CBFA2T3::GLIS2 pediatric acute megakaryoblastic leukemia is sensitive to BCL-XL inhibition by navitoclax and DT2216

ABSTRACT

Acute megakaryoblastic leukemia (AMKL) is a rare, developmentally restricted, and highly lethal cancer of early childhood. The paucity and hypocellularity (due to myelofibrosis) of primary patient samples hamper the discovery of cell- and genotype-specific treatments. AMKL is driven by mutually exclusive chimeric fusion oncogenes in two-thirds of the cases, with CBFA2T3::GLIS2 (CG2) and NUP98 fusions (NUP98r) representing the highest-fatality subgroups. We established CD34+ cord blood-derived CG2 models (n = 6) that sustain serial transplantation and recapitulate human leukemia regarding immunophenotype, leukemia-initiating cell frequencies, comutational landscape, and gene expression signature, with distinct upregulation of the prosurvival factor B-cell lymphoma 2 (BCL2). Cell membrane proteomic analyses highlighted CG2 surface markers preferentially expressed on leukemic cells compared with CD34+ cells (eg, NCAM1 and CD151). AMKL differentiation block in the mega-erythroid progenitor space was confirmed by single-cell profiling. Although CG2 cells were rather resistant to BCL2 genetic knockdown or selective pharmacological inhibition with venetoclax, they were vulnerable to strategies that target the megakaryocytic prosurvival factor BCL-XL (BCL2L1), including in vitro and in vivo treatment with BCL2/BCL-XL/BCL-W inhibitor navitoclax and DT2216, a selective BCL-XL proteolysis-targeting chimera degrader developed to limit thrombocytopenia in patients. NUP98r AMKL were also sensitive to BCL-XL inhibition but not the NUP98r monocytic leukemia, pointing to a lineage-specific dependency. Navitoclax or DT2216 treatment in combination with low-dose cytarabine further reduced leukemic burden in mice. This work extends the cellular and molecular diversity set of human AMKL models and uncovers BCL-XL as a therapeutic vulnerability in CG2 and NUP98r AMKL.

Recent Advances in Cancer Drug Discovery Through the Use of Phenotypic Reporter Systems, Connectivity Mapping, and Pooled CRISPR Screening

Multi-omic approaches offer an unprecedented overview of the development, plasticity, and resistance of cancer. However, the translation from anti-cancer compounds identified in vitro to clinically active drugs have a notoriously low success rate. Here, we review how technical advances in cell culture, robotics, computational biology, and development of reporter systems have transformed drug discovery, enabling screening approaches tailored to clinically relevant functional readouts (e.g., bypassing drug resistance). Illustrating with selected examples of “success stories,” we describe the process of phenotype-based high-throughput drug screening to target malignant cells or the immune system. Second, we describe computational approaches that link transcriptomic profiling of cancers with existing pharmaceutical compounds to accelerate drug repurposing. Finally, we review how CRISPR-based screening can be applied for the discovery of mechanisms of drug resistance and sensitization. Overall, we explore how the complementary strengths of each of these approaches allow them to transform the paradigm of pre-clinical drug development.

Acute inflammatory response via neutrophil activation protects against the development of chronic pain

The transition from acute to chronic pain is critically important but not well understood. Here, we investigated the pathophysiological mechanisms underlying the transition from acute to chronic low back pain (LBP) and performed transcriptome-wide analysis in peripheral immune cells of 98 participants with acute LBP, followed for 3 months. Transcriptomic changes were compared between patients whose LBP was resolved at 3 months with those whose LBP persisted. We found thousands of dynamic transcriptional changes over 3 months in LBP participants with resolved pain but none in those with persistent pain. Transient neutrophil-driven up-regulation of inflammatory responses was protective against the transition to chronic pain. In mouse pain assays, early treatment with a steroid or nonsteroidal anti-inflammatory drug (NSAID) also led to prolonged pain despite being analgesic in the short term; such a prolongation was not observed with other analgesics. Depletion of neutrophils delayed resolution of pain in mice, whereas peripheral injection of neutrophils themselves, or S100A8/A9 proteins normally released by neutrophils, prevented the development of long-lasting pain induced by an anti-inflammatory drug. Analysis of pain trajectories of human subjects reporting acute back pain in the UK Biobank identified elevated risk of pain persistence for subjects taking NSAIDs. Thus, despite analgesic efficacy at early time points, the management of acute inflammation may be counterproductive for long-term outcomes of LBP sufferers.

Humoral Immunity to Allogeneic Immunoproteasome-Expressing Mesenchymal Stromal Cells Requires Efferocytosis by Endogenous Phagocytes

The extensive use of mesenchymal stromal cells (MSCs) over the last decade has revolutionized modern medicine. From the delivery of pharmacological proteins to regenerative medicine and immune modulation, these cells have proven to be highly pleiotropic and responsive to their surrounding environment. Nevertheless, their role in promoting inflammation has been fairly limited by the questionable use of interferon-gamma, as this approach has also been proven to enhance the cells' immune-suppressive abilities. Alternatively, we have previously shown that de novo expression of the immunoproteasome (IPr) complex instills potent antigen cross-presentation capabilities in MSCs. Interestingly, these cells were found to express the major histocompatibility class (MHC) II protein, which prompted us to investigate their ability to stimulate humoral immunity. Using a series of in vivo studies, we found that administration of allogeneic ovalbumin (OVA)-pulsed MSC-IPr cells elicits a moderate antibody titer, which was further enhanced by the combined use of pro-inflammatory cytokines. The generated antibodies were functional as they blocked CD4 T-cell activation following their co-culture with OVA-pulsed MSC-IPr and mitigated E.G7 tumor growth in vivo. The therapeutic potency of MSC-IPr was, however, dependent on efferocytosis, as phagocyte depletion prior to vaccination abrogated MSC-IPr-induced humoral responses while promoting their survival in the host. In contrast, antibody-mediated neutralization of CD47, a potent "do not eat me signal", enhanced antibody titer levels. These observations highlight the major role played by myeloid cells in supporting antibody production by MSC-IPr and suggest that the immune outcome is dictated by a net balance between efferocytosis-stimulating and -inhibiting signals.

UM171A-induced ROS promote antigen cross-presentation of immunogenic peptides by bone marrow-derived mesenchymal stromal cells

Background

Mesenchymal stromal cells (MSCs) have been extensively used in the clinic due to their exquisite tissue repair capacity. However, they also hold promise in the field of cellular vaccination as they can behave as conditional antigen presenting cells in response to interferon (IFN)-gamma treatment under a specific treatment regimen. This suggests that the immune function of MSCs can be pharmacologically modulated. Given the capacity of the agonist pyrimido-indole derivative UM171a to trigger the expression of various antigen presentation-related genes in human hematopoietic progenitor cells, we explored the potential use of UM171a as a means to pharmacologically instill and/or promote antigen presentation by MSCs.

Methods

Besides completing a series of flow-cytometry-based phenotypic analyses, several functional antigen presentation assays were conducted using the SIINFEKL-specific T-cell clone B3Z. Anti-oxidants and electron transport chain inhibitors were also used to decipher UM171a’s mode of action in MSCs. Finally, the potency of UM171a-treated MSCs was evaluated in the context of therapeutic vaccination using immunocompetent C57BL/6 mice with pre-established syngeneic EG.7T-cell lymphoma.

Results

Treatment of MSCs with UM171a triggered potent increase in H2-K^b cell surface levels along with the acquisition of antigen cross-presentation abilities. Mechanistically, such effects occurred in response to UM171a-mediated production of mitochondrial-derived reactive oxygen species as their neutralization using anti-oxidants or Antimycin-A mitigated MSCs’ ability to cross-present antigens. Processing and presentation of the immunogenic ovalbumin-derived SIINFEKL peptide was caused by de novo expression of the Psmb8 gene in response to UM171a-triggered oxidative stress. When evaluated for their anti-tumoral properties in the context of therapeutic vaccination, UM171a-treated MSC administration to immunocompetent mice with pre-established T-cell lymphoma controlled tumor growth resulting in 40% survival without the need of additional supportive therapy and/or standard-of-care.

Conclusions

Altogether, our findings reveal a new immune-related function for UM171a and clearly allude to a direct link between UM171a-mediated ROS induction and antigen cross-presentation by MSCs. The fact that UM171a treatment modulates MSCs to become antigen-presenting cells without the use of IFN-gamma opens-up a new line of investigation to search for additional agents capable of converting immune-suppressive MSCs to a cellular tool easily adaptable to vaccination.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s13287-021-02693-z.

Engineering immunoproteasome-expressing mesenchymal stromal cells: A potent cellular vaccine for lymphoma and melanoma in mice

Dendritic cells (DCs) excel at cross-presenting antigens, but their effectiveness as cancer vaccine is limited. Here, we describe a vaccination approach using mesenchymal stromal cells (MSCs) engineered to express the immunoproteasome complex (MSC-IPr). Such modification instills efficient antigen cross-presentation abilities associated with enhanced major histocompatibility complex class I and CD80 expression, de novo production of interleukin-12, and higher chemokine secretion. This cross-presentation capacity of MSC-IPr is highly dependent on their metabolic activity. Compared with DCs, MSC-IPr hold the ability to cross-present a vastly different epitope repertoire, which translates into potent re-activation of T cell immunity against EL4 and A20 lymphomas and B16 melanoma tumors. Moreover, therapeutic vaccination of mice with pre-established tumors efficiently controls cancer growth, an effect further enhanced when combined with antibodies targeting PD-1, CTLA4, LAG3, or 4-1BB under both autologous and allogeneic settings. Therefore, MSC-IPr constitute a promising subset of non-hematopoietic antigen-presenting cells suitable for designing universal cell-based cancer vaccines.

Bioexploration and Phylogenetic Placement of Entomopathogenic Fungi of the Genus Beauveria in Soils of Lebanon Cedar Forests

The cedar forests of Lebanon have been threatened by the outbreak caused by climate change of a web-spinning sawfly, Cephalcia tannourinensis (Hymenoptera: Pamphiliidae), which negatively impacted the survival of one of the oldest tree species on earth. In this study, we investigated the occurrence of naturally soil-inhabiting entomopathogenic fungi for their role in containing the massive outbreak of this insect. We used a combination of fungal bioexploration methods, including insect bait and selective media. Morphological features and multilocus phylogeny-based on Sanger sequencing of the transcripts encoding the translation elongation factor 1-alpha (TEF-α), RNA polymerase II second largest subunit (RBP2), and the nuclear intergenic region (Bloc) were used for species identification. The occurrence rate of entomopathogenic fungi (EPF) varied with location, soil structure, forest structure, and isolation method. From 15 soil samples positive for fungal occurrence, a total of 249 isolates was obtained from all locations using different isolation methods. The phylogenetic analysis confirmed the existence of two novel indigenous species: Beauveria tannourinensis sp. nov. and Beauveria ehdenensis sp. nov. In conclusion, the present survey was successful (1) in optimizing the isolation methods for EPF, (2) investigating the natural occurrence of Beauveria spp. in outbreak areas of C. tannourinensis, and (3) in characterizing the presence of new Beauveria species in Lebanese cedar forest soil.

Repurposing Cilostazol for Raynaud's Phenomenon

Raynaud 's Phenomenon (RP) results from exaggerated cold-induced vasoconstriction. RP patients suffer from vasospastic attacks and compromised digital blood perfusion leading to a triple color change at the level the fingers. Severe RP may cause ulcers and threaten tissue viability. Many drugs have been used to alleviate the symptoms of RP. These include calcium-channel blockers, cGMP-specific phosphodiesterase type 5 inhibitors, prostacyclin analogs, and angiotensin receptor blockers. Despite their variety, these drugs do not treat RP but rather alleviate its symptoms. To date, no drug for RP has been yet approved by the U.S Food and Drugs Administration. Cilostazol is a selective inhibitor of phosphodiesterase-III, originally prescribed to treat intermittent claudication. Owing to its antiplatelet and vasodilating properties, cilostazol is being repurposed as a potential drug for RP. This review focuses on the different lines of action of cilostazol serving to enhance blood perfusion in RP patients.

Thymoproteasome-Expressing Mesenchymal Stromal Cells Confer Protective Anti-Tumor Immunity via Cross-Priming of Endogenous Dendritic Cells

Proteasomes are complex macromolecular structures existing in various forms to regulate a myriad of cellular processes. Besides degrading unwanted or misfolded proteins (proteostasis), distinct immune functions were ascribed for the immunoproteasome and thymoproteasome (TPr) complexes. For instance, antigen degradation during ongoing immune responses mainly relies on immunoproteasome activity, whereas intrathymic CD8 T-cell development requires peptide generation by the TPr complex. Despite these substantial differences, the functional contribution of the TPr to peripheral T-cell immunity remains ill-defined. We herein explored whether the use of mesenchymal stromal cells (MSCs) engineered to exhibit altered proteasomal activity through de novo expression of the TPr complex can be exploited as a novel anti-cancer vaccine capable of triggering potent CD8 T-cell activation. Phenotypic and molecular characterization of MSC-TPr revealed a substantial decrease in MHCI (H2-K^b and H2-D^d) expression along with elevated secretion of various chemokines (CCL2, CCL9, CXCL1, LIX, and CX3CL1). In parallel, transcriptomic analysis pinpointed the limited ability of MSC-TPr to present endogenous antigens, which is consistent with their low expression levels of the peptide-loading proteins TAP, CALR, and PDAI3. Nevertheless, MSC-TPr cross-presented peptides derived from captured soluble proteins. When tested for their protective capacity, MSC-TPr triggered modest anti-tumoral responses despite efficient generation of effector memory CD4 and CD8 T cells. In contrast, clodronate administration prior to vaccination dramatically enhanced the MSC-TPr-induced anti-tumoral immunity clearly highlighting a refractory role mediated by phagocytic cells. Thus, our data elute to a DC cross-priming-dependant pathway in mediating the therapeutic effect of MSC-TPr.

Integrative Transcriptome Analyses Empower the Anti-COVID-19 Drug Arsenal

The beginning of the 21st century has been marked by three distinct waves of zoonotic coronavirus outbreaks into the human population. The COVID-19 (coronavirus disease 2019) pandemic is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and emerged as a global threat endangering the livelihoods of millions worldwide. Currently, and despite collaborative efforts, diverse therapeutic strategies from ongoing clinical trials are still debated. To address the need for such an immediate call of action, we leveraged the largest dataset of drug-induced transcriptomic perturbations, public SARS-CoV-2 transcriptomic datasets, and expression profiles from normal lung transcriptomes. Most importantly, our unbiased systems biology approach prioritized more than 50 repurposable drug candidates (e.g., corticosteroids, Janus kinase and Bruton kinase inhibitors). Further clinical investigation of these FDA-approved candidates as monotherapy or in combination with an antiviral regimen (e.g., remdesivir) could lead to promising outcomes in patients with COVID-19.

TACIMA-218: A Novel Pro-Oxidant Agent Exhibiting Selective Antitumoral Activity

We report the discovery, via a unique high-throughput screening strategy, of a novel bioactive anticancer compound: Thiol Alkylating Compound Inducing Massive Apoptosis (TACIMA)-218. We demonstrate that this molecule engenders apoptotic cell death in genetically diverse murine and human cancer cell lines, irrespective of their p53 status, while sparing normal cells. TACIMA-218 causes oxidative stress in the absence of protective antioxidants normally induced by Nuclear factor erythroid 2-related factor 2 activation. As such, TACIMA-218 represses RNA translation and triggers cell signaling cascade alterations in AKT, p38, and JNK pathways. In addition, TACIMA-218 manifests thiol-alkylating properties resulting in the disruption of redox homeostasis along with key metabolic pathways. When administered to immunocompetent animals as a monotherapy, TACIMA-218 has no apparent toxicity and induces complete regression of pre-established lymphoma and melanoma tumors. In sum, TACIMA-218 is a potent oxidative stress inducer capable of selective cancer cell targeting.

A comparison of artificial intelligence-based algorithms for the identification of patients with depressed right ventricular function from 2-dimentional echocardiography parameters and clinical features

Background

Recognizing low right ventricular (RV) function from 2-dimentiontial echocardiography (2D-ECHO) is challenging when parameters are contradictory. We aim to develop a model to predict low RV function integrating the various 2D-ECHO parameters in reference to cardiac magnetic resonance (CMR)-the gold standard.

Methods

We retrospectively identified patients who underwent a 2D-ECHO and a CMR within 3 months of each other at our institution (American University of Beirut Medical Center). We extracted three parameters (TAPSE, S' and FAC_RV) that are classically used to assess RV function. We have assessed the ability of 2D-ECHO derived parameters and clinical features to predict RV function measured by the gold standard CMR. We compared outcomes from four machine learning algorithms, widely used in the biomedical community to solve classification problems.

Results

One hundred fifty-five patients were identified and included in our study. Average age was 43±17.1 years old and 52/156 (33.3%) were females. According to CMR, 21 patients were identified to have RV dysfunction, with an RVEF of 34.7%±6.4%, as opposed to 54.7%±6.7% in the normal RV population (P<0.0001). The Random Forest model was able to detect low RV function with an AUC =0.80, while general linear regression performed poorly in our population with an AUC of 0.62.

Conclusions

In this study, we trained and validated an ML-based algorithm that could detect low RV function from clinical and 2D-ECHO parameters. The algorithm has two advantages: first, it performed better than general linear regression, and second, it integrated the various 2D-ECHO parameters.

A novel TRAF3IP2 variant causing familial scarring alopecia with mixed features of discoid lupus erythematosus and folliculitis decalvans

Discoid lupus erythematosus (DLE) is an autoimmune disorder with a poorly defined etiology. Despite epidemiologic gender and ethnic biases, a clear genetic basis for DLE remains elusive. In this study, we used exome and RNA sequencing technologies to characterize a consanguineous Lebanese family with four affected individuals who presented with classical scalp DLE and generalized folliculitis. Our results unraveled a novel biallelic variant c.1313C > A leading to a missense substitution p.(Thr438Asn) in TRAF3IP2(NM_147200.3). Expression studies in cultured cells revealed mis-localization of the mutated protein. Functional characterization of the mutated protein showed significant reduction in the physical interaction with the interleukin 17-A receptor (IL17RA), while interaction with TRAF6 was unaffected. By conducting a differential genome-wide transcriptomics analysis between affected and non-affected individuals, we showed that the hair follicle differentiation pathway is drastically suppressed, whereas cytokine and inflammation responses are significantly upregulated. Furthermore, our results were highly concordant with molecular signatures in patients with DLE from a public dataset. In conclusion, this is the first report on a new putative role for TRAF3IP2 in the etiology of DLE. The identified molecular features associated with this gene could pave the way for better DLE-targeted treatment.

A Novel Sulfonyl-Based Small Molecule Exhibiting Anti-cancer Properties

Phenotypic screening is an ideal strategy for the discovery of novel bioactive molecules. Using a customized high-throughput screening (HTS) assay employing primary T lymphocytes, we screened a small library of 4,398 compounds with unknown biological function/target to identify compounds eliciting immunomodulatory properties and discovered a sulfonyl-containing hit, we named InhiTinib. This compound inhibited interferon (IFN)-gamma production and proliferation of primary CD3⁺ T cells without inducing cell death. In contrast, InhiTinib triggered apoptosis in several murine and human cancer cell lines. Besides, the compound was well tolerated by immunocompetent mice, triggered tumor regression in animals with pre-established EL4 T-cell lymphomas, and prolonged the overall survival of mice harboring advanced tumors. Altogether, our data demonstrate the anti-cancer properties of InhiTinib, which can henceforth bridge to wider-scale biochemical and clinical tests following further in-depth pharmacodynamic studies.

gem-Difluorobisarylic derivatives: design, synthesis and anti-inflammatory effect

Introduction

New fluorinated diaryl ethers and bisarylic ketones were designed and evaluated for their anti-inflammatory effects in primary macrophages.

Methods

The synthesis of the designed molecules started from easily accessible and versatile gem-difluoro propargylic derivatives. The desired aromatic systems were obtained using Diels-Alder/aromatization sequences and this was followed by Pd-catalyzed coupling reactions and, when required, final functionalization steps. Both direct inhibitory effects on cyclooxygenase-1 or -2 activities, protein expression of cyclooxygenase-2 and nitric oxide synthase-II and the production of prostaglandin E₂, the pro-inflammatory nitric oxide and interleukin-6 were evaluated in primary murine bone marrow-derived macrophages in response to lipopolysaccharide. Docking of the designed molecules in cyclooxygenase-1 or -2 was performed.

Results

Only fluorinated compounds exerted anti-inflammatory activities by lowering the secretion of interleukin-6, nitric oxide, and prostaglandin E₂, and decreasing the protein expression of inducible nitric oxide synthase and cyclooxygenase-2 in mouse primary macrophages exposed to lipopolysaccharide, as well as cyclooxygenase activity for some inhibitors with different efficiencies depending on the R-groups. Docking observation suggested an inhibitory role of cyclooxygenase-1 or -2 for compounds A3, A4 and A5 in addition to their capacity to inhibit nitrite, interleukin-6, and nitric oxide synthase-II and cyclooxygenase-2 expression.

Conclusion

The new fluorinated diaryl ethers and bisarylic ketones have anti-inflammatory effects in macrophages. These fluorinated compounds have improved potential anti-inflammatory properties due to the fluorine residues in the bioactive molecules.

SIGN: similarity identification in gene expression

MOTIVATION

High-throughput molecular profiles of human cells have been used in predictive computational approaches for stratification of healthy and malignant phenotypes and identification of their biological states. In this regard, pathway activities have been used as biological features in unsupervised and supervised learning schemes.

RESULTS

We developed SIGN (Similarity Identification in Gene expressioN), a flexible open-source R package facilitating the use of pathway activities and their expression patterns to identify similarities between biological samples. We defined a new measure, the transcriptional similarity coefficient, which captures similarity of gene expression patterns, instead of quantifying overall activity, in biological pathways between the samples. To demonstrate the utility of SIGN in biomedical research, we establish that SIGN discriminates subtypes of breast tumors and patients with good or poor overall survival. SIGN outperforms the best models in DREAM challenge in predicting survival of breast cancer patients using the data from the Molecular Taxonomy of Breast Cancer International Consortium. In summary, SIGN can be used as a new tool for interrogating pathway activity and gene expression patterns in unsupervised and supervised learning schemes to improve prognostic risk estimation for cancer patients by the biomedical research community.

AVAILABILITY AND IMPLEMENTATION

An open-source R package is available (https://cran.r-project.org/web/packages/SIGN/).

PharmacoDB: an integrative database for mining in vitro anticancer drug screening studies

Recent cancer pharmacogenomic studies profiled large panels of cell lines against hundreds of approved drugs and experimental chemical compounds. The overarching goal of these screens is to measure sensitivity of cell lines to chemical perturbations, correlate these measures to genomic features, and thereby develop novel predictors of drug response. However, leveraging these valuable data is challenging due to the lack of standards for annotating cell lines and chemical compounds, and quantifying drug response. Moreover, it has been recently shown that the complexity and complementarity of the experimental protocols used in the field result in high levels of technical and biological variation in the in vitro pharmacological profiles. There is therefore a need for new tools to facilitate rigorous comparison and integrative analysis of large-scale drug screening datasets. To address this issue, we have developed PharmacoDB (pharmacodb.pmgenomics.ca), a database integrating the largest cancer pharmacogenomic studies published to date. Here, we describe how the curation of cell line and chemical compound identifiers maximizes the overlap between datasets and how users can leverage such data to compare and extract robust drug phenotypes. PharmacoDB provides a unique resource to mine a compendium of curated cancer pharmacogenomic datasets that are otherwise disparate and difficult to integrate.

Tissue specificity of in vitro drug sensitivity

Objectives

We sought to investigate the tissue specificity of drug sensitivities in large-scale pharmacological studies and compare these associations to those found in drug clinical indications.

Materials and Methods

We leveraged the curated cell line response data from PharmacoGx and applied an enrichment algorithm on drug sensitivity values' area under the drug dose-response curves (AUCs) with and without adjustment for general level of drug sensitivity.

Results

We observed tissue specificity in 63% of tested drugs, with 8% of total interactions deemed significant (false discovery rate <0.05). By restricting the drug-tissue interactions to those with AUC > 0.2, we found that in 52% of interactions, the tissue was predictive of drug sensitivity (concordance index > 0.65). When compared with clinical indications, the observed overlap was weak (Matthew correlation coefficient, MCC = 0.0003, P > .10).

Discussion

While drugs exhibit significant tissue specificity in vitro, there is little overlap with clinical indications. This can be attributed to factors such as underlying biological differences between in vitro models and patient tumors, or the inability of tissue-specific drugs to bring additional benefits beyond gold standard treatments during clinical trials.

Conclusion

Our meta-analysis of pan-cancer drug screening datasets indicates that most tested drugs exhibit tissue-specific sensitivities in a large panel of cancer cell lines. However, the observed preclinical results do not translate to the clinical setting. Our results suggest that additional research into showing parallels between preclinical and clinical data is required to increase the translational potential of in vitro drug screening.

AXL knockdown gene signature reveals a drug repurposing opportunity for a class of antipsychotics to reduce growth and metastasis of triple-negative breast cancer

Triple-Negative Breast Cancer (TNBC) is an aggressive cancer subtype that is associated with a poor prognosis due to its propensity to form metastases. The receptor tyrosine kinase AXL plays a role in tumor cell dissemination and its expression in breast cancers correlates with poor patient survival. Here, we explored whether already used drugs might elicit a gene signature similar to that seen with AXL knockdown in TNBC cells and which could, therefore, offer an opportunity for drug repurposing. To this end, we queried the Connectivity Map with an AXL gene signature which revealed a class of dopamine receptors antagonists named phenothiazines (Thioridazine, Fluphenazine and Trifluoperazine) typically used as anti-psychotics. We next tested if these drugs, similarly to AXL depletion, were able to limit growth and metastatic progression of TNBC cells and found that phenothiazines are able to reduce cell invasion, proliferation, viability and increase apoptosis of TNBC cells in vitro. Mechanistically, these drugs did not affect AXL activity but instead reduced PI3K/AKT/mTOR and ERK signaling. When administered to mice bearing TNBC xenografts, phenothiazines were able to reduce tumor growth and metastatic burden. Collectively, these results suggest that these antipsychotics display anti-tumor and anti-metastatic activity and that they could potentially be repurposed, in combination with standard chemotherapy, for the treatment of TNBC.

Transcriptomic Alterations in Lung Adenocarcinoma Unveil New Mechanisms Targeted by the TBX2 Subfamily of Tumor Suppressor Genes

T-box (TBX) transcription factors are evolutionary conserved genes and master transcriptional regulators. In mammals, TBX2 subfamily (TBX2, TBX3, TBX4, and TBX5) genes are expressed in the developing lung bud and tracheae. Our group previously showed that the expression of TBX2 subfamily was significantly high in human normal lungs, but markedly suppressed in lung adenocarcinoma (LUAD). To further elucidate their role in LUAD pathogenesis, we first confirmed abundant expression of protein products of the four members by immunostaining in adult human normal lung tissues. We also found overall suppressed expression of these genes and their corresponding proteins in a panel of human LUAD cell lines. Transient over-expression of each of the genes in human (NCI-H1299), and mouse (MDA-F471) derived lung cancer cells was found to significantly inhibit growth and proliferation as well as induce apoptosis. Genome-wide transcriptomic analyses on NCI-H1299 cells, overexpressing TBX2 gene subfamily, unraveled novel regulatory pathways. These included, among others, inhibition of cell cycle progression but more importantly activation of the histone demethylase pathway. When using a pattern-matching algorithm, we showed that TBX's overexpression mimic molecular signatures from azacitidine treated NCI-H1299 cells which in turn are inversely correlated to expression profiles of both human and murine lung tumors relative to matched normal lung. In conclusion, we showed that the TBX2 subfamily genes play a critical tumor suppressor role in lung cancer pathogenesis through regulating its methylating pattern, making them putative candidates for epigenetic therapy in LUAD.

Abstract 5513: Role of the evolutionarily conserved TBX2 subfamily of transcription factors in the molecular pathogenesis of human lung adenocarcinoma

T-box (TBX) transcription factors are evolutionary conserved genes and master regulators of transcription repression and activation. In mammals, 18 members were described functionally and structurally, of which the TBX2 subfamily (TBX2, TBX3, TBX4, TBX5) genes were shown to be expressed early on in the developing lung bud and tracheae. Despite these insights into the role of the TBX2 subfamily in normal lung organogenesis, little is known about the role of these genes in pathological pulmonary conditions in humans; particularly lung cancer, an aggressive malignancy that is the leading cause of cancer-deaths worldwide. To fill this void, our group previously surveyed the expression of TBX2 subfamily in various publicly available datasets and found that all four members were preferentially and highly expressed in human normal lung, but markedly and consistently suppressed in lung adenocarcinoma (LUAD) the most common histological subtype of lung cancer. We also showed that the subfamily was also suppressed in preneoplastic lesions preceding the development of LUADs. Following the above and to further elucidate the role of the TBX2 subfamily in LUAD pathogenesis, we first probed and confirmed abundant expression of protein products of the four members by immunostaining in adult human normal lung tissues. On the other hand, quantitative real-time PCR and western blotting analyses demonstrated overall suppressed expression of the genes and corresponding proteins in a panel of human LUAD cell lines. Transient over-expression of each of the four genes in human LUAD cell lines (H1299 and H1944) was found to overall significantly inhibit cancer cell growth and proliferation. Additionally, over-expression of the four genes induced apoptosis, evidenced by sub-G0/G1 accumulation following cell cycle analysis, in both cell lines (ranging from 40% to 90% compared to control). To understand genome-wide effects of TBX2 subfamily in LUAD, we interrogated global expression programs downstream of these transcription factors by RNA-Seq in H1299 cells engineered to over-express the four members separately. We unraveled novel signaling cues signifying canonical pathways found in our analysis to be directly regulated by members of the TBX2 subfamily. These included, among others, inhibition of cell cycle progression and glycolysis, suppression of pathways mediated by epidermal growth factor (EGFR) and WNT signaling and activation of the major anti-tumor immune marker interferon gamma (IFNG). All in all, our findings point to tumor suppressor roles for TBX2 subfamily in human LUAD pathogenesis and suggest “oncophenotypes” downstream of these factors as putative targets for lung cancer therapy.

Citation Format: Athar Khalil, Nehme El-Hachem, Batoul Dekmak, Humam Kadara, Georges Nemer. Role of the evolutionarily conserved TBX2 subfamily of transcription factors in the molecular pathogenesis of human lung adenocarcinoma [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 5513.

A Novel Role for CSRP1 in a Lebanese Family with Congenital Cardiac Defects

Despite an obvious role for consanguinity in congenital heart disease (CHD), most studies fail to document a monogenic model of inheritance except for few cases. We hereby describe a first-degree cousins consanguineous Lebanese family with 7 conceived children: 2 died in utero of unknown causes, 3 have CHD, and 4 have polydactyly. The aim of the study is to unveil the genetic variant(s) causing these phenotypes using next generation sequencing (NGS) technology. Targeted exome sequencing identified a heterozygous duplication in CSRP1 which leads to a potential frameshift mutation at position 154 of the protein. This mutation is inherited from the father, and segregates only with the CHD phenotype. The in vitro characterization demonstrates that the mutation dramatically abrogates its transcriptional activity over cardiac promoters like NPPA. In addition, it differentially inhibits the physical association of CSRP1 with SRF, GATA4, and with the newly described partner herein TBX5. Whole exome sequencing failed to show any potential variant linked to polydactyly, but revealed a novel missense mutation in TRPS1. This mutation is inherited from the healthy mother, and segregating only with the cardiac phenotype. Both TRPS1 and CSRP1 physically interact, and the mutations in each abrogate their partnership. Our findings add fundamental knowledge into the molecular basis of CHD, and propose the di-genic model of inheritance as responsible for such malformations.

Gene isoforms as expression-based biomarkers predictive of drug response in vitro

Next-generation sequencing technologies have recently been used in pharmacogenomic studies to characterize large panels of cancer cell lines at the genomic and transcriptomic levels. Among these technologies, RNA-sequencing enable profiling of alternatively spliced transcripts. Given the high frequency of mRNA splicing in cancers, linking this feature to drug response will open new avenues of research in biomarker discovery. To identify robust transcriptomic biomarkers for drug response across studies, we develop a meta-analytical framework combining the pharmacological data from two large-scale drug screening datasets. We use an independent pan-cancer pharmacogenomic dataset to test the robustness of our candidate biomarkers across multiple cancer types. We further analyze two independent breast cancer datasets and find that specific isoforms of IGF2BP2, NECTIN4, ITGB6, and KLHDC9 are significantly associated with AZD6244, lapatinib, erlotinib, and paclitaxel, respectively. Our results support isoform expressions as a rich resource for biomarkers predictive of drug response.

Defining the biological basis of radiomic phenotypes in lung cancer

Medical imaging can visualize characteristics of human cancer noninvasively. Radiomics is an emerging field that translates these medical images into quantitative data to enable phenotypic profiling of tumors. While radiomics has been associated with several clinical endpoints, the complex relationships of radiomics, clinical factors, and tumor biology are largely unknown. To this end, we analyzed two independent cohorts of respectively 262 North American and 89 European patients with lung cancer, and consistently identified previously undescribed associations between radiomic imaging features, molecular pathways, and clinical factors. In particular, we found a relationship between imaging features, immune response, inflammation, and survival, which was further validated by immunohistochemical staining. Moreover, a number of imaging features showed predictive value for specific pathways; for example, intra-tumor heterogeneity features predicted activity of RNA polymerase transcription (AUC = 0.62, p=0.03) and intensity dispersion was predictive of the autodegration pathway of a ubiquitin ligase (AUC = 0.69, p<10^-4). Finally, we observed that prognostic biomarkers performed highest when combining radiomic, genetic, and clinical information (CI = 0.73, p<10^-9) indicating complementary value of these data. In conclusion, we demonstrate that radiomic approaches permit noninvasive assessment of both molecular and clinical characteristics of tumors, and therefore have the potential to advance clinical decision-making by systematically analyzing standard-of-care medical images.

DOI:http://dx.doi.org/10.7554/eLife.23421.001

Medical imaging covers a wide range of techniques that are used to look inside the body, including X-rays, MRI scans and ultrasound. A process called radiomics uses computer algorithms to process the data collected by these techniques to identify and precisely measure a large number of features that would not otherwise be quantifiable by human experts. By doing so, radiomics can automatically measure the radiographic characteristics of a tumor. For example, radiomics can establish the size, shape and texture of a tumor to help to diagnose cancer and guide its treatment.

Research has suggested that radiomics can predict certain clinical characteristics of cancer, such as how far through the body the cancer has spread, how likely it is to respond to treatment, and how likely a patient is to survive. However, these radiomic characteristics have not yet been precisely linked to the biological processes that drive how cancer develops and spreads.

Cancers develop as a result of genetic changes that activate “molecular pathways” in the cells and trigger processes such as cell division and inflammation. To work out exactly which changes are behind a particular tumor, a sample of the tumor from biopsy or surgery is analyzed using genomics techniques. Linking radiomics features to the molecular processes active in a tumor can generate further information that can complement the molecular data. Images are routinely collected on all cancer patients yet molecular data is not. Hence, in some cases, the images can be used to infer the molecular underpinnings of cancer in individual patients.

Grossmann et al. have now analyzed radiomic, genomic and clinical data collected from approximately 350 patients with lung cancer. The analysis revealed links between biological processes normally detected by genomics – in particular, inflammatory responses – and radiomics features. Furthermore, these features could also be associated with clinical characteristics, such as tumor type and patient survival rates. These results were further validated by using a technique called immunohistochemical staining on tumor tissue obtained by surgery.

Further investigation revealed that certain radiomics features can predict the state of molecular pathways that are key to cancer development (such as the inflammatory response). Furthermore, Grossmann et al. found that combining data from radiomics, genomics and clinical parameters predicts how the cancer will progress better than any of these parameters can predict on their own. These results demonstrate the complementary value of radiomic data to genomic and clinical data.

There are many different algorithms that can be used to process images for radiomics. Before radiomics can be used clinically to assess the biological processes underlying the tumors of patients, a specific algorithm needs to be decided upon and then tested in prospective clinical trials.

DOI:http://dx.doi.org/10.7554/eLife.23421.002

Integrative Cancer Pharmacogenomics to Infer Large-Scale Drug Taxonomy

Identification of drug targets and mechanism of action (MoA) for new and uncharacterized anticancer drugs is important for optimization of treatment efficacy. Current MoA prediction largely relies on prior information including side effects, therapeutic indication, and chemoinformatics. Such information is not transferable or applicable for newly identified, previously uncharacterized small molecules. Therefore, a shift in the paradigm of MoA predictions is necessary toward development of unbiased approaches that can elucidate drug relationships and efficiently classify new compounds with basic input data. We propose here a new integrative computational pharmacogenomic approach, referred to as Drug Network Fusion (DNF), to infer scalable drug taxonomies that rely only on basic drug characteristics toward elucidating drug-drug relationships. DNF is the first framework to integrate drug structural information, high-throughput drug perturbation, and drug sensitivity profiles, enabling drug classification of new experimental compounds with minimal prior information. DNF taxonomy succeeded in identifying pertinent and novel drug-drug relationships, making it suitable for investigating experimental drugs with potential new targets or MoA. The scalability of DNF facilitated identification of key drug relationships across different drug categories, providing a flexible tool for potential clinical applications in precision medicine. Our results support DNF as a valuable resource to the cancer research community by providing new hypotheses on compound MoA and potential insights for drug repurposing. Cancer Res; 77(11); 3057-69. ©2017 AACR.

AutoDock and AutoDockTools for Protein-Ligand Docking: Beta-Site Amyloid Precursor Protein Cleaving Enzyme 1(BACE1) as a Case Study

Computational docking and scoring techniques have revolutionized structural bioinformatics by providing unprecedented insights on key aspects of ligand-receptor interaction. Docking is used for optimizing known drugs and for identifying novel binders by predicting their binding mode and affinity. AutoDock and AutoDockTools are free of charge techniques that have been extensively cited in the literature as essential tools in structure-based drug design. Moreover, these methods are fast enough to permit virtual screening of ligand libraries containing tens of thousands of compounds. However using Autodock requires some knowledge in programming which creates a limitation for biologists and makes them prone for commercial applications. Here, we selected a relevant target involved in the progression of Alzheimer disease and provided a fully reproducible docking protocol. This example will show how docking techniques would be an important asset to identify new BACE1 inhibitors. The following friendly user tutorial targets both undergraduate and graduate students, allowing them to understand docking as a computational tool for structure-based drug design.

Revisiting inconsistency in large pharmacogenomic studies

In 2013, we published a comparative analysis of mutation and gene expression profiles and drug sensitivity measurements for 15 drugs characterized in the 471 cancer cell lines screened in the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE). While we found good concordance in gene expression profiles, there was substantial inconsistency in the drug responses reported by the GDSC and CCLE projects. We received extensive feedback on the comparisons that we performed. This feedback, along with the release of new data, prompted us to revisit our initial analysis. We present a new analysis using these expanded data, where we address the most significant suggestions for improvements on our published analysis - that targeted therapies and broad cytotoxic drugs should have been treated differently in assessing consistency, that consistency of both molecular profiles and drug sensitivity measurements should be compared across cell lines, and that the software analysis tools provided should have been easier to run, particularly as the GDSC and CCLE released additional data. Our re-analysis supports our previous finding that gene expression data are significantly more consistent than drug sensitivity measurements. Using new statistics to assess data consistency allowed identification of two broad effect drugs and three targeted drugs with moderate to good consistency in drug sensitivity data between GDSC and CCLE. For three other targeted drugs, there were not enough sensitive cell lines to assess the consistency of the pharmacological profiles. We found evidence of inconsistencies in pharmacological phenotypes for the remaining eight drugs. Overall, our findings suggest that the drug sensitivity data in GDSC and CCLE continue to present challenges for robust biomarker discovery. This re-analysis provides additional support for the argument that experimental standardization and validation of pharmacogenomic response will be necessary to advance the broad use of large pharmacogenomic screens.

Revisiting inconsistency in large pharmacogenomic studies

In 2013, we published a comparative analysis of mutation and gene expression profiles and drug sensitivity measurements for 15 drugs characterized in the 471 cancer cell lines screened in the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE). While we found good concordance in gene expression profiles, there was substantial inconsistency in the drug responses reported by the GDSC and CCLE projects. We received extensive feedback on the comparisons that we performed. This feedback, along with the release of new data, prompted us to revisit our initial analysis. We present a new analysis using these expanded data, where we address the most significant suggestions for improvements on our published analysis - that targeted therapies and broad cytotoxic drugs should have been treated differently in assessing consistency, that consistency of both molecular profiles and drug sensitivity measurements should be compared across cell lines, and that the software analysis tools provided should have been easier to run, particularly as the GDSC and CCLE released additional data. Our re-analysis supports our previous finding that gene expression data are significantly more consistent than drug sensitivity measurements. Using new statistics to assess data consistency allowed identification of two broad effect drugs and three targeted drugs with moderate to good consistency in drug sensitivity data between GDSC and CCLE. For three other targeted drugs, there were not enough sensitive cell lines to assess the consistency of the pharmacological profiles. We found evidence of inconsistencies in pharmacological phenotypes for the remaining eight drugs. Overall, our findings suggest that the drug sensitivity data in GDSC and CCLE continue to present challenges for robust biomarker discovery. This re-analysis provides additional support for the argument that experimental standardization and validation of pharmacogenomic response will be necessary to advance the broad use of large pharmacogenomic screens.

Revisiting inconsistency in large pharmacogenomic studies

In 2013, we published a comparative analysis mutation and gene expression profiles and drug sensitivity measurements for 15 drugs characterized in the 471 cancer cell lines screened in the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE). While we found good concordance in gene expression profiles, there was substantial inconsistency in the drug responses reported by the GDSC and CCLE projects. We received extensive feedback on the comparisons that we performed. This feedback, along with the release of new data, prompted us to revisit our initial analysis. Here we present a new analysis using these expanded data in which we address the most significant suggestions for improvements on our published analysis - that targeted therapies and broad cytotoxic drugs should have been treated differently in assessing consistency, that consistency of both molecular profiles and drug sensitivity measurements should both be compared across cell lines, and that the software analysis tools we provided should have been easier to run, particularly as the GDSC and CCLE released additional data.             Our re-analysis supports our previous finding that gene expression data are significantly more consistent than drug sensitivity measurements. The use of new statistics to assess data consistency allowed us to identify two broad effect drugs and three targeted drugs with moderate to good consistency in drug sensitivity data between GDSC and CCLE. For three other targeted drugs, there were not enough sensitive cell lines to assess the consistency of the pharmacological profiles. We found evidence of inconsistencies in pharmacological phenotypes for the remaining eight drugs.             Overall, our findings suggest that the drug sensitivity data in GDSC and CCLE continue to present challenges for robust biomarker discovery. This re-analysis provides additional support for the argument that experimental standardization and validation of pharmacogenomic response will be necessary to advance the broad use of large pharmacogenomic screens.

Assessment of pharmacogenomic agreement

In 2013 we published an analysis demonstrating that drug response data and gene-drug associations reported in two independent large-scale pharmacogenomic screens, Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE), were inconsistent. The GDSC and CCLE investigators recently reported that their respective studies exhibit reasonable agreement and yield similar molecular predictors of drug response, seemingly contradicting our previous findings. Reanalyzing the authors' published methods and results, we found that their analysis failed to account for variability in the genomic data and more importantly compared different drug sensitivity measures from each study, which substantially deviate from our more stringent consistency assessment. Our comparison of the most updated genomic and pharmacological data from the GDSC and CCLE confirms our published findings that the measures of drug response reported by these two groups are not consistent. We believe that a principled approach to assess the reproducibility of drug sensitivity predictors is necessary before envisioning their translation into clinical settings.

Characterization of Conserved Toxicogenomic Responses in Chemically Exposed Hepatocytes across Species and Platforms

BACKGROUND

Genome-wide expression profiling is increasingly being used to identify transcriptional changes induced by drugs and environmental stressors. In this context, the Toxicogenomics Project-Genomics Assisted Toxicity Evaluation system (TG-GATEs) project generated transcriptional profiles from rat liver samples and human/rat cultured primary hepatocytes exposed to more than 100 different chemicals.

OBJECTIVES

To assess the capacity of the cell culture models to recapitulate pathways induced by chemicals in vivo, we leveraged the TG-GATEs data set to compare the early transcriptional responses observed in the liver of rats treated with a large set of chemicals with those of cultured rat and human primary hepatocytes challenged with the same compounds in vitro.

METHODS

We developed a new pathway-based computational pipeline that efficiently combines gene set enrichment analysis (GSEA) using pathways from the Reactome database with biclustering to identify common modules of pathways that are modulated by several chemicals in vivo and in vitro across species.

RESULTS

We found that some chemicals induced conserved patterns of early transcriptional responses in in vitro and in vivo settings, and across human and rat genomes. These responses involved pathways of cell survival, inflammation, xenobiotic metabolism, oxidative stress, and apoptosis. Moreover, our results support the transforming growth factor beta receptor (TGF-βR) signaling pathway as a candidate biomarker associated with exposure to environmental toxicants in primary human hepatocytes.

CONCLUSIONS

Our integrative analysis of toxicogenomics data provides a comprehensive overview of biochemical perturbations affected by a large panel of chemicals. Furthermore, we show that the early toxicological response occurring in animals is recapitulated in human and rat primary hepatocyte cultures at the molecular level, indicating that these models reproduce key pathways in response to chemical stress. These findings expand our understanding and interpretation of toxicogenomics data from human hepatocytes exposed to environmental toxicants.

PharmacoGx: an R package for analysis of large pharmacogenomic datasets

Pharmacogenomics holds great promise for the development of biomarkers of drug response and the design of new therapeutic options, which are key challenges in precision medicine. However, such data are scattered and lack standards for efficient access and analysis, consequently preventing the realization of the full potential of pharmacogenomics. To address these issues, we implemented PharmacoGx, an easy-to-use, open source package for integrative analysis of multiple pharmacogenomic datasets. We demonstrate the utility of our package in comparing large drug sensitivity datasets, such as the Genomics of Drug Sensitivity in Cancer and the Cancer Cell Line Encyclopedia. Moreover, we show how to use our package to easily perform Connectivity Map analysis. With increasing availability of drug-related data, our package will open new avenues of research for meta-analysis of pharmacogenomic data.

AVAILABILITY AND IMPLEMENTATION

PharmacoGx is implemented in R and can be easily installed on any system. The package is available from CRAN and its source code is available from GitHub.

CONTACT

bhaibeka@uhnresearch.ca or benjamin.haibe.kains@utoronto.ca

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Public data and open source tools for multi-assay genomic investigation of disease

Molecular interrogation of a biological sample through DNA sequencing, RNA and microRNA profiling, proteomics and other assays, has the potential to provide a systems level approach to predicting treatment response and disease progression, and to developing precision therapies. Large publicly funded projects have generated extensive and freely available multi-assay data resources; however, bioinformatic and statistical methods for the analysis of such experiments are still nascent. We review multi-assay genomic data resources in the areas of clinical oncology, pharmacogenomics and other perturbation experiments, population genomics and regulatory genomics and other areas, and tools for data acquisition. Finally, we review bioinformatic tools that are explicitly geared toward integrative genomic data visualization and analysis. This review provides starting points for accessing publicly available data and tools to support development of needed integrative methods.

Importance of collection in gene set enrichment analysis of drug response in cancer cell lines

Gene set enrichment analysis (GSEA) associates gene sets and phenotypes, its use is predicated on the choice of a pre-defined collection of sets. The defacto standard implementation of GSEA provides seven collections yet there are no guidelines for the choice of collections and the impact of such choice, if any, is unknown. Here we compare each of the standard gene set collections in the context of a large dataset of drug response in human cancer cell lines. We define and test a new collection based on gene co-expression in cancer cell lines to compare the performance of the standard collections to an externally derived cell line based collection. The results show that GSEA findings vary significantly depending on the collection chosen for analysis. Henceforth, collections should be carefully selected and reported in studies that leverage GSEA.

New analogues of 13-hydroxyocatdecadienoic acid and 12-hydroxyeicosatetraenoic acid block human blood platelet aggregation and cyclooxygenase-1 activity

Background

Thromboxane A₂ is derived from arachidonic acid through the action of cyclooxygenases and thromboxane synthase. It is mainly formed in blood platelets upon activation and plays an important role in aggregation. Aspirin is effective in reducing the incidence of complications following acute coronary syndrome and stroke. The anti-thrombotic effect of aspirin is obtained through the irreversible inhibition of cyclooxygenases. Analogues of 12-hydroxyeicosatetraenoic acid and 13-hydroxyocatdecadienoic acid were shown previously to modulate platelet activation and to block thromboxane receptors.

Results and discussion

We synthesized 10 compounds based on the structures of analogues of 12-hydroxyeicosatetraenoic acid and 13-hydroxyocatdecadienoic acid and evaluated their effect on platelet aggregation triggered by arachidonic acid. The structure activity relationship was evaluated. Five compounds showed a significant inhibition of platelet aggregation and highlighted the importance of the lipidic hydrophobic hydrocarbon chain and the phenol group. Their IC₅₀ ranged from 7.5 ± 0.8 to 14.2 ± 5.7 μM (Mean ± S.E.M.). All five compounds decreased platelet aggregation and thromboxane synthesis in response to collagen whereas no modification of platelet aggregation in response to thromboxane receptor agonist, U46619, was observed. Using COS-7 cells overexpressing human cyclooxygenase-1, we showed that these compounds are specific inhibitors of cyclooxygenase-1 with IC₅₀ ranging from 1.3 to 12 μM. Docking observation of human recombinant cyclooxygenase-1 supported a role of the phenol group in the fitting of cyclooxygenase-1, most likely related to hydrogen bonding with the Tyr 355 of cyclooxygenase-1.

Conclusions

In conclusion, the compounds we synthesized at first based on the structures of analogues of 12 lipoxygenase metabolites showed a role of the phenol group in the anti-platelet and anti-cyclooxygenase-1 activities. These compounds mediate their effects via blockade of cyclooxygenase-1.