Metrics other than potency reveal systematic variation in responses to cancer drugs

Preclinical Drug Response Metric Based on Cellular Response Phenotype Provides Better Pharmacogenomic Variables with Phenotype Relevance

High-throughput screening of drug response in cultured cell lines is essential for studying therapeutic mechanisms and identifying molecular variants associated with sensitivity to drugs. Assessment of drug response is typically performed by constructing a dose-response curve of viability and summarizing it to a representative, such as IC₅₀. However, this is limited by its dependency on the assay duration and lack of reflections regarding actual cellular response phenotypes. To address these limitations, we consider how each response-phenotype contributes to the overall growth behavior and propose an alternative method of drug response screening that takes into account the cellular response phenotype. In conventional drug response screening methods, the ranking of sensitivity depends on either the metric used to construct the dose-response curve or the representative factor used to summarize the curve. This ambiguity in conventional assessment methods is due to the fact that assessment methods are not consistent with the underlying principles of population dynamics. Instead, the suggested phenotype metrics provide all phenotypic rates of change that shape overall growth behavior at a given dose and better response classification, including the phenotypic mechanism of overall growth inhibition. This alternative high-throughput drug-response screening would improve preclinical pharmacogenomic analysis and the understanding of a therapeutic mechanism of action.

iTReX: Interactive exploration of mono- and combination therapy dose response profiling data

High throughput screening methods, measuring the sensitivity and resistance of tumor cells to drug treatments have been rapidly evolving. Not only do these screens allow correlating response profiles to tumor genomic features for developing novel predictors of treatment response, but they can also add evidence for therapy decision making in precision oncology. Recent analysis methods developed for either assessing single agents or combination drug efficacies enable quantification of dose-response curves with restricted symmetric fit settings. Here, we introduce iTReX, a user-friendly and interactive Shiny/R application, for both the analysis of mono- and combination therapy responses. The application features an extended version of the drug sensitivity score (DSS) based on the integral of an advanced five-parameter dose-response curve model and a differential DSS for combination therapy profiling. Additionally, iTReX includes modules that visualize drug target interaction networks and support the detection of matches between top therapy hits and the sample omics features to enable the identification of druggable targets and biomarkers. iTReX enables the analysis of various quantitative drug or therapy response readouts (e.g. luminescence, fluorescence microscopy) and multiple treatment strategies (drug treatments, radiation). Using iTReX we validate a cost-effective drug combination screening approach and reveal the application's ability to identify potential sample-specific biomarkers based on drug target interaction networks. The iTReX web application is accessible at https://itrex.kitz-heidelberg.de.

Kite-Shaped Molecules Block SARS-CoV-2 Cell Entry at a Post-Attachment Step

Anti-viral small molecules are currently lacking for treating coronavirus infection. The long development timescales for such drugs are a major problem, but could be shortened by repurposing existing drugs. We therefore screened a small library of FDA-approved compounds for potential severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) antivirals using a pseudovirus system that allows a sensitive read-out of infectivity. A group of structurally-related compounds, showing moderate inhibitory activity with IC₅₀ values in the 2–5 μM range, were identified. Further studies demonstrated that these “kite-shaped” molecules were surprisingly specific for SARS-CoV-1 and SARS-CoV-2 and that they acted early in the entry steps of the viral infectious cycle, but did not affect virus attachment to the cells. Moreover, the compounds were able to prevent infection in both kidney- and lung-derived human cell lines. The structural homology of the hits allowed the production of a well-defined pharmacophore that was found to be highly accurate in predicting the anti-viral activity of the compounds in the screen. We discuss the prospects of repurposing these existing drugs for treating current and future coronavirus outbreaks.

Two-level modeling approach to identify the regulatory dynamics capturing drug response heterogeneity in single-cells

Single-cell multimodal technologies reveal the scales of cellular heterogeneity impairing cancer treatment, yet cell response dynamics remain largely underused to decipher the mechanisms of drug resistance they take part in. As the phenotypic heterogeneity of a clonal cell population informs on the capacity of each single-cell to recapitulate the whole range of observed behaviors, we developed a modeling approach utilizing single-cell response data to identify regulatory reactions driving population heterogeneity in drug response. Dynamic data of hundreds of HeLa cells treated with TNF-related apoptosis-inducing ligand (TRAIL) were used to characterize the fate-determining kinetic parameters of an apoptosis receptor reaction model. Selected reactions sets were augmented to incorporate a mechanism that leads to the separation of the opposing response phenotypes. Using a positive feedback loop motif to identify the reaction set, we show that caspase-8 is able to encapsulate high levels of heterogeneity by introducing a response delay and amplifying the initial differences arising from natural protein expression variability. Our approach enables the identification of fate-determining reactions that drive the population response heterogeneity, providing regulatory targets to curb the cell dynamics of drug resistance.

IRS1 phosphorylation underlies the non-stochastic probability of cancer cells to persist during EGFR inhibition therapy

Stochastic transition of cancer cells between drug-sensitive and drug-tolerant persister phenotypes has been proposed to play a key role in non-genetic resistance to therapy. Yet, we show here that cancer cells actually possess a highly stable inherited chance to persist (CTP) during therapy. This CTP is non-stochastic, determined pre-treatment and has a unimodal distribution ranging from 0 to almost 100%. Notably, CTP is drug specific. We found that differential serine/threonine phosphorylation of the insulin receptor substrate 1 (IRS1) protein determines the CTP of lung and of head and neck cancer cells under epidermal growth factor receptor inhibition, both in vitro and in vivo. Indeed, the first-in-class IRS1 inhibitor NT219 was highly synergistic with anti-epidermal growth factor receptor therapy across multiple in vitro and in vivo models. Elucidation of drug-specific mechanisms that determine the degree and stability of cellular CTP may establish a framework for the elimination of cancer persisters, using new rationally designed drug combinations.

CREB signaling activity correlates with differentiation and survival in medulloblastoma

While there has been significant progress in the molecular characterization of the childhood brain cancer medulloblastoma, the tumor proteome remains less explored. However, it is important to obtain a complete understanding of medulloblastoma protein biology, since interactions between proteins represent potential new drug targets. Using previously generated phosphoprotein signaling-profiles of a large cohort of primary medulloblastoma, we discovered that phosphorylation of transcription factor CREB strongly correlates with medulloblastoma survival and associates with a differentiation phenotype. We further found that during normal cerebellar development, phosphorylated CREB was selectively expressed in differentiating cerebellar granule neuron progenitor (CGNP) cells. In line, we observed increased differentiation in CGNPs treated with Forskolin, Bmp6 and Bmp12 (Gdf7), which induce CREB phosphorylation. Lastly, we demonstrated that inducing CREB activation via PKA-mediated CREB signaling, but not Bmp/MEK/ERK mediated signalling, enhances medulloblastoma cell sensitivity to chemotherapy.

MuSyC is a consensus framework that unifies multi-drug synergy metrics for combinatorial drug discovery

Drug combination discovery depends on reliable synergy metrics but no consensus exists on the correct synergy criterion to characterize combined interactions. The fragmented state of the field confounds analysis, impedes reproducibility, and delays clinical translation of potential combination treatments. Here we present a mass-action based formalism to quantify synergy. With this formalism, we clarify the relationship between the dominant drug synergy principles, and present a mapping of commonly used frameworks onto a unified synergy landscape. From this, we show how biases emerge due to intrinsic assumptions which hinder their broad applicability and impact the interpretation of synergy in discovery efforts. Specifically, we describe how traditional metrics mask consequential synergistic interactions, and contain biases dependent on the Hill-slope and maximal effect of single-drugs. We show how these biases systematically impact synergy classification in large combination screens, potentially misleading discovery efforts. Thus the proposed formalism can provide a consistent, unbiased interpretation of drug synergy, and accelerate the translatability of synergy studies.

The lack of a unifying metric characterizing combinatorial drug interactions has impeded the development of combinatorial therapies. Here, the authors present MuSyC, a consensus synergy metric that overcomes several caveats associated with other, popular metrics.

A Review of Current In Silico Methods for Repositioning Drugs and Chemical Compounds

Drug repositioning is a new way of applying the existing therapeutics to new disease indications. Due to the exorbitant cost and high failure rate in developing new drugs, the continued use of existing drugs for treatment, especially anti-tumor drugs, has become a widespread practice. With the assistance of high-throughput sequencing techniques, many efficient methods have been proposed and applied in drug repositioning and individualized tumor treatment. Current computational methods for repositioning drugs and chemical compounds can be divided into four categories: (i) feature-based methods, (ii) matrix decomposition-based methods, (iii) network-based methods, and (iv) reverse transcriptome-based methods. In this article, we comprehensively review the widely used methods in the above four categories. Finally, we summarize the advantages and disadvantages of these methods and indicate future directions for more sensitive computational drug repositioning methods and individualized tumor treatment, which are critical for further experimental validation.

In vitro studies of deferasirox derivatives as potential organelle-targeting traceable anti-cancer therapeutics

We report here strategic functionalization of the FDA approved chelator deferasirox (1) in an effort to produce organelle-targeting iron chelators with enhanced activity against A549 lung cancer cells. Derivative 8 was found to have improved antiproliferative activity relative to 1. Fluorescent cell imaging revealed that compound 8 preferentially localises within the lysosome.

Kite-shaped molecules block SARS-CoV-2 cell entry at a post-attachment step.. [DOI: 10.1101/2021.05.29.446272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Anti-viral small molecules are currently lacking for treating coronavirus infection. The long development timescales for such drugs are a major problem, but could be shortened by repurposing existing drugs. We therefore screened a small library of FDA-approved compounds for potential severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) antivirals using a pseudovirus system that allows a sensitive read-out of infectivity. A group of structurally-related compounds, showing moderate inhibitory activity with IC50values in the 1-5µM range, were identified. Further studies demonstrated that these ‘kite-shaped’ molecules were surprisingly specific for SARS-CoV and SARS-CoV-2 and that they acted early in the entry steps of the viral infectious cycle, but did not affect virus attachment to the cells. Moreover the compounds were able to prevent infection in both kidney- and lung-derived human cell lines. The structural homology of the hits allowed the production of a well-defined pharmacophore that was found to be highly accurate in predicting the anti-viral activity of the compounds in the screen. We discuss the prospects of repurposing these existing drugs for treating current and future coronavirus outbreaks.

Medical Device Industry Approaches for Addressing Sources of Failing Cytotoxicity Scores

To ensure patient safety, medical device manufacturers are required by the Food and Drug Administration and other regulatory bodies to perform biocompatibility evaluations on their devices per standards, such as the AAMI-approved ISO 10993-1:2018 (ANSI/AAMI/ISO 10993-1:2018).However, some of these biological tests (e.g., systemic toxicity studies) have long lead times and are costly, which may hinder the release of new medical devices. In recent years, an alternative method using a risk-based approach for evaluating the toxicity (or biocompatibility) profile of chemicals and materials used in medical devices has become more mainstream. This approach is used as a complement to or substitute for traditional testing methods (e.g., systemic toxicity endpoints). Regardless of the approach, the one test still used routinely in initial screening is the cytotoxicity test, which is based on an in vitro cell culture system to evaluate potential biocompatibility effects of the final finished form of a medical device. However, it is known that this sensitive test is not always compatible with specific materials and can lead to failing cytotoxicity scores and an incorrect assumption of potential biological or toxicological adverse effects. This article discusses the common culprits of in vitro cytotoxicity failures, as well as describes the regulatory-approved methodology for cytotoxicity testing and the approach of using toxicological risk assessment to address clinical relevance of cytotoxicity failures for medical devices. Further, discrepancies among test results from in vitro tests, use of published half-maximal inhibitory concentration data, and the derivation of their relationship to tolerable exposure limits, reference doses, or no observed adverse effect levels are highlighted to demonstrate that although cytotoxicity tests in general are regarded as a useful sensitive screening assays, specific medical device materials are not compatible with these cellular/in vitro systems. For these cases, the results should be analyzed using more clinically relevant approaches (e.g., through chemical analysis or written risk assessment).

Treatment scheduling effects on the evolution of drug resistance in heterogeneous cancer cell populations

The effect of scheduling of targeted therapy combinations on drug resistance is underexplored in triple-negative breast cancer (TNBC). TNBC constitutes heterogeneous cancer cell populations the composition of which can change dynamically during treatment resulting in the selection of resistant clones with a fitness advantage. We evaluated crizotinib (ALK/MET inhibitor) and navitoclax (ABT-263; Bcl-2/Bcl-xL inhibitor) combinations in a large design consisting of 696 two-cycle sequential and concomitant treatment regimens with varying treatment dose, duration, and drug holiday length over a 26-day period in MDA-MB-231 TNBC cells and found that patterns of resistance depend on the schedule and sequence in which the drugs are given. Further, we tracked the clonal dynamics and mechanisms of resistance using DNA-integrated barcodes and single-cell RNA sequencing. Our study suggests that longer formats of treatment schedules in vitro screening assays are required to understand the effects of resistance and guide more realistically in vivo and clinical studies.

Medical Device Industry Approaches for Addressing Sources of Failing Cytotoxicity Scores

To ensure patient safety, medical device manufacturers are required by the Food and Drug Administration and other regulatory bodies to perform biocompatibility evaluations on their devices per standards, such as the AAMI-approved ISO 10993-1:2018 (ANSI/AAMI/ISO 10993-1:2018).However, some of these biological tests (e.g., systemic toxicity studies) have long lead times and are costly, which may hinder the release of new medical devices. In recent years, an alternative method using a risk-based approach for evaluating the toxicity (or biocompatibility) profile of chemicals and materials used in medical devices has become more mainstream. This approach is used as a complement to or substitute for traditional testing methods (e.g., systemic toxicity endpoints). Regardless of the approach, the one test still used routinely in initial screening is the cytotoxicity test, which is based on an in vitro cell culture system to evaluate potential biocompatibility effects of the final finished form of a medical device. However, it is known that this sensitive test is not always compatible with specific materials and can lead to failing cytotoxicity scores and an incorrect assumption of potential biological or toxicological adverse effects. This article discusses the common culprits of in vitro cytotoxicity failures, as well as describes the regulatory-approved methodology for cytotoxicity testing and the approach of using toxicological risk assessment to address clinical relevance of cytotoxicity failures for medical devices. Further, discrepancies among test results from in vitro tests, use of published half-maximal inhibitory concentration data, and the derivation of their relationship to tolerable exposure limits, reference doses, or no observed adverse effect levels are highlighted to demonstrate that although cytotoxicity tests in general are regarded as a useful sensitive screening assays, specific medical device materials are not compatible with these cellular/in vitro systems. For these cases, the results should be analyzed using more clinically relevant approaches (e.g., through chemical analysis or written risk assessment).

Drug GRADE: An Integrated Analysis of Population Growth and Cell Death Reveals Drug-Specific and Cancer Subtype-Specific Response Profiles

When evaluating anti-cancer drugs, two different measurements are used: relative viability, which scores an amalgam of proliferative arrest and cell death, and fractional viability, which specifically scores the degree of cell killing. We quantify relationships between drug-induced growth inhibition and cell death by counting live and dead cells using quantitative microscopy. We find that most drugs affect both proliferation and death, but in different proportions and with different relative timing. This causes a non-uniform relationship between relative and fractional response measurements. To unify these measurements, we created a data visualization and analysis platform called drug GRADE, which characterizes the degree to which death contributes to an observed drug response. GRADE captures drug- and genotype-specific responses, which are not captured using traditional pharmacometrics. This study highlights the idiosyncratic nature of drug-induced proliferative arrest and cell death. Furthermore, we provide a metric for quantitatively evaluating the relationship between these behaviors.

Patient-derived organoids as a predictive biomarker for treatment response in cancer patients

Effective predictive biomarkers are needed to enable personalized medicine and increase treatment efficacy and survival for cancer patients, thereby reducing toxic side effects and treatment costs. Patient-derived organoids (PDOs) enable individualized tumour response testing. Since 2018, 17 publications have examined PDOs as a potential predictive biomarker in the treatment of cancer patients. We review and provide a pooled analysis of the results regarding the use of PDOs in individualized tumour response testing, focusing on evidence for analytical validity, clinical validity and clinical utility. We identify future perspectives to accelerate the implementation of PDOs as a predictive biomarker in the treatment of cancer patients.

ZINC40099027 activates human focal adhesion kinase by accelerating the enzymatic activity of the FAK kinase domain

Focal adhesion kinase (FAK) regulates gastrointestinal epithelial restitution and healing. ZINC40099027 (Zn27) activates cellular FAK and promotes intestinal epithelial wound closure in vitro and in mice. However, whether Zn27 activates FAK directly or indirectly remains unknown. We evaluated Zn27 potential modulation of the key phosphatases, PTP-PEST, PTP1B, and SHP2, that inactivate FAK, and performed in vitro kinase assays with purified FAK to assess direct Zn27-FAK interaction. In human Caco-2 cells, Zn27-stimulated FAK-Tyr-397 phosphorylation despite PTP-PEST inhibition and did not affect PTP1B-FAK interaction or SHP2 activity. Conversely, in vitro kinase assays demonstrated that Zn27 directly activates both full-length 125 kDa FAK and its 35 kDa kinase domain. The ATP-competitive FAK inhibitor PF573228 reduced basal and ZN27-stimulated FAK phosphorylation in Caco-2 cells, but Zn27 increased FAK phosphorylation even in cells treated with PF573228. Increasing PF573228 concentrations completely prevented activation of 35 kDa FAK in vitro by a normally effective Zn27 concentration. Conversely, increasing Zn27 concentrations dose-dependently activated kinase activity and overcame PF573228 inhibition of FAK, suggesting the direct interactions of Zn27 with FAK may be competitive. Zn27 increased the maximal activity (Vmax ) of FAK. The apparent Km of the substrate also increased under laboratory conditions less relevant to intracellular ATP concentrations. These results suggest that Zn27 is highly potent and enhances FAK activity via allosteric interaction with the FAK kinase domain to increase the Vmax of FAK for ATP. Understanding Zn27 enhancement of FAK activity will be important to redesign and develop a clinical drug that can promote mucosal wound healing.

Screening of metabolic modulators identifies new strategies to target metabolic reprogramming in melanoma

The prognosis of metastatic melanoma remains poor due to de novo or acquired resistance to immune and targeted therapies. Previous studies have shown that melanoma cells have perturbed metabolism and that cellular metabolic pathways represent potential therapeutic targets. To support the discovery of new drug candidates for melanoma, we examined 180 metabolic modulators, including phytochemicals and anti-diabetic compounds, for their growth-inhibitory activities against melanoma cells, alone and in combination with the BRAF inhibitor vemurafenib. Two positive hits from this screen, 4-methylumbelliferone (4-MU) and ursolic acid (UA), were subjected to validation and further characterization. Metabolic analysis showed that 4-MU affected cellular metabolism through inhibition of glycolysis and enhanced the effect of vemurafenib to reduce the growth of melanoma cells. In contrast, UA reduced mitochondrial respiration, accompanied by an increase in the glycolytic rate. This metabolic switch potentiated the growth-inhibitory effect of the pyruvate dehydrogenase kinase inhibitor dichloroacetate. Both drug combinations led to increased production of reactive oxygen species, suggesting the involvement of oxidative stress in the cellular response. These results support the potential use of metabolic modulators for combination therapies in cancer and may encourage preclinical validation and clinical testing of such treatment strategies in patients with metastatic melanoma.

A statistical framework for assessing pharmacological responses and biomarkers using uncertainty estimates

High-throughput testing of drugs across molecular-characterised cell lines can identify candidate treatments and discover biomarkers. However, the cells' response to a drug is typically quantified by a summary statistic from a best-fit dose-response curve, whilst neglecting the uncertainty of the curve fit and the potential variability in the raw readouts. Here, we model the experimental variance using Gaussian Processes, and subsequently, leverage uncertainty estimates to identify associated biomarkers with a new Bayesian framework. Applied to in vitro screening data on 265 compounds across 1074 cancer cell lines, our models identified 24 clinically established drug-response biomarkers, and provided evidence for six novel biomarkers by accounting for association with low uncertainty. We validated our uncertainty estimates with an additional drug screen of 26 drugs, 10 cell lines with 8 to 9 replicates. Our method is applicable to any dose-response data without replicates, and improves biomarker discovery for precision medicine.

Regulation of GABAA and 5-HT Receptors Involved in Anxiolytic Mechanisms of Jujube Seed: A System Biology Study Assisted by UPLC-Q-TOF/MS and RT-qPCR Method

The increase of the prevalence of anxiety greatly impacts the quality of life in China and globally. As the most popular traditional Chinese medicinal ingredient for nourishing health and tranquilizing mind, Jujube seed (Ziziphus jujuba Mill., Rhamnaceae) (SZJ) has been proved to exert anxiolytic effects in previous reports. In this study, a system biology method assisted by UPLC-Q-TOF/MS and RT-qPCR was developed to systematically demonstrate the anxiolytic mechanisms of SZJ. A total of 35 phytochemicals were identified from SZJ extract (Ziziphus jujuba Mill. var. spinosa [Bunge] Hu ex H.F. Chow), which interact with 71 anxiolytic targets. Protein-protein interaction, genes cluster, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis were subsequently conducted, and results demonstrated that regulation of serotonergic and GABAergic synapse pathways were dominantly involved in the anxiolytic mechanisms of SZJ extract. The effects of SZJ extract on mRNA expressions of multiple GABA_A (gamma-aminobutyric acid type A) and 5-HT (serotonin) receptors subtypes were further validated in human neuroblastoma SH-SY5Y cells using RT-qPCR. Results showed that SZJ extract (250 μg/mL) significantly up-regulated the mRNA level of GABRA1 and GABRA3 as well as HTR1A, HTR2A, and HTR2B in non-H₂O₂ treated SH-SY5Y cells. However, it exerted an inhibitive effect on the overexpressed mRNA of GABRA1, GABRA2, HTR1A, and HTR2A in H₂O₂ treated SH-SY5Y cells. Taken together, our findings suggest that anxiolytic mechanisms of SZJ mostly involve the regulation of GABAergic and serotonergic synapse pathways, especially a two-way modulation of GABRA1, HTR1A, and HTR2A. Our current results provide potential direction for future investigation of SZJ as an anxiolytic agent.

Q-Rank: Reinforcement Learning for Recommending Algorithms to Predict Drug Sensitivity to Cancer Therapy

In personalized medicine, a challenging task is to identify the most effective treatment for a patient. In oncology, several computational models have been developed to predict the response of drugs to therapy. However, the performance of these models depends on multiple factors. This paper presents a new approach, called Q-Rank, to predict the sensitivity of cell lines to anti-cancer drugs. Q-Rank integrates different prediction algorithms and identifies a suitable algorithm for a given application. Q-Rank is based on reinforcement learning methods to rank prediction algorithms on the basis of relevant features (e.g., omics characterization). The best-ranked algorithm is recommended and used to predict the response of drugs to therapy. Our experimental results indicate that Q-Rank outperforms the integrated models in predicting the sensitivity of cell lines to different drugs.

Profiling the Non-genetic Origins of Cancer Drug Resistance with a Single-Cell Functional Genomics Approach Using Predictive Cell Dynamics

Non-genetic heterogeneity observed in clonal cell populations is an immediate cause of drug resistance that remains challenging to profile because of its transient nature. Here, we coupled three single-cell technologies to link the predicted drug response of a cell to its own genome-wide transcriptomic profile. As a proof of principle, we analyzed the response to tumor-necrosis-factor-related apoptosis-inducing ligand (TRAIL) in HeLa cells to demonstrate that cell dynamics can discriminate the transient transcriptional states at the origin of cell decisions such as sensitivity and resistance. Our same-cell approach, named fate-seq, can reveal the molecular factors regulating the efficacy of a drug in clonal cells, providing therapeutic targets of non-genetic drug resistance otherwise confounded in gene expression noise. A record of this paper's transparent peer review process is included in the Supplemental Information.

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.

Expansion of the structure-activity relationship of branched chain fatty acids: Effect of unsaturation and branching group size on anticancer activity

Branched chain fatty acids (BCFAs) are a class of fatty acid with promising anticancer activity. The BCFA 13-methyltetradecanoic acid (13-MTD) inhibits tumour growth in vivo without toxicity but efficacy is limited by moderate potency, a property shared by all known BCFAs. The mechanism of action of BCFAs has not been fully elucidated, and in the absence of a clearly defined target optimisation of BCFA potency must rely on structure-activity relationships. Our current understanding of the structural features that promote BCFA anticancer activity is limited by the low structural diversity of reported BCFAs.The aim of this study was to examine the effects of two new structural modifications- unsaturation and branching group size- on BCFA activity. Thus, homologous series of saturated and cis-Δ11 unsaturated BCFAs were synthesised bearing methyl, ethyl, propyl and butyl branching groups, and were screened in vitro for activity against three human cancer cell lines. Potencies of the new BCFAs were compared to 13-MTD and an unbranched monounstaurated fatty acid (MUFA) bearing a cis-Δ11 double bond. The principal findings to emerge were that the anticancer activity of BCFAs was adversly affected by larger branching groups but significantly improved by incorporation of a cis-Δ11 double bond into the BCFA alkyl chain. This study provides new structure-activity relationship insights that may be used to develop BCFAs with improved potency and therapeutic potential.

Kinetic Heterogeneity of Cancer Cell Fractional Killing

Lethal drugs can induce incomplete cell death in a population of cancer cells, a phenomenon referred to as fractional killing. Here, we show that high-throughput population-level time-lapse imaging can be used to quantify fractional killing in response to hundreds of different drug treatments in parallel. We find that stable intermediate levels of fractional killing are uncommon, with many drug treatments resulting in complete or near-complete eradication of all cells, if given enough time. The kinetics of fractional killing over time vary substantially as a function of drug, drug dose, and genetic background. At the molecular level, the antiapoptotic protein MCL1 is an important determinant of the kinetics of fractional killing in response to MAPK pathway inhibitors but not other lethal stimuli. These studies suggest that fractional killing is governed by diverse lethal stimulus-specific mechanisms.

Optimum concentration-response curve metrics for supervised selection of discriminative cellular phenotypic endpoints for chemical hazard assessment

High-content imaging (HCI) provides quantitative and information-rich measurements of chemical effects on human in vitro cell models. Identification of discriminative phenotypic endpoints from cellular features obtained from HCI is required for accurate assessments of potential chemical hazards. However, the use of suboptimal metrics to quantify the concentration-response curves (CRC) of chemicals based on these features may obscure discriminative features, and lead to non-predictive endpoints and poor chemical classifications or hazard assessments. Here, we present a systematic and data-driven study on the performances of different CRC metrics in identifying image-based phenotypic features that can accurately classify the effects of reference chemicals with known in vivo toxicities. We studied four previous HCI in vitro nephro- or pulmono-toxicity datasets, which contain phenotypic feature measurements from different cell and feature types. Within a feature type, we found that efficacy metrics at higher chemical concentrations tend to give higher classification accuracy, whereas potency metrics do not have obvious trends across different response levels. Across different cell and feature types, efficacy metrics generally gave higher classification accuracy than potency metrics and area under the curve (AUC). Our results suggest that efficacy metrics, especially at higher concentrations, are more likely to help us to identify discriminative phenotypic endpoints. Therefore, HCI experiments for toxicological applications should include measurements at sufficiently high chemical concentrations, and efficacy metrics should always be analyzed. The identified features may be used as specific toxicity endpoints for further chemical hazard assessment.

Structural Refinement of the Tubulin Ligand (+)-Discodermolide to Attenuate Chemotherapy-Mediated Senescence

The natural product (+)-discodermolide (DDM) is a microtubule stabilizing agent and potent inducer of senescence. We refined the structure of DDM and evaluated the activity of novel congeners in triple negative breast and ovarian cancers, malignancies that typically succumb to taxane resistance. Previous structure-activity analyses identified the lactone and diene as moieties conferring anticancer activity, thus identifying priorities for the structural refinement studies described herein. Congeners possessing the monodiene with a simplified lactone had superior anticancer efficacy relative to taxol, particularly in resistant models. Specifically, one of these congeners, B2, demonstrated 1) improved pharmacologic properties, specifically increased maximum response achievable and area under the curve, and decreased EC₅₀; 2) a uniform dose-response profile across genetically heterogeneous cancer cell lines relative to taxol or DDM; 3) reduced propensity for senescence induction relative to DDM; 4) superior long-term activity in cancer cells versus taxol or DDM; and 5) attenuation of metastatic characteristics in treated cancer cells. To contrast the binding of B2 versus DDM in tubulin, X-ray crystallography studies revealed a shift in the position of the lactone ring associated with removal of the C2-methyl and C3-hydroxyl. Thus, B2 may be more adaptable to changes in the taxane site relative to DDM that could account for its favorable properties. In conclusion, we have identified a DDM congener with broad range anticancer efficacy that also has decreased risk of inducing chemotherapy-mediated senescence. SIGNIFICANCE STATEMENT: Here, we describe the anticancer activity of novel congeners of the tubulin-polymerizing molecule (+)-discodermolide. A lead molecule is identified that exhibits an improved dose-response profile in taxane-sensitive and taxane-resistant cancer cell models, diminished risk of chemotherapy-mediated senescence, and suppression of tumor cell invasion endpoints. X-ray crystallography studies identify subtle changes in the pose of binding to β-tubulin that could account for the improved anticancer activity. These findings support continued preclinical development of discodermolide, particularly in the chemorefractory setting.

QSAR-derived affinity fingerprints (part 2): modeling performance for potency prediction

Affinity fingerprints report the activity of small molecules across a set of assays, and thus permit to gather information about the bioactivities of structurally dissimilar compounds, where models based on chemical structure alone are often limited, and model complex biological endpoints, such as human toxicity and in vitro cancer cell line sensitivity. Here, we propose to model in vitro compound activity using computationally predicted bioactivity profiles as compound descriptors. To this aim, we apply and validate a framework for the calculation of QSAR-derived affinity fingerprints (QAFFP) using a set of 1360 QSAR models generated using K_i, K_d, IC₅₀ and EC₅₀ data from ChEMBL database. QAFFP thus represent a method to encode and relate compounds on the basis of their similarity in bioactivity space. To benchmark the predictive power of QAFFP we assembled IC₅₀ data from ChEMBL database for 18 diverse cancer cell lines widely used in preclinical drug discovery, and 25 diverse protein target data sets. This study complements part 1 where the performance of QAFFP in similarity searching, scaffold hopping, and bioactivity classification is evaluated. Despite being inherently noisy, we show that using QAFFP as descriptors leads to errors in prediction on the test set in the ~ 0.65-0.95 pIC₅₀ units range, which are comparable to the estimated uncertainty of bioactivity data in ChEMBL (0.76-1.00 pIC₅₀ units). We find that the predictive power of QAFFP is slightly worse than that of Morgan2 fingerprints and 1D and 2D physicochemical descriptors, with an effect size in the 0.02-0.08 pIC₅₀ units range. Including QSAR models with low predictive power in the generation of QAFFP does not lead to improved predictive power. Given that the QSAR models we used to compute the QAFFP were selected on the basis of data availability alone, we anticipate better modeling results for QAFFP generated using more diverse and biologically meaningful targets. Data sets and Python code are publicly available at https://github.com/isidroc/QAFFP_regression .

FAK activates AKT-mTOR signaling to promote the growth and progression of MMTV-Wnt1-driven basal-like mammary tumors

BACKGROUND

Breast cancer is a heterogeneous disease. Hence, stratification of patients based on the subtype of breast cancer is key to its successful treatment. Among all the breast cancer subtypes, basal-like breast cancer is the most aggressive subtype with limited treatment options. Interestingly, we found focal adhesion kinase (FAK), a cytoplasmic tyrosine kinase, is highly overexpressed and activated in basal-like breast cancer.

METHODS

To understand the role of FAK in this subtype, we generated mice with conditional deletion of FAK and a knock-in mutation in its kinase domain in MMTV-Wnt1-driven basal-like mammary tumors. Tumor initiation, growth, and metastasis were characterized for these mice cohorts. Immunohistochemical and transcriptomic analysis of Wnt1-driven tumors were also performed to elucidate the mechanisms underlying FAK-dependent phenotypes. Pharmacological inhibition of FAK and mTOR in human basal-like breast cancer cell lines was also tested.

RESULTS

We found that in the absence of FAK or its kinase function, growth and metastasis of the tumors were significantly suppressed. Furthermore, immunohistochemical analyses of cleaved caspase 3 revealed that loss of FAK results in increased tumor cell apoptosis. To further investigate the mechanism by which FAK regulates survival of the Wnt1-driven tumor cells, we prepared an isogenic pair of mammary tumor cells with and without FAK and found that FAK ablation increased their sensitivity to ER stress-induced cell death, as well as reduced tumor cell migration and tumor sphere formation. Comparative transcriptomic profiling of the pair of tumor cells and gene set enrichment analysis suggested mTOR pathway to be downregulated upon loss of FAK. Immunoblot analyses further confirmed that absence of FAK results in reduction of AKT and downstream mTOR pathways. We also found that inhibition of FAK and mTOR pathways both induces apoptosis, indicating the importance of these pathways in regulating cell survival.

CONCLUSIONS

In summary, our studies show that in a basal-like tumor model, FAK is required for survival of the tumor cells and can serve as a potential therapeutic target.

Comparison of Drug Inhibitory Effects ([Formula: see text]) in Monolayer and Spheroid Cultures

Traditionally, the monolayer (two-dimensional) cell cultures are used for initial evaluation of the effectiveness of anticancer drugs. In particular, these experiments provide the [Formula: see text] curves that determine drug concentration that can inhibit growth of a tumor colony by half when compared to the cells grown with no exposure to the drug. Low [Formula: see text] value means that the drug is effective at low concentrations, and thus will show lower systemic toxicity when administered to the patient. However, in these experiments cells are grown in a monolayer, all well exposed to the drug, while in vivo tumors expand as three-dimensional multicellular masses, where inner cells have a limited access to the drug. Therefore, we performed computational studies to compare the [Formula: see text] curves for cells grown as a two-dimensional monolayer and a cross section through a three-dimensional spheroid. Our results identified conditions (drug diffusivity, drug action mechanisms and cell proliferation capabilities) under which these [Formula: see text] curves differ significantly. This will help experimentalists to better determine drug dosage for future in vivo experiments and clinical trials.

Betulinic Acid and Brosimine B Hybrid Derivatives as Potential Agents against Female Cancers

Background:

Cancer is a multifactorial disease, representing one of the leading causes of death worldwide. On a global estimate, breast cancer is the most frequently occurring cancer in women and cervical cancer, the fourth most common. Both types of cancer remain the major cause of cancer-related mortality in developing countries. A strategy for rational drug design is hybridization, which aims to bring together in one molecule, two or more pharmacophores in order to reach several biological targets.

Objective:

The objective of this work was to develop new hybrids based on natural pharmacophores: Betulinic acid (1) and brosimine b (2), active in female cancer cell lines.

Methods:

The coupling reactions were carried out by Steglich esterification. Different compounds were designed for the complete and simplified structural hybridization of molecules. The anticancer activities of the compounds were evaluated in human cervical adenocarcinoma (HeLa), human cervical metastatic epidermoid carcinoma (ME-180), and human breast adenocarcinoma (MCF-7) cell lines.

Results:

Hybrid 3 presented higher potency (IC50 = 9.2 ± 0.5μM) and SI (43.5) selectively in MCF-7 cells (in relation to Vero cells) with its cytotoxic effect occurring via apoptosis. In addition, compound 6 showed activity in MCF-7 and HeLa cells with intermediate potency, but with high efficacy, acting via apoptosis as well.

Conclusion:

In this context, we showed that the combination of two complex structures generated the development of hybrids with differing inhibitory profiles and apoptotic modes of action, thus representing potential alternatives in female cancer treatment.

Understanding the effect of measurement time on drug characterization

In order to determine correct dosage of chemotherapy drugs, the effect of the drug must be properly quantified. There are two important values that characterize the effect of the drug: εmax is the maximum possible effect of a drug, and IC50 is the drug concentration where the effect diminishes by half. There is currently a problem with the way these values are measured because they are time-dependent measurements. We use mathematical models to determine how the εmax and IC50 values depend on measurement time and model choice. Seven ordinary differential equation models (ODE) are used for the mathematical analysis; the exponential, Mendelsohn, logistic, linear, surface, Bertalanffy, and Gompertz models. We use the models to simulate tumor growth in the presence and absence of treatment with a known IC50 and εmax. Using traditional methods, we then calculate the IC50 and εmax values over fifty days to show the time-dependence of these values for all seven mathematical models. The general trend found is that the measured IC50 value decreases and the measured εmax increases with increasing measurement day for most mathematical models. Unfortunately, the measured values of IC50 and εmax rarely matched the values used to generate the data. Our results show that there is no optimal measurement time since models predict that IC50 estimates become more accurate at later measurement times while εmax is more accurate at early measurement times.

Application of a Biphasic Mathematical Model of Cancer Cell Drug Response for Formulating Potent and Synergistic Targeted Drug Combinations to Triple Negative Breast Cancer Cells

Triple negative breast cancer is a collection of heterogeneous breast cancers that are immunohistochemically negative for estrogen receptor, progesterone receptor, and ErbB2 (due to deletion or lack of amplification). No dominant proliferative driver has been identified for this type of cancer, and effective targeted therapy is lacking. In this study, we hypothesized that triple negative breast cancer cells are multi-driver cancer cells, and evaluated a biphasic mathematical model for identifying potent and synergistic drug combinations for multi-driver cancer cells. The responses of two triple negative breast cancer cell lines, MDA-MB-231 and MDA-MB-468, to a panel of targeted therapy drugs were determined over a broad range of concentrations. The analyses of the drug responses by the biphasic mathematical model revealed that both cell lines were indeed dependent on multiple drivers, and inhibitors of individual drivers caused a biphasic response: a target-specific partial inhibition at low nM concentrations, and an off-target toxicity at μM concentrations. We further demonstrated that combinations of drugs, targeting each driver, cause potent, synergistic, and cell-specific cell killing. Immunoblotting analysis of the effects of the individual drugs and drug combinations on the signaling pathways supports the above conclusion. These results support a multi-driver proliferation hypothesis for these triple negative breast cancer cells, and demonstrate the applicability of the biphasic mathematical model for identifying effective and synergistic targeted drug combinations for triple negative breast cancer cells.

de la Torre A, M. Muñiz F, Pertino MW, Alarcón B, Ormazabal V, Nova-Lamperti E, Zúñiga FA, Jiménez CA. Synthesis and Cytotoxic Analysis of Novel Myrtenyl Grafted Pseudo-Peptides Revealed Potential Candidates for Anticancer Therapy

Myrtenal is a natural monoterpene isolated from essential oils of several plants and their derivates have shown to have several biological properties including cytotoxicity. The cytotoxic activity of these derivates are being investigated for their antitumor effect leading to the development of potential anticancer agents. In this study, novels Myrtenyl grafted pseudo-peptides were designed, synthesized and functionally characterized as possible therapeutic agents for cancer treatment. Thirteen novel Myrtenyl grafted pseudo-peptides were prepared in high atom economy and efficiency by a classic Ugi-4CR and sequential post-modification. Their structures were confirmed by NMR, and ESI-MS, and its cytotoxic activity was evaluated in three cancer cell lines and primary CD4+ T cells at different proliferative cycles. Our results revealed that some of these compounds showed significant cytotoxicity against human gastric, breast and colon adenocarcinoma cells lines, but not against human dermal fibroblast cell line. Moreover, from the thirteen novel myrtenyl synthesized the compound (1R,5S)-N-{[1-(3-chlorophenyl)-1H-1,2,3-triazol-4-yl]methyl}-N-[2-(cyclohexylamino)-2–oxoethyl]-6,6-dimethylbicyclo[3.1.1]hept-2-ene-2-carboxamide (3b) proved to be the best candidate in terms of acceptable EC₅₀, and E_max values in cancer cell lines and at inducing cytotoxicity in CD4+ T cells undergoing active proliferation, without affecting non-proliferating T cells. Overall, the synthesis and characterization of our Myrtenyl derivates revealed novel potential anticancer candidates with selective cytotoxic activity.

Measurement and models accounting for cell death capture hidden variation in compound response

Cancer cell sensitivity or resistance is almost universally quantified through a direct or surrogate measure of cell number. However, compound responses can occur through many distinct phenotypic outcomes, including changes in cell growth, apoptosis, and non-apoptotic cell death. These outcomes have divergent effects on the tumor microenvironment, immune response, and resistance mechanisms. Here, we show that quantifying cell viability alone is insufficient to distinguish between these compound responses. Using an alternative assay and drug-response analysis amenable to high-throughput measurement, we find that compounds with identical viability outcomes can have very different effects on cell growth and death. Moreover, additive compound pairs with distinct growth/death effects can appear synergistic when only assessed by viability. Overall, these results demonstrate an approach to incorporating measurements of cell death when characterizing a pharmacologic response.

A Perspective on Expanding Our Understanding of Cancer Treatments by Integrating Approaches from the Biological and Physical Sciences

Multicellular systems such as cancer suffer from immense complexity. It is imperative to capture the heterogeneity of these systems across scales to achieve a deeper understanding of the underlying biology and develop effective treatment strategies. In this perspective article, we will discuss how recent technologies and approaches from the biological and physical sciences have transformed traditional ways of measuring, interpreting, and treating cancer. During the SLAS 2019 Annual Meeting, SBI² hosted a Special Interest Group (SIG) on this topic. Academic and industry leaders engaged in discussions surrounding what biological model systems are appropriate to study cancer complexity, what assays are necessary to interrogate this complexity, and how physical sciences approaches may be useful to detangle this complexity. In particular, we examined the utility of mathematical models in predicting cancer progression and treatment response when tightly integrated with reproducible, quantitative, and dynamic biological measurements achieved using high-content imaging and analysis. The dialogue centered around the impetus for convergent biosciences, bringing new perspectives to cancer research to further understand this complex adaptive system and successfully intervene therapeutically.

Glioblastoma Multiforme Stem Cell Cycle Arrest by Alkylaminophenol Through the Modulation of EGFR and CSC Signaling Pathways

Cancer stem cells (CSCs), a small subpopulation of cells existing in the tumor microenvironment promoting cell proliferation and growth. Targeting the stemness of the CSC population would offer a vital therapeutic opportunity. 3,4-Dihydroquinolin-1(2H)-yl)(p-tolyl)methyl)phenol (THTMP), a small synthetic phenol compound, is proposed to play a significant role in controlling the CSC proliferation and survival. We assessed the potential therapeutic effects of THTMP on glioblastoma multiforme (GBM) and its underlying mechanism in various signaling pathways. To fully comprehend the effect of THTMP on the CSCs, CD133⁺ GBM stem cell (GSC) and CD133^- GBM Non-stem cancer cells (NSCC) population from LN229 and SNB19 cell lines was used. Cell cycle arrest, apoptosis assay and transcriptome analysis were performed for individual cell population. THTMP strongly inhibited NSCC and in a subtle way for GSC in a time-dependent manner and inhibit the resistance variants better than that of temozolomide (TMZ). THTMP arrest the CSC cell population at both G1/S and G2/M phase and induce ROS-mediated apoptosis. Gene expression profiling characterize THTMP as an inhibitor of the p53 signaling pathway causing DNA damage and cell cycle arrest in CSC population. We show that the THTMP majorly affects the EGFR and CSC signaling pathways. Specifically, modulation of key genes involved in Wnt, Notch and Hedgehog, revealed the significant role of THTMP in disrupting the CSCs’ stemness and functions. Moreover, THTMP inhibited cell growth, proliferation and metastasis of multiple mesenchymal patient-tissue derived GBM-cell lines. THTMP arrests GBM stem cell cycle through the modulation of EGFR and CSC signaling pathways.

Phenotype-based probabilistic analysis of heterogeneous responses to cancer drugs and their combination efficacy

Cell-to-cell variability generates subpopulations of drug-tolerant cells that diminish the efficacy of cancer drugs. Efficacious combination therapies are thus needed to block drug-tolerant cells via minimizing the impact of heterogeneity. Probabilistic models such as Bliss independence have been developed to evaluate drug interactions and their combination efficacy based on probabilities of specific actions mediated by drugs individually and in combination. In practice, however, these models are often applied to conventional dose-response curves in which a normalized parameter with a value between zero and one, generally referred to as fraction of cells affected (fa), is used to evaluate the efficacy of drugs and their combined interactions. We use basic probability theory, computer simulations, time-lapse live cell microscopy, and single-cell analysis to show that fa metrics may bias our assessment of drug efficacy and combination effectiveness. This bias may be corrected when dynamic probabilities of drug-induced phenotypic events, i.e. induction of cell death and inhibition of division, at a single-cell level are used as metrics to assess drug efficacy. Probabilistic phenotype metrics offer the following three benefits. First, in contrast to the commonly used fa metrics, they directly represent probabilities of drug action in a cell population. Therefore, they deconvolve differential degrees of drug effect on tumor cell killing versus inhibition of cell division, which may not be correlated for many drugs. Second, they increase the sensitivity of short-term drug response assays to cell-to-cell heterogeneities and the presence of drug-tolerant subpopulations. Third, their probabilistic nature allows them to be used directly in unbiased evaluation of synergistic efficacy in drug combinations using probabilistic models such as Bliss independence. Altogether, we envision that probabilistic analysis of single-cell phenotypes complements currently available assays via improving our understanding of heterogeneity in drug response, thereby facilitating the discovery of more efficacious combination therapies to block drug-tolerant cells.

Assessment of modelling strategies for drug response prediction in cell lines and xenografts

Data from several large high-throughput drug response screens have become available to the scientific community recently. Although many efforts have been made to use this information to predict drug sensitivity, our ability to accurately predict drug response based on genetic data remains limited. In order to systematically examine how different aspects of modelling affect the resulting prediction accuracy, we built a range of models for seven drugs (erlotinib, pacliatxel, lapatinib, PLX4720, sorafenib, nutlin-3 and nilotinib) using data from the largest available cell line and xenograft drug sensitivity screens. We found that the drug response metric, the choice of the molecular data type and the number of training samples have a substantial impact on prediction accuracy. We also compared the tasks of drug response prediction with tissue type prediction and found that, unlike for drug response, tissue type can be predicted with high accuracy. Furthermore, we assessed our ability to predict drug response in four xenograft cohorts (treated either with erlotinib, gemcitabine or paclitaxel) using models trained on cell line data. We could predict response in an erlotinib-treated cohort with a moderate accuracy (correlation ≈ 0.5), but were unable to correctly predict responses in cohorts treated with gemcitabine or paclitaxel.

Biphasic Mathematical Model of Cell-Drug Interaction That Separates Target-Specific and Off-Target Inhibition and Suggests Potent Targeted Drug Combinations for Multi-Driver Colorectal Cancer Cells

Quantifying the response of cancer cells to a drug, and understanding the mechanistic basis of the response, are the cornerstones for anti-cancer drug discovery. Classical single target-based IC₅₀ measurements are inadequate at describing cancer cell responses to targeted drugs. In this study, based on an analysis of targeted inhibition of colorectal cancer cell lines, we develop a new biphasic mathematical model that accurately describes the cell–drug response. The model describes the drug response using three kinetic parameters: ratio of target-specific inhibition, F₁, potency of target-specific inhibition, K_d1, and potency of off-target toxicity, K_d2. Determination of these kinetic parameters also provides a mechanistic basis for predicting effective combination targeted therapy for multi-driver cancer cells. The experiments confirmed that a combination of inhibitors, each blocking a driver pathway and having a distinct target-specific effect, resulted in a potent and synergistic blockade of cell viability, improving potency over mono-agent treatment by one to two orders of magnitude. We further demonstrate that mono-driver cancer cells represent a special scenario in which F₁ becomes nearly 100%, and the drug response becomes monophasic. Application of this model to the responses of >400 cell lines to kinase inhibitor dasatinib revealed that the ratio of biphasic versus monophasic responses is about 4:1. This study develops a new mathematical model of quantifying cancer cell response to targeted therapy, and suggests a new framework for developing rational combination targeted therapy for colorectal and other multi-driver cancers.

A normalized drug response metric improves accuracy and consistency of anticancer drug sensitivity quantification in cell-based screening

Accurate quantification of drug effects is crucial for identifying pharmaceutically actionable cancer vulnerabilities. Current cell viability-based measurements often lead to biased response estimates due to varying growth rates and experimental artifacts that explain part of the inconsistency in high-throughput screening results. We developed an improved drug scoring model, normalized drug response (NDR), which makes use of both positive and negative control conditions to account for differences in cell growth rates, and experimental noise to better characterize drug-induced effects. We demonstrate an improved consistency and accuracy of NDR compared to existing metrics in assessing drug responses of cancer cells in various culture models and experimental setups. Notably, NDR reliably captures both toxicity and viability responses, and differentiates a wider spectrum of drug behavior, including lethal, growth-inhibitory and growth-stimulatory modes, based on a single viability readout. The method will therefore substantially reduce the time and resources required in cell-based drug sensitivity screening.

Network-Based Matching of Patients and Targeted Therapies for Precision Oncology

The extensive acquisition of high-throughput molecular profiling data across model systems (human tumors and cancer cell lines) and drug sensitivity data, makes precision oncology possible - allowing clinicians to match the right drug to the right patient. Current supervised models for drug sensitivity prediction, often use cell lines as exemplars of patient tumors and for model training. However, these models are limited in their ability to accurately predict drug sensitivity of individual cancer patients to a large set of drugs, given the paucity of patient drug sensitivity data used for testing and high variability across different drugs. To address these challenges, we developed a multilayer network-based approach to impute individual patients' responses to a large set of drugs. This approach considers the triplet of patients, cell lines and drugs as one inter-connected holistic system. We first use the omics profiles to construct a patient-cell line network and determine best matching cell lines for patient tumors based on robust measures of network similarity. Subsequently, these results are used to impute the "missing link" between each individual patient and each drug, called Personalized Imputed Drug Sensitivity Score (PIDS-Score), which can be construed as a measure of the therapeutic potential of a drug or therapy. We applied our method to two subtypes of lung cancer patients, matched these patients with cancer cell lines derived from 19 tissue types based on their functional proteomics profiles, and computed their PIDS-Scores to 251 drugs and experimental compounds. We identified the best representative cell lines that conserve lung cancer biology and molecular targets. The PIDS-Score based top sensitive drugs for the entire patient cohort as well as individual patients are highly related to lung cancer in terms of their targets, and their PIDS-Scores are significantly associated with patient clinical outcomes. These findings provide evidence that our method is useful to narrow the scope of possible effective patient-drug matchings for implementing evidence-based personalized medicine strategies.

Stochastically modeling multiscale stationary biological processes

Large scale biological responses are inherently uncertain, in part as a consequence of noisy systems that do not respond deterministically to perturbations and measurement errors inherent to technological limitations. As a result, they are computationally difficult to model and current approaches are notoriously slow and computationally intensive (multiscale stochastic models), fail to capture the effects of noise across a system (chemical kinetic models), or fail to provide sufficient biological fidelity because of broad simplifying assumptions (stochastic differential equations). We use a new approach to modeling multiscale stationary biological processes that embraces the noise found in experimental data to provide estimates of the parameter uncertainties and the potential mis-specification of models. Our approach models the mean stationary response at each biological level given a particular expected response relationship, capturing variation around this mean using conditional Monte Carlo sampling that is statistically consistent with training data. A conditional probability distribution associated with a biological response can be reconstructed using this method for a subset of input values, which overcomes the parameter identification problem. Our approach could be applied in addition to dynamical modeling methods (see above) to predict uncertain biological responses over experimental time scales. To illustrate this point, we apply the approach to a test case in which we model the variation associated with measurements at multiple scales of organization across a reproduction-related Adverse Outcome Pathway described for teleosts.

Characterization of cancer omics and drug perturbations in panels of lung cancer cells

To better understand the disruptions of transcriptional regulations and gene expression in lung cancers, we constructed a multi-omics catalogue of the responses of lung cancer cells to a series of chemical compounds. We generated and analyzed 3,240 RNA-seq and 3,393 ATAC-seq libraries obtained from 23 cell lines treated with 95 well-annotated compounds. To demonstrate the power of the created multi-omics resource, we attempted to identify drugs that could induce the designated changes alone or in combination. The basal multi-omics information was first integrated into co-expression modules. Among these modules, we identified a stress response module that may be a promising drug intervention target, as new combinations of compounds that could be used to regulate this module and the consequent phenotypic appearance of cancer cells have been identified. We believe that the multi-omics profiles generated in this study and the strategy used to stratify them will lead to more rational and efficient development of anticancer drugs.

Drug responses are conserved across patient-derived xenograft models of melanoma leading to identification of novel drug combination therapies

BACKGROUND

Patient-derived xenograft (PDX) mouse tumour models can predict response to therapy in patients. Predictions made from PDX cultures (PDXC) would allow for more rapid and comprehensive evaluation of potential treatment options for patients, including drug combinations.

METHODS

We developed a PDX library of BRAF-mutant metastatic melanoma, and a high-throughput drug-screening (HTDS) platform utilising clinically relevant drug exposures. We then evaluated 34 antitumor agents across eight melanoma PDXCs, compared drug response to BRAF and MEK inhibitors alone or in combination with PDXC and the corresponding PDX, and investigated novel drug combinations targeting BRAF inhibitor-resistant melanoma.

RESULTS

The concordance of cancer-driving mutations across patient, matched PDX and subsequent PDX generations increases as variant allele frequency (VAF) increases. There was a high correlation in the magnitude of response to BRAF and MEK inhibitors between PDXCs and corresponding PDXs. PDXCs and corresponding PDXs from metastatic melanoma patients that progressed on standard-of-care therapy demonstrated similar resistance patterns to BRAF and MEK inhibitor therapy. Importantly, HTDS identified novel drug combinations to target BRAF-resistant melanoma.

CONCLUSIONS

The biological consistency observed between PDXCs and PDXs suggests that PDXCs may allow for a rapid and comprehensive identification of treatments for aggressive cancers, including combination therapies.

TRAIL-induced variation of cell signaling states provides nonheritable resistance to apoptosis

TNFα-related apoptosis-inducing ligand (TRAIL), specifically initiates programmed cell death, but often fails to eradicate all cells, making it an ineffective therapy for cancer. This fractional killing is linked to cellular variation that bulk assays cannot capture. Here, we quantify the diversity in cellular signaling responses to TRAIL, linking it to apoptotic frequency across numerous cell systems with single-cell mass cytometry (CyTOF). Although all cells respond to TRAIL, a variable fraction persists without apoptotic progression. This cell-specific behavior is nonheritable where both the TRAIL-induced signaling responses and frequency of apoptotic resistance remain unaffected by prior exposure. The diversity of signaling states upon exposure is correlated to TRAIL resistance. Concomitantly, constricting the variation in signaling response with kinase inhibitors proportionally decreases TRAIL resistance. Simultaneously, TRAIL-induced de novo translation in resistant cells, when blocked by cycloheximide, abrogated all TRAIL resistance. This work highlights how cell signaling diversity, and subsequent translation response, relates to nonheritable fractional escape from TRAIL-induced apoptosis. This refined view of TRAIL resistance provides new avenues to study death ligands in general.

Pharmacogenomic analysis of patient-derived tumor cells in gynecologic cancers

Background

Gynecologic malignancy is one of the leading causes of mortality in female adults worldwide. Comprehensive genomic analysis has revealed a list of molecular aberrations that are essential to tumorigenesis, progression, and metastasis of gynecologic tumors. However, targeting such alterations has frequently led to treatment failures due to underlying genomic complexity and simultaneous activation of various tumor cell survival pathway molecules. A compilation of molecular characterization of tumors with pharmacological drug response is the next step toward clinical application of patient-tailored treatment regimens.

Results

Toward this goal, we establish a library of 139 gynecologic tumors including epithelial ovarian cancers (EOCs), cervical, endometrial tumors, and uterine sarcomas that are genomically and/or pharmacologically annotated and explore dynamic pharmacogenomic associations against 37 molecularly targeted drugs. We discover lineage-specific drug sensitivities based on subcategorization of gynecologic tumors and identify TP53 mutation as a molecular determinant that elicits therapeutic response to poly (ADP-Ribose) polymerase (PARP) inhibitor. We further identify transcriptome expression of inhibitor of DNA biding 2 (ID2) as a potential predictive biomarker for treatment response to olaparib.

Conclusions

Together, our results demonstrate the potential utility of rapid drug screening combined with genomic profiling for precision treatment of gynecologic cancers.

Double PIK3CA mutations in cis increase oncogenicity and sensitivity to PI3Kα inhibitors

Activating mutations in PIK3CA are frequent in human breast cancer, and phosphoinositide 3-kinase alpha (PI3Kα) inhibitors have been approved for therapy. To characterize determinants of sensitivity to these agents, we analyzed PIK3CA-mutant cancer genomes and observed the presence of multiple PIK3CA mutations in 12 to 15% of breast cancers and other tumor types, most of which (95%) are double mutations. Double PIK3CA mutations are in cis on the same allele and result in increased PI3K activity, enhanced downstream signaling, increased cell proliferation, and tumor growth. The biochemical mechanisms of dual mutations include increased disruption of p110α binding to the inhibitory subunit p85α, which relieves its catalytic inhibition, and increased p110α membrane lipid binding. Double PIK3CA mutations predict increased sensitivity to PI3Kα inhibitors compared with single-hotspot mutations.

Synergistic activity of Hsp90 inhibitors and anticancer agents in pancreatic cancer cell cultures

Heat shock protein 90 (Hsp90) is a widely investigated target for anticancer therapy. The experimental Hsp90 inhibitors ICPD47 and ICPD62 demonstrated anticancer activity against colorectal, osteosarcoma and cervical cancer cell lines. However, their anticancer activity has not been investigated against pancreatic cancer cell lines yet, and there are no data about synergistic activity of these compounds in combination with clinically used anticancer agents. Pancreatic cancer cell lines, MIA PaCa-2 and PANC-1 were exposed to ICPD47 and ICPD62 alone and in combinations with antimetabolites gemcitabine (GEM), 5-fluorouracil (5-FU) and topoisomerase inhibitor doxorubicin (DOX). Effects on cell viability were determined by MTT assay. The synergistic activity was evaluated using Chou-Talalay method. Also, 3D cell cultures were formed using 3D Bioprinting method and the activity of each compound and their combinations was examined by measuring the size change of spheroids. The strongest synergistic activities were determined in combinations using all ratios of ICPD47 with GEM and ICPD62 with GEM in MIA PaCa-2 cell line (combination index <0.5). The combinations of ICPD47 with 5-FU and ICPD47 with GEM in a ratio of 1:5 showed the greatest effect on tumour spheroid growth in both cell lines. The ICPD47 in combination with mild hyperthermia showed significant results, where the EC₅₀ value in PANC-1 cell line dropped from 4.04 ± 0.046 to 1.68 ± 0.004 µM. The ICPD47 and ICPD62 under the same conditions could act synergistically with GEM, 5-FU and DOX and is worth of further investigations, and studies of synergistic effect is a promising path for more efficient anticancer therapies.