Analysis of compound synergy in high-throughput cellular screens by population-based lifetime modeling

Exploring the resistance mechanism of triple-negative breast cancer to paclitaxel through the scRNA-seq analysis

BACKGROUND

The triple negative breast cancer (TNBC) is the most malignant subtype of breast cancer with high aggressiveness. Although paclitaxel-based chemotherapy scenario present the mainstay in TNBC treatment, paclitaxel resistance is still a striking obstacle for cancer cure. So it is imperative to probe new therapeutic targets through illustrating the mechanisms underlying paclitaxel chemoresistance.

METHODS

The Single cell RNA sequencing (scRNA-seq) data of TNBC cells treated with paclitaxel at different points were downloaded from the Gene Expression Omnibus (GEO) database. The Seurat R package was used to filter and integrate the scRNA-seq expression matrix. Cells were further clustered by the FindClusters function, and the gene marker of each subset was defined by FindAllMarkers function. Then, the hallmark score of each cell was calculated by AUCell R package, the biological function of the highly expressed interest genes was analyzed by the DAVID database. Subsequently, we performed pseudotime analysis to explore the change patterns of drug resistance genes and SCENIC analysis to identify the key transcription factors (TFs). Finally, the inhibitors of which were also analyzed by the CTD database.

RESULTS

We finally obtained 6 cell subsets from 2798 cells, which were marked as AKR1C3+, WNT7A+, FAM72B+, RERG+, IDO1+ and HEY1+HCC1143 cell subsets, among which the AKR1C3+, IDO1+ and HEY1+ cell subsets proportions increased with increasing treatment time, and then were regarded as paclitaxel resistance subsets. Hallmark score and pseudotime analysis showed that these paclitaxel resistance subsets were associated with the inflammatory response, virus and interferon response activation. In addition, the gene regulatory networks (GRNs) indicated that 3 key TFs (STAT1, CEBPB and IRF7) played vital role in promoting resistance development, and five common inhibitors targeted these TFs as potential combination therapies of paclitaxel were identified.

CONCLUSION

In this study, we identified 3 paclitaxel resistance relevant IFs and their inhibitors, which offers essential molecular basis for paclitaxel resistance and beneficial guidance for the combination of paclitaxel in clinical TNBC therapy.

Natural Cyclophilin A Inhibitors Suppress the Growth of Cancer Stem Cells in Non-Small Cell Lung Cancer by Disrupting Crosstalk between CypA/CD147 and EGFR

Non-small cell lung cancer (NSCLC) is a fatal malignant tumor with a high mortality rate. Cancer stem cells (CSCs) play pivotal roles in tumor initiation and progression, treatment resistance, and NSCLC recurrence. Therefore, the development of novel therapeutic targets and anticancer drugs that effectively block CSC growth may improve treatment outcomes in patients with NSCLC. In this study, we evaluated, for the first time, the effects of natural cyclophilin A (CypA) inhibitors, including 23-demethyl 8,13-deoxynargenicin (C9) and cyclosporin A (CsA), on the growth of NSCLC CSCs. C9 and CsA more sensitively inhibited the proliferation of epidermal growth factor receptor (EGFR)-mutant NSCLC CSCs than EGFR wild-type NSCLC CSCs. Both compounds suppressed the self-renewal ability of NSCLC CSCs and NSCLC-CSC-derived tumor growth in vivo. Furthermore, C9 and CsA inhibited NSCLC CSC growth by activating the intrinsic apoptotic pathway. Notably, C9 and CsA reduced the expression levels of major CSC markers, including integrin α6, CD133, CD44, ALDH1A1, Nanog, Oct4, and Sox2, through dual downregulation of the CypA/CD147 axis and EGFR activity in NSCLC CSCs. Our results also show that the EGFR tyrosine kinase inhibitor afatinib inactivated EGFR and decreased the expression levels of CypA and CD147 in NSCLC CSCs, suggesting close crosstalk between the CypA/CD147 and EGFR pathways in regulating NSCLC CSC growth. In addition, combined treatment with afatinib and C9 or CsA more potently inhibited the growth of EGFR-mutant NSCLC CSCs than single-compound treatments. These findings suggest that the natural CypA inhibitors C9 and CsA are potential anticancer agents that suppress the growth of EGFR-mutant NSCLC CSCs, either as monotherapy or in combination with afatinib, by interfering with the crosstalk between CypA/CD147 and EGFR.

Drug Combination Modeling: Methods and Applications in Drug Development

Combination therapies have become increasingly researched and used in the treatment and management of complex diseases due to their ability to increase the chances for better efficacy and decreased toxicity. To evaluate drug combinations in drug development, pharmacokinetic and pharmacodynamic interactions between drugs in combination can be quantified using mathematical models; however, it can be difficult to deduce which models to use and how to use them to aid in clinical trial simulations to simulate the effect of a drug combination. This review paper aims to provide an overview of the various methods used to evaluate combination drug interaction for use in clinical trial development and a practical guideline on how combination modeling can be used in the settings of clinical trials.

Unbiased High-Throughput Drug Combination Pilot Screening Identifies Synergistic Drug Combinations Effective against Patient-Derived and Drug-Resistant Melanoma Cell Lines

We describe the development, optimization, and validation of 384-well growth inhibition assays for six patient-derived melanoma cell lines (PDMCLs), three wild type (WT) for BRAF and three with V600E-BRAF mutations. We conducted a pilot drug combination (DC) high-throughput screening (HTS) of 45 pairwise 4×4 DC matrices prepared from 10 drugs in the PDMCL assays: two B-Raf inhibitors (BRAFi), a MEK inhibitor (MEKi), and a methylation agent approved for melanoma; cytotoxic topoisomerase II and DNA methyltransferase chemotherapies; and drugs targeting the base excision DNA repair enzyme APE1 (apurinic/apyrimidinic endonuclease-1/redox effector factor-1), SRC family tyrosine kinases, the heat shock protein 90 (HSP90) molecular chaperone, and histone deacetylases.Pairwise DCs between dasatinib and three drugs approved for melanoma therapy-dabrafenib, vemurafenib, or trametinib-were flagged as synergistic in PDMCLs. Exposure to fixed DC ratios of the SRC inhibitor dasatinib with the BRAFis or MEKis interacted synergistically to increase PDMCL sensitivity to growth inhibition and enhance cytotoxicity independently of PDMCL BRAF status. These DCs synergistically inhibited the growth of mouse melanoma cell lines that either were dabrafenib-sensitive or had acquired resistance to dabrafenib with cross resistance to vemurafenib, trametinib, and dasatinib. Dasatinib DCs with dabrafenib, vemurafenib, or trametinib activated apoptosis and increased cell death in melanoma cells independently of their BRAF status or their drug resistance phenotypes. These preclinical in vitro studies provide a data-driven rationale for the further investigation of DCs between dasatinib and BRAFis or MEKis as candidates for melanoma combination therapies with the potential to improve outcomes and/or prevent or delay the emergence of disease resistance.

Targeted therapy and drug resistance in triple-negative breast cancer: the EGFR axis

Targeting of estrogen receptor is commonly used as a first-line treatment for hormone-positive breast cancer patients, and is considered as a keystone of systemic cancer therapy. Likewise, HER2-targeted therapy significantly improved the survival of HER2-positive breast cancer patients, indicating that targeted therapy is a powerful therapeutic strategy for breast cancer. However, for triple-negative breast cancer (TNBC), an aggressive breast cancer subtype, there are no clinically approved targeted therapies, and thus, an urgent need to identify potent, highly effective therapeutic targets. In this mini-review, we describe general strategies to inhibit tumor growth by targeted therapies and briefly discuss emerging resistance mechanisms. Particularly, we focus on therapeutic targets for TNBC and discuss combination therapies targeting the epidermal growth factor receptor (EGFR) and associated resistance mechanisms.

Designing combination therapies with modeling chaperoned machine learning

Chemotherapy resistance is a major challenge to the effective treatment of cancer. Thus, a systematic pipeline for the efficient identification of effective combination treatments could bring huge biomedical benefit. In order to facilitate rational design of combination therapies, we developed a comprehensive computational model that incorporates the available biological knowledge and relevant experimental data on the life-and-death response of individual cancer cells to cisplatin or cisplatin combined with the TNF-related apoptosis-inducing ligand (TRAIL). The model's predictions, that a combination treatment of cisplatin and TRAIL would enhance cancer cell death and exhibit a "two-wave killing" temporal pattern, was validated by measuring the dynamics of p53 accumulation, cell fate, and cell death in single cells. The validated model was then subjected to a systematic analysis with an ensemble of diverse machine learning methods. Though each method is characterized by a different algorithm, they collectively identified several molecular players that can sensitize tumor cells to cisplatin-induced apoptosis (sensitizers). The identified sensitizers are consistent with previous experimental observations. Overall, we have illustrated that machine learning analysis of an experimentally validated mechanistic model can convert our available knowledge into the identity of biologically meaningful sensitizers. This knowledge can then be leveraged to design treatment strategies that could improve the efficacy of chemotherapy.

Confirmation of Selected Synergistic Cancer Drug Combinations Identified in an HTS Campaign and Exploration of Drug Efflux Transporter Contributions to the Mode of Synergy

Systematic unbiased high-throughput screening (HTS) of drug combinations (DCs) in well-characterized tumor cell lines is a data-driven strategy to identify novel DCs with potential to be developed into effective therapies. Four DCs from a DC HTS campaign were selected for confirmation; only one appears in clinicaltrials.gov and limited preclinical in vitro data indicates that the drug pairs interact synergistically. Nineteen DC-tumor cell line sets were confirmed to interact synergistically in three pharmacological interaction models. We developed an imaging assay to quantify accumulation of the ABCG2 efflux transporter substrate Hoechst. Gefitinib and raloxifene enhanced Hoechst accumulation in ABCG2 (BCRP)-expressing cells, consistent with inhibition of ABCG2 efflux. Both drugs also inhibit ABCB1 efflux. Mitoxantrone, daunorubicin, and vinorelbine are substrates of one or more of the ABCG2, ABCB1, or ABCC1 efflux transporters expressed to varying extents in the selected cell lines. Interactions between ABC drug efflux transporter inhibitors and substrates may have contributed to the observed synergy; however, other mechanisms may be involved. Novel synergistic DCs identified by HTS were confirmed in vitro, and plausible mechanisms of action studied. Similar approaches may justify the testing of novel HTS-derived DCs in mouse xenograft human cancer models and support the clinical evaluation of effective in vivo DCs in patients.

Osteogenic differentiation of skeletal muscle progenitor cells is activated by the DNA damage response

Heterotopic ossification (HO) is a pathological condition characterized by the deposition of mineralized tissue in ectopic locations such as the skeletal muscle. The precise cellular origin and molecular mechanisms underlying HO are still debated. In our study we focus on the differentiation of mesoangioblasts (MABs), a population of multipotent skeletal muscle precursors. High-content screening for small molecules that perturb MAB differentiation decisions identified Idoxuridine (IdU), an antiviral and radiotherapy adjuvant, as a molecule that promotes MAB osteogenic differentiation while inhibiting myogenesis. IdU-dependent osteogenesis does not rely on the canonical BMP-2/SMADs osteogenic pathway. At pro-osteogenic conditions IdU induces a mild DNA Damage Response (DDR) that activates ATM and p38 eventually promoting the phosphorylation of the osteogenesis master regulator RUNX2. By interfering with this pathway IdU-induced osteogenesis is severely impaired. Overall, our study suggests that induction of the DDR promotes osteogenesis in muscle resident MABs thereby offering a new mechanism that may be involved in the ectopic deposition of mineralized tissue in the muscle.

Implementation of the NCI-60 Human Tumor Cell Line Panel to Screen 2260 Cancer Drug Combinations to Generate >3 Million Data Points Used to Populate a Large Matrix of Anti-Neoplastic Agent Combinations (ALMANAC) Database

Animal and clinical studies demonstrate that cancer drug combinations (DCs) are more effective than single agents. However, it is difficult to predict which DCs will be more efficacious than individual drugs. Systematic DC high-throughput screening (HTS) of 100 approved drugs in the National Cancer Institute's panel of 60 cancer cell lines (NCI-60) produced data to help select DCs for further consideration. We miniaturized growth inhibition assays into 384-well format, increased the fetal bovine serum amount to 10%, lengthened compound exposure to 72 h, and used a homogeneous detection reagent. We determined the growth inhibition 50% values of individual drugs across 60 cell lines, selected drug concentrations for 4 × 4 DC matrices (DCMs), created DCM master and replica daughter plate sets, implemented the HTS, quality control reviewed the data, and analyzed the results. A total of 2620 DCMs were screened in 60 cancer cell lines to generate 3.04 million data points for the NCI ALMANAC (A Large Matrix of Anti-Neoplastic Agent Combinations) database. We confirmed in vitro a synergistic drug interaction flagged in the DC HTS between the vinca-alkaloid microtubule assembly inhibitor vinorelbine (Navelbine) tartrate and the epidermal growth factor-receptor tyrosine kinase inhibitor gefitinib (Iressa) in the SK-MEL-5 melanoma cell line. Seventy-five percent of the DCs examined in the screen are not currently in the clinical trials database. Selected synergistic drug interactions flagged in the DC HTS described herein were subsequently confirmed by the NCI in vitro, evaluated mechanistically, and were shown to have greater than single-agent efficacy in mouse xenograft human cancer models. Enrollment is open for two clinical trials for DCs that were identified in the DC HTS. The NCI ALMANAC database therefore constitutes a valuable resource for selecting promising DCs for confirmation, mechanistic studies, and clinical translation.

Inhibition of Cytochrome P450 Activities by Extracts of Hyptis verticillata Jacq.: Assessment for Potential HERB-Drug Interactions

Understanding the potential for adverse drug reactions (ADRs), from herb-drug interactions, is a key aspect of medicinal plant safety, with particular relevance for public health in countries where medicinal plant use is highly prevalent. We undertook an in-depth assessment of extracts of Hyptis verticillata Jacq., via its impact on activities of key cytochrome P450 (CYP) enzymes (CYPs 1A1, 1A2, 1B1, 3A4 and 2D6), its antioxidant properties (determined by DPPH assays) and chemical characterisation (using LC-MS). The dried plant aqueous extract demonstrated potent inhibition of the activities of CYPs 1A1 (7.6 µg/mL), 1A2 (1.9 µg/mL), 1B1 (9.4 µg/mL) and 3A4 (6.8 µg/mL). Further analysis of other crude extracts demonstrated potent inhibition of CYP1A2 activity for a dried plant ethanol extract (1.5 µg/mL), fresh plant ethanol extract (3.9 µg/mL), and moderate activity for a fresh plant aqueous extract (27.8 µg/mL). All four extracts demonstrated strong antioxidant activity, compared to the positive control (ascorbic acid, 1.3 µg/mL), with the dried plant ethanol extract being the most potent (1.6 µg/mL). Analysis of the dried plant aqueous extract confirmed the identity of seven phytochemicals, five lignans and two triterpenes. Individual screening of these phytochemicals against the activity of CYP1A2 identified yatein as a moderate inhibitor (71.9 μM), likely to contribute to the plant extract’s potent bioactivity. Further analysis on the impact of this plant on key drug metabolizing enzymes in vivo appears warranted for likely ADRs, as well as furthering development as a potential chemopreventive agent.

Evaluation of drug combination effect using a Bliss independence dose-response surface model

To test the anticancer effect of combining two drugs targeting different biological pathways, the popular way to show synergistic effect of drug combination is a heat map or surface plot based on the percent excess the Bliss prediction using the average response measures at each combination dose. Such graphs, however, are inefficient in the drug screening process and it doesn't give a statistical inference on synergistic effect. To make a statistically rigorous and robust conclusion for drug combination effect, we present a two-stage Bliss independence response surface model to estimate an overall interaction index (τ) with 95% confidence interval (CI). By taking into all data points account, the overall τ with 95% CI can be applied to determine if the drug combination effect is synergistic overall. Using some example data, the two-stage model was compared to a couple of classic models following Bliss rule. The data analysis results obtained from our model reflect the pattern shown from other models. The application of overall τ helps investigators to make decision easier and accelerate the preclinical drug screening.

[Mechanism of action and preclinical development of afatinib]

Afatinib, together with gefitinib and erlotinib, is approved for first-line treatment of advanced non-small cell lung cancer (NSCLC) with activating mutations of the epidermal growth factor receptor (EGFR). This is an irreversible inhibitor of the ErbB family, acting on EGFR (HER1, ErbB1), ErbB2 (HER2) and ErbB4 (HER4). Covalent attachment to cysteine residues in the catalytic domain of EGFR, HER2 and ErbB4 inhibits the tyrosine kinase activity (TKIs) of these receptors, decreasing auto- and transphosphorylation between ErbB dimers, and thus blocking the activity of downstream signalling pathways related to growth and apoptosis suppression. In preclinical models, this has resulted in a reduction in tumour size. Furthermore, due to its mechanism of action, afatinib may be more potent than the first-generation EGFR TKIs (gefitinib and erlotinib) and may even be able to overcome acquired resistance to such treatments. Finally, because of the demonstrated synergism with other chemotherapeutic and target agents, it could be interesting to enhance its clinical development in combination with other drugs.

Heterogeneous Mechanisms of Primary and Acquired Resistance to Third-Generation EGFR Inhibitors

PURPOSE

To identify novel mechanisms of resistance to third-generation EGFR inhibitors in patients with lung adenocarcinoma that progressed under therapy with either AZD9291 or rociletinib (CO-1686).

EXPERIMENTAL DESIGN

We analyzed tumor biopsies from seven patients obtained before, during, and/or after treatment with AZD9291 or rociletinib (CO-1686). Targeted sequencing and FISH analyses were performed, and the relevance of candidate genes was functionally assessed in in vitro models.

RESULTS

We found recurrent amplification of either MET or ERBB2 in tumors that were resistant or developed resistance to third-generation EGFR inhibitors and show that ERBB2 and MET activation can confer resistance to these compounds. Furthermore, we identified a KRASG12S mutation in a patient with acquired resistance to AZD9291 as a potential driver of acquired resistance. Finally, we show that dual inhibition of EGFR/MEK might be a viable strategy to overcome resistance in EGFR-mutant cells expressing mutant KRAS CONCLUSIONS: Our data suggest that heterogeneous mechanisms of resistance can drive primary and acquired resistance to third-generation EGFR inhibitors and provide a rationale for potential combination strategies. Clin Cancer Res; 22(19); 4837-47. ©2016 AACR.

Afatinib-based combination regimens for the treatment of solid tumors: rationale, emerging strategies and recent progress

In oncology, there is a clinical need for novel combination therapy regimens that maximize efficacy and delay resistance to individual treatment modalities. Given the role of aberrant ErbB receptor signaling in the pathogenesis of many human cancers, there is rationale for incorporating afatinib, an irreversible pan-ErbB tyrosine kinase inhibitor, into such combinations. This review focuses on: pharmacological properties of afatinib that facilitate its use in combination; preclinical rationale for the combination of afatinib with other agents; and recently completed, and ongoing, clinical trials of afatinib-based combinations across tumor types. Based on these data, we emphasize a number of areas of high unmet medical need that could benefit from afatinib-based combinations, including patients with relapsed/refractory non-small-cell lung cancer.

Preclinical evaluation of afatinib (BIBW2992) in esophageal squamous cell carcinoma (ESCC)

Esophageal squamous cell carcinoma (ESCC) is the eighth most common cancer worldwide. Epidermal growth factor receptors (EGFR) are often overexpressed in esophageal cancers, thus anti-EGFR inhibitors have been evaluated in ESCC. Afatinib was an irreversible inhibitor of these ErbB family receptors. This study characterized the preclinical activity of afatinib in five ESCC cell lines: HKESC-1, HKESC-2, KYSE510, SLMT-1 and EC-1. ESCC cell lines were sensitive to afatinib with IC50 concentrations at lower micro-molar range (at 72 hour incubation: HKESC-1 = 0.002 μM, HKESC-2 = 0.002 μM, KYSE510 = 1.090 μM, SLMT-1 = 1.161 μM and EC-1 = 0.109 μM) with a maximum growth inhibition over 95%. Afatinib can strongly induce G0/G1 cell cycle arrest in HKESC-2 and EC-1 in a dose- and time-dependent manner. The phosphorylation of ErbB family downstream effectors such as pAKT, pS6 and pMAPK were significantly inhibited in HKESC-2 and EC-1. Apoptosis was observed in both cell lines at 24 hours after exposure to afatinib, as determined by the presence of cleaved PARP. Afatinib could effectively inhibit HKESC-2 tumor growth in mice without obvious toxicity. Afatinib alone has shown excellent growth inhibitory effect on ESCC in both in vitro and in vivo models, however, no synergistic effect was observed when it was combined with chemotherapeutic agents such as 5-fluorouracil (5-FU) and cisplatin. In summary, afatinib can inhibit cell proliferation effectively by arresting the cells in G0/G1 phase, as well as inducing apoptosis in ESCC. These findings warrant further studies of afatinib as therapeutic agent in treating ESCC.

Pharmacologically active metabolites, combination screening and target identification-driven drug repositioning in antituberculosis drug discovery

There has been renewed interest in alternative strategies to address bottlenecks in antibiotic development. These include the repurposing of approved drugs for use as novel anti-infective agents, or their exploitation as leads in drug repositioning. Such approaches are especially attractive for tuberculosis (TB), a disease which remains a leading cause of morbidity and mortality globally and, increasingly, is associated with the emergence of drug-resistance. In this review article, we introduce a refinement of traditional drug repositioning and repurposing strategies involving the development of drugs that are based on the active metabolite(s) of parental compounds with demonstrated efficacy. In addition, we describe an approach to repositioning the natural product antibiotic, fusidic acid, for use against Mycobacterium tuberculosis. Finally, we consider the potential to exploit the chemical matter arising from these activities in combination screens and permeation assays which are designed to confirm mechanism of action (MoA), elucidate potential synergies in polypharmacy, and to develop rules for drug permeability in an organism that poses a special challenge to new drug development.

A comprehensive review of the preclinical efficacy profile of the ErbB family blocker afatinib in cancer

Afatinib (also known as BIBW 2992) has recently been approved in several countries for the treatment of a distinct type of epidermal growth factor receptor (EGFR)-mutated non-small cell lung cancer. This manuscript comprehensively reviews the preclinical data on afatinib, an irreversible inhibitor of the tyrosine kinase activity of members of the epidermal growth factor receptor family (ErbB) including EGFR, HER2 and ErbB4. Afatinib covalently binds to cysteine 797 of the EGFR and the corresponding cysteines 805 and 803 in HER2 and ErbB4, respectively. Such covalent binding irreversibly inhibits the tyrosine kinase activity of these receptors, resulting in reduced auto- and transphosphorylation within the ErbB dimers and inhibition of important steps in the signal transduction of all ErbB receptor family members. Afatinib inhibits cellular growth and induces apoptosis in a wide range of cells representative for non-small cell lung cancer, breast cancer, pancreatic cancer, colorectal cancer, head and neck squamous cell cancer and several other cancer types exhibiting abnormalities of the ErbB network. This translates into tumour shrinkage in a variety of in vivo rodent models of such cancers. Afatinib retains inhibitory effects on signal transduction and in vitro and in vivo cancer cell growth in tumours resistant to reversible EGFR inhibitors, such as those exhibiting the T790M mutations. Several combination treatments have been explored to prevent and/or overcome development of resistance to afatinib, the most promising being those with EGFR- or HER2-targeted antibodies, other tyrosine kinase inhibitors or inhibitors of downstream signalling molecules.

Network pharmacology strategies toward multi-target anticancer therapies: from computational models to experimental design principles

Polypharmacology has emerged as novel means in drug discovery for improving treatment response in clinical use. However, to really capitalize on the polypharmacological effects of drugs, there is a critical need to better model and understand how the complex interactions between drugs and their cellular targets contribute to drug efficacy and possible side effects. Network graphs provide a convenient modeling framework for dealing with the fact that most drugs act on cellular systems through targeting multiple proteins both through on-target and off-target binding. Network pharmacology models aim at addressing questions such as how and where in the disease network should one target to inhibit disease phenotypes, such as cancer growth, ideally leading to therapies that are less vulnerable to drug resistance and side effects by means of attacking the disease network at the systems level through synergistic and synthetic lethal interactions. Since the exponentially increasing number of potential drug target combinations makes pure experimental approach quickly unfeasible, this review depicts a number of computational models and algorithms that can effectively reduce the search space for determining the most promising combinations for experimental evaluation. Such computational-experimental strategies are geared toward realizing the full potential of multi-target treatments in different disease phenotypes. Our specific focus is on system-level network approaches to polypharmacology designs in anticancer drug discovery, where we give representative examples of how network-centric modeling may offer systematic strategies toward better understanding and even predicting the phenotypic responses to multi-target therapies.

Combination of a proteomics approach and reengineering of meso scale network models for prediction of mode-of-action for tyrosine kinase inhibitors

In drug discovery, the characterisation of the precise modes of action (MoA) and of unwanted off-target effects of novel molecularly targeted compounds is of highest relevance. Recent approaches for identification of MoA have employed various techniques for modeling of well defined signaling pathways including structural information, changes in phenotypic behavior of cells and gene expression patterns after drug treatment. However, efficient approaches focusing on proteome wide data for the identification of MoA including interference with mutations are underrepresented. As mutations are key drivers of drug resistance in molecularly targeted tumor therapies, efficient analysis and modeling of downstream effects of mutations on drug MoA is a key to efficient development of improved targeted anti-cancer drugs. Here we present a combination of a global proteome analysis, reengineering of network models and integration of apoptosis data used to infer the mode-of-action of various tyrosine kinase inhibitors (TKIs) in chronic myeloid leukemia (CML) cell lines expressing wild type as well as TKI resistance conferring mutants of BCR-ABL. The inferred network models provide a tool to predict the main MoA of drugs as well as to grouping of drugs with known similar kinase inhibitory activity patterns in comparison to drugs with an additional MoA. We believe that our direct network reconstruction approach, demonstrated on proteomics data, can provide a complementary method to the established network reconstruction approaches for the preclinical modeling of the MoA of various types of targeted drugs in cancer treatment. Hence it may contribute to the more precise prediction of clinically relevant on- and off-target effects of TKIs.

Differential protein stability and ALK inhibitor sensitivity of EML4-ALK fusion variants

PURPOSE

ALK rearrangement-positive lung cancers can be effectively treated with ALK inhibitors. However, the magnitude and duration of response is heterogeneous. In addition, acquired resistance limits the efficacy of ALK inhibitors, with most upfront resistance mechanisms being unknown.

EXPERIMENTAL DESIGN

By making use of the Ba/F3 cell line model, we analyzed the cytotoxic efficacy of ALK kinase inhibitors as a function of different EML4-ALK fusion variants v1, v2, v3a, and v3b as well as of three artificially designed EML4-ALK deletion constructs and the ALK fusion genes KIF5b-ALK and NPM1-ALK. In addition, the intracellular localization, the sensitivity to HSP90 inhibition and the protein stability of ALK fusion proteins were studied.

RESULTS

Different ALK fusion genes and EML4-ALK variants exhibited differential sensitivity to the structurally diverse ALK kinase inhibitors crizotinib and TAE684. In addition, differential sensitivity correlated with differences in protein stability in EML4-ALK-expressing cells. Furthermore, the sensitivity to HSP90 inhibition also varied depending on the ALK fusion partner but differed from ALK inhibitor sensitivity patterns. Finally, combining inhibitors of ALK and HSP90 resulted in synergistic cytotoxicity.

CONCLUSIONS

Our results might explain some of the heterogeneous responses of ALK-positive tumors to ALK kinase inhibition observed in the clinic. Thus, targeted therapy of ALK-positive lung cancer should take into account the precise ALK genotype. Furthermore, combining ALK and HSP90 inhibitors might enhance tumor shrinkage in EML4-ALK-driven tumors.

Combinatorial drug therapy for cancer in the post-genomic era

Over the past decade, whole genome sequencing and other 'omics' technologies have defined pathogenic driver mutations to which tumor cells are addicted. Such addictions, synthetic lethalities and other tumor vulnerabilities have yielded novel targets for a new generation of cancer drugs to treat discrete, genetically defined patient subgroups. This personalized cancer medicine strategy could eventually replace the conventional one-size-fits-all cytotoxic chemotherapy approach. However, the extraordinary intratumor genetic heterogeneity in cancers revealed by deep sequencing explains why de novo and acquired resistance arise with molecularly targeted drugs and cytotoxic chemotherapy, limiting their utility. One solution to the enduring challenge of polygenic cancer drug resistance is rational combinatorial targeted therapy.

Classifying compound mechanism of action for linking whole cell phenotypes to molecular targets

Drug development programs have proven successful when performed at a whole cell level, thus incorporating solubility and permeability into the primary screen. However, linking those results to the target within the cell has been a major setback. The Phenotype Microarray system, marketed and sold by Biolog, seeks to address this need by assessing the phenotype in combination with a variety of chemicals with known mechanism of action (MOA). We have evaluated this system for usefulness in deducing the MOA for three test compounds. To achieve this, we constructed a database with 21 known antimicrobials, which served as a comparison for grouping our unknown MOA compounds. Pearson correlation and Ward linkage calculations were used to generate a dendrogram that produced clustering largely by known MOA, although there were exceptions. Of the three unknown compounds, one was definitively placed as an antifolate. The second and third compounds' MOA were not clearly identified, likely because the unique MOA was not represented within the database. The availability of the database generated in this report for Staphylococcus aureus ATCC 29213 will increase the accessibility of this technique to other investigators. From our analysis, the Phenotype Microarray system can group compounds with clear MOA, but the distinction of unique or broadly acting MOA at this time is less clear.

Brainstorming: weighted voting prediction of inhibitors for protein targets

The "Brainstorming" approach presented in this paper is a weighted voting method that can improve the quality of predictions generated by several machine learning (ML) methods. First, an ensemble of heterogeneous ML algorithms is trained on available experimental data, then all solutions are gathered and a consensus is built between them. The final prediction is performed using a voting procedure, whereby the vote of each method is weighted according to a quality coefficient calculated using multivariable linear regression (MLR). The MLR optimization procedure is very fast, therefore no additional computational cost is introduced by using this jury approach. Here, brainstorming is applied to selecting actives from large collections of compounds relating to five diverse biological targets of medicinal interest, namely HIV-reverse transcriptase, cyclooxygenase-2, dihydrofolate reductase, estrogen receptor, and thrombin. The MDL Drug Data Report (MDDR) database was used for selecting known inhibitors for these protein targets, and experimental data was then used to train a set of machine learning methods. The benchmark dataset (available at http://bio.icm.edu.pl/∼darman/chemoinfo/benchmark.tar.gz ) can be used for further testing of various clustering and machine learning methods when predicting the biological activity of compounds. Depending on the protein target, the overall recall value is raised by at least 20% in comparison to any single machine learning method (including ensemble methods like random forest) and unweighted simple majority voting procedures.