Systems Biology Research Articles

Stochastic modeling of a gene regulatory network driving B cell development in germinal centers.

Germinal centers (GCs) are the key histological structures of the adaptive immune system, responsible for the development and selection of B cells producing high-affinity antibodies against antigens. Due to their level of complexity, unexpected malfunctioning may lead to a range of pathologies, including various malignant formations. One promising way to improve the understanding of malignant transformation is to study the underlying gene regulatory networks (GRNs) associated with cell development and differentiation. Evaluation and inference of the GRN structure from gene expression data is a challenging task in systems biology: recent achievements in single-cell (SC) transcriptomics allow the generation of SC gene expression data, which can be used to sharpen the knowledge on GRN structure. In order to understand whether a particular network of three key gene regulators (BCL6, IRF4, BLIMP1), influenced by two external stimuli signals (surface receptors BCR and CD40), is able to describe GC B cell differentiation, we used a stochastic model to fit SC transcriptomic data from a human lymphoid organ dataset. The model is defined mathematically as a piecewise-deterministic Markov process. We showed that after parameter tuning, the model qualitatively recapitulates mRNA distributions corresponding to GC and plasmablast stages of B cell differentiation. Thus, the model can assist in validating the GRN structure and, in the future, could lead to better understanding of the different types of dysfunction of the regulatory mechanisms.

BioKC: a collaborative platform for curation and annotation of molecular interactions.

Curation of biomedical knowledge into systems biology diagrammatic or computational models is essential for studying complex biological processes. However, systems-level curation is a laborious manual process, especially when facing ever-increasing growth of domain literature. New findings demonstrating elaborate relationships between multiple molecules, pathways and cells have to be represented in a format suitable for systems biology applications. Importantly, curation should capture the complexity of molecular interactions in such a format together with annotations of the involved elements and support stable identifiers and versioning. This challenge calls for novel collaborative tools and platforms allowing to improve the quality and the output of the curation process. In particular, community-based curation, an important source of curated knowledge, requires support in role management, reviewing features and versioning. Here, we present Biological Knowledge Curation (BioKC), a web-based collaborative platform for the curation and annotation of biomedical knowledge following the standard data model from Systems Biology Markup Language (SBML). BioKC offers a graphical user interface for curation of complex molecular interactions and their annotation with stable identifiers and supporting sentences. With the support of collaborative curation and review, it allows to construct building blocks for systems biology diagrams and computational models. These building blocks can be published under stable identifiers and versioned and used as annotations, supporting knowledge building for modelling activities.

Chemical Organization Theory as a General Modeling Framework for Self-Sustaining Systems

This paper summarizes and reviews Chemical Organization Theory (COT), a formalism for the analysis of complex, self-organizing systems across multiple disciplines. Its elements are resources and reactions. A reaction maps a set of resources onto another set, thus representing an elementary process that transforms resources into new resources. Reaction networks self-organize into invariant subnetworks, called ‘organizations’, which are attractors of their dynamics. These are characterized by closure (no new resources are added) and self-maintenance (no existing resources are lost). Thus, they provide a simple model of autopoiesis: the organization persistently recreates its own components. The resilience of organizations in the face of perturbations depends on properties such as the size of their basin of attraction and the redundancy of their reaction pathways. Application domains of COT include the origin of life, systems biology, cognition, ecology, Gaia theory, sustainability, consciousness, and social systems.

DeepFGRN: inference of gene regulatory network with regulation type based on directed graph embedding.

The inference of gene regulatory networks (GRNs) from gene expression profiles has been a key issue in systems biology, prompting many researchers to develop diverse computational methods. However, most of these methods do not reconstruct directed GRNs with regulatory types because of the lack of benchmark datasets or defects in the computational methods. Here, we collect benchmark datasets and propose a deep learning-based model, DeepFGRN, for reconstructing fine gene regulatory networks (FGRNs) with both regulation types and directions. In addition, the GRNs of real species are always large graphs with direction and high sparsity, which impede the advancement of GRN inference. Therefore, DeepFGRN builds a node bidirectional representation module to capture the directed graph embedding representation of the GRN. Specifically, the source and target generators are designed to learn the low-dimensional dense embedding of the source and target neighbors of a gene, respectively. An adversarial learning strategy is applied to iteratively learn the real neighbors of each gene. In addition, because the expression profiles of genes with regulatory associations are correlative, a correlation analysis module is designed. Specifically, this module not only fully extracts gene expression features, but also captures the correlation between regulators and target genes. Experimental results show that DeepFGRN has a competitive capability for both GRN and FGRN inference. Potential biomarkers and therapeutic drugs for breast cancer, liver cancer, lung cancer and coronavirus disease 2019 are identified based on the candidate FGRNs, providing a possible opportunity to advance our knowledge of disease treatments.

Gene networks and systems biology in Alzheimer's disease: Insights from multi-omics approaches.

Despite numerous studies in the field of dementia and Alzheimer's disease (AD), a comprehensive understanding of this devastating disease remains elusive. Bulk transcriptomics have provided insights into the underlying genetic factors at a high level. Subsequent technological advancements have focused on single-cell omics, encompassing techniques such as single-cell RNA sequencing and epigenomics, enabling the capture of RNA transcripts and chromatin states at a single cell or nucleus resolution. Furthermore, the emergence of spatial omics has allowed the study of gene responses in the vicinity of amyloid beta plaques or across various brain regions. With the vast amount of data generated, utilizing gene regulatory networks to comprehensively study this disease has become essential. This review delves into some techniques employed in the field of AD, explores the discoveries made using these techniques, and provides insights into the future of the field.

Bioinformatics and system biology approach to identify the influences among COVID-19, influenza, and HIV on the regulation of gene expression.

Coronavirus disease (COVID-19), caused by SARS-CoV-2, has emerged as a infectious disease, coexisting with widespread seasonal and sporadic influenza epidemics globally. Individuals living with HIV, characterized by compromised immune systems, face an elevated risk of severe outcomes and increased mortality when affected by COVID-19. Despite this connection, the molecular intricacies linking COVID-19, influenza, and HIV remain unclear. Our research endeavors to elucidate the shared pathways and molecular markers in individuals with HIV concurrently infected with COVID-19 and influenza. Furthermore, we aim to identify potential medications that may prove beneficial in managing these three interconnected illnesses. Sequencing data for COVID-19 (GSE157103), influenza (GSE185576), and HIV (GSE195434) were retrieved from the GEO database. Commonly expressed differentially expressed genes (DEGs) were identified across the three datasets, followed by immune infiltration analysis and diagnostic ROC analysis on the DEGs. Functional enrichment analysis was performed using GO/KEGG and Gene Set Enrichment Analysis (GSEA). Hub genes were screened through a Protein-Protein Interaction networks (PPIs) analysis among DEGs. Analysis of miRNAs, transcription factors, drug chemicals, diseases, and RNA-binding proteins was conducted based on the identified hub genes. Finally, quantitative PCR (qPCR) expression verification was undertaken for selected hub genes. The analysis of the three datasets revealed a total of 22 shared DEGs, with the majority exhibiting an area under the curve value exceeding 0.7. Functional enrichment analysis with GO/KEGG and GSEA primarily highlighted signaling pathways associated with ribosomes and tumors. The ten identified hub genes included IFI44L, IFI44, RSAD2, ISG15, IFIT3, OAS1, EIF2AK2, IFI27, OASL, and EPSTI1. Additionally, five crucial miRNAs (hsa-miR-8060, hsa-miR-6890-5p, hsa-miR-5003-3p, hsa-miR-6893-3p, and hsa-miR-6069), five essential transcription factors (CREB1, CEBPB, EGR1, EP300, and IRF1), and the top ten significant drug chemicals (estradiol, progesterone, tretinoin, calcitriol, fluorouracil, methotrexate, lipopolysaccharide, valproic acid, silicon dioxide, cyclosporine) were identified. This research provides valuable insights into shared molecular targets, signaling pathways, drug chemicals, and potential biomarkers for individuals facing the complex intersection of COVID-19, influenza, and HIV. These findings hold promise for enhancing the precision of diagnosis and treatment for individuals with HIV co-infected with COVID-19 and influenza.

Progress in polystyrene biodegradation by insect gut microbiota.

Polystyrene (PS) is frequently used in the plastics industry. However, its structural stability and difficulty to break down lead to an abundance of plastic waste in the environment, resulting in micro-nano plastics (MNPs). As MNPs are severe hazards to both human and environmental health, it is crucial to develop innovative treatment technologies to degrade plastic waste. The biodegradation of plastics by insect gut microorganisms has gained attention as it is environmentally friendly, efficient, and safe. However, our knowledge of the biodegradation of PS is still limited. This review summarizes recent research advances on PS biodegradation by gut microorganisms/enzymes from insect larvae of different species, and schematic pathways of the degradation process are discussed in depth. Additionally, the prospect of using modern biotechnology, such as genetic engineering and systems biology, to identify novel PS-degrading microbes/functional genes/enzymes and to realize new strategies for PS biodegradation is highlighted. Challenges and limitations faced by the application of genetically engineered microorganisms (GEMs) and multiomics technologies in the field of plastic pollution bioremediation are also discussed. This review encourages the further exploration of the biodegradation of PS by insect gut microbes/enzymes, offering a cutting-edge perspective to identify PS biodegradation pathways and create effective biodegradation strategies.

CEGAR-Based Approach for Solving Combinatorial Optimization Modulo Quantified Linear Arithmetics Problems

Bioinformatics has always been a prolific domain for generating complex satisfiability and optimization problems. For instance, the synthesis of multi-scale models of biological networks has recently been associated with the resolution of optimization problems mixing Boolean logic and universally quantified linear constraints (OPT+qLP), which can be benchmarked on real-world models. In this paper, we introduce a Counter-Example-Guided Abstraction Refinement (CEGAR) to solve such problems efficiently. Our CEGAR exploits monotone properties inherent to linear optimization in order to generalize counter-examples of Boolean relaxations. We implemented our approach by extending Answer Set Programming (ASP) solver Clingo with a quantified linear constraints propagator. Our prototype enables exploiting independence of sub-formulas to further exploit the generalization of counter-examples. We evaluate the impact of refinement and partitioning on two sets of OPT+qLP problems inspired by system biology. Additionally, we conducted a comparison with the state-of-the-art ASP solver Clingo[lpx] that handles non-quantified linear constraints, showing the advantage of our CEGAR approach for solving large problems.

Scalable Enumeration of Trap Spaces in Boolean Networks via Answer Set Programming

Boolean Networks (BNs) are widely used as a modeling formalism in several domains, notably systems biology and computer science. A fundamental problem in BN analysis is the enumeration of trap spaces, which are hypercubes in the state space that cannot be escaped once entered. Several methods have been proposed for enumerating trap spaces, however they often suffer from scalability and efficiency issues, particularly for large and complex models. To our knowledge, the most efficient and recent methods for the trap space enumeration all rely on Answer Set Programming (ASP), which has been widely applied to the analysis of BNs. Motivated by these considerations, our work proposes a new method for enumerating trap spaces in BNs using ASP. We evaluate the method on a mix of 250+ real-world and 400+ randomly generated BNs, showing that it enables analysis of models beyond the capabilities of existing tools (namely pyboolnet, mpbn, trappist, and trapmvn).

Abstract 3762: 11,000-plex aptamer based proteomics of bladder cancer urine for the discovery of novel disease biomarkers

Abstract In search of more accurate, non-invasive biomarkers of bladder cancer (BC), a comprehensive aptamer-based screen of urine proteins was conducted using urine samples from a BC cohort, followed by systems biology analyses. Following an initial 1317-plex screen, an extended 11,000 plex aptamer-based proteomic screen is in progress. Based on the initial screen, 30 urine proteins were next ELISA validated in an independent cohort of 68 subjects, and the results are shown in Table 1. Of 21 urine proteins that discriminated BC from urology clinic controls (UC), urine D-dimer displayed the highest ROC AUC value (0.96) and sensitivity (95%) (Table 1). Furthermore, 9 urine proteins significantly discriminated more advanced BC (T1-T4) from earlier stages (Ta-Tis), with IL-8 and MMP-12 being the best performers. Urine MMP12 exhibited the highest specificity of 80% at fixed sensitivity for identifying advanced BC. Systems biology analysis implicated molecular functions related to extracellular matrix, collagen, integrin, heparin and transmembrane tyrosine kinase signaling in BC susceptibility, with HNF4A and NFKB1 emerging as key molecular regulators. STEM analysis of the dysregulated pathways implicated a functional role for the immune system, complement and interleukins in BC disease progression. Urine C2, D-dimer, and Elastase were additionally ELISA validated in a second BC cohort of Chinese ethnicity. Thus, comprehensive proteomics and independent ELISA validation have uncovered novel urine biomarkers for initial BC diagnosis and subsequent disease progression, that warrant further head to head comparison with current diagnostic tests. Citation Format: Chandra Mohan, Kamala Vanarsa, Jessica Castillo, Long Wang, Yair Lotan. 11,000-plex aptamer based proteomics of bladder cancer urine for the discovery of novel disease biomarkers [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 3762.

Abstract 6548: Leveraging compact feature sets for TCGA-based molecular subtype classification on new samples

Abstract The NCI's The Cancer Genome Atlas (TCGA) project profiled over 10,000 tumor samples over the course of 10 years. As different tissue-specific working groups reviewed all of the available data, these patient samples were separated into distinct molecular subtypes, and these clusters were reported in various marker papers. While these assignments provided invaluable information about the common patterns of molecular characteristics in different types of cancer there was no consistent methodology for assigning new samples to these defined molecular subtypes.The NCI's Tumor Molecular Pathology group was formulated to create machine learning-based models that could be applied to non-TCGA samples and determine their TCGA mapped subtypes. Five modeling systems, JADBio, SKGrid by the Oregon Health and Science University, CloudForest by the Institute of Systems Biology, AKLIMATE by University of California Santa Cruz and subSCOPE by BC Cancer’s Genome Sciences Centre, were trained to recognize TCGA subtypes using multi-omic measurements from gene expression, DNA methylation, miRNA expression, copy number, and somatic mutation calls. While the TCGA samples were profiled using multi-omic technologies, single platform and/or compact feature set models also were assessed for their ability to assign these classifications. Each machine learning system created predictive models for 106 subtypes from 26 cancer types using as few features as possible, with a maximum of 100 features allowed for scored models. A set of 411,706 models was developed, composed of results of each of the learning methods across the various omic platforms. Top models, both multi-omic and single platform, were selected for each cancer type. On average, models were able to achieve an overall weighted F1 score of 0.895 with 42 features. While the top models for each cancer type had an overall weighted F1 mean performance of 0.936 with a mean of 29 features, in 20 of the 26 cancer types models using only gene expression provided the best performance. Analysis of features selected by the models showed some known onco-drivers were selected by many models, but many times different models would utilize features of different genes with similar levels of performance. Network-level analysis revealed that many genes of these selected features operated within the same pathways.Transferability of these models to external datasets was tested, taking TCGA breast cancer trained models and applying them to AURORA and METABRIC datasets. Interestingly, despite the data platform difference between TCGA (RNAseq) and METABRIC (microarray), model performance saw only minimal degradation of F1 values in transfer. This set of models and the training dataset will provide new opportunities for researchers and translational scientists to connect new tumors to the subtypes seen in the TCGA cohorts. Citation Format: Kyle Ellrott, Chris K. Wong, Christina Yau, Mauro A. Castro, Jordan Lee, Brian Karlberg, Jasleen K. Grewal, Vincenzo Lagani, Bahar Tercan, Verena Friedl, Toshinori Hinoue, Vladislav Uzunangelov, Lindsay Westlake, Xavier Loinaz, Ina Felau, Peggy Wang, Anab Kemal, Samantha J. Caesar-Johnson, Ilya Shmulevich, Alexander J. Lazar, Ioannis Tsamardinos, Katherine A. Hoadley, The Cancer Genome Atlas Analysis Network, Gordon A. Robertson, Theo A. Knijnenburg, Christopher C. Benz, Joshua M. Stuart, Jean C. Zenklusen, Andrew D. Cherniack, Peter W. Laird. Leveraging compact feature sets for TCGA-based molecular subtype classification on new samples [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 6548.

Abstract 3651: Mutational analysis and spatial phenotyping to decipher racial disparities in pancreatic adenocarcinoma

Abstract Background: Pancreatic cancer has a 5-year survival of only 12%. This is due to the lack of effective treatments and virtually no early detection methods. To compound this already poor prognosis, African Americans face a 20% higher incidence and worse mortality compared to non-African American patients. With a substantial portion of the data and tools employed for studying pancreatic cancer failing to adequately represent this demographic we aim to gain more insight into the differences associated with worse outcomes for African Americans with pancreatic cancer. To achieve this, we conducted an in-depth analysis of tumor mutational burden using whole exome sequencing and the cutting-edge PhenoCycler®-Fusion 2.0 spatial biology platform, to uncover disparities in spatial immune and metabolic phenotypes. Methods: Using an ultrahigh-plex discovery panel of markers encompassing cell lineage, immune checkpoints, tissue architecture, activation, metabolism, proliferation, and stress, we sought to analyze the differences in spatial phenotypes, cellular neighborhoods and functional pathways across the two groups. Additionally, we performed laser capture microdissection on a cohort of patients containing equal numbers of African American and non-African American pancreatic cancer samples. Tumor, stroma, and adjacent normal areas were identified by a board-certified pathologist. These annotations were used to laser capture on 10-micron sections with 7 sections for each patient. We performed whole exome sequencing on these sections and different areas and compared between groups. Results: Through our investigation using these two technologies, we can find differences in the tumor microenvironment between African Americans and non-African Americans. We can see potential associations in the increased mortality and incidence of this group while also building a foundation of racially diverse data to be used for future studies. Whole exome sequencing has revealed distinct mutational patterns among African American patients, involving common genes that exhibit unique mutation profiles when compared to those previously observed in non-African American patients. Moreover, whole-slide spatial phenotyping reveals the differential tumor-immune landscapes and the key cellular and molecular niches that contribute to tumor progression and prognosis. These findings underscore the imperative need for a more systems biology exploration of the variations within these groups, with the aim of developing more effective and personalized treatment approaches. Citation Format: Daniel James Salas-Escabillas, Ning Ma, Bassem Ben Cheikh, Aditya Pratapa, Thais Pichardo, Kyra Langley, Julie Clark, Nina Steele, Niyati Jhaveri, David Kwon, Howard C. Crawford. Mutational analysis and spatial phenotyping to decipher racial disparities in pancreatic adenocarcinoma [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 3651.

Abstract 6220: TRIP13 augments pancreatic cancer progression- An integrated systems biology study

Abstract Background- Pancreatic cancer (PanCa) is one of the most aggressive forms of cancer and its incidence rate is continuously increasing every year. It is expected that by 2030, PanCa will become the 2nd leading cause of cancer related deaths in the United States due to the lack of understanding of the complexity of disease, early diagnostic methods, and extremely poor survival. Despite great advancements in biomedical research, there are very limited modalities available for the early detection of PanCa and its treatment. Thus, understanding of disease biology and identification of newer diagnostic and therapeutic modalities are high priority research in the field. Our group has identified Thyroid Receptor Interacting Protein -13 (TRIP13) as an oncogenic protein which contributes to early events of PanCa. Methodology- High dimensional omics and structural elucidation of TRIP13 was conducted using publicly available databases like Consurf, Phosphosite, GTEx and GEPIA2. Molecular biology techniques were applied to cross validate all our bioinformatics data. qPCR, immunoblotting and IHC analysis was performed in progressive PDAC cell lines and human tissues to decipher the expression level of TRIP13 in various pathological staging including functional enrichment analysis using Linkdomics. Results- The structural elucidation analysis revealed that most of the functionally exposed residues, phosphorylation and ubiquitylation sites situated at AAA domain of TRIP13. As compared to normal, TRIP13 is significantly over expressed in pancreatic tumor samples. Pathological staging 2 and 1 (early stages) showed relatively higher expression of TRIP13 as compared advanced stage. Of the total 7 isoforms of TRIP13, only one isoform (TRIP13-001) has shown the over expression in pancreatic tumor samples and shown association with poor survival. In correlation studies, TRIP13 has shown positive association with other pancreatic cancer biomarkers (CEACAM5, S100A4, MUC1, MSLN, CA125). The functional enrichment analyses suggest that TRIP13 is involved in important patho-physiological pathways like DNA repair, viral carcinogenesis, cellular senescence, and cell cycle. Our wet lab experiments also support our computational biology observation. Conclusion- This integrated computational biology study suggests a potential role of TRIP13 in pancreatic cancer progression via modulation of important oncological pathways. Wet lab experimentations also validated the involvement of TRIP13 in pancreatic cancer progression. TRIP13 expression may be associated with patient prognosis. Thus, its inhibitory small molecules can be useful for boosting pancreatic cancer therapies in clinical settings. Key words- TRIP13, Pancreatic cancer, Early events of cancer, Integrative Biology, Transcriptomics, Computational biology Citation Format: Swati Dhasmana, Anupam Dhasmana, Stella Rios, Iris A. Perez, Sheema Khan, Farrukh Afaq, Upender Manne, Murali M. Yallapu, Subhash C. Chauhan. TRIP13 augments pancreatic cancer progression- An integrated systems biology study [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 6220.

Abstract 6227: A systems biology approach for identifying targetable vulnerabilities in Ewing sarcoma

Abstract Background: Targeting fusion oncoproteins like the canonical EWS/ETS fusions in Ewing sarcoma remain an elusive challenge. To circumvent these challenges, we have utilized transcriptome-based systems biology approaches that identify targetable Master Regulator proteins (MRs) critical to maintaining the cancer cell state. OncoTarget analysis generates a rank order list of targetable MRs while OncoTreat analysis uses cognate cell lines matched to patient tumors to identify drugs that invert the MR signature. We used OncoTarget and OncoTreat analyses to identify existing drugs which could be repurposed to target MRs active in Ewing sarcoma. Methods: A gene regulatory interactome was developed from 122 pediatric sarcoma samples to enable identification of MR targets using OncoTarget. To enable OncoTreat analysis, 2 cognate cell lines were subjected to high-throughput drug perturbational screens. Cells were treated with sub-lethal concentrations of 335 compounds (FDA approved or investigational drugs). Ex vivo drug screens using PDX-derived cells were performed as a complementary assay to validate predictions. In vivo validation was performed in Ewing sarcoma PDXs. Results: The MR architecture of 3 Ewing sarcoma PDX models was evaluated using OncoTarget. OncoTreat analysis identified 43 drugs predicted to be active in at least 1 Ewing sarcoma PDX while OncoTarget analysis identified 20 MR targets predicted in at least 1 Ewing sarcoma PDX. DOT1L and AURKB were predicted targets across multiple Ewing sarcoma PDXs. Ex vivo screening of dissociated cells from 2 Ewing sarcoma PDXs was performed to select the most potent agents for in vivo validation. Three OncoTreat (clarithromycin, tivozanib and obataclax) and two OncoTarget (DOT1L targeted with EPZ-5676 and AURKB targeted with baracertib) predicted drugs were evaluated in a STAG2 mutant Ewing sarcoma PDX and all demonstrated significant abrogation of tumor growth including complete regression in baracertib-treated animals (p<0.01). Conclusion: These systems biology approaches can identify non-oncogene encoded vulnerabilities in Ewing sarcoma and hold the potential for expanding the number of therapeutic options for tumors driven by untargetable oncoproteins. Citation Format: Filemon S. Dela Cruz, Prabhjot S. Mundi, Armaan Siddiquee, Kristina Guillan, Daoqi You, Glorymar Ibanez Sanchez, Andoyo Ndengu, Andrea Califano, Andrew L. Kung. A systems biology approach for identifying targetable vulnerabilities in Ewing sarcoma [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 6227.

Abstract 645: A systems biology approach uncovers the anticancer mechanisms of curcumin

Abstract Introduction: Curcumin, the active ingredient of turmeric, generates reactive oxygen species, increases p53 expression, inhibits NFⲕB signaling, and can induce apoptosis. Despite these potential anticancer effects, there has not yet been a systematic analysis of the effects of curcumin on a large panel of cancer cell lines. We hypothesized that curcumin has differential effects based on the specific cell line and its tissue of origin. This study assessed curcumin’s anticancer properties via cell survival assays and gene expression analyses. Methods: Cell survival assays were performed by the CTD2 Centre at the Broad Institute. 860 human cancer cell lines were seeded into 1536-well plates, and 24 hours after plating, either DMSO control or curcumin was added to the wells at 16 different concentrations by serial dilution. 72 hours later, cellular ATP levels were measured with CellTiter-Glo as an indication of cell survival. For gene expression analysis, the L1000 assay from the Broad Institute was used. 9 core cell lines were treated with 10 µM of curcumin in 384-well plates. After 6 and 24 hours of incubation, a gene expression signature was identified with the Affymetrix GeneChip HG-U133 Plus 2.0 Array. This signature was compared against signatures from other small molecule and genetic perturbations contained in the CMap database. Afterwards, connectivity scores (incorporating an enrichment score, nominal p-value, and false discovery rate) were used to rank the L1000 signatures by similarity to the signature induced by curcumin. Results: Curcumin was most potent against SEM (IC50 = 1.00 µM; B-acute lymphoblastic leukemia), RPMI-8226 (IC50= 1.62 µM; myeloma), and SCC-4 (IC50 = 1.81 µM; squamous cell carcinoma) cell lines. Lymphoid and peripheral nervous system cancers were generally the most sensitive to curcumin, whereas prostate cancers were the least sensitive. Curcumin-induced gene expression changes resembled GPR87 and PRKRA knockdowns, which are associated with reductions in cancer cell survival, chemoresistance, and proliferation in various cancer cell lines. However, these transcriptomic changes also resembled CERS2 downregulation, which is associated with increased cell migration. In vitro, curcumin treatment mimicked 2’,5’-dideoxyadenosine, BMS-299897, and eudesmic acid administration. Conclusion: Curcumin induces changes in cell survival and gene expression that support its anticancer properties in a variety of cancer cell lines. However, separating these effects from CERS2 downregulation may be an important component of future translational research for curcumin and its analogs. Citation Format: Adin Aggarwal, Jacqueline H. Law, Clement Lo, Kenneth W. Yip. A systems biology approach uncovers the anticancer mechanisms of curcumin [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 645.

Abstract 934: RNA-based precision medicine predicts sensitivity to selinexor in select pancreatic ductal adenocarcinoma patients

Abstract Sensitivity to therapeutic agents is impacted both by genetic heterogeneity between patients or among clones within a tumor and by heterogeneity of epigenetic cell states. Prior work demonstrates that human pancreatic tumors comprise malignant cells in a mixture of multiple cell states with distinct genetic dependencies, leading to a reservoir of chemoresistance across the tumor; targeting just one or two malignant cell states is insufficient to achieve durable therapeutic responses. One approach to overcome this challenge is to identify agents with efficacy against malignant cells in multiple cell states. However, in the absence of genetic distinctions between cell states, identifying therapeutic vulnerabilities for each malignant cell state present in a patient’s tumor in real time remains a challenge. OncoTreat is a CLIA certified algorithm that matches drugs to patients based on tumor gene expression profiles. The approach utilizes regulatory network analysis, a systems biology framework that transforms gene expression data into regulatory protein activity profiles, in a manner that facilitates the identification of master transcriptional regulators of cell state. We carried out a Phase 1 clinical trial of the OncoTreat framework to examine feasibility for implementing RNA-based precision medicine in patients with metastatic pancreatic ductal adenocarcinoma (2nd line and beyond). Patient-derived xenografts were established from subjects prior to 1st line therapy, and selected agents matched by OncoTreat were assessed co-clinically while subjects received standard of care regimens, with the potential for subsequent treatment with successful agents upon progression. Two subjects in the study matched to selenexor, an XPO1 inhibitor that is FDA approved for multiple myeloma. Co-clinical studies of selinexor in patient-derived xenograft models generated from the matched subjects showed prolonged survival compared to control regimens. Single-cell RNA sequencing analysis of these tumors found that the activity of master regulators confirmed that the activity of selinexor-responsive master regulators were inverted in response to treatment in vivo, reducing the fraction of malignant cells in the tumors. Strikingly, selenexor treatment was associated with significant inhibition of RAS/MAPK signaling, suggesting a potential novel role for selinexor targeting Ras. Together, these data indicate a potential therapeutic vulnerability for a subset of PDAC patients that can be predicted by a CLIA-certified RNA-based precision medicine platform. Citation Format: Alvaro Curiel-Garcia, Lorenzo Tomassoni, Tanner C. Dalton, Amanda R. Decker-Farrell, Carmine F. Palermo, Daniel R. Ross, Stephen A. Sastra, Urszula N. Wasko, Isabel M. Goncalves, Prabhot Mundi, Basil S. Bakir, Rachael A. Safyan, Hanina Hibshoosh, Gulam A. Manji, Andrea Califano, Kenneth P. Olive. RNA-based precision medicine predicts sensitivity to selinexor in select pancreatic ductal adenocarcinoma patients [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 934.

Abstract 6236: Systems biology modeling identifies SMARCD3 as a master regulator facilitating everolimus resistance in ER+ breast cancer

Abstract Breast cancer is the most common malignancy in women and estrogen receptor positive (ER+) breast cancers represent nearly 75% of all breast tumors. Everolimus, an mTORC1 inhibitor, in combination with exemestane has been approved for patients with metastatic ER+ breast cancer. However, many patients eventually develop resistance to everolimus and leads to poor survival outcomes. Characterizing everolimus-resistant cells can reveal targetable molecular phenotypes that can eventually lead to new therapeutic targets. In this study, we elucidate systems-level phenotypic distinctions between everolimus sensitive and refractory cells. We generated several isogenic ER+ breast cancer cell lines resistant or sensitive to everolimus. We then leveraged transcriptomic profiles from sensitive and resistant cells before or after everolimus treatment to decipher the underlying mechanisms essential to the resistant state. Using linear mixed effect models, we identified several meta-phenotypes that were distinct between sensitive and resistant cell states. These included growth-factor signaling, cell cycle, metabolism, stemness, and apoptosis meta-phenotypes. Our analyses also identified strong activation of growth-factor receptors including IGF1R/INSR and ESR1 in the resistant cells, which were maintained despite everolimus treatment. Next, we adopted a transcriptional regulatory network reconstruction framework to identify the master regulators responsible for the activation of resistance meta-phenotypes. We identified SMARCD3 as the key master regulator controlling expression of genes across diverse resistance meta-phenotypes, including growth-factor signaling. Further, using transcriptomic profiles from a neoadjuvant trial of 23 post-menopausal ER+ breast cancers treated with everolimus, we found that SMARCD3 expression was elevated in resistant tumors. To confirm the role of SMARCD3 in everolimus resistance, we repressed transcription of the SMARCD3 gene using CRISPR interference to re-sensitize resistant cells to everolimus treatment. In conclusion, we used context-specific cell transcriptomic profiles and integrative systems-biology approaches to elucidate mechanisms of resistance in response to everolimus treatment in ER+ breast cancer. Our findings suggest SMARCD3 upregulation promotes resistance to everolimus treatment and may serve as a novel therapeutic target in ER+ breast cancer. Citation Format: Eric F. Medina, Patrick A. Cosgrove, Eleni Farmaki, Vince K. Grolmusz, Feng Chi, Jason I. Griffiths, Andrea H. Bild, Aritro Nath. Systems biology modeling identifies SMARCD3 as a master regulator facilitating everolimus resistance in ER+ breast cancer [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 6236.

Abstract 4490: Targeting NQO1 with Episesaminol: A new therapeutic option identified for colorectal cancer using integrated systems biology and structural biology-based approach

Abstract Introduction: Colon and/or rectal cancer together constitute colorectal cancer (CRC), the 2nd most lethal and the 3rd common cancer worldwide. Drug resistance to modern chemotherapies results in decreased therapeutic efficacy and CRC treatment failure. Herein, multi-layered strategies are used for a better identification of the newer therapeutics. Methodology: Different publicly available datasets were used for the identification of the differentially expressed genes (DEGs) between normal and CRC tissues. miRNAs, lncRNAs and small molecules interacting with those DEGs were mined from various databases. A complex network of interacting mRNA-miRNAs-lncRNAs-small molecules was created using the Cytoscape. Hub RNAs, significant modules and cliques were identified using Cytohubba and MClique plugins. The mRNAs from the top 30 cliques were analyzed by expression, oncoprint, survival and gene ontology. Polyphenols from the PhenolExplorer database were also assessed for their druggability and toxicity properties to finally obtain druggable polyphenols (DPs). A two-step molecular docking was done using PyRx for mRNAs from top 30 cliques and DPs and the top ten complexes were docked again using AutoDock. Molecular dynamics simulation (MDS) is performed for the best fit protein-proposed DP vs protein-known drug using GROMACS. Results: 1915 DEGs, 2609 miRNAs, 1411 lncRNAs and 8790 small molecules (other than selected DPs) interactions were considered for generating a comprehensive interaction network. The 27 mRNAs from the top 30 cliques were subjected to an array of analyses. NCAPD2, CDK4 and FASN were found to be involved in different stages of CRC. IRF2BPL, CMPK1, CCNF and UST were found as survival biomarkers, whereas FAT4 was found to be the only genetically altered mRNA. Functional enrichment supported the analysis revealing significant CRC-associated gene ontology as well as pathways. 56 DPs when docked with the DEGs, the two-step docking revealed NQO1-Episesaminol to be the best target-drug complex. Results of docking were comparable when MDS was performed for NOQ1•Episesaminol Vs NQO1•Doxorubicin (approved drug). Conclusion: In the current interaction network and MDS-based integrated analysis, significant druggable CRC biomarkers and their newer therapeutic solutions were identified. MDS reveals NQO1•Episesaminol as a potential target•drug combination for CRC. Citation Format: Sohini Chakraborty, Gaurav Sharma, Sricheta Karmakar, Prarthana Chatterjee, Saptarshi Sinha, Pradipta Ghosh, Satarupa Banerjee. Targeting NQO1 with Episesaminol: A new therapeutic option identified for colorectal cancer using integrated systems biology and structural biology-based approach [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 4490.

Abstract 7100: Quantitative proteomics reveal the mechanism of TTFields therapy for glioblastoma

Abstract Glioblastoma (GBM) is a highly malignant brain tumor, and conventional treatments such as surgical resection and chemotherapy have limited effectiveness, with a high rate of recurrence and poor prognosis for patients. In 2011, the FDA approved a novel therapy for recurrent GBM known as Tumor Treating Fields (TTFields). TTFields use alternating intermediate-frequency (200 kHz) and low-intensity (1-3 V/cm) electric fields to interfere with tumor cells, inducing apoptosis and inhibiting tumor growth. This study aimed to explore the molecular mechanisms of TTFields therapy at the systems biology level using quantitative proteomics technology. We analyzed U87 cells before and after TTFields treatment using quantitative proteomics and identified a total of 7679 proteins. Among them, 804 proteins were significantly upregulated after TTFields treatment, and 1141 proteins were significantly downregulated. Pathway enrichment analysis of differentially expressed proteins showed that upregulated proteins were mainly associated with cell proliferation, cell cycle, and transcription, possibly representing compensatory responses to the TTFields treatment. Further analysis revealed a significant upregulation of the tumor proliferation key protein PARP1, which was validated by protein immunoblotting experiments. Based on this analysis, we hypothesized that combining TTFields with Olaparib (a PARP1 inhibitor) might enhance the sensitivity of TTFields. Subsequently, we performed cell proliferation experiments to evaluate the concomitant application strategy, and the results demonstrated that Olaparib in combination with TTFields treatment significantly reduced the survival rate of U87 cells compared to single-drug treatment. In conclusion, using quantitative proteomics, this study revealed the biological processes underlying the inhibitory effects of TTFields on GBM proliferation. Under TTFields intervention, the expression of PARP1 was upregulated, and the use of a PARP1 inhibitor in combination significantly increased the sensitivity of TTFields. Therefore, this study not only elucidated the molecular mechanisms of TTFields therapy for GBM treatment at the systems biology level, but also provided support for further enhancing the efficacy of TTFields. Citation Format: Qi Mei, Yanmei Gao, Rong Li, Chenxu Wang, Guangyuan Hu, He Huang. Quantitative proteomics reveal the mechanism of TTFields therapy for glioblastoma [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 7100.

Abstract 450: GCN2 protects cancer cells from hypertranslation and metabolic crisis

Abstract It is widely assumed that cancer cells rely on high levels of protein synthesis to support growth and proliferation. However, protein synthesis, including ribosome biogenesis, is a highly resource- and energy-demanding process that needs to be coordinated with protein degradation and metabolic programs. GCN2 is an evolutionarily conserved kinase that is primarily activated by uncharged tRNAs when amino acid levels drop and activates the Integrated Stress Response (ISR), which encompasses a global reduction in translation and increased amino acid uptake and generation. GCN2 has been shown to support cancer cell survival under conditions of nutrient scarcity. Here, we used an integrated systems biology approach to delineate the multifaceted role of GCN2 in cancer cells that do not suffer from nutrient shortages. We carried out a broad array of multiomic analyses (RNA-seq, TMT-labelling based proteomics, Ribo-seq, LC-MS metabolomics) of melanoma cells in which GCN2 was inhibited (GCN2iB) or genetically depleted (constitutive and inducible shRNA) in cell culture and in vivo in murine xenografts, supplemented by puromycinylation-based quantification of global protein synthesis. GCN2 inhibition in A375 cells triggered a transcriptome and proteome response that was dominated by the induction of processes linked to protein and ribosome synthesis while suppressing metabolic pathways such as glycolysis and the proteasome. We confirmed these observations in vivo via RNA-seq of tumor samples from mice xenografted with A375 cells in which GCN2 was knocked down by induced shRNA expression and from xenografted mice that were treated orally with GCN2iB. Ribo-seq further demonstrated that, upon GCN2 inhibition, A375 cells expand their translational machinery. Notably, we identified 50 ribosomal genes that were translated at higher rate in GCN2-inhibited cells and whose expression was regulated exclusively at the translational level. GCN2 inhibition/depletion also resulted in an approximately 1.8-fold increase in global protein synthesis and altered the druggable proteome. Moreover, metabolite profiling of GCN2iB-treated cells revealed a loss of metabolic homeostasis including substantial alterations in glycolysis and both amino acid and lipid metabolism. In addition, we observed a significant decrease in ATP levels despite increased translation of mitochondrial ETC complex proteins. Proteomic and translatomic analyses revealed a failure of cells to increase proteasomal subunits and to maintain the production of ECM proteins. Last, but not least, we did not observe a role of GCN2 in preventing or resolving ribosome collisions. Taken together, our findings reveal GCN2 as a master regulator of translation in cancer cells that do not suffer from nutrient scarcity that keeps protein synthesis in check to maintain metabolic homeostasis. Citation Format: Monica Roman-Trufero, Istvan T. Kleijn, Kevin Blighe, Paula Saavedra-Garcia, Jinglin Zhou, Abigail Gaffar, Marilena Christoforou, Mariia Yuneva, Audrey M. Michel, Glenn R. Masson, Vahid Shahrezaei, Holger W. Auner. GCN2 protects cancer cells from hypertranslation and metabolic crisis [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 450.