Search

Supplementary Figure from Identification of a Molecularly-Defined Subset of Breast and Ovarian Cancer Models that Respond to WEE1 or ATR Inhibition, Overcoming PARP Inhibitor Resistance

Supplementary Data from Identification of a Molecularly-Defined Subset of Breast and Ovarian Cancer Models that Respond to WEE1 or ATR Inhibition, Overcoming PARP Inhibitor Resistance

Purpose:PARP inhibitors (PARPi) induce synthetic lethality in homologous recombination repair (HRR)-deficient tumors and are used to treat breast, ovarian, pancreatic, and prostate cancers. Multiple PARPi resistance mechanisms exist, most resulting in restoration of HRR and protection of stalled replication forks. ATR inhibition was highlighted as a unique approach to reverse both aspects of resistance. Recently, however, a PARPi/WEE1 inhibitor (WEE1i) combination demonstrated enhanced antitumor activity associated with the induction of replication stress, suggesting another approach to tackling PARPi resistance.Experimental Design:We analyzed breast and ovarian patient-derived xenoimplant models resistant to PARPi to quantify WEE1i and ATR inhibitor (ATRi) responses as single agents and in combination with PARPi. Biomarker analysis was conducted at the genetic and protein level. Metabolite analysis by mass spectrometry and nucleoside rescue experiments ex vivo were also conducted in patient-derived models.Results:Although WEE1i response was linked to markers of replication stress, including STK11/RB1 and phospho-RPA, ATRi response associated with ATM mutation. When combined with olaparib, WEE1i could be differentiated from the ATRi/olaparib combination, providing distinct therapeutic strategies to overcome PARPi resistance by targeting the replication stress response. Mechanistically, WEE1i sensitivity was associated with shortage of the dNTP pool and a concomitant increase in replication stress.Conclusions:Targeting the replication stress response is a valid therapeutic option to overcome PARPi resistance including tumors without an underlying HRR deficiency. These preclinical insights are now being tested in several clinical trials where the PARPi is administered with either the WEE1i or the ATRi.

Cáncer de mama y de ovario; Inhibición WEE1 Càncer de mama i d'ovari; Inhibició WEE1 Breast and ovarian cancer; WEE1 inhibition Purpose: PARP inhibitors (PARPi) induce synthetic lethality in homologous recombination repair (HRR)-deficient tumors and are used to treat breast, ovarian, pancreatic, and prostate cancers. Multiple PARPi resistance mechanisms exist, most resulting in restoration of HRR and protection of stalled replication forks. ATR inhibition was highlighted as a unique approach to reverse both aspects of resistance. Recently, however, a PARPi/WEE1 inhibitor (WEE1i) combination demonstrated enhanced antitumor activity associated with the induction of replication stress, suggesting another approach to tackling PARPi resistance. Experimental Design: We analyzed breast and ovarian patient-derived xenoimplant models resistant to PARPi to quantify WEE1i and ATR inhibitor (ATRi) responses as single agents and in combination with PARPi. Biomarker analysis was conducted at the genetic and protein level. Metabolite analysis by mass spectrometry and nucleoside rescue experiments ex vivo were also conducted in patient-derived models. Results: Although WEE1i response was linked to markers of replication stress, including STK11/RB1 and phospho-RPA, ATRi response associated with ATM mutation. When combined with olaparib, WEE1i could be differentiated from the ATRi/olaparib combination, providing distinct therapeutic strategies to overcome PARPi resistance by targeting the replication stress response. Mechanistically, WEE1i sensitivity was associated with shortage of the dNTP pool and a concomitant increase in replication stress. Conclusions: Targeting the replication stress response is a valid therapeutic option to overcome PARPi resistance including tumors without an underlying HRR deficiency. These preclinical insights are now being tested in several clinical trials where the PARPi is administered with either the WEE1i or the ATRi. This work was supported by the Spanish Instituto de Salud Carlos III (ISCIII), an initiative of the Spanish Ministry of Economy and Innovation partially supported by European Regional Development FEDER Funds (FIS PI17/01080 to V. Serra, PI12/02606 to J. Balmaña); European Research Area-NET, Transcan-2 (AC15/00063), Asociación Española contra el Cáncer (AECC; LABAE16020PORTT), the Agència de Gestió d'Ajuts Universitaris i de Recerca (AGAUR; 2017 SGR 540), La Marató TV3 (654/C/2019), and ERAPERMED2019–215 to V. Serra. We also acknowledge the GHD-Pink program, the FERO Foundation, and the Orozco Family for supporting this study (to V. Serra). V. Serra was supported by the Miguel Servet Program (ISCIII; CPII19/00033); M. Castroviejo-Bermejo and C. Cruz (AIOC15152806CRUZ) by AECC; A. Herencia-Ropero by Generalitat de Catalunya-PERIS (SLT017/20/000081); M. Palafox by Juan de la Cierva (FJCI-2015–25412); A. Lau by AECC and Generalitat de Catalunya-PERIS (INVES20095LLOP, SLT002/16/00477); A. Gris-Oliver by FI-AGAUR (2015 FI_B 01075). This work was supported by Breast Cancer Research Foundation (BCRF-19–08), Instituto de Salud Carlos III Project Reference number AC15/00062, and the EC under the framework of the ERA-NET TRANSCAN-2 initiative co-financed by FEDER, Instituto de Salud Carlos III (CB16/12/00449 and PI19/01181), and Asociación Española Contra el Cáncer (to J. Arribas). The xenograft program in the Caldas laboratory was supported by Cancer Research UK and also received funding from an EU H2020 Network of Excellence (EuroCAN). The RPPA facility is funded by NCI #CA16672.

Resistance to EGFR inhibitors (EGFRi) presents a major obstacle in treating non-small cell lung cancer (NSCLC). One of the most exciting new ways to find potential resistance markers involves running functional genetic screens, such as CRISPR, followed by manual triage of significantly enriched genes. This triage process to identify ‘high value’ hits resulting from the CRISPR screen involves significant manual curation that requires specialized knowledge and can take even experts several months to comprehensively complete.To find key drivers of resistance faster we built a hybrid recommendation system on top of a heterogeneous biomedical knowledge graph integrating preclinical, clinical, and literature evidence. Genes were ranked based on trade-offs between diverse types of evidence linking them to potential mechanisms of EGFRi resistance. This unbiased approach identified 36 resistance markers from >3,000 genes, reducing hit identification time from months to minutes. In addition to reproducing known resistance markers, our method identified novel resistance mechanisms that we prospectively validated.

Recent innovation in the field of machine learning has been enabled by the confluence of three advances: rapid expansion of affordable computing power in the form of cloud computing environments, the accelerating pace of infrastructure associated with large-scale data collection and rapid methodological advancements, particularly neural network architecture improvements. Development and adoption of these advances have lagged in the health care domain largely due to restrictions around public use of data and siloed nature of these datasets with respect to providers, payers and clinical trial sponsors.

Combination effects of compounds are commonly discussed in relation to the treatment of complex and treatment resistance-prone illnesses like cancer, (auto-)immune disorders, and infectious diseases. However, traversing the whole combinatorial space experimentally is infeasible. Therefore, in the last decades, substantially efforts have been made in the field of (predictive) combination modeling. In this reference module, we will discuss the goals of drug combination modeling, the features and endpoints that are used to address combination effect, and the modeling strategies which have been developed. We conclude that data availability is the largest bottle neck in the combination modeling pipeline. Improved data management and availability is essential for drug combination modeling to provide treatment benefit.

Motivation While drug combination therapies are a well-established concept in cancer treatment, identifying novel synergistic combinations is challenging due to the size of combinatorial space. However, computational approaches have emerged as a time- and cost-efficient way to prioritize combinations to test, based on recently available large-scale combination screening data. Recently, Deep Learning has had an impact in many research areas by achieving new state-of-the-art model performance. However, Deep Learning has not yet been applied to drug synergy prediction, which is the approach we present here, termed DeepSynergy. DeepSynergy uses chemical and genomic information as input information, a normalization strategy to account for input data heterogeneity, and conical layers to model drug synergies. Results DeepSynergy was compared to other machine learning methods such as Gradient Boosting Machines, Random Forests, Support Vector Machines and Elastic Nets on the largest publicly available synergy dataset with respect to mean squared error. DeepSynergy significantly outperformed the other methods with an improvement of 7.2% over the second best method at the prediction of novel drug combinations within the space of explored drugs and cell lines. At this task, the mean Pearson correlation coefficient between the measured and the predicted values of DeepSynergy was 0.73. Applying DeepSynergy for classification of these novel drug combinations resulted in a high predictive performance of an AUC of 0.90. Furthermore, we found that all compared methods exhibit low predictive performance when extrapolating to unexplored drugs or cell lines, which we suggest is due to limitations in the size and diversity of the dataset. We envision that DeepSynergy could be a valuable tool for selecting novel synergistic drug combinations. Availability and implementation DeepSynergy is available via www.bioinf.jku.at/software/DeepSynergy. Supplementary information Supplementary data are available at Bioinformatics online.

The epidermal growth factor receptor (EGFR) is a validated therapeutic target for triple-negative breast cancer (TNBC). In the present study, we synthesize novel adamantanyl-based thiadiazolyl pyrazoles by introducing the adamantane ring to thiazolopyrazoline. On the basis of loss of cell viability in TNBC cells, 4-(adamantan-1-yl)-2-(3-(2,4-dichlorophenyl)-5-phenyl-4,5-dihydro-1H-pyrazol-1-yl)thiazole (APP) was identified as a lead compound. Using a Parzen-Rosenblatt Window classifier, APP was predicted to target the EGFR protein, and the same was confirmed by surface plasmon resonance. Further analysis revealed that APP suppressed the phosphorylation of EGFR at Y992, Y1045, Y1068, Y1086, Y1148, and Y1173 in TNBC cells. APP also inhibited the phosphorylation of ERK at Y204 and of STAT3 at Y705, implying that APP downregulates the activity of EGFR downstream effectors. Small interfering RNA mediated depletion of EGFR expression prevented the effect of APP in BT549 and MDA-MB-231 cells, indicating that APP specifically targets the EGFR. Furthermore, APP modulated the expression of the proteins involved in cell proliferation and survival. In addition, APP altered the expression of epithelial-mesenchymal transition related proteins and suppressed the invasion of TNBC cells. Hence, we report a novel and specific inhibitor of the EGFR signaling cascade.

The development of treatments involving combinations of drugs is a promising approach towards combating complex or multifactorial disorders. However, the large number of compound combinations that can be generated, even from small compound collections, means that exhaustive experimental testing is infeasible. The ability to predict the behaviour of compound combinations in biological systems, whittling down the number of combinations to be tested, is therefore crucial. Here, we review the current state-of-the-art in the field of compound combination modelling, with the aim to support the development of approaches that, as we hope, will finally lead to an integration of chemical with systems-level biological information for predicting the effect of chemical mixtures.

The Cancer Dependency Map Portal enables combined analyses of genomic and phenomic data such as genomic alterations and CRISPR gene dependencies but focuses on single genes or cell lines. This is well suited for verifying previously generated hypotheses such as the dependency of cells on PRMT5 in the setting of loss of function mutations in MTAP. To identify additional actionable de-novo vulnerabilities in-silico, we refined the genomic status of 1000 cell lines by 1. focusing on variants (driver mutations and copy number alterations) that have higher prevalence in a given cancer type in TCGA and 2. reclassifying variants as actionable only if they cause loss of function in known tumour suppressor genes or gain of function in oncogenes. This gives a clear line of sight, a more manageable set of variants and is analogous to Mendelian randomization. This approach was applied to lung squamous cell carcinoma to form a binary event matrix for the shortlisted genes and copy number alterations across a panel of lung squamous cell lines. For each of the variants, we used machine learning approaches to define a dependency signature from CRISPR dependencies for the matching cell lines that distinguish altered vs non-altered cell lines. The top ranking dependencies were further examined for biological relevance and potential toxicity, yielding novel targets to follow up on. The approach generalises well and can be applied to any other cancers with unmet needs. All data in this study was derived from public databases. By combining TCGA cancer specific prevalent genomic alterations in tumor suppressor and oncogenes we drew clear line of sight and associated these alterations to CRISPR based vulnerabilities. This yielded a manageable set of alterations, and for each of these we identified a set of essential genes/pathways to use in machine learning. These signatures were further refined by biological enrichment, targetability and toxicity in order to build a process for rational novel target ID generation. Citation Format: Miika Ahdesmäki, Joshua Armenia, Krishna Bulusu, Ben Sidders, Jonathan Dry, Ultan McDermott. Cancer dependency signatures for line of sight genomic alterations in squamous cell lung cancer yield rational targets [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3379.

Phospholipase A2 (PLA2) is known to regulate inflammation and hence it is considered as a validated drug-target by medicinal chemists. In this report, we have identified and considered a highly ranked ligand from the ZINC-drug-like compounds database that targets PLA2 via the MOLPRINT-2D based chemoinformatics drug-design approach. The computationally predicted lead molecule was found to contain a core moiety of a chromene ring, which is well known for its varied biological properties. Here, a novel and efficient retro-synthetic protocol for the synthesis of highly substituted chromene libraries was made. A one-pot synthesis of chromene was carried out using different aromatic primary alcohols, malononitrile and 4-hydroxy coumarin in the presence of a mild oxidant mixture called T3P®–DMSO, followed by a Suzuki coupling reaction to obtain the lead molecules. All of the tested compounds of the chromene series displayed inhibition of the venom PLA2 in the range of 12 to 68 μM. Among the tested compounds, 2-amino-4-(2′-methyl-[1,1′-biphenyl]-4-yl)-5-oxo-4,5-dihydropyrano[3,2-c]chromene-3-carbonitrile (7b) showed maximum inhibitory efficacy against venom PLA2 with an IC50 value of 12.5 μM. Furthermore, the designed PLA2 ligands bound to the active site of venom PLA2, whose binding affinity was comparable to nimesulide, indicating that the chromene moiety containing ligands could be novel lead-structures that serve as anti-inflammatory agents.

Even targeted chemotherapies against solid cancers show a moderate success increasing the need to novel targeting strategies. To address this problem, we designed a systems-level approach investigating the neighbourhood of mutated or differentially expressed cancer-related proteins in four major solid cancers (colon, breast, liver and lung). Using signalling and protein–protein interaction network resources integrated with mutational and expression datasets, we analysed the properties of the direct and indirect interactors (first and second neighbours) of cancer-related proteins, not found previously related to the given cancer type. We found that first neighbours have at least as high degree, betweenness centrality and clustering coefficient as cancer-related proteins themselves, indicating a previously unknown central network position. We identified a complementary strategy for mutated and differentially expressed proteins, where the affect of differentially expressed proteins having smaller network centrality is compensated with high centrality first neighbours. These first neighbours can be considered as key, so far hidden, components in cancer rewiring, with similar importance as mutated proteins. These observations strikingly suggest targeting first neighbours as a novel strategy for disrupting cancer-specific networks. Remarkably, our survey revealed 223 marketed drugs already targeting first neighbour proteins but applied mostly outside oncology, providing a potential list for drug repurposing against solid cancers. For the very central first neighbours, whose direct targeting would cause several side effects, we suggest a cancer-mimicking strategy by targeting their interactors (second neighbours of cancer-related proteins, having a central protein affecting position, similarly to the cancer-related proteins). Hence, we propose to include first neighbours to network medicine based approaches for (but not limited to) anticancer therapies., Cancer: Key proteins hiding in the neighbourhood Cancer is considered a systems disease in which the interactors of cancer-related proteins have a key role, also as targets to fight cancer. New therapeutic approaches are needed to improve success rates and to identify suitable proteins as novel, alternative drug targets. We designed a computational approach, combining mutation and differential expression data with network information, to analyse the interactions of cancer-related proteins in colon, breast, liver and lung cancer. We found that first (direct) neighbours, not linked previously to the given cancer type, are similarly important as mutated proteins known to be involved in cancer development. We found 223 drugs already in the clinic targeting these proteins but not yet used against cancer as their oncology relevance was hidden so far. Our observations open up new strategies for target selection and anti-cancer drug discovery.

PARP1/2 inhibitors (PARPi) are the first approved targeted DNA damage response (DDR) inhibitors and have been shown to have clinical benefit, particularly in tumors harboring mutations in BRCA1/BRCA2 and other genes of homologous recombination repair (HRR). However, resistance to PARPi monotherapy will impact on both the breadth and depth of response. We showed that HRR-mutant TNBC and ovarian cancer patient-derived tumor xenograft (PDX) models frequently exhibit innate or acquired PARP inhibitor resistance and that this resistance is linked to a proficient or reactivated HRR, indicated by the ability to form RAD51 foci. A key component of PARPi mechanism of action results from trapping PARP onto DNA, which has the potential to generate replication stress. Moreover, recent data demonstrate that PARP1, along with components of the HRR, are associated with the re-start and protection of stalled replication forks. Here, we assessed whether combinations of the PARPi olaparib together with inhibitors of the replication stress response (RSR) could reverse PARPi resistance in our PDX cohort and we analyzed the effects on RSR by immunofluorescence and immunoblotting. Our data demonstrate that 7/27 models exhibit WEE1i (AZD1775) single agent activity measured as tumor regressions (≤ -30% change in tumor volume), all of which were PARPi resistant. Of the PARPi and WEE1i resistant models, 5 responded to combination treatment and demonstrated a significant increase in the RSR markers pRPA32 and pan-γH2AX compared to either single agent treatment alone. For monotherapy ATRi treatment, there were three models showing tumor regression with AZD6738, all harbored ATM mutations, two also responding to WEE1i monotherapy and one responded to olaparib monotherapy. There were an additional two models that responded to the combination of PARPi plus ATRi, and one of these also responded to the WEE1i/PARPi combination. Together, our analysis demonstrates that by targeting the replication stress response we could cause tumor regression in 13/20 PARPi resistant PDX models and in 6 of these cases the response required PARP inhibition, with DDR signalling indicating that the PARPi was impacting on the RSR. Further insights will be presented into which genetic backgrounds and acquired PARPi resistant mechanisms are reversed by targeting either WEE1 or ATR, thus highlighting the potential for how PARPi resistance can be reversed by targeting alternative DDR dependencies. Citation Format: Mark J. O'Connor, Cristina Cruz, Marta Castroviejo-Bermejo, Urszula M. Polanska, Gemma N. Jones, Anderson Wang, Zhongwu Lai, Josep Forment, Krishna Bulusu, Alba Llop-Guevara, Brian Dougherty, Cristina Saura, Rachel Brough, Chris J. Lord, Alejandra Bruna, Carlos Caldas, Stephen Fawell, J Carl Barrett, Susan E. Critchlow, Judith Balmaña, Elaine Cadogan, Violeta Serra. Reversing PARP inhibitor resistance by targeting the replication stress response [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 932.

9020 Background: In the Phase III FLAURA trial (NCT02296125), osimertinib, a third generation EGFR-TKI, showed superior efficacy to comparator EGFR-TKIs as first line treatment for EGFR mutation-positive (EGFRm) advanced NSCLC. In an exploratory analysis, we investigated clinical outcomes associated with detection of plasma EGFRm at 3 or 6 weeks (wks) after start of treatment. Methods: Treatment-naïve patients (pts) with EGFRm (ex19del or L858R) locally advanced or metastatic NSCLC were randomized 1:1 to receive osimertinib 80 mg once daily (QD) or comparator EGFR-TKIs (gefitinib 250 mg QD or erlotinib 150 mg QD). Plasma EGFR mutation analysis was conducted at baseline (BL), wks 3 and 6 by droplet digital PCR. Clearance was defined as undetectable levels of EGFRm in ctDNA at wks 3/6, where they were detectable at BL. Progression-free survival (PFS) was investigated based on early clearance of EGFRm. Results: In total 489/556 (88%) pts (osimertinib: 244/279; comparator: 245/277) had evaluable ctDNA at BL and wks 3/6. Of these, 342/489 (70%; osimertinib: 168/244; comparator: 174/245) had detectable BL EGFRm and were included in this analysis. See table. Conclusions: Clearance of plasma EGFRm after 3/6 wks of EGFR-TKI therapy was associated with a numerical improvement in PFS. The efficacy of osimertinib was superior to comparator EGFR-TKIs regardless of clearance status. Clinical trial information: NCT02296125. [Table: see text]

Selective modulators of the γ-amino butyric acid (GABA

Selective modulators of the γ-amino butyric acid (GABAA) family of receptors have the potential to treat a range of disease states related to cognition, pain, and anxiety. While the development of various α subunit-selective modulators is currently underway for the treatment of anxiety disorders, a mechanistic understanding of the correlation between their bioactivity and efficacy, based on ligand–target interactions, is currently still lacking. In order to alleviate this situation, in the current study we have analyzed, using ligand- and structure-based methods, a data set of 5440 GABAA modulators. The Spearman correlation (ρ) between binding activity and efficacy of compounds was calculated to be 0.008 and 0.31 against the α1 and α2 subunits of GABA receptor, respectively; in other words, the compounds had little diversity in structure and bioactivity, but they differed significantly in efficacy. Two compounds were selected as a case study for detailed interaction analysis due to the small difference in their structures and affinities (ΔpKi(comp1_α1 – comp2_α1) = 0.45 log units, ΔpKi(comp1_α2 – comp2_α2) = 0 log units) as compared to larger relative efficacies (ΔRE(comp1_α1 – comp2_α1) = 1.03, ΔRE(comp1_α2 – comp2_α2) = 0.21). Docking analysis suggested that His-101 is involved in a characteristic interaction of the α1 receptor with both compounds 1 and 2. Residues such as Phe-77, Thr-142, Asn-60, and Arg-144 of the γ chain of the α1γ2 complex also showed interactions with heterocyclic rings of both compounds 1 and 2, but these interactions were disturbed in the case of α2γ2 complex docking results. Binding pocket stability analysis based on molecular dynamics identified three substitutions in the loop C region of the α2 subunit, namely, G200E, I201T, and V202I, causing a reduction in the flexibility of α2 compared to α1. These amino acids in α2, as compared to α1, were also observed to decrease the vibrational and dihedral entropy and to increase the hydrogen bond content in α2 in the apo state. However, freezing of both α1 and α2 was observed in the ligand-bound state, with an increased number of internal hydrogen bonds and increased entropy. Therefore, we hypothesize that the amino acid differences in the loop C region of α2 are responsible for conformational changes in the protein structure compared to α1, as well as for the binding modes of compounds and hence their functional signaling.

Author(s): Fechner, Uli; de Graaf, Chris; Torda, Andrew E; Gussregen, Stefan; Evers, Andreas; Matter, Hans; Hessler, Gerhard; Richmond, Nicola J; Schmidtke, Peter; Segler, Marwin HS; Waller, Mark P; Pleik, Stefanie; Shea, Joan-Emma; Levine, Zachary; Mullen, Ryan; van den Broek, Karina; Epple, Matthias; Kuhn, Hubert; Truszkowski, Andreas; Zielesny, Achim; Fraaije, Johannes Hans; Gracia, Ruben Serral; Kast, Stefan M; Bulusu, Krishna C; Bender, Andreas; Yosipof, Abraham; Nahum, Oren; Senderowitz, Hanoch; Krotzky, Timo; Schulz, Robert; Wolber, Gerhard; Bietz, Stefan; Rarey, Matthias; Zimmermann, Markus O; Lange, Andreas; Ruff, Manuel; Heidrich, Johannes; Onlia, Ionut; Exner, Thomas E; Boeckler, Frank M; Bermudez, Marcel; Firaha, Dzmitry S; Holloczki, Oldamur; Kirchner, Barbara; Tautermann, Christofer S; Volkamer, Andrea; Eid, Sameh; Turk, Samo; Rippmann, Friedrich; Fulle, Simone; Saleh, Noureldin; Saladino, Giorgio; Gervasio, Francesco L; Haensele, Elke; Banting, Lee; Whitley, David C; Oliveira Santos, Jana Sopkova-de; Bureau, Ronan; Clark, Timothy; Sandmann, Achim; Lanig, Harald; Kibies, Patrick; Heil, Jochen; Hoffgaard, Franziska; Frach, Roland; Engel, Julian; Smith, Steven; Basu, Debjit; Rauh, Daniel; Kohlbacher, Oliver; Boeckler, Frank M; Essex, Jonathan W; Bodnarchuk, Michael S; Ross, Gregory A; Finkelmann, Arndt R; Goller, Andreas H; Schneider, Gisbert; Husch, Tamara; Schutter, Christoph; Balducci, Andrea; Korth, Martin; Ntie-Kang, Fidele; Gunther, Stefan; Sippl, Wolfgang; Mbaze, Luc Meva'a

This abstract has been withheld from publication due to its inclusion in the AACR Annual Meeting 2018 Official Press Program. It will be posted online following its presentation. Citation Format: Jonathan R. Dry, Michael P. Menden, Krishna Bulusu, Justin Guinney, Julio Saez-Rodriguez. A large cancer pharmacogenomics combination screen powering crowd-sourced advancement of computational drug synergy predictions [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3886.

Drugs such as necopidem, saripidem, alpidem, zolpidem, and olprinone contain nitrogen-containing bicyclic, condensed-imidazo[1,2-α]pyridines as bioactive scaffolds. In this work, we report a high-yield one pot synthesis of 1-(2-methyl-8-aryl-substitued-imidazo[1,2-α]pyridin-3-yl)ethan-1-onefor the first-time. Subsequently, we performed in silico mode-of-action analysis and predicted that the synthesized imidazopyridines targets Phospholipase A2 (PLA2). In vitro analysis confirmed the predicted target PLA2 for the novel imidazopyridine derivative1-(2-Methyl-8-naphthalen-1-yl-imidazo [1,2-α]pyridine-3-yl)-ethanone (compound 3f) showing significant inhibitory activity towards snake venom PLA2 with an IC50 value of 14.3 μM. Evidently, the molecular docking analysis suggested that imidazopyridine compound was able to bind to the active site of the PLA2 with strong affinity, whose affinity values are comparable to nimesulide. Furthermore, we estimated the potential for oral bioavailability by Lipinski's Rule of Five. Hence, it is concluded that the compound 3f could be a lead molecule against snake venom PLA2.

Signal transducer and activator of transcription 3 (STAT3) is a transcription factor that regulates genes involved in cell growth, proliferation, and survival, and given its association with many types of cancers, it has recently emerged as a promising target for therapy. In this work, we present the synthesis of N-substituted azaspirane derivatives and their biological evaluation against hepatocellular carcinoma (HCC) cells (IC50 = 7.3 μm), thereby identifying 2-(1-(4-(2-cyanophenyl)1-benzyl-1H-indol-3-yl)-5-(4-methoxy-phenyl)-1-oxa-3-azaspiro(5,5) undecane (CIMO) as a potent inhibitor of the JAK-STAT pathway with selectivity over normal LO2 cells (IC50 > 100 μm). The lead compound, CIMO, suppresses proliferation of HCC cells and achieves this effect by reducing both constitutive and inducible phosphorylation of JAK1, JAK2, and STAT3. Interestingly, CIMO displayed inhibition of Tyr-705 phosphorylation, which is required for nuclear translocation of STAT3, but it has no effect on Ser-727 phosphorylation. CIMO accumulates cancer cells in the sub-G1 phase and decreases STAT3 in the nucleus and thereby causes down-regulation of genes regulated via STAT3. Suppression of STAT3 phosphorylation by CIMO and knockdown of STAT3 mRNA using siRNA transfection displayed a similar effect on the viability of HCC cells. Furthermore, CIMO significantly decreased the tumor development in an orthotopic HCC mouse model through the modulation of phospho-STAT3, Ki-67, and cleaved caspase-3 in tumor tissues. Thus, CIMO represents a chemically novel and biologically in vitro and in vivo validated compound, which targets the JAK-STAT pathway as a potential cancer treatment.

A new, simple, and microwave-assisted, solution-phase T3P®-DMSO mediated method for the preparation of a novel class of estrogen receptor alpha (ERα) ligands based on the 2-phenylquinoline scaffold was developed. Furthermore, the novel ERα ligands were tested for their bioactivity against ERα-positive and ERα-negative cell lines. The ligand (entry 4), with amine and nitro group substitution at C4 position, displayed significant cytotoxicity against MCF-7 and HepG2 cells with an IC50 value of 6 and 11 μM, respectively. On the other hand, ERα-negative cells displayed resistance to quinolines induced cytotoxicity with an IC50 value >100 Mm and they does not induce cytotoxicity in normal breast epithelial cells. Molecular docking analyses suggest a consistent binding mode for these ERα ligands in the ligand binding domain of the human ERα and predict the ligands to occupy the hydrophobic cavity in a similar fashion as estradiol or GW2368.

canSAR (http://cansar.icr.ac.uk) is a public integrative cancer-focused knowledgebase for the support of cancer translational research and drug discovery. Through the integration of biological, pharmacological, chemical, structural biology and protein network data, it provides a single information portal to answer complex multidisciplinary questions including--among many others--what is known about a protein, in which cancers is it expressed or mutated, and what chemical tools and cell line models can be used to experimentally probe its activity? What is known about a drug, its cellular sensitivity profile and what proteins is it known to bind that may explain unusual bioactivity? Here we describe major enhancements to canSAR including new data, improved search and browsing capabilities and new target, cancer cell line, protein family and 3D structure summaries and tools.

Large-scale systematic analyses of the underlying genetics and proteomics of cancer, as well as the systematic mapping of effects of RNAi and chemical screens on different cancer cells are empowering cancer drug discovery and paving the way to wider stratified medicine approaches to the treatment of cancer. However, this deluge of data poses an equally large challenge of effectively integrating such massive and heterogeneous datasets in a manner that allows researchers to benefit from a multidisciplinary view across different. To address this need, we have developed a multidisciplinary cancer platform, canSAR (http://cansar.icr.ac.uk). It integrates millions of experimental datapoints ranging from chemical and RNAi screening, to expression, drug ADMET studies and 3D structural complexes. In this presentation we describe canSAR and show examples of how it has been utilized in the knowledge-driven prioritization of targets for drug discovery. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2011 Nov 12-16; San Francisco, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2011;10(11 Suppl):Abstract nr CN01-02.

The effectiveness of most cancer targeted therapies is short lived since tumors evolve and develop resistance. Combinations of drugs offer the potential to overcome resistance, however the number of possible combinations is vast necessitating data-driven approaches to find optimal treatments tailored to a patient’s tumor. AstraZeneca carried out 11,576 experiments on 910 drug combinations across 85 cancer cell lines, recapitulating in vivo response profiles. These data, the largest openly available screen, were hosted by DREAM alongside deep molecular characterization from the Sanger Institute for a Challenge to computationally predict synergistic drug pairs and associated biomarkers. 160 teams participated to provide the most comprehensive methodological development and subsequent benchmarking to date. Winning methods incorporated prior knowledge of putative drug target interactions. For >60% of drug combinations synergy was reproducibly predicted with an accuracy matching biological replicate experiments, however 20% of drug combinations were poorly predicted by all methods. Genomic rationale for synergy predictions were identified, including antagonism unique to combined PIK3CB/D inhibition with the ADAM17 inhibitor where synergy is seen with other PI3K pathway inhibitors. All data, methods and code are freely available as a resource to the community.