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Predicting in vivo response to antineoplastics remains an elusive challenge. We performed a first-of-kind evaluation of two transcriptome-based precision cancer medicine methodologies to predict tumor sensitivity to a comprehensive repertoire of clinically relevant oncology drugs, whose mechanism of action we experimentally assessed in cognate cell lines. We enrolled patients with histologically distinct, poor-prognosis malignancies who had progressed on multiple therapies, and developed low-passage, patient-derived xenograft models that were used to validate 35 patient-specific drug predictions. Both OncoTarget, which identifies high-affinity inhibitors of individual master regulator (MR) proteins, and OncoTreat, which identifies drugs that invert the transcriptional activity of hyperconnected MR modules, produced highly significant 30-day disease control rates (68% and 91%, respectively). Moreover, of 18 OncoTreat-predicted drugs, 15 induced the predicted MR-module activity inversion in vivo. Predicted drugs significantly outperformed antineoplastic drugs selected as unpredicted controls, suggesting these methods may substantively complement existing precision cancer medicine approaches, as also illustrated by a case study. Significance: Complementary precision cancer medicine paradigms are needed to broaden the clinical benefit realized through genetic profiling and immunotherapy. In this first-in-class application, we introduce two transcriptome-based tumor-agnostic systems biology tools to predict drug response in vivo. OncoTarget and OncoTreat are scalable for the design of basket and umbrella clinical trials.

Supplementary Table 2: Transcriptomic analyses of the GEMMs A. GEMMs Interactome B. Protein activity C. Cluster analyses D. Pathway analyses

Index of Supplementary Data

Prioritizing treatments for individual patients with cancer remains challenging, and performing coclinical studies using patient-derived models in real time is often unfeasible. To circumvent these challenges, we introduce OncoLoop, a precision medicine framework that predicts drug sensitivity in human tumors and their preexisting high-fidelity (cognate) model(s) by leveraging drug perturbation profiles. As a proof of concept, we applied OncoLoop to prostate cancer using genetically engineered mouse models (GEMM) that recapitulate a broad spectrum of disease states, including castration-resistant, metastatic, and neuroendocrine prostate cancer. Interrogation of human prostate cancer cohorts by Master Regulator (MR) conservation analysis revealed that most patients with advanced prostate cancer were represented by at least one cognate GEMM-derived tumor (GEMM-DT). Drugs predicted to invert MR activity in patients and their cognate GEMM-DTs were successfully validated in allograft, syngeneic, and patient-derived xenograft (PDX) models of tumors and metastasis. Furthermore, OncoLoop-predicted drugs enhanced the efficacy of clinically relevant drugs, namely, the PD-1 inhibitor nivolumab and the AR inhibitor enzalutamide.Significance:OncoLoop is a transcriptomic-based experimental and computational framework that can support rapid-turnaround coclinical studies to identify and validate drugs for individual patients, which can then be readily adapted to clinical practice. This framework should be applicable in many cancer contexts for which appropriate models and drug perturbation data are available.This article is highlighted in the In This Issue feature, p. 247

Detailed Materials and Methods

Figure S1: Genomic alterations in prostate cancer represented in the GEMMs (related to Fig. 2). Figure S2: Additional phenotypic analyses of the GEMMs (related to Fig. 2). Figure S3: Phenotypic analysis of allograft and organoid models (related to Fig. 2). Figure S4: Additional transcriptomic analyses of the GEMMs (related to Fig. 3). Figure S5: Analyses of AR activity in GEMMS (related to Fig. 3). Figure S6: Regulatory sub-networks of the GEMM clusters (related to Fig. 3). Figure S7. MR-match of prostate cancer cells lines to human PCa (related to Figs. 5). Figure S8. Drug perturbation protein activity profiles from DU145 cells (related to Figs. 5, 6, 7). Figure S9. LNCaP Pharmacotyping to patients and GEMMs (related to Figs. 5). Figure S10: Additional validation of drug candidates (related to Figs 6, 7). Figure S11: Additional validation of drug candidates (related to Figs 6).

Prioritizing treatments for individual patients with cancer remains challenging, and performing coclinical studies using patient-derived models in real time is often unfeasible. To circumvent these challenges, we introduce OncoLoop, a precision medicine framework that predicts drug sensitivity in human tumors and their preexisting high-fidelity (cognate) model(s) by leveraging drug perturbation profiles. As a proof of concept, we applied OncoLoop to prostate cancer using genetically engineered mouse models (GEMM) that recapitulate a broad spectrum of disease states, including castration-resistant, metastatic, and neuroendocrine prostate cancer. Interrogation of human prostate cancer cohorts by Master Regulator (MR) conservation analysis revealed that most patients with advanced prostate cancer were represented by at least one cognate GEMM-derived tumor (GEMM-DT). Drugs predicted to invert MR activity in patients and their cognate GEMM-DTs were successfully validated in allograft, syngeneic, and patient-derived xenograft (PDX) models of tumors and metastasis. Furthermore, OncoLoop-predicted drugs enhanced the efficacy of clinically relevant drugs, namely, the PD-1 inhibitor nivolumab and the AR inhibitor enzalutamide. Significance: OncoLoop is a transcriptomic-based experimental and computational framework that can support rapid-turnaround coclinical studies to identify and validate drugs for individual patients, which can then be readily adapted to clinical practice. This framework should be applicable in many cancer contexts for which appropriate models and drug perturbation data are available. This article is highlighted in the In This Issue feature, p. 247

Ternary-complex directed enzyme catalysis underlies a vast array of biological processes and several clinical therapies including growth hormones, interferon, and heparin. Recently, interest in ternary catalysis drugs has increased significantly with the rapid expansion of research new technologies such as bispecific antibodies and proteolysis targeting chimeras (PROTAC’s). Here, we derive a general model for ternary complex catalysis that defines the timescales of these diverse processes in familiar terms from classical enzyme theory. This was accomplished by solving for the maximum velocity (Vmax) and adapting an under-appreciated strategy within Michaels and Menten’s original publication: integration of the velocity equation. Critically, these equations are simple, conceptually accessible, and enables rapid estimation timescales that are consistent with a wide range of published literature. Finally, we have combined these equations with “big data” from new thermodynamic and kinetic databases to build interactive online tools that enable non-computational investigators to graphically simulate their own systems: • https://douglasslab.com/Btmax_kinetics/ Overall, this work is part of a general trend to reconceptualize pharmacodynamics from classical binding equilibria (e.g. Langmuir-Hill equation) to a kinetic processes with a characteristic timescale.

Summary The Columbia Cancer Target Discovery and Development (CTD2) Center is developing PANACEA, a resource comprising dose-responses and RNA sequencing (RNA-seq) profiles of 25 cell lines perturbed with ∼400 clinical oncology drugs, to study a tumor-specific drug mechanism of action. Here, this resource serves as the basis for a DREAM Challenge assessing the accuracy and sensitivity of computational algorithms for de novo drug polypharmacology predictions. Dose-response and perturbational profiles for 32 kinase inhibitors are provided to 21 teams who are blind to the identity of the compounds. The teams are asked to predict high-affinity binding targets of each compound among ∼1,300 targets cataloged in DrugBank. The best performing methods leverage gene expression profile similarity analysis as well as deep-learning methodologies trained on individual datasets. This study lays the foundation for future integrative analyses of pharmacogenomic data, reconciliation of polypharmacology effects in different tumor contexts, and insights into network-based assessments of drug mechanisms of action., Graphical abstract, Highlights • Drug-perturbed RNA sequencing data can be used to identify drug targets • Technology-based drug-target definitions often subsume literature definitions • Literature and screening datasets provide complementary information on drug mechanisms, Douglass et al. report the results of a crowdsourced challenge to develop machine-learning algorithms that use drug-perturbed transcriptome data to rapidly predict drug targets on a proteomic scale. Winning methods effectively predicted off-target binding of clinical kinase inhibitors and clarified disparate literature on these drugs’ mechanisms of action.

Recently, academic and industrial interest in molecular glue-based therapeutics has grown dramatically. Traditional drugs are designed to act on single targets, whereas molecular glues simultaneously bind two targets. By forming a ternary complex, molecular glues can create new therapeutic effects, such as rewiring cellular machinery to degrade specific proteins. Unfortunately, rational design of these therapies is challenging as current pharmacological theory is based on binary complex equilibria. Here, we extend our previous ternary-complex equilibrium work(JACS, 2013, 135, 6092) to derive a set of kinetic models highly analogous to Michaelis-Menten kinetics. We identify the weakest binding affinity as the most important engineerable parameter in the design of ternary-complex based therapeutics. Finally, we. have combined these equations with “big data” from new thermodynamic and kinetic databases to build interactive online tools that enable non-computational investigators to simulate their own experimental systems: • https://douglasslab.com/ternary_equilibrium/ • https://douglasslab.com/Btmax_kinetics/

The Columbia Cancer Target Discovery and Development (CTD2) Center has developed PANACEA (PANcancer Analysis of Chemical Entity Activity), a collection of dose-response curves and perturbational profiles for 400 clinical oncology drugs in cell lines selected to optimally represent 19 cancer subtypes. This resource, developed to study tumor-specific drug mechanism of action, was instrumental in hosting a DREAM Challenge to assess computational models for de novo drug polypharmacology prediction. Dose-response and perturbational profiles for 32 kinase inhibitors were provided to 21 participating teams who were asked to predict high-affinity binding target among 255 possible protein kinases. Best performing methods leveraged both gene expression profile similarity analysis, and deep-learning methodologies trained on individual datasets. This study lays the foundation for future integrative analyses of pharmacogenomic data, reconciliation of polypharmacology effects in different tumor contexts, and insights into network-based assessment of context-specific drug mechanism of action.

Despite recent advances, prioritizing therapy at the individual patient level remains challenging. In fact, inter-patient tumor heterogeneity remains one of the major challenges in cancer therapy, making it difficult to optimize available treatments on an individual patient basis. Likewise, the systematic prediction of drug sensitivity in vivo is still a major challenge in translational research, where targeted therapeutics are currently selected based on the presence of either actionable oncogene dependencies or aberrant cellular mechanisms. A further challenge is the limited availability of models that faithfully recapitulate the biology, complexity, and heterogeneity of human tumors, including their interaction with a conserved microenvironment and a competent immune system. To address these challenges, we introduce OncoLoop, a highly-generalizable, network-based precision medicine framework to triangulate between available mouse models, human tumors, and large-scale drug perturbational assays with in vivo validation to predict personalized treatment. OncoLoop requires only transcriptomic data (i.e., RNA-seq profiles) and leverages regulatory network analysis to (a) identify cognate models based on conservation of patient-specific Master Regulator (MR) proteins and (b) prioritize drugs based on their ability to invert the activity of MR proteins (MR-inverters), using drug perturbation profiles in cognate cell lines. As proof-of-concept, we applied OncoLoop to prostate cancer using a series of genetically engineered mouse models (GEMMs) that capture a broad range of phenotypes, including metastatic, castration-resistant and neuroendocrine disease. Indeed, >70% of patients in published cohorts had at least one high-fidelity matched GEMM. Drugs targeting shared Master Regulator dependencies of a patient and its cognate GEMM(s) were predicted using perturbational profiles of >300 drugs in MR-matched cell lines, resulting in an 80% validation rate in GEMM allografts and human xenografts. This network-based approach is highly generalized and can be applied to both cancer and non-cancer-related contexts. Citation Format: Alessandro Vasciaveo, Min Zou, Juan M. Arriaga, Francisca Nunes de Almeida, Eugene F. Douglass, Michael Shen, Andrea Califano, Cory Abate-Shen. Accelerating clinically-translatable discoveries using a network- and RNA-based precision-oncology framework [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1905.

Realizing the promise of precision medicine will require breaking down communication barriers between genomic, screening and literature "big data." We discuss the opportunities and challenges of reconciling data science approaches with the scientific method and the critical role of chemical biologists as a bridge between data and mechanism. Finally we propose next steps on the road to precision medicine and give examples of new tools designed to catalyze these steps.

Most antiviral agents are designed to target virus-specific proteins and mechanisms rather than the host cell proteins that are critically dysregulated following virus-mediated reprogramming of the host cell transcriptional state. To overcome these limitations, we propose that elucidation and pharmacologic targeting of host cell Master Regulator proteins—whose aberrant activities govern the reprogramed state of infected-coronavirus cells—presents unique opportunities to develop novel mechanism-based therapeutic approaches to antiviral therapy, either as monotherapy or as a complement to established treatments. Specifically, we propose that a small module of host cell Master Regulator proteins (ViroCheckpoint) is hijacked by the virus to support its efficient replication and release. Conventional methodologies are not well suited to elucidate these potentially targetable proteins. By using the VIPER network-based algorithm, we successfully interrogated 12h, 24h, and 48h signatures from Calu-3 lung adenocarcinoma cells infected with SARS-CoV, to elucidate the time-dependent reprogramming of host cells and associated Master Regulator proteins. We used the NYS CLIA-certified Darwin OncoTreat algorithm, with an existing database of RNASeq profiles following cell perturbation with 133 FDA-approved and 195 late-stage experimental compounds, to identify drugs capable of virtually abrogating the virus-induced Master Regulator signature. This approach to drug prioritization and repurposing can be trivially extended to other viral pathogens, including SARS-CoV-2, as soon as the relevant infection signature becomes available.

Cellulose has been used as a raw material for the manufacture of membranes and fibers for many years. This review gives the background of the most recent methods of treating or dissolving cellulose, and its derivatives to form polymer films or membranes for a variety of applications. Indeed, some potential applications of bacterial cellulose, nanofibrillar cellulose (NFC) for films showing enhanced barrier characteristics are reviewed as well as the utilization of cellulose nanonocrystals (CNC) for production of highly oriented super strong films or thin films is discussed. Because of the success of the Lyocell process as well as the amine/metal thiocyanate solvent blends of cellulose and other polysaccharides like starch, chitosan, and other natural polymers. Consequently, the use of cellulose (or its derivatives) and another polysaccharide dissolved as a blend is also elaborated. It is our hope that the reader will want to follow up and investigate these new systems and use them to develop end u...

Despite considerable pan-cancer efforts, the link between genomics and transcriptomics in cancer remains relatively weak and mostly based on statistical rather than mechanistic principles. By performing integrative analysis of transcriptomic and mutational profiles on a sample-by-sample basis, via regulatory/signaling networks, we identified a repertoire of 407 Master-Regulator proteins responsible for canalizing the genetics of 20 TCGA cohorts into 112 transcriptionally-distinct tumor subtypes. Further analysis highlighted a highly-recurrent regulatory architecture (oncotecture) with Master-Regulators organized into 24 modular MR-Blocks, regulating highly-specific tumor-hallmark functions and predictive of patient outcome. Critically, >50% of the somatic alterations identified in individual samples were in proteins affecting Master-Regulator activity, thus yielding novel insight into mechanisms linking tumor genetics and transcriptional identity and establishing novel non-oncogene dependencies. Experimental validation of functional mutations upstream of the most conserved MR-Block confirmed their ability to affect MR-protein activity, suggesting that the proposed methodology may effectively complement and extend current pan-cancer knowledge.

Despite considerable efforts, the mechanisms linking genomic alterations to the transcriptional identity of cancer cells remain elusive. Integrative genomic analysis, using a network-based approach, identified 407 master regulator (MR) proteins responsible for canalizing the genetics of individual samples from 20 cohorts in The Cancer Genome Atlas (TCGA) into 112 transcriptionally distinct tumor subtypes. MR proteins could be further organized into 24 pan-cancer, master regulator block modules (MRBs), each regulating key cancer hallmarks and predictive of patient outcome in multiple cohorts. Of all somatic alterations detected in each individual sample, >50% were predicted to induce aberrant MR activity, yielding insight into mechanisms linking tumor genetics and transcriptional identity and establishing non-oncogene dependencies. Genetic and pharmacological validation assays confirmed the predicted effect of upstream mutations and MR activity on downstream cellular identity and phenotype. Thus, co-analysis of mutational and gene expression profiles identified elusive subtypes and provided testable hypothesis for mechanisms mediating the effect of genetic alterations.

Mice deficient in the nuclear hormone receptor RORγt have defective development of thymocytes, lymphoid organs, Th17 cells, and type 3 innate lymphoid cells. RORγt binds to oxysterols derived from cholesterol catabolism, but it is not clear whether these are its natural ligands. Here, we show that sterol lipids are necessary and sufficient to drive RORγt-dependent transcription. We combined overexpression, RNAi, and genetic deletion of metabolic enzymes to study RORγ-dependent transcription. Our results are consistent with the RORγt ligand(s) being a cholesterol biosynthetic intermediate (CBI) downstream of lanosterol and upstream of zymosterol. Analysis of lipids bound to RORγ identified molecules with molecular weights consistent with CBIs. Furthermore, CBIs stabilized the RORγ ligand-binding domain and induced coactivator recruitment. Genetic deletion of metabolic enzymes upstream of the RORγt-ligand(s) affected the development of lymph nodes and Th17 cells. Our data suggest that CBIs play a role in lymphocyte development potentially through regulation of RORγt. ispartof: Cell Metabolism vol:21 issue:2 pages:286-297 ispartof: location:United States status: published

This article reports the design, synthesis, and evaluation of a novel class of molecules of intermediate size (approximately 7000 Da), which possess both the targeting and effector functions of antibodies. These compounds—called synthetic antibody mimics targeting prostate cancer (SyAM-Ps)—bind simultaneously to prostate-specific membrane antigen and Fc gamma receptor I, thus eliciting highly selective cancer cell phagocytosis. SyAMs have the potential to combine the advantages of both small-molecule and biologic therapies, and may address many drawbacks associated with available treatments for cancer and other diseases.

Here we report on the structure-based optimization of antibody-recruiting molecules targeting HIV gp120 (ARM-H). These studies have leveraged a combination of medicinal chemistry, biochemical and cellular assay analysis, and computation. Our findings have afforded an optimized analog of ARM-H, which is ~1000 fold more potent in gp120-binding and MT-2 antiviral assays than our previously reported derivative. Furthermore, computational analysis, taken together with experimental data, provides evidence that azaindole- and indole-based attachment inhibitors bind gp120 at an accessory hydrophobic pocket beneath the CD4-binding site and can also adopt multiple unique binding modes in interacting with gp120. These results are likely to prove highly enabling in the development of novel HIV attachment inhibitors, and more broadly, they suggest novel applications for ARMs as probes of conformationally flexible systems.

The ability to profile the prevalence and functional activity of endogenous antibodies is of vast clinical and diagnostic importance. Serum antibodies are an important class of biomarkers and are also crucial elements of immune responses elicited by natural disease-causing agents as well as vaccines. In particular, materials for manipulating and/or enhancing immune responses toward disease-causing cells or viruses have exhibited significant promise for therapeutic applications. Antibody-recruiting molecules (ARMs), bifunctional organic molecules that redirect endogenous antibodies to pathological targets, thereby increasing their recognition and clearance by the immune system, have proven particularly interesting. Notably, although ARMs capable of hijacking antibodies against oligosaccharides and electron-poor aromatics have proven efficacious, systematic comparisons of the prevalence and effectiveness of natural anti-hapten antibody populations have not appeared in the literature. Herein we report head-to-head comparisons of three chemically simple antigens, which are known ligands for endogenous antibodies. Thus, we have chemically synthesized bifunctional molecules containing 2,4-dinitrophenyl (DNP), phosphorylcholine (PC), and rhamnose. We have then used a combination of ELISA, flow cytometry, and cell-viability assays to compare these antigens in terms of their abilities both to recruit natural antibody from human serum and also to direct serum-dependent cytotoxicity against target cells. These studies have revealed rhamnose to be the most efficacious of the synthetic antigens examined. Furthermore, analysis of 122 individual serum samples has afforded comprehensive insights into population-wide prevalence and isotype distributions of distinct anti-hapten antibody populations. In addition to providing a general platform for comparing and studying anti-hapten antibodies, these studies serve as a useful starting point for the optimization of antibody-recruiting molecules and other synthetic strategies for modulating human immunity.

Summary Using the novel ethylenediamine/potassium thiocyanate (ED/KSCN) solvent system developed in our labs, a simpler, environmentally friendlier method was developed to produce membranes using cellulose, proteins, and other polymers. In contrast to current industrial methods that use processes that are relatively expensive with toxic or dangerous solvents, the new system eliminated majority of those concerns. In this study, soy protein concentrate and cellulose was used to develop a nonporous composite membrane with good physical properties. Glutaraldehyde was applied as the crosslinking agent to stabilize the molecular network structure of the blended membranes. Results showed that nonporous membranes were produced that were strong, flexible, and the exposure to the crosslinking agent shown structural and thermal improvement of the network membranes. This resulting blend of biopolymer membranes with improved physical abilities can be useful for food packaging, filtration systems, or even medical applications.

Three-component systems are often more complex than their two-component counterparts. Although the reversible association of three components in solution is critical for a vast array of chemical and biological processes, no general physical picture of such systems has emerged. Here we have developed a general, comprehensive framework for understanding ternary complex equilibria, which relates directly to familiar concepts such as “EC50” and “IC50” from simpler (binary complex) equilibria. Importantly, application of our model to data from the published literature has enabled us to achieve new insights into complex systems ranging from coagulation to therapeutic dosing regimens for monoclonal antibodies. We also provide an Excel spreadsheet to assist readers in both conceptualizing and applying our models. Overall, our analysis has the potential to render complex three-component systems – which have previously been characterized as “analytically intractable” – readily comprehensible to theoreticians and experimentalists alike.

Analytical gold electrodes were polished mechanically and electrochemically and the true area of the electrode surface was measured by quantitative oxidative/reductive cycling of the electrode. A roughness factor for each electrode was determined from the ratio of the true area to the geometric area. The roughness is fully described by a combination of microscopic roughness (up to tens of nanometers) and macroscopic roughness (on the order of hundreds of nanometers) terms. The electrodes were then derivatized with a self-assembled monolayer (SAM) of dodecanethiol or a thioalkane azacrown and characterized by impedance spectroscopy. The behavior of the electrodes was modeled with either a Helmholtz or Randles equivalent circuit (depending on the SAM used) in which the capacitance was replaced with a constant phase element. From the model, an effective capacitance and an alpha factor that quantifies the nonideality of the SAM capacitance was obtained. The effective capacitance divided by the roughness factor yields the capacitance per unit true area, which is only a function of microscopic roughness. The relationship between this capacitance and the alpha factor indicates that microscopic roughness predominantly affects the nonideality of the film while macroscopic roughness predominantly affects the magnitude of the film's capacitance. Understanding the contribution of the electrode topography to the magnitude and ideality of the SAM capacitance is important in the construction of SAM-based capacitive sensors because it predicts the importance of electrode-electrode variations.

Multilayered photocurrent generating thin films were fabricated by templated noncovalent assembly via stepwise assembly of molecular components. Each of films I-IV contained an underlying self-assembled monolayer (SAM) consisting of an alkanethiol linked covalently to a 2,6-dicarboxypyridine ligand that served as a binding site for attaching additional molecular components. The SAM subsequently was functionalized by sequential deposition of Cu(II), Co(II), or Fe(III) ions followed by a variety of substituted 2,6-dicarboxypyridine ligands as a means to incorporate one or more layers of pyrene chromophores into the film. The films were characterized by contact angle measurements, ellipsometry, grazing incidence IR, cyclic voltammetry, and impedance spectroscopy after deposition of each layer, confirming the formation of ordered, stable layers. Following incorporation into a three-electrode system, photoexcitation resulted in the generation of a cathodic photocurrent in the presence of methyl viologen and an anodic photocurrent in the presence of triethanolamine. Using this strategy, systems were fabricated that produced up to 89 nA/cm(2) of reproducible photocurrent.