Integration of Pan-Cancer Cell Line and Single-Cell Transcriptomic Profiles Enables Inference of Therapeutic Vulnerabilities in Heterogeneous Tumors.

Single-cell RNA sequencing (scRNA-seq) greatly advanced the understanding of intratumoral heterogeneity by identifying distinct cancer cell subpopulations. However, translating biological differences into treatment strategies is challenging due to a lack of tools to facilitate efficient drug discovery that tackles heterogeneous tumors. Developing such approaches requires accurate prediction of drug response at the single-cell level to offer therapeutic options to specific cell subpopulations. Here, we developed a transparent computational framework (nicknamed scIDUC) to predict therapeutic efficacies on an individual cell basis by integrating single-cell transcriptomic profiles with large, data-rich pan-cancer cell line screening data sets. This method achieved high accuracy in separating cells into their correct cellular drug response statuses. In three distinct prospective tests covering different diseases (rhabdomyosarcoma, pancreatic ductal adenocarcinoma, and castration-resistant prostate cancer), the predicted results using scIDUC were accurate and mirrored biological expectations. In the first two tests, the framework identified drugs for cell subpopulations that were resistant to standard-of-care (SOC) therapies due to intrinsic resistance or tumor microenvironmental effects, and the results showed high consistency with experimental findings from the original studies. In the third test using newly generated SOC therapy-resistant cell lines, scIDUC identified efficacious drugs for the resistant line, and the predictions were validated with in vitro experiments. Together, this study demonstrates the potential of scIDUC to quickly translate scRNA-seq data into drug responses for individual cells, displaying the potential as a tool to improve the treatment of heterogenous tumors. SIGNIFICANCE: A versatile method that infers cell-level drug response in scRNA-seq data facilitates the development of therapeutic strategies to target heterogeneous subpopulations within a tumor and address issues such as treatment failure and resistance.

A Web Application for Predicting Drug Combination Efficacy Using Monotherapy Data and IDACombo.

We recently reported a computational method (IDACombo) designed to predict the efficacy of cancer drug combinations using monotherapy response data and the assumptions of independent drug action. Given the strong agreement between IDACombo predictions and measured drug combination efficacy in vitro and in clinical trials, we believe IDACombo can be of immediate use to researchers who are working to develop novel drug combinations. While we previously released our method as an R package, we have now created an R Shiny application to allow researchers without programming experience to easily utilize this method. The app provides a graphical interface which enables users to easily generate efficacy predictions with IDACombo using provided data from several high-throughput cell line screens or using custom, user-provided data.

Revealing Pan-Histology Immunomodulatory Targets in Pediatric Central Nervous System Tumors.

BACKGROUND: The application of immunotherapy for pediatric CNS malignancies has been limited by the poorly understood immune landscape in this context. The aim of this study was to uncover the mechanisms of immune suppression common among pediatric brain tumors. METHODS: We apply an immunologic clustering algorithm validated by The Cancer Genome Atlas Project to an independent pediatric CNS transcriptomic dataset. Within the clusters, the mechanisms of immunosuppression are explored via tumor microenvironment deconvolution and survival analyses to identify relevant immunosuppressive genes with translational relevance. RESULTS: High-grade diseases fall predominantly within an immunosuppressive subtype (C4) that independently lowers overall survival time and where common immune checkpoints (e.g., PDL1, CTLA4) are less relevant. Instead, we identify several alternative immunomodulatory targets with relevance across histologic diseases. Specifically, we show how the mechanism of EZH2 inhibition to enhance tumor immunogenicity in vitro via the upregulation of MHC class 1 is applicable to a pediatric CNS oncologic context. Meanwhile, we identify that the C3 (inflammatory) immune subtype is more common in low-grade diseases and find that immune checkpoint inhibition may be an effective way to curb progression for this subset. CONCLUSIONS: Three predominant immunologic clusters are identified across pediatric brain tumors. Among high-risk diseases, the predominant immune cluster is associated with recurrent immunomodulatory genes that influence immune infiltrate, including a subset that impacts survival across histologies.

Inferring therapeutic vulnerability within tumors through integration of pan-cancer cell line and single-cell transcriptomic profiles.

Single-cell RNA sequencing greatly advanced our understanding of intratumoral heterogeneity through identifying tumor subpopulations with distinct biologies. However, translating biological differences into treatment strategies is challenging, as we still lack tools to facilitate efficient drug discovery that tackles heterogeneous tumors. One key component of such approaches tackles accurate prediction of drug response at the single-cell level to offer therapeutic options to specific cell subpopulations. Here, we present a transparent computational framework (nicknamed scIDUC) to predict therapeutic efficacies on an individual-cell basis by integrating single-cell transcriptomic profiles with large, data-rich pan-cancer cell line screening datasets. Our method achieves high accuracy, with predicted sensitivities easily able to separate cells into their true cellular drug resistance status as measured by effect size (Cohen's d > 1.0). More importantly, we examine our method's utility with three distinct prospective tests covering different diseases (rhabdomyosarcoma, pancreatic ductal adenocarcinoma, and castration-resistant prostate cancer), and in each our predicted results are accurate and mirrored biological expectations. In the first two, we identified drugs for cell subpopulations that are resistant to standard-of-care (SOC) therapies due to intrinsic resistance or effects of tumor microenvironments. Our results showed high consistency with experimental findings from the original studies. In the third test, we generated SOC therapy resistant cell lines, used scIDUC to identify efficacious drugs for the resistant line, and validated the predictions with in-vitro experiments. Together, scIDUC quickly translates scRNA-seq data into drug response for individual cells, displaying the potential as a first-line tool for nuanced and heterogeneity-aware drug discovery.

Simplicity: web-based visualization and analysis of high-throughput cancer cell line screens.

High-throughput drug screens are a powerful tool for cancer drug development. However, the results of such screens are often made available only as raw data, which is intractable for researchers without informatic skills, or as highly processed summary statistics, which can lack essential information for translating screening results into clinically meaningful discoveries. To improve the usability of these datasets, we developed Simplicity, a robust and user-friendly web interface for visualizing, exploring, and summarizing raw and processed data from high-throughput drug screens. Importantly, Simplicity allows for easy recalculation of summary statistics at user-defined drug concentrations. This allows Simplicity's outputs to be used with methods that rely on statistics being calculated at clinically relevant doses. Simplicity can be freely accessed at https://oncotherapyinformatics.org/simplicity/.

Computational drug discovery for castration-resistant prostate cancers through in vitro drug response modeling.

Prostate cancer (PC) is the most frequently diagnosed malignancy and a leading cause of cancer deaths in US men. Many PC cases metastasize and develop resistance to systemic hormonal therapy, a stage known as castration-resistant prostate cancer (CRPC). Therefore, there is an urgent need to develop effective therapeutic strategies for CRPC. Traditional drug discovery pipelines require significant time and capital input, which highlights a need for novel methods to evaluate the repositioning potential of existing drugs. Here, we present a computational framework to predict drug sensitivities of clinical CRPC tumors to various existing compounds and identify treatment options with high potential for clinical impact. We applied this method to a CRPC patient cohort and nominated drugs to combat resistance to hormonal therapies including abiraterone and enzalutamide. The utility of this method was demonstrated by nomination of multiple drugs that are currently undergoing clinical trials for CRPC. Additionally, this method identified the tetracycline derivative COL-3, for which we validated higher efficacy in an isogenic cell line model of enzalutamide-resistant vs. enzalutamide-sensitive CRPC. In enzalutamide-resistant CRPC cells, COL-3 displayed higher activity for inhibiting cell growth and migration, and for inducing G1-phase cell cycle arrest and apoptosis. Collectively, these findings demonstrate the utility of a computational framework for independent validation of drugs being tested in CRPC clinical trials, and for nominating drugs with enhanced biological activity in models of enzalutamide-resistant CRPC. The efficiency of this method relative to traditional drug development approaches indicates a high potential for accelerating drug development for CRPC.

Simplicity: Web-Based Visualization and Analysis of High-Throughput Cancer Cell Line Screens.

High-throughput drug screens are a powerful tool for cancer drug development. However, the results of such screens are often made available only as raw data, which is intractable for researchers without informatics skills, or as highly processed summary statistics, which can lack essential information for translating screening results into clinically meaningful discoveries. To improve the usability of these datasets, we developed Simplicity, a robust and user-friendly web interface for visualizing, exploring, and summarizing raw and processed data from high- throughput drug screens. Importantly, Simplicity allows for easy recalculation of summary statistics at user-defined drug concentrations. This allows Simplicity's outputs to be used with methods that rely on statistics being calculated at clinically relevant doses. Simplicity can be freely accessed at https://oncotherapyinformatics.org/simplicity/.

Fluorescence Lifetime Imaging Probes for Cell-Based Measurements of Enzyme Activity.

Posttranslational modification (PTM) enzymes are important modulators of protein structure and function. They typically act by chemically modifying amino acids, often on side chain functional groups, to change the physiochemical landscape of the protein and thus its biophysical behavior. In particular, protein kinases are enzymes that transfer phosphate from ATP to serine, threonine, or tyrosine in protein substrates. They are key regulators of vital cellular pathways such as survival, proliferation, and apoptosis, and their dysregulation in the context of cancer has been widely investigated for the purpose of development of anticancer drugs. However, several critical questions pertaining to their physiology, such as heterogeneity of kinase signaling within and between cells, and other factors that may play into the mechanisms of drug resistance, remain unanswered. Many of the current strategies to measure kinase activity lack the scope, subcellular resolution, and real-time monitoring ability needed to obtain the type of information needed about their dynamics and localization in cells. While FRET-based biosensors are capable of dynamic single cell imaging, their applications can be limited by difficulties in multiplexing and the inherent inadequacies of steady state measurements. In this chapter, we describe our fluorescence lifetime imaging microscopy (FLIM) probe technology in which peptide kinase substrates, linked to cell-penetrating peptides and labeled with small molecule fluorophores, are used to report kinase activity through time-resolved fluorescence imaging to visualize and quantify changes to the probe's fluorescence lifetime. These can be multiplexed for more than one kinase at a time, and interpretation is not affected by differences in local intensity due to probe uptake and distribution or photobleaching. With careful choice of peptide substrate(s), fluorophore label, and imaging set-up, high specificity and spatiotemporal resolution can be achieved. Due to the mechanism by which the lifetime change occurs, this approach is compatible with other PTMs (such as acetylation, methylation), and so the considerations for kinase FLIM probe design described in this chapter should be broadly applicable for other PTMs as well.

Multiplexable fluorescence lifetime imaging (FLIM) probes for Abl and Src-family kinases.

Many commonly employed strategies to map kinase activities in live cells require expression of genetically encoded proteins (e.g. FRET sensors). In this work, we describe the development and preliminary application of a set of cell-penetrating, fluorophore labelled peptide substrates for fluorescence lifetime imaging (FLIM) of Abl and Src-family kinase activities. These probes do not rely on FRET pairs or genetically-encoded protein expression. We further demonstrate probe multiplexing and pixel-by-pixel quantification to estimate the relative proportion of modified probe, suggesting that this strategy will be useful for detailed mapping of single cell and subcellular dynamics of multiple kinases concurrently in live cells.

Assays for tyrosine phosphorylation in human cells.

Tyrosine kinases are important for many cellular processes and disruption of their regulation is a factor in diseases like cancer, therefore they are a major target of anticancer drugs. There are many ways to measure tyrosine kinase activity in cells by monitoring endogenous substrate phosphorylation, or by using peptide substrates and incubating them with cell lysates containing active kinases. However, most of these strategies rely on antibodies and/or are limited in how accurately they model the intracellular environment. In cases in which activity needs to be measured in cells, but endogenous substrates are not known and/or suitable phosphospecific antibodies are not available, cell-deliverable peptide substrates can be an alternative and can provide information on activation and inhibition of kinases in intact, live cells. In this chapter, we review this methodology and provide a protocol for measuring Abl kinase activity in human cells using enzyme-linked immunosorbent assay (ELISA) with a generic antiphosphotyrosine antibody for detection.

Stability of lyophilized albumin formulations: Role of excipient crystallinity and molecular mobility.

In freeze-dried protein formulations, the composition governs the physical forms of the excipients and hence their functionality. It is also necessary to understand the effect of composition on the molecular relaxation behavior, a key factor influencing protein stability. Mannitol (bulking agent) - trehalose (lyoprotectant) - bovine serum albumin (BSA) lyophiles with varying trehalose to BSA mass ratios were investigated. The crystalline phases were characterized by X-ray diffractometry. The secondary structure of albumin in lyophiles and reconstituted solutions was evaluated by IR spectroscopy and circular dichroism, respectively. Dielectric spectroscopy was used to obtain the relaxation time of freeze-dried samples. When trehalose to BSA ratio was 0.2, while mannitol crystallized predominantly as the δ-anhydrous polymorph, trehalose remained amorphous. At lower concentrations of BSA, mannitol crystallized in both hemihydrate and anhydrous forms, and trehalose as dihydrate. The extent of dehydration during subsequent drying was dictated by the trehalose to BSA ratio in the formulation. A gradual increase in the Johari-Goldstein relaxation time was observed as the concentration of trehalose increased in the formulation. BSA was more susceptible to stresses from thawing than drying.

Effects of Excipient Interactions on the State of the Freeze-Concentrate and Protein Stability.

PURPOSE: The physical state of excipients in freeze-dried formulations directly affects the stability of the active pharmaceutical ingredient (API). Crystallization of trehalose and mannitol in frozen solutions has been shown to be a function of composition. However, to date a detailed study of the effect of concentrations of the API and other excipients on the crystallinity of mannitol and trehalose in frozen solutions has not been reported. METHODS: The crystallinity of mannitol and trehalose in frozen solutions was characterized by Differential Scanning Calorimetry, X-ray diffractometry, and FTIR spectroscopy. The secondary structure of BSA was probed by FTIR, and Circular Dichroism spectroscopy in frozen and thawed solutions, respectively. RESULTS: Trehalose crystallization was accompanied by unfolding of BSA. BSA delayed and reduced the extent of mannitol and trehalose crystallization. Similar effects were observed upon adding D2O (≥5% w/w) and low concentrations of polysorbate 20 (≤0.2% w/w) with retention of BSA in its native conformation. At high BSA to trehalose mass ratio, the protein could stabilize itself in the frozen state, but unfolded upon thawing. CONCLUSIONS: The API and other excipients, in a concentration-dependent manner, influenced the physical state of the freeze concentrate as well as the stability of the API.

Mutual Influence of Mannitol and Trehalose on Crystallization Behavior in Frozen Solutions.

PURPOSE: Phase separation of trehalose during freeze-drying could render it ineffective as a lyoprotectant. The bulking agent, mannitol, on the other hand, should crystallize readily upon freezing. It is therefore imperative to understand the mutual interaction of these sugars during freezing to ensure preservation of the API during freeze-drying. METHODS: We investigated the effect of mannitol to trehalose ratio (R) on the crystallization behavior of both solutes using Differential Scanning Calorimetry, X-Ray Crystallography and FTIR Spectrosopy during controlled freezing and annealing. RESULTS: When R = 1, crystallization of both mannitol (as hemihydrate) and trehalose (as α-anhydrate) were observed. When R ≥ 1, extent of mannitol crystallization was directly proportional to the value of R. When R < 1, trehalose completely suppressed mannitol crystallization. At R > 1, the freeze concentrate was heterogeneous and characterized by two glass transitions - the lower temperature transition (Tg") attributed to systems containing "extra" unfrozen water. When heated above Tg", crystallization of mannitol and the associated unfrozen water resulted in Tg' (glass transition temperature of the freeze-concentrate). CONCLUSIONS: R and not the total solute concentration, dictates the composition of the freeze concentrate as well as the physical stability of the excipients.