Library Of Integrated Network-based Cellular Signatures Research Articles

Searching for LINCS to Stress: Using Text Mining to Automate Reference Chemical Curation.

Adaptive stress response pathways (SRPs) restore cellular homeostasis following perturbation but may activate terminal outcomes like apoptosis, autophagy, or cellular senescence if disruption exceeds critical thresholds. Because SRPs hold the key to vital cellular tipping points, they are targeted for therapeutic interventions and assessed as biomarkers of toxicity. Hence, we are developing a public database of chemicals that perturb SRPs to enable new data-driven tools to improve public health. Here, we report on the automated text-mining pipeline we used to build and curate the first version of this database. We started with 100 reference SRP chemicals gathered from published biomarker studies to bootstrap the database. Second, we used information retrieval to find co-occurrences of reference chemicals with SRP terms in PubMed abstracts and determined pairwise mutual information thresholds to filter biologically relevant relationships. Third, we applied these thresholds to find 1206 putative SRP perturbagens within thousands of substances in the Library of Integrated Network-Based Cellular Signatures (LINCS). To assign SRP activity to LINCS chemicals, domain experts had to manually review at least three publications for each of 1206 chemicals out of 181,805 total abstracts. To accomplish this efficiently, we implemented a machine learning approach to predict SRP classifications from texts to prioritize abstracts. In 5-fold cross-validation testing with a corpus derived from the 100 reference chemicals, artificial neural networks performed the best (F1-macro = 0.678) and prioritized 2479/181,805 abstracts for expert review, which resulted in 457 chemicals annotated with SRP activities. An independent analysis of enriched mechanisms of action and chemical use class supported the text-mined chemical associations (p < 0.05): heat shock inducers were linked with HSP90 and DNA damage inducers to topoisomerase inhibition. This database will enable novel applications of LINCS data to evaluate SRP activities and to further develop tools for biomedical information extraction from the literature.

AI identifies potent inducers of breast cancer stem cell differentiation based on adversarial learning from gene expression data.

Cancer stem cells (CSCs) are a subpopulation of cancer cells within tumors that exhibit stem-like properties and represent a potentially effective therapeutic target toward long-term remission by means of differentiation induction. By leveraging an artificial intelligence approach solely based on transcriptomics data, this study scored a large library of small molecules based on their predicted ability to induce differentiation in stem-like cells. In particular, a deep neural network model was trained using publicly available single-cell RNA-Seq data obtained from untreated human-induced pluripotent stem cells at various differentiation stages and subsequently utilized to screen drug-induced gene expression profiles from the Library of Integrated Network-based Cellular Signatures (LINCS) database. The challenge of adapting such different data domains was tackled by devising an adversarial learning approach that was able to effectively identify and remove domain-specific bias during the training phase. Experimental validation in MDA-MB-231 and MCF7 cells demonstrated the efficacy of five out of six tested molecules among those scored highest by the model. In particular, the efficacy of triptolide, OTS-167, quinacrine, granisetron and A-443654 offer a potential avenue for targeted therapies against breast CSCs.

Abstract 4889: Integrative multi-omic analysis reveals novel prognostic biological entities in pediatric medulloblastoma

Abstract Medulloblastoma is a cerebellar tumor for which relapses are associated with poor survival rates of less than 5%. Molecular heterogeneity and dose-limiting toxicities that occur with standard of care approaches, which include surgical resection, craniospinal irradiation, and chemotherapy, complicate the treatment of both primary and recurrent medulloblastoma due to adverse long-term sequela. While the integration of molecular analyses into the histopathology of pediatric medulloblastomas has changed the way these diseases are diagnosed, classified, and treated, there remains an unmet need to further understand the underpinnings of the disease to improve clinical outcomes and minimize neuronal, cognitive, and hormonal toxicities that occur with standard-of-care therapies. We therefore aimed to investigate novel subgroups of pediatric medulloblastoma as a basis for further defining biological properties and targets for the disease through multi-omic characterization. We derived single-nucleotide variant, copy number variant, expression, alternative splicing, and methylation array data from pediatric medulloblastomas profiled as part of the Children’s Brain Tumor Network (CBTN) and the Open Pediatric Cancer (OpenPedCan) projects. Using non-negative matrix factorization across the five datasets derived from genomic, transcriptomic, and epigenomic profiling, we identified 14 clusters found to be prognostically significant by Kaplan-Meier analysis of overall survival (p &amp;lt; 0.0001). Our model further subdivided sonic hedgehog (SHH) and Group 3/4 tumors, but not Wnt-driven tumors, suggesting that the latter forms a homogeneous molecular subgroup. Among our subgroups, we recapitulated the four classically defined medulloblastoma subtypes known to be associated with mutations in KDM6A, KMT2C, CTNNB1, PTCH1, TP53, KBTBD4, MYC, and MYCN. Cascading biological relationships across the copy number, methylation, and expression domains were observed among novel subgroups, potentially driving migratory, immuno-regulatory, GPCR-related, growth factor-related, and histone methylation biological programs. A comparison of subgroup expression profiles to transcriptional signatures from the Library of Integrated Network-Based Cellular Signatures (LINCS) program identified canonical oncogenic pathways unique to each subgroup as potentially targetable, including, but not limited to, MAPK, VEGFA, inflammatory, fatty acid metabolic, and PI3K/AKT signaling. Our work uncovers novel biological entities in pediatric medulloblastoma potentially driven by higher order mechanisms across the genome, transcriptome, and epigenome, with the possibility of informing novel precision medicine approaches for the disease. Citation Format: Komal Rathi, Varun Kesherwani, Ammar S. Naqvi, Yuankun Zhu, Alex Sickler, Xiaoyan Huang, Bo Zhang, Brian Rood, Adam C. Resnick, Adam A. Kraya. Integrative multi-omic analysis reveals novel prognostic biological entities in pediatric medulloblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4889.

Vinpocetine Mediates Therapeutic Activity Alone and in Combination with Chemotherapy in T-Cell Acute Lymphoblastic Leukemia

Introduction: T-cell acute lymphoblastic leukemia (T-ALL) is a highly aggressive cancer affecting immature T-cells and constitutes about 15% of pediatric acute lymphoblastic leukemia (ALL) cases. Despite advances in treatment, 20% of patients have or develop treatment-resistant disease, and their survival rates are only 15-25% even with additional intensive chemotherapy and hematopoietic stem cell transplant. Furthermore, the current intensive treatment regimens have long-term side effects in most patients. Therefore, there is a pressing need for new, more targeted, and less toxic treatment options for children with T-ALL. Genomic analysis of leukemia samples has enabled identification of chromosomal aberrations and gene mutations associated with T-ALL leukemia development and progression. Single cell analysis of genes expressed in pediatric T-ALL bone marrow samples at diagnosis identified blast-associated signature genes that correlated with poorer event-free survival, suggesting their involvement in leukemia progression and highlighting the potential for further investigation of the signature as an avenue to develop novel T-ALL therapies. A non-conventional genome-oriented approach was applied to the data utilizing the Library of Integrated Network-Based Cellular Signatures (LINCS) resource to identify drugs that are predicted to reverse the T-ALL blast signature. Vinpocetine, a vinca alkaloid derivative, was identified as a potential therapeutic candidate. Methods: Leukemia cell cultures were treated with incremental concentrations of vinpocetine, alone or in combination with a standard chemotherapeutic agent (vincristine or doxorubicin) for 72 hours and relative cell densities were determined by luminescent viability assay. Interactions between drugs were evaluated by mathematical modeling using the fractional product method. Orthotopic T-ALL patient-derived xenografts (PDXs) were established in immune-compromised NSG mice by intravenous injection and peripheral blood samples were collected at intervals for detection of leukemic blasts (human CD45+) by flow cytometry. Mice with low disease burden (&amp;lt;5% peripheral blasts) were randomized to groups and treated every third day with 10 or 20 mg/kg vinpocetine or an equivalent volume of vehicle administered intraperitoneally. Mice were weighed and their health status was evaluated at intervals to monitor for toxicity. Results: Treatment with vinpocetine resulted in a dose-dependent reduction in cell density in T-ALL cell line (Jurkat, HPB, and PEER) cultures relative to vehicle treatment, with IC50 values between 62.6 -64.4 uM, supporting the therapeutic potential of vinpocetine in T-ALL. Vinpocetine also interacted synergistically with vincristine or doxorubicin to provide enhanced therapeutic efficacy against Jurkat cells, indicated by a greater reduction in cell number compared to either single agent. This promising finding underscores the potential of vinpocetine as a supplemental therapy to improve responses or allow for dose reduction of existing chemotherapeutic agents for T-ALL therapy. In a preliminary dose-finding study, treatment with 20 mg/kg vinpocetine was well-tolerated with no overt evidence of toxicity and there was a slight trend toward decreased disease burden. Conclusion: Our study explores the therapeutic potential of vinpocetine for treatment of T-ALL. The observed inhibitory effects on leukemia cell expansion and enhanced efficacy with standard chemotherapies identify vinpocetine as a promising novel treatment approach for T-ALL. Dose escalation studies are needed to evaluate therapeutic effects of vinpocetine in mouse models.

Cellular Heterogeneity of Activated Primary Human Macrophages and Associated Drug-Gene Networks: From Biology to Precision Therapeutics.

Interferon-γ (IFNγ) signaling plays a complex role in atherogenesis. IFNγ stimulation of macrophages permits in vitro exploration of proinflammatory mechanisms and the development of novel immune therapies. We hypothesized that the study of macrophage subpopulations could lead to anti-inflammatory interventions. Primary human macrophages activated by IFNγ (M[IFNγ]) underwent analyses by single-cell RNA sequencing, time-course cell-cluster proteomics, metabolite consumption, immunoassays, and functional tests (phagocytic, efferocytotic, and chemotactic). RNA-sequencing data were analyzed in LINCS (Library of Integrated Network-Based Cellular Signatures) to identify compounds targeting M(IFNγ) subpopulations. The effect of compound BI-2536 was tested in human macrophages in vitro and in a murine model of atherosclerosis. Single-cell RNA sequencing identified 2 major clusters in M(IFNγ): inflammatory (M[IFNγ]i) and phagocytic (M[IFNγ]p). M(IFNγ)i had elevated expression of inflammatory chemokines and higher amino acid consumption compared with M(IFNγ)p. M(IFNγ)p were more phagocytotic and chemotactic with higher Krebs cycle activity and less glycolysis than M(IFNγ)i. Human carotid atherosclerotic plaques contained 2 such macrophage clusters. Bioinformatic LINCS analysis using our RNA-sequencing data identified BI-2536 as a potential compound to decrease the M(IFNγ)i subpopulation. BI-2536 in vitro decreased inflammatory chemokine expression and secretion in M(IFNγ) by shrinking the M(IFNγ)i subpopulation while expanding the M(IFNγ)p subpopulation. BI-2536 in vivo shifted the phenotype of macrophages, modulated inflammation, and decreased atherosclerosis and calcification. We characterized 2 clusters of macrophages in atherosclerosis and combined our cellular data with a cell-signature drug library to identify a novel compound that targets a subset of macrophages in atherosclerosis. Our approach is a precision medicine strategy to identify new drugs that target atherosclerosis and other inflammatory diseases.

In Silico Identification of Therapeutic Targets and Novel Drug Candidates for Malignant Peripheral Nerve Sheath Tumors.

Malignant peripheral nerve sheath tumors (MPNSTs) are an aggressive form of sarcomas with a poor prognosis and limited treatment options. Therefore, new therapeutic targets are urgently needed to identify novel drugs. Based on the Gene Expression Omnibus database, an integrated analysis was performed to identify differentially expressed genes (DEGs) in MPNSTs compared to neurofibromas (NFs). Then functional enrichment analyses, protein-protein interaction (PPI) network construction, and hub gene selection were conducted. We explored DEG-guided repurposable drugs to treat MPNST based on the Library of Integrated Network-Based Cellular Signatures (LINCS) database. Furthermore, the binding affinity between predicted drug candidates and the MPNST-associated hub gene was calculated using molecular docking. We identified 89 DEGs in common with all three MPNSTs datasets. In the PPI networks, twist family bHLH transcription factor 1 (Twist1) with higher node degrees was further evaluated as a therapeutic target. Cytochalasin-d, cabozantinib, everolimus, refametinib, and BGT-226 were extracted from the LINCS database, which showed lower normalized connectivity scores (-1.88, -1.81, -1.78, -1.76, and -1.72, respectively) and was considered as drug candidates. In addition, the results of molecular docking between the five drugs and Twist1 showed a binding affinity of -6.61, -7.03, -7.73, -3.94, and -7.07 kcal/mol, respectively. Overall, our results describe the importance of Twist1 in MPNST pathogenesis. Everolimus was also found to be a potential therapeutic drug for MPNSTs.

Interrogating colorectal cancer metastasis to liver: a search for clinically viable compounds and mechanistic insights in colorectal cancer Patient Derived Organoids

BackgroundApproximately 20–50% of patients presenting with localized colorectal cancer progress to stage IV metastatic disease (mCRC) following initial treatment and this is a major prognostic determinant. Here, we have interrogated a heterogeneous set of primary colorectal cancer (CRC), liver CRC metastases and adjacent liver tissue to identify molecular determinants of the colon to liver spreading. Screening Food and Drug Administration (FDA) approved drugs for their ability to interfere with an identified colon to liver metastasis signature may help filling an unmet therapeutic need.MethodsRNA sequencing of primary colorectal cancer specimens vs adjacent liver tissue vs synchronous and asynchronous liver metastases. Pathways enrichment analyses. The Library of Integrated Network-based Cellular Signatures (LINCS)-based and Connectivity Map (CMAP)-mediated identification of FDA-approved compounds capable to interfere with a 22 gene signature from primary CRC and liver metastases. Testing the identified compounds on CRC-Patient Derived Organoid (PDO) cultures. Microscopy and Fluorescence Activated Cell Sorting (FACS) based analysis of the treated PDOs.ResultsWe have found that liver metastases acquire features of the adjacent liver tissue while partially losing those of the primary tumors they derived from. We have identified a 22-gene signature differentially expressed among primary tumors and metastases and validated in public databases. A pharmacogenomic screening for FDA-approved compounds capable of interfering with this signature has been performed. We have validated some of the identified representative compounds in CRC-Patient Derived Organoid cultures (PDOs) and found that pentoxyfilline and, to a minor extent, dexketoprofen and desloratadine, can variably interfere with number, size and viability of the CRC –PDOs in a patient-specific way. We explored the pentoxifylline mechanism of action and found that pentoxifylline treatment attenuated the 5-FU elicited increase of ALDHhigh cells by attenuating the IL-6 mediated STAT3 (tyr705) phosphorylation.ConclusionsPentoxifylline synergizes with 5-Fluorouracil (5-FU) in attenuating organoid formation. It does so by interfering with an IL-6-STAT3 axis leading to the emergence of chemoresistant ALDHhigh cell subpopulations in 5-FU treated PDOs. A larger cohort of CRC-PDOs will be required to validate and expand on the findings of this proof-of-concept study.

Prioritized polycystic kidney disease drug targets and repurposing candidates from pre-cystic and cystic mouse Pkd2 model gene expression reversion

BackgroundAutosomal dominant polycystic kidney disease (ADPKD) is one of the most prevalent monogenic human diseases. It is mostly caused by pathogenic variants in PKD1 or PKD2 genes that encode interacting transmembrane proteins polycystin-1 (PC1) and polycystin-2 (PC2). Among many pathogenic processes described in ADPKD, those associated with cAMP signaling, inflammation, and metabolic reprogramming appear to regulate the disease manifestations. Tolvaptan, a vasopressin receptor-2 antagonist that regulates cAMP pathway, is the only FDA-approved ADPKD therapeutic. Tolvaptan reduces renal cyst growth and kidney function loss, but it is not tolerated by many patients and is associated with idiosyncratic liver toxicity. Therefore, additional therapeutic options for ADPKD treatment are needed.MethodsAs drug repurposing of FDA-approved drug candidates can significantly decrease the time and cost associated with traditional drug discovery, we used the computational approach signature reversion to detect inversely related drug response gene expression signatures from the Library of Integrated Network-Based Cellular Signatures (LINCS) database and identified compounds predicted to reverse disease-associated transcriptomic signatures in three publicly available Pkd2 kidney transcriptomic data sets of mouse ADPKD models. We focused on a pre-cystic model for signature reversion, as it was less impacted by confounding secondary disease mechanisms in ADPKD, and then compared the resulting candidates’ target differential expression in the two cystic mouse models. We further prioritized these drug candidates based on their known mechanism of action, FDA status, targets, and by functional enrichment analysis.ResultsWith this in-silico approach, we prioritized 29 unique drug targets differentially expressed in Pkd2 ADPKD cystic models and 16 prioritized drug repurposing candidates that target them, including bromocriptine and mirtazapine, which can be further tested in-vitro and in-vivo.ConclusionCollectively, these results indicate drug targets and repurposing candidates that may effectively treat pre-cystic as well as cystic ADPKD.Graphical

Drug repositioning prediction for psoriasis using the adverse event reporting database.

Inverse signals produced from disproportional analyses using spontaneous drug adverse event reports can be used for drug repositioning purposes. The purpose of this study is to predict drug candidates using a computational method that integrates reported drug adverse event data, disease-specific gene expression profiles, and drug-induced gene expression profiles. Drug and adverse events from 2015 through 2020 were downloaded from the United States Food and Drug Administration Adverse Event Reporting System (FAERS). The reporting odds ratio (ROR), information component (IC) and empirical Bayes geometric mean (EBGM) were used to calculate the inverse signals. Psoriasis was selected as the target disease. Disease specific gene expression profiles were obtained by the meta-analysis of the Gene Expression Omnibus (GEO). The reverse gene expression scores were calculated using the Library of Integrated Network-based Cellular Signatures (LINCS) and their correlations with the inverse signals were obtained. Reversal genes and the candidate compounds were identified. Additionally, these correlations were validated using the relationship between the reverse gene expression scores and the half-maximal inhibitory concentration (IC50) values from the Chemical European Molecular Biology Laboratory (ChEMBL). Inverse signals produced from a disproportional analysis can be used for drug repositioning and to predict drug candidates against psoriasis.

Repositioning linifanib as a potent anti-necroptosis agent for sepsis

Sepsis is a systemic inflammatory syndrome (SIRS) caused by acute microbial infection, and it has an extremely high mortality rate. Tumor necrosis factor-α (TNF-α)-induced necroptosis contributes to the pathophysiology of sepsis, so inhibiting necroptosis might be expected to improve clinical outcomes in septic patients. Here we predicted candidate drugs for treating sepsis in silico by combining genes differentially expressed in septic patients and controls combined with interrogation of the Library of Integrated Network-based Cellular Signatures (LINCS) L1000 perturbation database. Sixteen candidate drugs were screened out through bioinformatics analysis, and the top candidate linifanib was validated in cellular and mouse models of TNF-α-induced necroptosis. Cell viability was measured using a luminescent ATP assay, while the effects of linifanib on necroptosis were investigated by western blotting, immunoprecipitation, and RIPK1 kinase assays. Linifanib effectively protected cells from necroptosis and rescued SIRS mice from TNF-α-induced shock and death. In vitro, linifanib directly suppressed RIPK1 kinase activity. In vivo, linifanib effectively reduced overexpressed IL-6, a marker of sepsis severity, in the lungs of SIRS mice. Our preclinical evidence using an integrated in silico and experimental drug repositioning approach supports the potential clinical utility of linifanib in septic patients. Further clinical validation is now warranted.

Linking chemicals, genes and morphological perturbations to diseases

The progress in image-based high-content screening technology has facilitated high-throughput phenotypic profiling notably the quantification of cell morphology perturbation by chemicals. However, understanding the mechanism of action of a chemical and linking it to cell morphology and phenotypes remains a challenge in drug discovery.In this study, we intended to integrate molecules that induced transcriptomic perturbations and cellular morphological changes into a biological network in order to assess chemical-phenotypic relationships in humans. Such a network was enriched with existing disease information to suggest molecular and cellular profiles leading to phenotypes.Two datasets were used for this study. Firstly, we used the “Cell Painting morphological profiling assay” dataset, composed of 30,000 compounds tested on human osteosarcoma cells (named U2OS). Secondly, we used the “L1000 mRNA profiling assay” dataset, a collection of transcriptional expression data from cultured human cells treated with approximately 20,000 bioactive small molecules from the Library of Integrated Network-based Cellular Signatures (LINCS). Furthermore, pathways, gene ontology terms and disease enrichments were performed on the transcriptomics data.Overall, our study makes it possible to develop a biological network combining chemical-gene-pathway-morphological perturbation and disease relationships. It contains an ensemble of 9989 chemicals, 732 significant morphological features and 12,328 genes. Through diverse examples, we demonstrated that some drugs shared similar genes, pathways and morphological profiles that, taken together, could help in deciphering chemical-phenotype observations.

NeuroLINCS Proteomics: Defining human-derived iPSC proteomes and protein signatures of pluripotency

The National Institute of Health (NIH) Library of integrated network-based cellular signatures (LINCS) program is premised on the generation of a publicly available data resource of cell-based biochemical responses or “signatures” to genetic or environmental perturbations. NeuroLINCS uses human inducible pluripotent stem cells (hiPSCs), derived from patients and healthy controls, and differentiated into motor neuron cell cultures. This multi-laboratory effort strives to establish i) robust multi-omic workflows for hiPSC and differentiated neuronal cultures, ii) public annotated data sets and iii) relevant and targetable biological pathways of spinal muscular atrophy (SMA) and amyotrophic lateral sclerosis (ALS). Here, we focus on the proteomics and the quality of the developed workflow of hiPSC lines from 6 individuals, though epigenomics and transcriptomics data are also publicly available. Known and commonly used markers representing 73 proteins were reproducibly quantified with consistent expression levels across all hiPSC lines. Data quality assessments, data levels and metadata of all 6 genetically diverse human iPSCs analysed by DIA-MS are parsable and available as a high-quality resource to the public.

Evaluating the robustness of connectivity methods to noise for in silico drug repurposing studies

Drug repurposing is an approach to identify new therapeutic applications for existing drugs and small molecules. It is a field of growing research interest due to its time and cost effectiveness as compared with de novo drug discovery. One method for drug repurposing is to adopt a systems biology approach to associate molecular ‘signatures’ of drug and disease. Drugs which have an inverse relationship with the disease signature may be able to reverse the molecular effects of the disease and thus be candidates for repurposing. Conversely, drugs which mimic the disease signatures can inform on potential molecular mechanisms of disease. The relationship between these disease and drug signatures are quantified through connectivity scores. Identifying a suitable drug-disease scoring method is key for in silico drug repurposing, so as to obtain an accurate representation of the true drug-disease relationship. There are several methods to calculate these connectivity scores, notably the Kolmogorov-Smirnov (KS), Zhang and eXtreme Sum (XSum). However, these methods can provide discordant estimations of the drug-disease relationship, and this discordance can affect the drug-disease indication. Using the gene expression profiles from the Library of Integrated Network-Based Cellular Signatures (LINCS) database, we evaluated the methods based on their drug-disease connectivity scoring performance. In this first-of-its-kind analysis, we varied the quality of disease signatures by using only highly differential genes or by the inclusion of non-differential genes. Further, we simulated noisy disease signatures by introducing varying levels of noise into the gene expression signatures. Overall, we found that there was not one method that outperformed the others in all instances, but the Zhang method performs well in a majority of our analyses. Our results provide a framework to evaluate connectivity scoring methods, and considerations for deciding which scoring method to apply in future systems biology studies for drug repurposing.

EPCO-14. ISOSCELES: AN INTEGRATIVE FRAMEWORK FOR THE PREDICTION OF TREATMENT RESISTANT GLIOBLASTOMA CELLS

Abstract Glioblastoma (GBM) remains the most common and lethal adult primary brain cancer. Two of the most significant issues preventing the development of effective GBM treatments are inter- and intra-tumor heterogeneity. To address these issues, we developed a novel platform termed ISOSCELES (Inferred cell Sensitivity Operating on the integration of Single-Cell Expression and L1000 Expression Signatures). ISOSCELES integrates single-cell gene expression data in individual GBM tumors with perturbation-response data derived from the NIH Library of Integrated Network-Based Cellular Signatures (LINCS) L1000 dataset to predict sensitive and resistant tumor cell populations. Importantly, we analyzed the predictive power of ISOSCELES in an in vivo xenograft model and demonstrated that ISOSCELES reveals the GBM cell identities primed for lineage expansion during treatment with the aurora kinase inhibitor alisertib. These studies suggest that ISOSCELES can be used to identify sensitive and resistant cell populations to targeted therapies in GBM, which can inform treatment decisions in ongoing and future clinical trials.

DrSim: Similarity Learning for Transcriptional Phenotypic Drug Discovery

Transcriptional phenotypic drug discovery has achieved great success, and various compound perturbation-based data resources, such as connectivity map (CMap) and library of integrated network-based cellular signatures (LINCS), have been presented. Computational strategies fully mining these resources for phenotypic drug discovery have been proposed. Among them, the fundamental issue is to define the proper similarity between transcriptional profiles. Traditionally, such similarity has been defined in an unsupervised way. However, due to the high dimensionality and the existence of high noise in high-throughput data, similarity defined in the traditional way lacks robustness and has limited performance. To this end, we present DrSim, which is a learning-based framework that automatically infers similarity rather than defining it. We evaluated DrSim on publicly available in vitro and in vivo datasets in drug annotation and repositioning. The results indicated that DrSim outperforms the existing methods. In conclusion, by learning transcriptional similarity, DrSim facilitates the broad utility of high-throughput transcriptional perturbation data for phenotypic drug discovery. The source code and manual of DrSim are available at https://github.com/bm2-lab/DrSim/.

Getting Started with LINCS Datasets and Tools.

The Library of Integrated Network-based Cellular Signatures (LINCS) was an NIH Common Fund program that aimed to expand our knowledge about human cellular responses to chemical, genetic, and microenvironment perturbations. Responses to perturbations were measured by transcriptomics, proteomics, cellular imaging, and other high content assays. The second phase of the LINCS program, which lasted 7 years, involved the engagement of six data and signature generation centers (DSGCs) and one data coordination and integration center (DCIC). The DSGCs and the DCIC developed several digital resources, including tools, databases, and workflows that aim to facilitate the use of the LINCS data and integrate this data with other publicly available data. The digital resources developed by the DSGCs and the DCIC can be used to gain new biological and pharmacological insights that can lead to the development of novel therapeutics. This protocol provides step-by-step instructions for processing the LINCS data into signatures, and utilizing the digital resources developed by the LINCS consortia for hypothesis generation and knowledge discovery. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Navigating L1000 tools and data in CLUE.io Basic Protocol 2: Computing signatures from the L1000 data with the CD method Basic Protocol 3: Analyzing lists of differentially expressed genes and querying them against the L1000 data with BioJupies and the Bulk RNA-seq Appyter Basic Protocol 4: Utilizing the L1000FWD resource for drug discovery Basic Protocol 5: KINOMEscan and the KINOMEscan Appyter Basic Protocol 6: LINCS P100 and GCP Proteomics Assays Basic Protocol 7: The LINCS Joint Project (LJP) Basic Protocol 8: The LINCS Data Portals and SigCom LINCS Basic Protocol 9: Creating and analyzing signatures with iLINCS.

Abstract 3074: (Phospho)proteomics profiling of patient-derived colon cancer organoids for the discovery of putative drug targets

Abstract Introduction: Colorectal cancer is the second cause of cancer related deaths worldwide, with the poor survival outcomes attributable to the presence of metastases. To tackle this problem, (phospho)proteomics analysis enables valuable insights into changes of protein expression and signalling in cancer that can be perturbed by drugs. To study mechanisms driving metastasis and perform subsequent drug testing, patient derived organoids (PDOs) are in development as preclinical models. PDOs are obtained by 3D culture of tumour tissue ex-vivo, which enables them to retain the heterogeneity and architecture of the tumor source. To identify putative drug targets for colon cancer metastasis, we performed comparative (phospho)proteomics analysis of metastatic colon tissues and their matched PDOs. Methods: (Phospho)proteomics profiling was performed on PDOs generated from 10 patients with colon metastases to the liver, 10 matched tumour tissues and 4 normal colon mucosa. Unsupervised analysis of the (phospho)proteomic landscape was used to identify differentially expressed genes and pathways in colon metastases and their matched PDOs, compared to normal colon mucosa. We focused on phosphosites, proteins and pathways showing consistently altered expression in tumour tissues and PDOs. Kinase-substrate enrichment analysis was used to identify aberrantly activated kinases, based on the abundance of phosphorylation on the substrates. Putative drugs were selected using the consensus expression response to multiple small molecule drugs across cell lines and conditions from the Library of Integrated Network-Based Cellular Signatures (LINCS) database. Drug treatment of drug candidates was performed using the MTT Cell Proliferation Assay. Results: Using the approach described above we identified 103 differentially expressed proteins and 236 phosphosites between tumour tissues and normals, which were also detected in PDOs, with overall high correlation of t statistics (0.6 Spearman’s rho) between tumor tissues and PDOs. MYC-targets, G2M checkpoints and E2F-targets were amongst the top positively enriched pathways in common, and the LINCS analysis identified multi-kinase inhibitor Nintedanib and NFKB pathway-targeting KIN0-260 as putative drugs for upregulated proteins. The kinase CSNK2A1 was overactivated in both groups, pointing towards the use of CK2 inhibitors for drug testing. Treatment at 5 days with Nintedanib and KIN001-260 on the PDOs resulted in significant reduction of cell viability compared with 5-FU. Conclusion: Our study provides evidence that PDOs recapitulate relevant tumor features at the proteomic and phosphoproteomic levels, supporting the utility of this ex-vivo model as a tool for drug sensitivity testing for metastatic colon cancer. Citation Format: Gina Faye Boot, Federica Panebianco, John Gallon, Charlotte Kiu Yan Ng, Salvatore Piscuoglio. (Phospho)proteomics profiling of patient-derived colon cancer organoids for the discovery of putative drug targets [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3074.

Abstract 5594: Systematic identification of the chemo-immunotherapy synergism to overcome immunotherapy resistance in cancer

Abstract Immune checkpoint blockades (ICB) have prompted a revolution in cancer treatment, but its low response rate and resistance remain a major problem. Clinical trials have reported the combination of ICB with chemotherapy can improve patient treatment outcome, but the synergy potential of most chemo drugs with ICBs are not systematically evaluated due to the limited insight of drug induced alterations in tumor microenvironment. Here, we reported a novel algorithm termed post-treatment-based synergy prediction for chemotherapy and ICB combination (POSEIDON). POSEIDON integrates one million drug-induced transcriptomic changes of T cell and cancer cells from the Library of Integrated Network-Based Cellular Signatures (LINCS) and ICB-induced single cell transcriptomic changes of 500K cells from tumor microenvironments across multiple cancer types. Using the gene expression signatures induced by chemotherapy and immunotherapy, POSEIDON utilized a reversal-orthogonal framework to predict the chemo compound that can potentially overcome ICB resistance and improve treatment beneficial. In an anti-PD1 treated melanoma patient cohort, POSEIDON identified a group of VEGFR inhibitors and HDAC inhibitors, including tivozanib and mocetinostat, showing the highest synergy potential with anti-PD1 therapy to reverse the anti-PD1 resistance through regulating T cell activities. In a triple negative breast cancer patient cohort who received the combination of anti-PDL1 and paclitaxel, POSEIDON also predicted several HDAC inhibitors (e.g., mocetinostat) and thymidylate synthase inhibitors (e.g., trifluridine) to synergize with the combination of anti-PDL1 plus paclitaxel and reverse the combination resistance through activating the immune response in tumor microenvironment. In addition to the synergy through tumor microenvironment, POSEIDON is also capable of predicting the synergy through tumor intrinsic mechanism. POSEIDON identified the genetic inhibition of EZH2 can potentially reverse anti-PD1 resistance via upregulation of tumor antigen presentation and interferon signaling. The synergy between EZH2 inhibitor and anti-PD1 was further validated in vivo by treating the anti-PD1 resistant 4T1.2 mouse model with FDA approved EZH2 inhibitor tazemetostat and PD1 antibody. Our data showed that, while tazemetostat or anti-PD1 alone failed to inhibit tumor growth, the combination of tazemetostat and anti-PD1 can significantly reduce the tumor size and increase overall survival. Collectively, our study demonstrated that POSEIDON can provide reliable prediction of chemo-immunotherapy synergism by integrating the post-treatment transcriptomic profiles from patient tumor microenvironment and cell lines. We envision that the systematic identification of chemo-immunotherapy synergism will help cancer patients to cope with immunotherapy resistance in the short future. Citation Format: Yue Wang, Yifei Wang, Xiaofei Wang, Zehua Wang, Min Zhang, Da Yang. Systematic identification of the chemo-immunotherapy synergism to overcome immunotherapy resistance in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5594.

Investigation of drugs for the prevention of doxorubicin-induced cardiac events using big data analysis

AimDoxorubicin, an anthracycline anti-tumour agent, is an essential chemotherapeutic drug; however, the adverse events associated with doxorubicin usage, including cardiotoxicity, prevent patients from continuing treatment. Here, we used databases to explore existing approved drugs with potential preventative effects against doxorubicin-induced cardiac events and examined their efficacy and mechanisms. MethodsThe Gene Expression Omnibus (GEO), Library of Integrated Network-based Cellular Signatures (LINCS), and Food and Drug Administration Adverse Events Reporting System (FAERS) databases were used to extract candidate prophylactic drugs. Mouse models of doxorubicin-induced cardiac events were generated by intraperitoneal administration of 20 mg/kg of doxorubicin on Day 1 and oral administration of prophylactic candidate drugs for 6 consecutive days beginning the day before doxorubicin administration. On Day 6, mouse hearts were extracted and examined for mRNA expression of apoptosis-related genes. ResultsGEO analysis showed that doxorubicin administration upregulated 490 genes and downregulated 862 genes, and LINCS data identified sirolimus, verapamil, minoxidil, prednisolone, guanabenz, and mosapride as drugs capable of counteracting these genetic alterations. Examination of the effects of these drugs on cardiac toxicity using FAERS identified sirolimus and mosapride as new prophylactic drug candidates. In model mice, mosapride and sirolimus suppressed the Bax/Bcl-2 mRNA ratio, which is elevated in doxorubicin-induced cardiotoxicity. These drugs also suppressed the expression of inflammatory cytokines Il1b and Il6 and markers associated with myocardial fibrosis, including Lgal3 and Timp1. ConclusionThese findings suggest that doxorubicin-induced cardiac events are suppressed by the administration of mosapride and sirolimus.

SigCom LINCS: data and metadata search engine for a million gene expression signatures.

Millions of transcriptome samples were generated by the Library of Integrated Network-based Cellular Signatures (LINCS) program. When these data are processed into searchable signatures along with signatures extracted from Genotype-Tissue Expression (GTEx) and Gene Expression Omnibus (GEO), connections between drugs, genes, pathways and diseases can be illuminated. SigCom LINCS is a webserver that serves over a million gene expression signatures processed, analyzed, and visualized from LINCS, GTEx, and GEO. SigCom LINCS is built with Signature Commons, a cloud-agnostic skeleton Data Commons with a focus on serving searchable signatures. SigCom LINCS provides a rapid signature similarity search for mimickers and reversers given sets of up and down genes, a gene set, a single gene, or any search term. Additionally, users of SigCom LINCS can perform a metadata search to find and analyze subsets of signatures and find information about genes and drugs. SigCom LINCS is findable, accessible, interoperable, and reusable (FAIR) with metadata linked to standard ontologies and vocabularies. In addition, all the data and signatures within SigCom LINCS are available via a well-documented API. In summary, SigCom LINCS, available at https://maayanlab.cloud/sigcom-lincs, is a rich webserver resource for accelerating drug and target discovery in systems pharmacology.