Kinetic Trajectories of Glucose Uptake in Single Cancer Cells Reveal a Drug-Induced Cell-State Change Within Hours of Drug Treatment.

The development of drug resistance is a nearly universal phenomenon in patients with glioblastoma multiforme (GBM) brain tumors. Upon treatment, GBM cancer cells may initially undergo a drug-induced cell-state change to a drug-tolerant, slow-cycling state. The kinetics of that process are not well understood, in part due to the heterogeneity of GBM tumors and tumor models, which can confound the interpretation of kinetic data. Here, we resolve drug-adaptation kinetics in a patient-derived in vitro GBM tumor model characterized by the epithelial growth factor receptor (EGFR) variant(v)III oncogene treated with an EGFR inhibitor. We use radiolabeled 18F-fluorodeoxyglucose (FDG) to monitor the glucose uptake trajectories of single GBM cancer cells over a 12 h period of drug treatment. Autocorrelation analysis of the single-cell glucose uptake trajectories reveals evidence of a drug-induced cell-state change from a high- to low-glycolytic phenotype after 5-7 h of drug treatment. Information theoretic analysis of a bulk transcriptome kinetic series of the GBM tumor model delineated the underlying molecular mechanisms driving the cellular state change, including a shift from a stem-like mesenchymal state to a more differentiated, slow-cycling astrocyte-like state. Our results demonstrate that complex drug-induced cancer cell-state changes of cancer cells can be captured via measurements of single cell metabolic trajectories and reveal the extremely facile nature of drug adaptation.

Signaling and transcriptional dynamics underlying early adaptation to oncogenic BRAF inhibition.

A major contributor to poor sensitivity to anti-cancer kinase inhibitor therapy is drug-induced cellular adaptation, whereby remodeling of signaling and gene regulatory networks permits a drug-tolerant phenotype. Here, we resolve the scale and kinetics of critical subcellular events following oncogenic kinase inhibition and preceding cell cycle re-entry, using mass spectrometry-based phosphoproteomics and RNA sequencing to capture molecular snapshots within the first minutes, hours, and days of BRAF kinase inhibitor exposure in a human BRAF -mutant melanoma model of adaptive therapy resistance. By enriching specific phospho-motifs associated with mitogenic kinase activity, we monitored the dynamics of thousands of growth- and survival-related protein phosphorylation events under oncogenic BRAF inhibition and drug removal. We observed early and sustained inhibition of the BRAF-ERK axis, gradual downregulation of canonical cell cycle-dependent signals, and three distinct and reversible phase transitions toward quiescence. Statistical inference of kinetically-defined signaling and transcriptional modules revealed a concerted response to oncogenic BRAF inhibition and a dominant compensatory induction of SRC family kinase (SFK) signaling, which we found to be at least partially driven by accumulation of reactive oxygen species via impaired redox homeostasis. This induction sensitized cells to co-treatment with an SFK inhibitor across a panel of patient-derived melanoma cell lines and in an orthotopic mouse xenograft model, underscoring the translational potential for measuring the early temporal dynamics of signaling and transcriptional networks under therapeutic challenge.

Integrative systems biology reveals NKG2A-biased immune responses correlate with protection in infectious disease, autoimmune disease, and cancer.

Infection, autoimmunity, and cancer are principal human health challenges of the 21st century. Often regarded as distinct ends of the immunological spectrum, recent studies hint at potential overlap between these diseases. For example, inflammation can be pathogenic in infection and autoimmunity. T resident memory (TRM) cells can be beneficial in infection and cancer. However, these findings are limited by size and scope; exact immunological factors shared across diseases remain elusive. Here, we integrate large-scale deeply clinically and biologically phenotyped human cohorts of 526 patients with infection, 162 with lupus, and 11,180 with cancer. We identify an NKG2A+ immune bias as associative with protection against disease severity, mortality, and autoimmune/post-acute chronic disease. We reveal that NKG2A+ CD8+ T cells correlate with reduced inflammation and increased humoral immunity and that they resemble TRM cells. Our results suggest NKG2A+ biases as a cross-disease factor of protection, supporting suggestions of immunological overlap between infection, autoimmunity, and cancer.

An NKG2A biased immune response confers protection for infection, autoimmune disease, and cancer.

Infection, autoimmunity, and cancer are the principal human health challenges of the 21st century and major contributors to human death and disease. Often regarded as distinct ends of the immunological spectrum, recent studies have hinted there may be more overlap between these diseases than appears. For example, pathogenic inflammation has been demonstrated as conserved between infection and autoimmune settings. T resident memory (TRM) cells have been highlighted as beneficial for infection and cancer. However, these findings are limited by patient number and disease scope; exact immunological factors shared across disease remain elusive. Here, we integrate large-scale deeply clinically and biologically phenotyped human cohorts of 526 patients with infection, 162 with lupus, and 11,180 with cancer. We identify an NKG2A+ immune bias as associative with protection against disease severity, mortality, and autoimmune and post-acute chronic disease. We reveal that NKG2A+ CD8+ T cells correlate with reduced inflammation, increased humoral immunity, and resemble TRM cells. Our results suggest that an NKG2A+ bias is a pan-disease immunological factor of protection and thus supports recent suggestions that there is immunological overlap between infection, autoimmunity, and cancer. Our findings underscore the promotion of an NKG2A+ biased response as a putative therapeutic strategy.

Effects of HLA single chain trimer design on peptide presentation and stability.

MHC class I "single-chain trimer" molecules, coupling MHC heavy chain, ß2-microglobulin, and a specific peptide into a single polypeptide chain, are widely used in research. To more fully understand caveats associated with this design that may affect its use for basic and translational studies, we evaluated a set of engineered single-chain trimers with combinations of stabilizing mutations across eight different classical and non-classical human class I alleles with 44 different peptides, including a novel human/murine chimeric design. While, overall, single-chain trimers accurately recapitulate native molecules, care was needed in selecting designs for studying peptides longer or shorter than 9-mers, as single-chain trimer design could affect peptide conformation. In the process, we observed that predictions of peptide binding were often discordant with experiment and that yields and stabilities varied widely with construct design. We also developed novel reagents to improve the crystallizability of these proteins and confirmed novel modes of peptide presentation.

Large libraries of single-chain trimer peptide-MHCs enable antigen-specific CD8+ T cell discovery and analysis.

The discovery and characterization of antigen-specific CD8+ T cell clonotypes typically involves the labor-intensive synthesis and construction of peptide-MHC tetramers. We adapt single-chain trimer (SCT) technologies into a high throughput platform for pMHC library generation, showing that hundreds can be rapidly prepared across multiple Class I HLA alleles. We use this platform to explore the impact of peptide and SCT template mutations on protein expression yield, thermal stability, and functionality. SCT libraries were an efficient tool for identifying T cells recognizing commonly reported viral epitopes. We then construct SCT libraries to capture SARS-CoV-2 specific CD8+ T cells from COVID-19 participants and healthy donors. The immunogenicity of these epitopes is validated by functional assays of T cells with cloned TCRs captured using SCT libraries. These technologies should enable the rapid analyses of peptide-based T cell responses across several contexts, including autoimmunity, cancer, or infectious disease.

Non-viral precision T cell receptor replacement for personalized cell therapy.

T cell receptors (TCRs) enable T cells to specifically recognize mutations in cancer cells1-3. Here we developed a clinical-grade approach based on CRISPR-Cas9 non-viral precision genome-editing to simultaneously knockout the two endogenous TCR genes TRAC (which encodes TCRα) and TRBC (which encodes TCRß). We also inserted into the TRAC locus two chains of a neoantigen-specific TCR (neoTCR) isolated from circulating T cells of patients. The neoTCRs were isolated using a personalized library of soluble predicted neoantigen-HLA capture reagents. Sixteen patients with different refractory solid cancers received up to three distinct neoTCR transgenic cell products. Each product expressed a patient-specific neoTCR and was administered in a cell-dose-escalation, first-in-human phase I clinical trial ( NCT03970382 ). One patient had grade 1 cytokine release syndrome and one patient had grade 3 encephalitis. All participants had the expected side effects from the lymphodepleting chemotherapy. Five patients had stable disease and the other eleven had disease progression as the best response on the therapy. neoTCR transgenic T cells were detected in tumour biopsy samples after infusion at frequencies higher than the native TCRs before infusion. This study demonstrates the feasibility of isolating and cloning multiple TCRs that recognize mutational neoantigens. Moreover, simultaneous knockout of the endogenous TCR and knock-in of neoTCRs using single-step, non-viral precision genome-editing are achieved. The manufacture of neoTCR engineered T cells at clinical grade, the safety of infusing up to three gene-edited neoTCR T cell products and the ability of the transgenic T cells to traffic to the tumours of patients are also demonstrated.

Large libraries of single-chain trimer peptide-MHCs enable rapid antigen-specific CD8+ T cell discovery and analysis.

CD8 + cytotoxic T cell responses against viral infection represent a major element of the adaptive immune response. We describe the development of a peptide antigen - major histompatibility complex (pMHC) library representing the full SARS-CoV-2 viral proteome, and comprised of 634 pMHC multimers representing the A*02.01, A*24.02, and B*07.02 HLA alleles, as well as specific antigens associated with the cytomegalovirus (CMV). These libraries were used to capture non-expanded CD8 + T cells from blood samples collected from 64 infected individuals, and then analyzed using single cell RNA-seq. The discovery and characterization of antigen-specific CD8 + T cell clonotypes typically involves the labor-intensive synthesis and construction of peptide-MHC tetramers. We adapted single-chain trimer (SCT) technologies into a high throughput platform for pMHC library generation, showing that hundreds can be rapidly prepared across multiple Class I HLA alleles. We used this platform to explore the impact of peptide and SCT template mutations on protein expression yield, thermal stability, and functionality. SCT libraries were an efficient tool for identifying T cells recognizing commonly reported viral epitopes. We then constructed SCT libraries designed to capture SARS-CoV-2 specific CD8 + T cells from COVID-19 participants and healthy donors. The immunogenicity of these epitopes was validated by functional assays of T cells with cloned TCRs captured using SCT libraries. These technologies should enable the rapid analyses of peptide-based T cell responses across several contexts, including autoimmunity, cancer, or infectious disease.

Computational Modelling for Electrical Impedance Spectroscopy-Based Diagnosis of Oral Potential Malignant Disorders (OPMD).

A multiscale modelling approach has been applied to the simulation of the electrical properties of oral tissue, for the purpose of informing an electrical impedance-based method of oral potential malignant disorder (OPMD) diagnosis. Finite element models of individual cell types, with geometry informed by histological analysis of human oral tissue (normal, hyperplastic and dysplastic), were generated and simulated to obtain electrical parameters. These were then used in a histology-informed tissue scale model, including the electrode geometry of the ZedScan tetrapolar impedance-measurement device. The simulations offer insight into the feasibility of distinguishing moderate dysplasia from severe dysplasia or healthy tissue. For some oral sites, simulated spectra agreed with real measurements previously collected using ZedScan. However, similarities between simulated spectra for dysplastic, keratinised and non-dysplastic but hyperkeratinised tissue suggest that significant keratinisation could cause some OPMD tissues to exhibit larger than expected impedance values. This could lead to misidentification of OPMD spectra as healthy. Sources of uncertainty within the models were identified and potential remedies proposed.

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.

Physical and in silico immunopeptidomic profiling of a cancer antigen prostatic acid phosphatase reveals targets enabling TCR isolation.

Tissue-specific antigens can serve as targets for adoptive T cell transfer-based cancer immunotherapy. Recognition of tumor by T cells is mediated by interaction between peptide-major histocompatibility complexes (pMHCs) and T cell receptors (TCRs). Revealing the identity of peptides bound to MHC is critical in discovering cognate TCRs and predicting potential toxicity. We performed multimodal immunopeptidomic analyses for human prostatic acid phosphatase (PAP), a well-recognized tissue antigen. Three physical methods, including mild acid elution, coimmunoprecipitation, and secreted MHC precipitation, were used to capture a thorough signature of PAP on HLA-A*02:01. Eleven PAP peptides that are potentially A*02:01-restricted were identified, including five predicted strong binders by NetMHCpan 4.0. Peripheral blood mononuclear cells (PBMCs) from more than 20 healthy donors were screened with the PAP peptides. Seven cognate TCRs were isolated which can recognize three distinct epitopes when expressed in PBMCs. One TCR shows reactivity toward cell lines expressing both full-length PAP and HLA-A*02:01. Our results show that a combined multimodal immunopeptidomic approach is productive in revealing target peptides and defining the cloned TCR sequences reactive with prostatic acid phosphatase epitopes.

Protein Catalyzed Capture (PCC) Agents for Antigen Targeting.

The protein catalyzed capture agent (PCC) method is a powerful combinatorial screening strategy for discovering synthetic macrocyclic peptide ligands, called PCCs, to designated protein epitopes. The foundational concept of the PCC method is the use of in situ click chemistry to survey large combinatorial libraries of peptides for ligands to designated biological targets. State-of-the-art PCC screens integrate synthetic libraries of constrained macrocyclic peptides with epitope-specific targeting strategies to identify high-affinity (<100 nM) binders de novo. Automated instrumentation can accelerate PCC discovery to a rapid 2-week timeframe. Here, we describe methods to perform combinatorial screens that yield epitope-targeted PCCs.

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.

Early IFN-α signatures and persistent dysfunction are distinguishing features of NK cells in severe COVID-19.

Longitudinal analyses of the innate immune system, including the earliest time points, are essential to understand the immunopathogenesis and clinical course of coronavirus disease (COVID-19). Here, we performed a detailed characterization of natural killer (NK) cells in 205 patients (403 samples; days 2 to 41 after symptom onset) from four independent cohorts using single-cell transcriptomics and proteomics together with functional studies. We found elevated interferon (IFN)-α plasma levels in early severe COVD-19 alongside increased NK cell expression of IFN-stimulated genes (ISGs) and genes involved in IFN-α signaling, while upregulation of tumor necrosis factor (TNF)-induced genes was observed in moderate diseases. NK cells exert anti-SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) activity but are functionally impaired in severe COVID-19. Further, NK cell dysfunction may be relevant for the development of fibrotic lung disease in severe COVID-19, as NK cells exhibited impaired anti-fibrotic activity. Our study indicates preferential IFN-α and TNF responses in severe and moderate COVID-19, respectively, and associates a prolonged IFN-α-induced NK cell response with poorer disease outcome.

Resolution of tissue signatures of therapy response in patients with recurrent GBM treated with neoadjuvant anti-PD1.

The response of patients with recurrent glioblastoma multiforme to neoadjuvant immune checkpoint blockade has been challenging to interpret due to the inter-patient and intra-tumor heterogeneity. We report on a comparative analysis of tumor tissues collected from patients with recurrent glioblastoma and high-risk melanoma, both treated with neoadjuvant checkpoint blockade. We develop a framework that uses multiplex spatial protein profiling, machine learning-based image analysis, and data-driven computational models to investigate the pathophysiological and molecular factors within the tumor microenvironment that influence treatment response. Using melanoma to guide the interpretation of glioblastoma analyses, we interrogate the protein expression in microscopic compartments of tumors, and determine the correlates of cytotoxic CD8+ T cells, tumor growth, treatment response, and immune cell-cell interaction. This work reveals similarities shared between glioblastoma and melanoma, immunosuppressive factors that are unique to the glioblastoma microenvironment, and potential co-targets for enhancing the efficacy of neoadjuvant immune checkpoint blockade.

Unique challenges for glioblastoma immunotherapy-discussions across neuro-oncology and non-neuro-oncology experts in cancer immunology. Meeting Report from the 2019 SNO Immuno-Oncology Think Tank.

Cancer immunotherapy has made remarkable advances with over 50 separate Food and Drug Administration (FDA) approvals as first- or second-line indications since 2015. These include immune checkpoint blocking antibodies, chimeric antigen receptor-transduced T cells, and bispecific T-cell-engaging antibodies. While multiple cancer types now benefit from these immunotherapies, notable exceptions thus far include brain tumors, such as glioblastoma. As such, it seems critical to gain a better understanding of unique mechanistic challenges underlying the resistance of malignant gliomas to immunotherapy, as well as to acquire insights into the development of future strategies. An Immuno-Oncology Think Tank Meeting was held during the 2019 Annual Society for Neuro-Oncology Scientific Conference. Discussants in the fields of neuro-oncology, neurosurgery, neuro-imaging, medical oncology, and cancer immunology participated in the meeting. Sessions focused on topics such as the tumor microenvironment, myeloid cells, T-cell dysfunction, cellular engineering, and translational aspects that are critical and unique challenges inherent with primary brain tumors. In this review, we summarize the discussions and the key messages from the meeting, which may potentially serve as a basis for advancing the field of immune neuro-oncology in a collaborative manner.

Key Parameters of Tumor Epitope Immunogenicity Revealed Through a Consortium Approach Improve Neoantigen Prediction.

Many approaches to identify therapeutically relevant neoantigens couple tumor sequencing with bioinformatic algorithms and inferred rules of tumor epitope immunogenicity. However, there are no reference data to compare these approaches, and the parameters governing tumor epitope immunogenicity remain unclear. Here, we assembled a global consortium wherein each participant predicted immunogenic epitopes from shared tumor sequencing data. 608 epitopes were subsequently assessed for T cell binding in patient-matched samples. By integrating peptide features associated with presentation and recognition, we developed a model of tumor epitope immunogenicity that filtered out 98% of non-immunogenic peptides with a precision above 0.70. Pipelines prioritizing model features had superior performance, and pipeline alterations leveraging them improved prediction performance. These findings were validated in an independent cohort of 310 epitopes prioritized from tumor sequencing data and assessed for T cell binding. This data resource enables identification of parameters underlying effective anti-tumor immunity and is available to the research community.

Raman-guided subcellular pharmaco-metabolomics for metastatic melanoma cells.

Non-invasively probing metabolites within single live cells is highly desired but challenging. Here we utilize Raman spectro-microscopy for spatial mapping of metabolites within single cells, with the specific goal of identifying druggable metabolic susceptibilities from a series of patient-derived melanoma cell lines. Each cell line represents a different characteristic level of cancer cell de-differentiation. First, with Raman spectroscopy, followed by stimulated Raman scattering (SRS) microscopy and transcriptomics analysis, we identify the fatty acid synthesis pathway as a druggable susceptibility for differentiated melanocytic cells. We then utilize hyperspectral-SRS imaging of intracellular lipid droplets to identify a previously unknown susceptibility of lipid mono-unsaturation within de-differentiated mesenchymal cells with innate resistance to BRAF inhibition. Drugging this target leads to cellular apoptosis accompanied by the formation of phase-separated intracellular membrane domains. The integration of subcellular Raman spectro-microscopy with lipidomics and transcriptomics suggests possible lipid regulatory mechanisms underlying this pharmacological treatment. Our method should provide a general approach in spatially-resolved single cell metabolomics studies.

Visualizing Subcellular Enrichment of Glycogen in Live Cancer Cells by Stimulated Raman Scattering.

Glycogen, a branched glucose polymer, helps regulate glucose homeostasis through immediate storage and release of glucose. Reprogramming of glycogen metabolism has recently been suggested to play an emerging role in cancer progression and tumorigenesis. However, regulation of metabolic rewiring for glycogen synthesis and breakdown in cancer cells remains less understood. Despite the availability of various glycogen detection methods, selective visualization of glycogen in living cells with high spatial resolution has proven to be highly challenging. Here, we present an optical imaging strategy to visualize glycogen in live cancer cells with minimal perturbation by combining stimulated Raman scattering microscopy with metabolic incorporation of deuterium-labeled glucose. We revealed the subcellular enrichment of glycogen in live cancer cells and achieved specific glycogen mapping through distinct spectral identification. Using this method, different glycogen metabolic phenotypes were characterized in a series of patient-derived BRAF mutant melanoma cell lines. Our results indicate that cell lines manifesting high glycogen storage level showed increased tolerance to glucose deficiency among the studied melanoma phenotypes. This method opens up the possibility for noninvasive study of complex glycogen metabolism at subcellular resolution and may help reveal new features of glycogen regulation in cancer systems.

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.