness among distinct cell subpopulations within tumors. A few methods have employed scRNA-seq data to predict the drug response of individual cells to date, but their performance is far from satisfactory. In this study, we propose SSDA4Drug, a semi-supervised few-shot transfer learning method for inferring drug-resistant cancer cells. SSDA4Drug extracts pharmacogenomic features from both bulk and single-cell transcriptomic data using semi-supervised adversarial domain adaptation. This allows us to transfer knowledge of drug sensitivity from bulk-level cell lines to single cells. We conduct extensive performance evaluation experiments across multiple independent scRNA-seq datasets, demonstrating SSDA4Drug's superior performance over current state-of-the-art methods. Remarkably, with only one or two labeled target-domain samples, SSDA4Drug significantly boosts the predictive performance of single-cell drug responses. Moreover, SSDA4Drug accurately recapitulates the temporally dynamic changes of drug responses during continuous drug exposure of tumor cells, and successfully identifies reversible drug-responsive states in lung cancer cells, which initially acquire resistance through drug exposure but later restore sensitivity during drug holidays. Also, our predicted drug responses consistently align with the developmental patterns of drug sensitivity observed along the evolutionary trajectory of oral squamous cell carcinoma cells. In addition, our derived SHAP values and integrated gradients effectively pinpoint the key genes involved in drug resistance in prostate cancer cells. These findings highlight the exceptional performance of our method in determining single-cell drug responses. This powerful tool holds the potential for identifying drug-resistant tumor cell subpopulations, paving the way for advancements in precision medicine and novel drug development.

Integration of transcriptomics, proteomics and loss-of-function screening reveals WEE1 as a target for combination with dasatinib against proneural glioblastoma

Glioblastoma is characterized by a pronounced resistance to therapy with dismal prognosis. Transcriptomics classify glioblastoma into proneural (PN), mesenchymal (MES) and classical (CL) subtypes that show differential resistance to targeted therapies. The aim of this study was to provide a viable approach for identifying combination therapies in glioblastoma subtypes. Proteomics and phosphoproteomics were performed on dasatinib inhibited glioblastoma stem cells (GSCs) and complemented by an shRNA loss-of-function screen to identify genes whose knockdown sensitizes GSCs to dasatinib. Proteomics and screen data were computationally integrated with transcriptomic data using the SamNet 2.0 algorithm for network flow learning to reveal potential combination therapies in PN GSCs. In vitro viability assays and tumor spheroid models were used to verify the synergy of identified therapy. Further in vitro and TCGA RNA-Seq data analyses were utilized to provide a mechanistic explanation of these effects. Integration of data revealed the cell cycle protein WEE1 as a potential combination therapy target for PN GSCs. Validation experiments showed a robust synergistic effect through combination of dasatinib and the WEE1 inhibitor, MK-1775, in PN GSCs. Combined inhibition using dasatinib and MK-1775 propagated DNA damage in PN GCSs, with GCSs showing a differential subtype-driven pattern of expression of cell cycle genes in TCGA RNA-Seq data. The integration of proteomics, loss-of-function screens and transcriptomics confirmed WEE1 as a target for combination with dasatinib against PN GSCs. Utilizing this integrative approach could be of interest for studying resistance mechanisms and revealing combination therapy targets in further tumor entities.

mTOR in metabolic homeostasis and disease

The ability to maintain cellular metabolic homeostasis is critical to life, in which mTOR plays an important role. This kinase integrates upstream nutrient signals and performs essential functions in physiology and metabolism by increasing metabolism and suppressing autophagy. Thus, dysregulation of mTOR activity leads to diseases, especially metabolic diseases such as cancer, type 2 diabetes and neurological disorders. Therefore, inhibition of overactivated mTOR becomes a rational approach to treat a variety of metabolic diseases. In this review, we discuss how mTOR responds to upstream signals and how mTOR regulates metabolic processes, including protein, nucleic acid, and lipid metabolism. Furthermore, we discuss the possible causes and consequences of dysregulated mTOR signaling activity, and summarize relevant applications, such as inhibition of mTOR activity to treat these diseases. This review will advance our comprehensive knowledge of the association between mTOR and metabolic homeostasis, which has significant ramifications for human health.

Quantitative Assessment of Preanalytic Variables on Clinical Evaluation of PI3/AKT/mTOR Signaling Activity in Diffuse Glioma

Biomarker-driven therapeutic clinical trials require the implementation of standardized, evidence-based practices for sample collection. In diffuse glioma, phosphatidylinositol 3 (PI3)-kinase/AKT/mTOR (PI3/AKT/mTOR) signaling is an attractive therapeutic target for which window-of-opportunity clinical trials could facilitate the identification of promising new agents. Yet, the relevant preanalytic variables and optimal tumor sampling methods necessary to measure pathway activity are unknown. To address this, we used a murine model for isocitrate dehydrogenase (IDH)-wildtype glioblastoma (GBM) and human tumor tissue, including IDH-wildtype GBM and IDH-mutant diffuse glioma. First, we determined the impact of delayed time-to-formalin fixation, or cold ischemia time (CIT), on the quantitative assessment of cellular expression of 6 phosphoproteins that are readouts of PI3K/AK/mTOR activity (phosphorylated-proline-rich Akt substrate of 40 kDa (p-PRAS40, T246), -mechanistic target of rapamycin (p-mTOR; S2448); -AKT (p-AKT, S473); -ribosomal protein S6 (p-RPS6, S240/244 and S235/236), and -eukaryotic initiation factor 4E-binding protein 1 (p-4EBP1, T37/46). With CITs ≥ 2 hours, typical of routine clinical handling, all had reduced or altered expression with p-RPS6 (S240/244) exhibiting relatively greater stability. A similar pattern was observed using patient tumor samples from the operating room with p-4EBP1 more sensitive to delayed fixation than p-RPS6 (S240/244). Many clinical trials utilize unstained slides for biomarker evaluation. Thus, we evaluated the impact of slide storage conditions on the detection of p-RPS6 (S240/244), p-4EBP1, and p-AKT. After 5 months, storage at -80°C was required to preserve the expression of p-4EBP1 and p-AKT, whereas p-RPS6 (240/244) expression was not stable regardless of storage temperature. Biomarker heterogeneity impacts optimal tumor sampling. Quantification of p-RPS6 (240/244) expression in multiple regionally distinct human tumor samples from 8 patients revealed significant intratumoral heterogeneity. Thus, the accurate assessment of PI3K/AKT/mTOR signaling in diffuse glioma must overcome intratumoral heterogeneity and multiple preanalytic factors, including time-to-formalin fixation, slide storage conditions, and phosphoprotein of interest.

A heterogeneous pharmaco-transcriptomic landscape induced by targeting a single oncogenic kinase

Over-activation of the epidermal growth factor receptor (EGFR) is a hallmark of glioblastoma. However, EGFR-targeted therapies have led to minimal clinical response. While delivery of EGFR inhibitors (EGFRis) to the brain constitutes a major challenge, how additional drug-specific features alter efficacy remains poorly understood. We apply highly multiplex single-cell chemical genomics to define the molecular response of glioblastoma to EGFRis. Using a deep generative framework, we identify shared and drug-specific transcriptional programs that group EGFRis into distinct molecular classes. We identify programs that differ by the chemical properties of EGFRis, including induction of adaptive transcription and modulation of immunogenic gene expression. Finally, we demonstrate that pro-immunogenic expression changes associated with a subset of tyrphostin family EGFRis increase the ability of T-cells to target glioblastoma cells.

Repeat surgery of recurrent glioma for molecularly informed treatment in the age of precision oncology: A risk-benefit analysis

PURPOSE

Surgery for recurrent glioma provides cytoreduction and tissue for molecularly informed treatment. With mostly heavily pretreated patients involved, it is unclear whether the benefits of repeat surgery outweigh its potential risks.

METHODS

Patients receiving surgery for recurrent glioma WHO grade 2-4 with the goal of tissue sampling for targeted therapies were analyzed retrospectively. Complication rates (surgical, neurological) were compared to our institutional glioma surgery cohort. Tissue molecular diagnostic yield, targeted therapies and post-surgical survival rates were analyzed.

RESULTS

Between 2017 and 2022, tumor board recommendation for targeted therapy through molecular diagnostics was made for 180 patients. Of these, 70 patients (38%) underwent repeat surgery. IDH-wildtype glioblastoma was diagnosed in 48 patients (69%), followed by IDH-mutant astrocytoma (n = 13; 19%) and oligodendroglioma (n = 9; 13%). Gross total resection (GTR) was achieved in 50 patients (71%). Tissue was processed for next-generation sequencing in 64 cases (91%), and for DNA methylation analysis in 58 cases (83%), while immunohistochemistry for mTOR phosphorylation was performed in 24 cases (34%). Targeted therapy was recommended in 35 (50%) and commenced in 21 (30%) cases. Postoperatively, 7 patients (11%) required revision surgery, compared to 7% (p = 0.519) and 6% (p = 0.359) of our reference cohorts of patients undergoing first and second craniotomy, respectively. Non-resolving neurological deterioration was documented in 6 cases (10% vs. 8%, p = 0.612, after first and 4%, p = 0.519, after second craniotomy). Median survival after repeat surgery was 399 days in all patients and 348 days in GBM patients after repeat GTR.

CONCLUSION

Surgery for recurrent glioma provides relevant molecular diagnostic information with a direct consequence for targeted therapy under a reasonable risk of postoperative complications. With satisfactory postoperative survival it can therefore complement a multi-modal glioma therapy approach.

Intrinsic and Microenvironmental Drivers of Glioblastoma Invasion

Gliomas are diffusely infiltrating brain tumors whose prognosis is strongly influenced by their extent of invasion into the surrounding brain tissue. While lower-grade gliomas present more circumscribed borders, high-grade gliomas are aggressive tumors with widespread brain infiltration and dissemination. Glioblastoma (GBM) is known for its high invasiveness and association with poor prognosis. Its low survival rate is due to the certainty of its recurrence, caused by microscopic brain infiltration which makes surgical eradication unattainable. New insights into GBM biology at the single-cell level have enabled the identification of mechanisms exploited by glioma cells for brain invasion. In this review, we explore the current understanding of several molecular pathways and mechanisms used by tumor cells to invade normal brain tissue. We address the intrinsic biological drivers of tumor cell invasion, by tackling how tumor cells interact with each other and with the tumor microenvironment (TME). We focus on the recently discovered neuronal niche in the TME, including local as well as distant neurons, contributing to glioma growth and invasion. We then address the mechanisms of invasion promoted by astrocytes and immune cells. Finally, we review the current literature on the therapeutic targeting of the molecular mechanisms of invasion.

Multiplex single-cell chemical genomics reveals the kinase dependence of the response to targeted therapy

Chemical genetic screens are a powerful tool for exploring how cancer cells' response to drugs is shaped by their mutations, yet they lack a molecular view of the contribution of individual genes to the response to exposure. Here, we present sci-Plex-Gene-by-Environment (sci-Plex-GxE), a platform for combined single-cell genetic and chemical screening at scale. We highlight the advantages of large-scale, unbiased screening by defining the contribution of each of 522 human kinases to the response of glioblastoma to different drugs designed to abrogate signaling from the receptor tyrosine kinase pathway. In total, we probed 14,121 gene-by-environment combinations across 1,052,205 single-cell transcriptomes. We identify an expression signature characteristic of compensatory adaptive signaling regulated in a MEK/MAPK-dependent manner. Further analyses aimed at preventing adaptation revealed promising combination therapies, including dual MEK and CDC7/CDK9 or nuclear factor κB (NF-κB) inhibitors, as potent means of preventing transcriptional adaptation of glioblastoma to targeted therapy.

Non-Mass Spectrometric Targeted Single-Cell Metabolomics

Metabolic assays serve as pivotal tools in biomedical research, offering keen insights into cellular physiological and pathological states. While mass spectrometry (MS)-based metabolomics remains the gold standard for comprehensive, multiplexed analyses of cellular metabolites, innovative technologies are now emerging for the targeted, quantitative scrutiny of metabolites and metabolic pathways at the single-cell level. In this review, we elucidate an array of these advanced methodologies, spanning synthetic and surface chemistry techniques, imaging-based methods, and electrochemical approaches. We summarize the rationale, design principles, and practical applications for each method, and underscore the synergistic benefits of integrating single-cell metabolomics (scMet) with other single-cell omics technologies. Concluding, we identify prevailing challenges in the targeted scMet arena and offer a forward-looking commentary on future avenues and opportunities in this rapidly evolving field.

Multiplex single-cell chemical genomics reveals the kinase dependence of the response to targeted therapy

Chemical genetic screens are a powerful tool for exploring how cancer cells' response to drugs is shaped by their mutations, yet they lack a molecular view of the contribution of individual genes to the response to exposure. Here, we present sci-Plex-Gene-by-Environment (sci-Plex-GxE), a platform for combined single-cell genetic and chemical screening at scale. We highlight the advantages of large-scale, unbiased screening by defining the contribution of each of 522 human kinases to the response of glioblastoma to different drugs designed to abrogate signaling from the receptor tyrosine kinase pathway. In total, we probed 14,121 gene-by-environment combinations across 1,052,205 single-cell transcriptomes. We identify an expression signature characteristic of compensatory adaptive signaling regulated in a MEK/MAPK-dependent manner. Further analyses aimed at preventing adaptation revealed promising combination therapies, including dual MEK and CDC7/CDK9 or NF-kB inhibitors, as potent means of preventing transcriptional adaptation of glioblastoma to targeted therapy.

Mass spectrometry-based phosphoproteomics in clinical applications

Protein phosphorylation is an essential post-translational modification that regulates many aspects of cellular physiology, and dysregulation of pivotal phosphorylation events is often responsible for disease onset and progression. Clinical analysis on disease-relevant phosphoproteins, while quite challenging, provides unique information for precision medicine and targeted therapy. Among various approaches, mass spectrometry (MS)-centered characterization features discovery-driven, high-throughput and in-depth identification of phosphorylation events. This review highlights advances in sample preparation and instrument in MS-based phosphoproteomics and recent clinical applications. We emphasize the preeminent data-independent acquisition method in MS as one of the most promising future directions and biofluid-derived extracellular vesicles as an intriguing source of the phosphoproteome for liquid biopsy.

Deep Learning in Phosphoproteomics: Methods and Application in Cancer Drug Discovery

Protein phosphorylation is a key post-translational modification (PTM) that is a central regulatory mechanism of many cellular signaling pathways. Several protein kinases and phosphatases precisely control this biochemical process. Defects in the functions of these proteins have been implicated in many diseases, including cancer. Mass spectrometry (MS)-based analysis of biological samples provides in-depth coverage of phosphoproteome. A large amount of MS data available in public repositories has unveiled big data in the field of phosphoproteomics. To address the challenges associated with handling large data and expanding confidence in phosphorylation site prediction, the development of many computational algorithms and machine learning-based approaches have gained momentum in recent years. Together, the emergence of experimental methods with high resolution and sensitivity and data mining algorithms has provided robust analytical platforms for quantitative proteomics. In this review, we compile a comprehensive collection of bioinformatic resources used for the prediction of phosphorylation sites, and their potential therapeutic applications in the context of cancer.

Enabling Single-Cell Drug Response Annotations from Bulk RNA-Seq Using SCAD

The single-cell RNA sequencing (scRNA-seq) quantifies the gene expression of individual cells, while the bulk RNA sequencing (bulk RNA-seq) characterizes the mixed transcriptome of cells. The inference of drug sensitivities for individual cells can provide new insights to understand the mechanism of anti-cancer response heterogeneity and drug resistance at the cellular resolution. However, pharmacogenomic information related to their corresponding scRNA-Seq is often limited. Therefore, a transfer learning model is proposed to infer the drug sensitivities at single-cell level. This framework learns bulk transcriptome profiles and pharmacogenomics information from population cell lines in a large public dataset and transfers the knowledge to infer drug efficacy of individual cells. The results suggest that it is suitable to learn knowledge from pre-clinical cell lines to infer pre-existing cell subpopulations with different drug sensitivities prior to drug exposure. In addition, the model offers a new perspective on drug combinations. It is observed that drug-resistant subpopulation can be sensitive to other drugs (e.g., a subset of JHU006 is Vorinostat-resistant while Gefitinib-sensitive); such finding corroborates the previously reported drug combination (Gefitinib + Vorinostat) strategy in several cancer types. The identified drug sensitivity biomarkers reveal insights into the tumor heterogeneity and treatment at cellular resolution.

CAR T Cell Therapy in Glioblastoma: Overcoming Challenges Related to Antigen Expression

Glioblastoma (GBM) is the most common primary brain tumor, yet prognosis remains dismal with current treatment. Immunotherapeutic strategies have had limited effectiveness to date in GBM, but recent advances hold promise. One such immunotherapeutic advance is chimeric antigen receptor (CAR) T cell therapy, where autologous T cells are extracted and engineered to express a specific receptor against a GBM antigen and are then infused back into the patient. There have been numerous preclinical studies showing promising results, and several of these CAR T cell therapies are being tested in clinical trials for GBM and other brain cancers. While results in tumors such as lymphomas and diffuse intrinsic pontine gliomas have been encouraging, early results in GBM have not shown clinical benefit. Potential reasons for this are the limited number of specific antigens in GBM, their heterogenous expression, and their loss after initiating antigen-specific therapy due to immunoediting. Here, we review the current preclinical and clinical experiences with CAR T cell therapy in GBM and potential strategies to develop more effective CAR T cells for this indication.

Screening of predicted synergistic multi-target therapies in glioblastoma identifies new treatment strategies

Background

IDH-wildtype glioblastoma (GBM) is a highly malignant primary brain tumor with a median survival of 15 months after standard of care, which highlights the need for improved therapy. Personalized combination therapy has shown to be successful in many other tumor types and could be beneficial for GBM patients.

Methods

We performed the largest drug combination screen to date in GBM, using a high-throughput effort where we selected 90 drug combinations for their activity onto 25 patient-derived GBM cultures. 43 drug combinations were selected for interaction analysis based on their monotherapy efficacy and were tested in a short-term (3 days) as well as long-term (18 days) assay. Synergy was assessed using dose-equivalence and multiplicative survival metrics.

Results

We observed a consistent synergistic interaction for 15 out of 43 drug combinations on patient-derived GBM cultures. From these combinations, 11 out of 15 drug combinations showed a longitudinal synergistic effect on GBM cultures. The highest synergies were observed in the drug combinations Lapatinib with Thapsigargin and Lapatinib with Obatoclax Mesylate, both targeting epidermal growth factor receptor and affecting the apoptosis pathway. To further elaborate on the apoptosis cascade, we investigated other, more clinically relevant, apoptosis inducers and observed a strong synergistic effect while combining Venetoclax (BCL targeting) and AZD5991 (MCL1 targeting).

Conclusions

Overall, we have identified via a high-throughput drug screening several new treatment strategies for GBM. Moreover, an exceptionally strong synergistic interaction was discovered between kinase targeting and apoptosis induction which is suitable for further clinical evaluation as multi-targeted combination therapy.

Phosphoproteomic Analysis Defines BABAM1 as mTORC2 Downstream Effector Promoting DNA Damage Response in Glioblastoma Cells

Glioblastoma (GBM) is a devastating primary brain cancer with a poor prognosis. GBM is associated with an abnormal mechanistic target of rapamycin (mTOR) signaling pathway, consisting of two distinct kinase complexes: mTORC1 and mTORC2. The complexes play critical roles in cell proliferation, survival, migration, metabolism, and DNA damage response. This study investigated the aberrant mTORC2 signaling pathway in GBM cells by performing quantitative phosphoproteomic analysis of U87MG cells under different drug treatment conditions. Interestingly, a functional analysis of phosphoproteome revealed that mTORC2 inhibition might be involved in double-strand break (DSB) repair. We further characterized the relationship between mTORC2 and BRISC and BRCA1-A complex member 1 (BABAM1). We demonstrated that pBABAM1 at Ser29 is regulated by mTORC2 to initiate DNA damage response, contributing to DNA repair and cancer cell survival. Accordingly, the inactivation of mTORC2 significantly ablated pBABAM1 (Ser29), reduced DNA repair activities in the nucleus, and promoted apoptosis of the cancer cells. Furthermore, we also recognized that histone H2AX phosphorylation at Ser139 (γH2AX) could be controlled by mTORC2 to repair the DNA. These results provided a better understanding of the mTORC2 function in oncogenic DNA damage response and might lead to specific mTORC2 treatments for brain cancer patients in the future.

Deep Phosphoproteome Landscape of Interhemispheric Functionality of Neuroanatomical Regions of the Human Brain

Post-translational modifications (PTMs) are one of the compulsive and predominant biological processes that regulate the diverse molecular mechanism, modulate the onset of disease, and are the reason behind the functional diversity of proteins. Despite the widespread research findings in neuroproteomics, one of the key drawbacks has been the lack of proteome-level knowledge of hemispheric lateralization. We have investigated the proteome level expression in different neuroanatomical regions under the Human Brain Proteome Project (HBPP) and developed the global interhemispheric brain proteome map (Brainprot) earlier. Furthermore, this study has extended to decipher the phosphoproteome map of human brain interhemispheric regions through high-resolution mass spectrometry. The phosphoproteomics examination of 12 unique interhemispheric neurological brain regions using Orbitrap fusion liquid chromatography with tandem mass spectrometry provided comprehensive coverage of 996 phosphoproteins, 2010 phosphopeptides, and 3567 phosphosites. Moreover, interhemispheric phosphoproteome profiling has been categorized according to synaptic ontologies and interhemispheric expression to understand the functionality. Finally, we have integrated the phosphosites data under the PhosphoMap section in the Inter-Hemispheric Brain Proteome Map Portal (https://www.brainprot.org/) for the advancement and support of the ongoing neuroproteomics research worldwide. Data is available via ProteomeXchange with the identifier PXD031188.

Leveraging extrachromosomal DNA to fine-tune trials of targeted therapy for glioblastoma: opportunities and challenges

Glioblastoma evolution is facilitated by intratumour heterogeneity, which poses a major hurdle to effective treatment. Evidence indicates a key role for oncogene amplification on extrachromosomal DNA (ecDNA) in accelerating tumour evolution and thus resistance to treatment, particularly in glioblastomas. Oncogenes contained within ecDNA can reach high copy numbers and expression levels, and their unequal segregation can result in more rapid copy number changes in response to therapy than is possible through natural selection of intrachromosomal genomic loci. Notably, targeted therapies inhibiting oncogenic pathways have failed to improve glioblastoma outcomes. In this Perspective, we outline reasons for this disappointing lack of clinical translation and present the emerging evidence implicating ecDNA as an important driver of tumour evolution. Furthermore, we suggest that through detection of ecDNA, patient selection for clinical trials of novel agents can be optimized to include those most likely to benefit based on current understanding of resistance mechanisms. We discuss the challenges to successful translation of this approach, including accurate detection of ecDNA in tumour tissue with novel technologies, development of faithful preclinical models for predicting the efficacy of novel agents in the presence of ecDNA oncogenes, and understanding the mechanisms of ecDNA formation during cancer evolution and how they could be attenuated therapeutically. Finally, we evaluate the feasibility of routine ecDNA characterization in the clinic and how this process could be integrated with other methods of molecular stratification to maximize the potential for clinical translation of precision medicines.

Computational quantification and characterization of independently evolving cellular subpopulations within tumors is critical to inhibit anti-cancer therapy resistance

BACKGROUND

Drug resistance continues to be a major limiting factor across diverse anti-cancer therapies. Contributing to the complexity of this challenge is cancer plasticity, in which one cancer subtype switches to another in response to treatment, for example, triple-negative breast cancer (TNBC) to Her2-positive breast cancer. For optimal treatment outcomes, accurate tumor diagnosis and subsequent therapeutic decisions are vital. This study assessed a novel approach to characterize treatment-induced evolutionary changes of distinct tumor cell subpopulations to identify and therapeutically exploit anticancer drug resistance.

METHODS

In this research, an information-theoretic single-cell quantification strategy was developed to provide a high-resolution and individualized assessment of tumor composition for a customized treatment approach. Briefly, this single-cell quantification strategy computes cell barcodes based on at least 100,000 tumor cells from each experiment and reveals a cell-specific signaling signature (CSSS) composed of a set of ongoing processes in each cell.

RESULTS

Using these CSSS-based barcodes, distinct subpopulations evolving within the tumor in response to an outside influence, like anticancer treatments, were revealed and mapped. Barcodes were further applied to assign targeted drug combinations to each individual tumor to optimize tumor response to therapy. The strategy was validated using TNBC models and patient-derived tumors known to switch phenotypes in response to radiotherapy (RT).

CONCLUSIONS

We show that a barcode-guided targeted drug cocktail significantly enhances tumor response to RT and prevents regrowth of once-resistant tumors. The strategy presented herein shows promise in preventing cancer treatment resistance, with significant applicability in clinical use.

Next Steps for Immunotherapy in Glioblastoma

Outcomes for glioblastoma (GBM) patients undergoing standard of care treatment remain poor. Here we discuss the portfolio of previously investigated immunotherapies for glioblastoma, including vaccine therapy and checkpoint inhibitors, as well as novel emerging therapeutic approaches. In addition, we explore the factors that potentially influence response to immunotherapy, which should be considered in future research aimed at improving immunotherapy efficacy.

Constraint-Based Reconstruction and Analyses of Metabolic Models: Open-Source Python Tools and Applications to Cancer

The influence of metabolism on signaling, epigenetic markers, and transcription is highly complex yet important for understanding cancer physiology. Despite the development of high-resolution multi-omics technologies, it is difficult to infer metabolic activity from these indirect measurements. Fortunately, genome-scale metabolic models and constraint-based modeling provide a systems biology framework to investigate the metabolic states and define the genotype-phenotype associations by integrations of multi-omics data. Constraint-Based Reconstruction and Analysis (COBRA) methods are used to build and simulate metabolic networks using mathematical representations of biochemical reactions, gene-protein reaction associations, and physiological and biochemical constraints. These methods have led to advancements in metabolic reconstruction, network analysis, perturbation studies as well as prediction of metabolic state. Most computational tools for performing these analyses are written for MATLAB, a proprietary software. In order to increase accessibility and handle more complex datasets and models, community efforts have started to develop similar open-source tools in Python. To date there is a comprehensive set of tools in Python to perform various flux analyses and visualizations; however, there are still missing algorithms in some key areas. This review summarizes the availability of Python software for several components of COBRA methods and their applications in cancer metabolism. These tools are evolving rapidly and should offer a readily accessible, versatile way to model the intricacies of cancer metabolism for identifying cancer-specific metabolic features that constitute potential drug targets.

Tumor Drug Concentration and Phosphoproteomic Profiles After Two Weeks of Treatment With Sunitinib in Patients with Newly Diagnosed Glioblastoma

PURPOSE

Tyrosine kinase inhibitors (TKI) have poor efficacy in patients with glioblastoma (GBM). Here, we studied whether this is predominantly due to restricted blood-brain barrier penetration or more to biological characteristics of GBM.

PATIENTS AND METHODS

Tumor drug concentrations of the TKI sunitinib after 2 weeks of preoperative treatment was determined in 5 patients with GBM and compared with its in vitro inhibitory concentration (IC50) in GBM cell lines. In addition, phosphotyrosine (pTyr)-directed mass spectrometry (MS)-based proteomics was performed to evaluate sunitinib-treated versus control GBM tumors.

RESULTS

The median tumor sunitinib concentration of 1.9 μmol/L (range 1.0-3.4) was 10-fold higher than in concurrent plasma, but three times lower than sunitinib IC50s in GBM cell lines (median 5.4 μmol/L, 3.0-8.5; P = 0.01). pTyr-phosphoproteomic profiles of tumor samples from 4 sunitinib-treated versus 7 control patients revealed 108 significantly up- and 23 downregulated (P < 0.05) phosphopeptides for sunitinib treatment, resulting in an EGFR-centered signaling network. Outlier analysis of kinase activities as a potential strategy to identify drug targets in individual tumors identified nine kinases, including MAPK10 and INSR/IGF1R.

CONCLUSIONS

Achieved tumor sunitinib concentrations in patients with GBM are higher than in plasma, but lower than reported for other tumor types and insufficient to significantly inhibit tumor cell growth in vitro. Therefore, alternative TKI dosing to increase intratumoral sunitinib concentrations might improve clinical benefit for patients with GBM. In parallel, a complex profile of kinase activity in GBM was found, supporting the potential of (phospho)proteomic analysis for the identification of targets for (combination) treatment.

Rise of the SARS-CoV-2 Variants: can proteomics be the silver bullet?

INTRODUCTION

The challenges posed by emergent strains of SARS-CoV-2 need to be tackled by contemporary scientific approaches, with proteomics playing a significant role.

AREAS COVERED

In this review, we provide a brief synthesis of the impact of proteomics technologies in elucidating disease pathogenesis and classifiers for the prognosis of COVID-19 and propose proteomics methodologies that could play a crucial role in understanding emerging variants and their altered disease pathology. From aiding the design of novel drug candidates to facilitating the identification of T cell vaccine targets, we have discussed the impact of proteomics methods in COVID-19 research. Techniques varied as mass spectrometry, single-cell proteomics, multiplexed ELISA arrays, high-density proteome arrays, surface plasmon resonance, immunopeptidomics, and in silico docking studies that have helped augment the fight against existing diseases were useful in preparing us to tackle SARS-CoV-2 variants. We also propose an action plan for a pipeline to combat emerging pandemics using proteomics technology by adopting uniform standard operating procedures and unified data analysis paradigms.

EXPERT OPINION

The knowledge about the use of diverse proteomics approaches for COVID-19 investigation will provide a framework for future basic research, better infectious disease prevention strategies, improved diagnostics, and targeted therapeutics.

Cancer cell heterogeneity and plasticity: A paradigm shift in glioblastoma

Phenotypic plasticity has emerged as a major contributor to intra-tumoral heterogeneity and treatment resistance in cancer. Increasing evidence shows that glioblastoma (GBM) cells display prominent intrinsic plasticity and reversibly adapt to dynamic microenvironmental conditions. Limited genetic evolution at recurrence further suggests that resistance mechanisms also largely operate at the phenotypic level. Here we review recent literature underpinning the role of GBM plasticity in creating gradients of heterogeneous cells including those that carry cancer stem cell (CSC) properties. A historical perspective from the hierarchical to the nonhierarchical concept of CSCs towards the recent appreciation of GBM plasticity is provided. Cellular states interact dynamically with each other and with the surrounding brain to shape a flexible tumor ecosystem, which enables swift adaptation to external pressure including treatment. We present the key components regulating intra-tumoral phenotypic heterogeneity and the equilibrium of phenotypic states, including genetic, epigenetic, and microenvironmental factors. We further discuss plasticity in the context of intrinsic tumor resistance, where a variable balance between preexisting resistant cells and adaptive persisters leads to reversible adaptation upon treatment. Innovative efforts targeting regulators of plasticity and mechanisms of state transitions towards treatment-resistant states are needed to restrict the adaptive capacities of GBM.

Predicting heterogeneity in clone-specific therapeutic vulnerabilities using single-cell transcriptomic signatures

While understanding molecular heterogeneity across patients underpins precision oncology, there is increasing appreciation for taking intra-tumor heterogeneity into account. Based on large-scale analysis of cancer omics datasets, we highlight the importance of intra-tumor transcriptomic heterogeneity (ITTH) for predicting clinical outcomes. Leveraging single-cell RNA-seq (scRNA-seq) with a recommender system (CaDRReS-Sc), we show that heterogeneous gene-expression signatures can predict drug response with high accuracy (80%). Using patient-proximal cell lines, we established the validity of CaDRReS-Sc's monotherapy (Pearson r>0.6) and combinatorial predictions targeting clone-specific vulnerabilities (>10% improvement). Applying CaDRReS-Sc to rapidly expanding scRNA-seq compendiums can serve as in silico screen to accelerate drug-repurposing studies. Availability: https://github.com/CSB5/CaDRReS-Sc .

The Origins of Phenotypic Heterogeneity in Cancer

Heterogeneity is a pervasive feature of cancer, and understanding the sources and regulatory mechanisms underlying heterogeneity could provide key insights to help improve the diagnosis and treatment of cancer. In this review, we discuss the origin of heterogeneity in the phenotype of individual cancer cells. Genotype-phenotype (G-P) maps are widely used in evolutionary biology to represent the complex interactions of genes and the environment that lead to phenotypes that impact fitness. Here, we present the rationale of an extended G-P (eG-P) map with a cone structure in cancer. The eG-P cone is formed by cells that are similar at the genome layer but gradually increase variability in the epigenome, transcriptome, proteome, metabolome and signalome layers to produce large variability at the phenome layer. Experimental evidence from single-cell -omics analyses supporting the cancer eG-P cone concept is presented, and the impact of epimutations and the interaction of cancer and tumor microenvironmental eG-P cones are integrated with the current understanding of cancer biology. The eG-P cone concept uncovers potential therapeutic strategies to reduce cancer evolution and improve cancer treatment. More methods to study phenotypes in single cells will be key to better understand cancer cell fitness in tumor biology and therapeutics.

Status Quo and Trends of Intra-Arterial Therapy for Brain Tumors: A Bibliometric and Clinical Trials Analysis

Intra-arterial drug delivery circumvents the first-pass effect and is believed to increase both efficacy and tolerability of primary and metastatic brain tumor therapy. The aim of this update is to report on pertinent articles and clinical trials to better understand the research landscape to date and future directions. Elsevier's Scopus and ClinicalTrials.gov databases were reviewed in August 2021 for all possible articles and clinical trials of intra-arterial drug injection as a treatment strategy for brain tumors. Entries were screened against predefined selection criteria and various parameters were summarized. Twenty clinical trials and 271 articles satisfied all inclusion criteria. In terms of articles, 201 (74%) were primarily clinical and 70 (26%) were basic science, published in a total of 120 different journals. Median values were: publication year, 1986 (range, 1962-2021); citation count, 15 (range, 0-607); number of authors, 5 (range, 1-18). Pertaining to clinical trials, 9 (45%) were phase 1 trials, with median expected start and completion years in 2011 (range, 1998-2019) and 2022 (range, 2008-2025), respectively. Only one (5%) trial has reported results to date. Glioma was the most common tumor indication reported in both articles (68%) and trials (75%). There were 215 (79%) articles investigating chemotherapy, while 13 (65%) trials evaluated targeted therapy. Transient blood-brain barrier disruption was the commonest strategy for articles (27%) and trials (60%) to optimize intra-arterial therapy. Articles and trials predominately originated in the United States (50% and 90%, respectively). In this bibliometric and clinical trials analysis, we discuss the current state and trends of intra-arterial therapy for brain tumors. Most articles were clinical, and traditional anti-cancer agents and drug delivery strategies were commonly studied. This was reflected in clinical trials, of which only a single study had reported outcomes. We anticipate future efforts to involve novel therapeutic and procedural strategies based on recent advances in the field.

Microfluidic Single-Cell Proteomics Assay Chip: Lung Cancer Cell Line Case Study

Cancer is a dynamic disease involving constant changes. With these changes, cancer cells become heterogeneous, resulting in varying sensitivity to chemotherapy. The heterogeneity of cancer cells plays a key role in chemotherapy resistance and cancer recurrence. Therefore, for effective treatment, cancer cells need to be analyzed at the single-cell level by monitoring various proteins and investigating their heterogeneity. We propose a microfluidic chip for a single-cell proteomics assay that is capable of analyzing complex cellular signaling systems to reveal the heterogeneity of cancer cells. The single-cell assay chip comprises (i) microchambers (n = 1376) for manipulating single cancer cells, (ii) micropumps for rapid single-cell lysis, and (iii) barcode immunosensors for detecting nine different secretory and intracellular proteins to reveal the correlation among cancer-related proteins. Using this chip, the single-cell proteomics of a lung cancer cell line, which may be easily masked in bulk analysis, were evaluated. By comparing changes in the level of protein secretion and heterogeneity in response to combinations of four anti-cancer drugs, this study suggests a new method for selecting the best combination of anti-cancer drugs. Subsequent preclinical and clinical trials should enable this platform to become applicable for patient-customized therapies.

An overview of multiplexed analyses of CAR T-cell therapies: insights and potential

INTRODUCTION

Cancer immunotherapy is a rapidly growing field with exponential advancement in engineered immune cell-based therapies. For instance, an engineered chimeric antigen receptor (CAR) can be introduced in T-cells or other immune cells and adoptively transferred to target and kill cancer cells in hematologic malignancies or solid tumors. The first CAR-T-cell (CAR-T) therapy has been developed against CD19, a B-cell marker expressed on lymphoma and lymphoblastic leukemia. To allow for personalized treatment, proteomics approaches could provide insights into biomarkers for CAR-T therapy efficacy and toxicity.

AREAS COVERED

We researched the most recent technology methods of biomarker evaluation used in the laboratory and clinical setting. Publications of CAR-T biomarkers were then systematically reviewed to provide a narrative of the most validated biomarkers of CAR-T efficacy and toxicity. Examples of biomarkers include CAR-T functionality and phenotype as well as interleukin-6 and other cytokines.

EXPERT COMMENTARY

Biomarkers of CAR-T efficacy and toxicity have been identified, but still need to be validated and standardized across institutions. Moreover, few are used in the clinical setting due to limitations in real-time technology. Expansion of biomarker research could provide better understanding of patient response and risk of life-threatening side effects with potential for improved precision medicine.

Digitonin-facilitated delivery of imaging probes enables single-cell analysis of AKT signalling activities in suspension cells

Analyzing intracellular signalling protein activities in living cells promises a better understanding of the signalling cascade and related biological processes. We have previously developed cyclic peptide-based probes for analyzing intracellular AKT signalling activities, but these peptide probes were not cell-permeable. Implementing fusogenic liposomes as delivery vehicles could circumvent the problem when analyzing adherent cells, but it remained challenging to study suspension cells using similar approaches. Here, we present a method for delivering these imaging probes into suspension cells using digitonin, which could transiently perforate the cell membrane. Using U87, THP-1, and Jurkat cells as model systems representing suspended adherent cells, myeloid cells, and lymphoid cells, we demonstrated that low concentrations of digitonin enabled a sufficient amount of probes to enter the cytosol without affecting cell viability. We further combined this delivery method with a microwell single-cell chip and interrogated the AKT signalling dynamics in THP-1 and Jurkat cells, followed by immunofluorescence-based quantitation of AKT expression levels. We resolved the cellular heterogeneity in AKT signalling activities and showed that the kinetic patterns of AKT signalling and the AKT expression levels were related in THP-1 cells, but decoupled in Jurkat cells. We expect that our approach can be adapted to study other suspension cells.

Interaction between Ras and Src clones causes interdependent tumor malignancy via Notch signaling in Drosophila

Cancer tissue often comprises multiple tumor clones with distinct oncogenic alterations such as Ras or Src activation, yet the mechanism by which tumor heterogeneity drives cancer progression remains elusive. Here, we show in Drosophila imaginal epithelium that clones of Ras- or Src-activated benign tumors interact with each other to mutually promote tumor malignancy. Mechanistically, Ras-activated cells upregulate the cell-surface ligand Delta while Src-activated cells upregulate its receptor Notch, leading to Notch activation in Src cells. Elevated Notch signaling induces the transcriptional repressor Zfh1/ZEB1, which downregulates E-cadherin and cell death gene hid, leading to Src-activated invasive tumors. Simultaneously, Notch activation in Src cells upregulates the cytokine Unpaired/IL-6, which activates JAK-STAT signaling in neighboring Ras cells. Elevated JAK-STAT signaling upregulates the BTB-zinc-finger protein Chinmo, which downregulates E-cadherin and thus generates Ras-activated invasive tumors. Our findings provide a mechanistic explanation for how tumor heterogeneity triggers tumor progression via cell-cell interactions.

"Zooming in" on Glioblastoma: Understanding Tumor Heterogeneity and its Clinical Implications in the Era of Single-Cell Ribonucleic Acid Sequencing

Glioblastoma (GBM) is the most common primary brain malignancy in adults and one of the most aggressive of all human cancers. It is highly recurrent and treatment-resistant, in large part due to its infiltrative nature and inter- and intratumoral heterogeneity. This heterogeneity entails varying genomic landscapes and cell types within and between tumors and the tumor microenvironment (TME). In GBM, heterogeneity is a driver of treatment resistance, recurrence, and poor prognosis, representing a substantial impediment to personalized medicine. Over the last decade, sequencing technologies have facilitated deeper understanding of GBM heterogeneity by "zooming in" progressively further on tumor genomics and transcriptomics. Initial efforts employed bulk ribonucleic acid (RNA) sequencing, which examines composite gene expression of whole tumor specimens. While groundbreaking at the time, this bulk RNAseq masks the crucial contributions of distinct tumor subpopulations to overall gene expression. This work progressed to the use of bulk RNA sequencing in anatomically and spatially distinct tumor subsections, which demonstrated previously underappreciated genomic complexity of GBM. A revolutionary next step forward has been the advent of single-cell RNA sequencing (scRNAseq), which examines gene expression at the single-cell level. scRNAseq has enabled us to understand GBM heterogeneity in unprecedented detail. We review seminal studies in our progression of understanding GBM heterogeneity, with a focus on scRNAseq and the insights that it has provided into understanding the GBM tumor mass, peritumoral space, and TME. We highlight preclinical and clinical implications of this work and consider its potential to impact neuro-oncology and to improve patient outcomes via personalized medicine.

Proteomics of resistance to Notch1 inhibition in acute lymphoblastic leukemia reveals targetable kinase signatures

Notch1 is a crucial oncogenic driver in T-cell acute lymphoblastic leukemia (T-ALL), making it an attractive therapeutic target. However, the success of targeted therapy using γ-secretase inhibitors (GSIs), small molecules blocking Notch cleavage and subsequent activation, has been limited due to development of resistance, thus restricting its clinical efficacy. Here, we systematically compare GSI resistant and sensitive cell states by quantitative mass spectrometry-based phosphoproteomics, using complementary models of resistance, including T-ALL patient-derived xenografts (PDX) models. Our datasets reveal common mechanisms of GSI resistance, including a distinct kinase signature that involves protein kinase C delta. We demonstrate that the PKC inhibitor sotrastaurin enhances the anti-leukemic activity of GSI in PDX models and completely abrogates the development of acquired GSI resistance in vitro. Overall, we highlight the potential of proteomics to dissect alterations in cellular signaling and identify druggable pathways in cancer.

NOTCH1 is a driver of T-cell acute lymphoblastic leukemia that can be inhibited by γ-secretase inhibitors (GSIs), but their clinical efficacy is limited. Here, the authors compare the phosphoproteomes of GSI resistant and sensitive models, and identify potential kinase targets to overcome GSI resistance.

Residual Disease in Glioma Recurrence: A Dangerous Liaison with Senescence

With a dismally low median survival of less than two years after diagnosis, Glioblastoma (GBM) is the most lethal type of brain cancer. The standard-of-care of surgical resection, followed by DNA-damaging chemo-/radiotherapy, is often non-curative. In part, this is because individual cells close to the resection border remain alive and eventually undergo renewed proliferation. These residual, therapy-resistant cells lead to rapid recurrence, against which no effective treatment exists to date. Thus, new experimental approaches need to be developed against residual disease to prevent GBM survival and recurrence. Cellular senescence is an attractive area for the development of such new approaches. Senescence can occur in healthy cells when they are irreparably damaged. Senescent cells develop a chronic secretory phenotype that is generally considered pro-tumorigenic and pro-migratory. Age is a negative prognostic factor for GBM stage, and, with age, senescence steadily increases. Moreover, chemo-/radiotherapy can provide an additional increase in senescence close to the tumor. In light of this, we will review the importance of senescence in the tumor-supportive brain parenchyma, focusing on the invasion and growth of GBM in residual disease. We will propose a future direction on the application of anti-senescence therapies against recurrent GBM.

Overview of Trends in Global Single Cell Research Based on Bibliometric Analysis and LDA Model (2009–2019)

Purpose

This article aims to describe the global research profile and the development trends of single cell research from the perspective of bibliometric analysis and semantic mining.

Design/methodology/approach

The literatures on single cell research were extracted from Clarivate Analytic's Web of Science Core Collection between 2009 and 2019. Firstly, bibliometric analyses were performed with Thomson Data Analyzer (TDA). Secondly, topic identification and evolution trends of single cell research was conducted through the LDA topic model. Thirdly, taking the post-discretized method which is used for topic evolution analysis for reference, the topics were also be dispersed to countries to detect the spatial distribution.

Findings

The publication of single cell research shows significantly increasing tendency in the last decade. The topics of single cell research field can be divided into three categories, which respectively refers to single cell research methods, mechanism of biological process, and clinical application of single cell technologies. The different trends of these categories indicate that technological innovation drives the development of applied research. The continuous and rapid growth of the topic strength in the field of cancer diagnosis and treatment indicates that this research topic has received extensive attention in recent years. The topic distributions of some countries are relatively balanced, while for the other countries, several topics show significant superiority.

Research limitations

The analyzed data of this study only contain those were included in the Web of Science Core Collection.

Practical implications

This study provides insights into the research progress regarding single cell field and identifies the most concerned topics which reflect potential opportunities and challenges. The national topic distribution analysis based on the post-discretized analysis method extends topic analysis from time dimension to space dimension.

Originality/value

This paper combines bibliometric analysis and LDA model to analyze the evolution trends of single cell research field. The method of extending post-discretized analysis from time dimension to space dimension is distinctive and insightful.

ConBr lectin modulates MAPKs and Akt pathways and triggers autophagic glioma cell death by a mechanism dependent upon caspase-8 activation

Glioblastoma multiforme is the most aggressive type of glioma, with limited treatment and poor prognosis. Despite some advances over the last decade, validation of novel and selective antiglioma agents remains a challenge in clinical pharmacology. Prior studies have shown that leguminous lectins may exert various biological effects, including antitumor properties. Accordingly, this study aimed to evaluate the mechanisms underlying the antiglioma activity of ConBr, a lectin extracted from the Canavalia brasiliensis seeds. ConBr at lower concentrations inhibited C6 glioma cell migration while higher levels promoted cell death dependent upon carbohydrate recognition domain (CRD) structure. ConBr increased p38^MAPK and JNK and decreased ERK1/2 and Akt phosphorylation. Moreover, ConBr inhibited mTORC1 phosphorylation associated with accumulation of autophagic markers, such as acidic vacuoles and LC3 cleavage. Inhibition of early steps of autophagy with 3-methyl-adenine (3-MA) partially protected whereas the later autophagy inhibitor Chloroquine (CQ) had no protective effect upon ConBr cytotoxicity. ConBr also augmented caspase-3 activation without affecting mitochondrial function. Noteworthy, the caspase-8 inhibitor IETF-fmk attenuated ConBr induced autophagy and C6 glioma cell death. Finally, ConBr did not show cytotoxicity against primary astrocytes, suggesting a selective antiglioma activity. In summary, our results indicate that ConBr requires functional CRD lectin domain to exert antiglioma activity, and its cytotoxicity is associated with MAPKs and Akt pathways modulation and autophagy- and caspase-8- dependent cell death.

Her2 promotes early dissemination of breast cancer by suppressing the p38 pathway through Skp2-mediated proteasomal degradation of Tpl2

While mechanisms for metastasis were extensively studied in cancer cells from patients with detectable tumors, pathways underlying metastatic dissemination from early lesions before primary tumors appear are poorly understood. Her2 promotes breast cancer early dissemination by suppressing p38, but how Her2 downregulates p38 is unclear. Here, we demonstrate that in early lesion breast cancer models, Her2 inhibits p38 by inducing Skp2 through Akt-mediated phosphorylation, which promotes ubiquitination and proteasomal degradation of Tpl2, a p38 MAP3K. The early disseminating cells are Her2⁺Skp2^highTpl2^lowp-p38^lowE-cadherin^low in the MMTV-Her2 breast cancer model. In human breast carcinoma, high Skp2 and low Tpl2 expression are associated with the Her2⁺ status; Tpl2 expression positively correlates with that of activated p38; Skp2 expression negatively correlates with that of Tpl2 and activated p38. Moreover, the Her2-Akt-Skp2-Tpl2-p38 axis plays a key role in the disseminating phenotypes in early lesion breast cancer cells; inhibition of Tpl2 enhances early dissemination in vivo. These findings identify the Her2-Akt-Skp2-Tpl2-p38 cascade as a novel mechanism mediating breast cancer early dissemination and a potential target for novel therapies targeting early metastatic dissemination.

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.

Investigation of cancer drug resistance mechanisms by phosphoproteomics

Cancer cell mutations can be identified by genomic and transcriptomic techniques. However, they are not sufficient to understand the full complexity of cancer heterogeneity. Analyses of proteins expressed in cancers and their modification profiles show how these mutations could be translated at the functional level. Protein phosphorylation is a major post-translational modification critical for regulating several cellular functions. The covalent addition of phosphate groups to serine, threonine, and tyrosine is catalyzed by protein kinases. Over the past years, kinases were strongly associated with cancer, thus inhibition of protein kinases emanated as novel cancer treatment. However, cancers frequently develop drug resistance. Therefore, a better understanding of drug effects on tumors is urgently needed. In this perspective, phosphoproteomics arose as advanced tool to monitor cancer therapies and to discover novel drugs. This review highlights the role of phosphoproteomics in predicting sensitivity or resistance of cancers towards tyrosine kinase inhibitors and cytotoxic drugs. It also shows the importance of phosphoproteomics in identifying biomarkers that could be applied in clinical diagnostics to predict responses to drugs.

Unsupervised machine learning reveals risk stratifying glioblastoma tumor cells

A goal of cancer research is to reveal cell subsets linked to continuous clinical outcomes to generate new therapeutic and biomarker hypotheses. We introduce a machine learning algorithm, Risk Assessment Population IDentification (RAPID), that is unsupervised and automated, identifies phenotypically distinct cell populations, and determines whether these populations stratify patient survival. With a pilot mass cytometry dataset of 2 million cells from 28 glioblastomas, RAPID identified tumor cells whose abundance independently and continuously stratified patient survival. Statistical validation within the workflow included repeated runs of stochastic steps and cell subsampling. Biological validation used an orthogonal platform, immunohistochemistry, and a larger cohort of 73 glioblastoma patients to confirm the findings from the pilot cohort. RAPID was also validated to find known risk stratifying cells and features using published data from blood cancer. Thus, RAPID provides an automated, unsupervised approach for finding statistically and biologically significant cells using cytometry data from patient samples.

Recent advances in mass spectrometry based clinical proteomics: applications to cancer research

Cancer biomarkers have transformed current practices in the oncology clinic. Continued discovery and validation are crucial for improving early diagnosis, risk stratification, and monitoring patient response to treatment. Profiling of the tumour genome and transcriptome are now established tools for the discovery of novel biomarkers, but alterations in proteome expression are more likely to reflect changes in tumour pathophysiology. In the past, clinical diagnostics have strongly relied on antibody-based detection strategies, but these methods carry certain limitations. Mass spectrometry (MS) is a powerful method that enables increasingly comprehensive insights into changes of the proteome to advance personalized medicine. In this review, recent improvements in MS-based clinical proteomics are highlighted with a focus on oncology. We will provide a detailed overview of clinically relevant samples types, as well as, consideration for sample preparation methods, protein quantitation strategies, MS configurations, and data analysis pipelines currently available to researchers. Critical consideration of each step is necessary to address the pressing clinical questions that advance cancer patient diagnosis and prognosis. While the majority of studies focus on the discovery of clinically-relevant biomarkers, there is a growing demand for rigorous biomarker validation. These studies focus on high-throughput targeted MS assays and multi-centre studies with standardized protocols. Additionally, improvements in MS sensitivity are opening the door to new classes of tumour-specific proteoforms including post-translational modifications and variants originating from genomic aberrations. Overlaying proteomic data to complement genomic and transcriptomic datasets forges the growing field of proteogenomics, which shows great potential to improve our understanding of cancer biology. Overall, these advancements not only solidify MS-based clinical proteomics' integral position in cancer research, but also accelerate the shift towards becoming a regular component of routine analysis and clinical practice.

Multi-omic single-cell snapshots reveal multiple independent trajectories to drug tolerance in a melanoma cell line

The determination of individual cell trajectories through a high-dimensional cell-state space is an outstanding challenge for understanding biological changes ranging from cellular differentiation to epigenetic responses of diseased cells upon drugging. We integrate experiments and theory to determine the trajectories that single BRAFV600E mutant melanoma cancer cells take between drug-naive and drug-tolerant states. Although single-cell omics tools can yield snapshots of the cell-state landscape, the determination of individual cell trajectories through that space can be confounded by stochastic cell-state switching. We assayed for a panel of signaling, phenotypic, and metabolic regulators at points across 5 days of drug treatment to uncover a cell-state landscape with two paths connecting drug-naive and drug-tolerant states. The trajectory a given cell takes depends upon the drug-naive level of a lineage-restricted transcription factor. Each trajectory exhibits unique druggable susceptibilities, thus updating the paradigm of adaptive resistance development in an isogenic cell population.

Profiling Cell Signaling Networks at Single-cell Resolution

Signaling networks process intra- and extracellular information to modulate the functions of a cell. Deregulation of signaling networks results in abnormal cellular physiological states and often drives diseases. Network responses to a stimulus or a drug treatment can be highly heterogeneous across cells in a tissue because of many sources of cellular genetic and non-genetic variance. Signaling network heterogeneity is the key to many biological processes, such as cell differentiation and drug resistance. Only recently, the emergence of multiplexed single-cell measurement technologies has made it possible to evaluate this heterogeneity. In this review, we categorize currently established single-cell signaling network profiling approaches by their methodology, coverage, and application, and we discuss the advantages and limitations of each type of technology. We also describe the available computational tools for network characterization using single-cell data and discuss potential confounding factors that need to be considered in single-cell signaling network analyses.

Receptor tyrosine kinase targeting in glioblastoma: performance, limitations and future approaches

From all central nervous system tumors, gliomas are the most common. Nowadays, researchers are looking for more efficient treatments for these tumors, as well as ways for early diagnosis. Receptor tyrosine kinases (RTKs) are major targets for oncology and the development of small-molecule RTK inhibitors has been proven successful in cancer treatment. Mutations or aberrant activation of the RTKs and their intracellular signaling pathways are linked to several malignant diseases, including glioblastoma. The progress in the understanding of malignant glioma evolution has led to RTK targeted therapies with high capacity to improve the therapeutic response while reducing toxicity. In this review, we present the most important RTKs (i.e. EGFR, IGFR, PDGFR and VEGFR) currently used for developing cancer therapeutics together with the potential of RTK-related drugs in glioblastoma treatment. Also, we focus on some therapeutic agents that are currently at different stages of research or even in clinical phases and proved to be suitable as re-purposing candidates for glioblastoma treatment.

Translating Cancer Molecular Variability into Personalized Information Using Bulk and Single Cell Approaches

Cancer research is striving toward new frontiers of assigning the correct personalized drug(s) to a given patient. However, extensive tumor heterogeneity poses a major obstacle. Tumors of the same type often respond differently to therapy, due to patient-specific molecular aberrations and/or untargeted tumor subpopulations. It is frequently not possible to determine a priori which patients will respond to a certain therapy or how an efficient patient-specific combined therapy should be designed. Large-scale datasets have been growing at an accelerated pace and various technologies and analytical tools for single cell and bulk level analyses are being developed to extract significant individualized signals from such heterogeneous data. However, personalized therapies that dramatically alter the course of the disease remain scarce, and most tumors still respond poorly to medical care. In this review, the basic concepts of bulk and single cell approaches are discussed, as well as their emerging role in individualized designs of drug therapies, including the advantages and limitations of their applications in personalized medicine.

Genetic Hallmarks and Heterogeneity of Glioblastoma in the Single-Cell Omics Era

Glioblastoma multiforme is the most common and aggressive malignant primary brain tumor. As implied by its name, the disease displays impressive intrinsic heterogeneity. Among other complications, inter- and intratumoral diversity hamper glioblastoma research and therapy, typically leaving patients with little hope for long-term survival. Extensive genetic analyses, including omics, characterize several recurrent mutations. However, confounding factors mask crucial aspects of the pathology to conventional bulk approaches. In recent years, single-cell omics have made their first appearance in cancer research, and the methodology is about to reach its full potential for glioblastoma too. Here, recent glioblastoma single-cell omics investigations are reviewed, and most promising routes toward less grim prognoses and more efficient therapeutics are discussed.

Crosstalks of GSK3 signaling with the mTOR network and effects on targeted therapy of cancer

The introduction of therapeutics targeting specific tumor-promoting oncogenic or non-oncogenic signaling pathways has revolutionized cancer treatment. Mechanistic (previously mammalian) target of rapamycin (mTOR), a highly conserved Ser/Thr kinase, is a central hub of the phosphatidylinositol 3-kinase (PI3K)/Akt/mTOR network, one of the most frequently deregulated signaling pathways in cancer, that makes it an attractive target for therapy. Numerous mTOR inhibitors have progressed to clinical trials and two of them have been officially approved as anticancer therapeutics. However, mTOR-targeting drugs have met with a very limited success in cancer patients. Frequently, the primary impediment to a successful targeted therapy in cancer is drug-resistance, either from the very beginning of the therapy (innate resistance) or after an initial response and upon repeated drug treatment (evasive or acquired resistance). Drug-resistance leads to treatment failure and relapse/progression of the disease. Resistance to mTOR inhibitors depends, among other reasons, on activation/deactivation of several signaling pathways, included those regulated by glycogen synthase kinase-3 (GSK3), a protein that targets a vast number of substrates in its repertoire, thereby orchestrating many processes that include cell proliferation and survival, metabolism, differentiation, and stemness. A detailed knowledge of the rewiring of signaling pathways triggered by exposure to mTOR inhibitors is critical to our understanding of the consequences such perturbations cause in tumors, including the emergence of drug-resistant cells. Here, we provide the reader with an updated overview of intricate circuitries that connect mTOR and GSK3 and we relate them to the efficacy (or lack of efficacy) of mTOR inhibitors in cancer cells.

PDZ-Binding Kinase-Dependent Transcriptional Regulation of CCNB2 Promotes Tumorigenesis and Radio-Resistance in Glioblastoma

Increasing evidence has indicated that PDZ binding kinase (PBK) promotes proliferation, invasion, and therapeutic resistance in a variety of cancer types. However, the physiological function and therapy-resistant role of PBK in GBM remain underexplored. In this study, PBK was identified as one of the most therapy-resistant genes with significantly elevated expression level in GBM. Moreover, the high expression level of PBK was essential for GBM tumorigenesis and radio-resistance both in vitro and in vivo. Clinically, aberrant activation of PBK was correlated with poor clinical prognosis. In addition, inhibition of PBK dramatically enhanced the efficacy of radiation therapy in GBM cells. Mechanically, PBK-dependent transcriptional regulation of CCNB2 was critical for tumorigenesis and radio-resistance in GBM cells. Collectively, PBK promotes tumorigenesis and radio-resistance in GBM and may serve as a novel therapeutic target for GBM treatment.