Proteomic Expression Data Research Articles

Integration across biophysical scales identifies molecular and cellular correlates of person-to-person variability in human brain connectivity

Brain connectivity arises from interactions across biophysical scales, ranging from molecular to cellular to anatomical to network level. To date, there has been little progress toward integrated analysis across these scales. To bridge this gap, from a unique cohort of 98 individuals, we collected antemortem neuroimaging and genetic data, as well as postmortem dendritic spine morphometric, proteomic and gene expression data from the superior frontal and inferior temporal gyri. Through the integration of the molecular and dendritic spine morphology data, we identified hundreds of proteins that explain interindividual differences in functional connectivity and structural covariation. These proteins are enriched for synaptic structures and functions, energy metabolism and RNA processing. By integrating data at the genetic, molecular, subcellular and tissue levels, we link specific biochemical changes at synapses to connectivity between brain regions. These results demonstrate the feasibility of integrating data from vastly different biophysical scales to provide a more comprehensive understanding of brain connectivity.

A Bioconductor workflow for processing, evaluating, and interpreting expression proteomics data

Background Expression proteomics involves the global evaluation of protein abundances within a system. In turn, differential expression analysis can be used to investigate changes in protein abundance upon perturbation to such a system. Methods Here, we provide a workflow for the processing, analysis and interpretation of quantitative mass spectrometry-based expression proteomics data. This workflow utilises open-source R software packages from the Bioconductor project and guides users end-to-end and step-by-step through every stage of the analyses. As a use-case we generated expression proteomics data from HEK293 cells with and without a treatment. Of note, the experiment included cellular proteins labelled using tandem mass tag (TMT) technology and secreted proteins quantified using label-free quantitation (LFQ). Results The workflow explains the software infrastructure before focusing on data import, pre-processing and quality control. This is done individually for TMT and LFQ datasets. The application of statistical differential expression analysis is demonstrated, followed by interpretation via gene ontology enrichment analysis. Conclusions A comprehensive workflow for the processing, analysis and interpretation of expression proteomics is presented. The workflow is a valuable resource for the proteomics community and specifically beginners who are at least familiar with R who wish to understand and make data-driven decisions with regards to their analyses.

DEWNA: dynamic entropy weight network analysis and its application to the DNA-binding proteome in A549 cells with cisplatin-induced damage.

Cisplatin is one of the most commonly used chemotherapy drugs for treating solid tumors. As a genotoxic agent, cisplatin binds to DNA and forms platinum-DNA adducts that cause DNA damage and activate a series of signaling pathways mediated by various DNA-binding proteins (DBPs), ultimately leading to cell death. Therefore, DBPs play crucial roles in the cellular response to cisplatin and in determining cell fate. However, systematic studies of DBPs responding to cisplatin damage and their temporal dynamics are still lacking. To address this, we developed a novel and user-friendly stand-alone software, DEWNA, designed for dynamic entropy weight network analysis to reveal the dynamic changes of DBPs and their functions. DEWNA utilizes the entropy weight method, multiscale embedded gene co-expression network analysis and generalized reporter score-based analysis to process time-course proteome expression data, helping scientists identify protein hubs and pathway entropy profiles during disease progression. We applied DEWNA to a dataset of DBPs from A549 cells responding to cisplatin-induced damage across 8 time points, with data generated by data-independent acquisition mass spectrometry (DIA-MS). The results demonstrate that DEWNA can effectively identify protein hubs and associated pathways that are significantly altered in response to cisplatin-induced DNA damage, and offer a comprehensive view of how different pathways interact and respond dynamically over time to cisplatin treatment. Notably, we observed the dynamic activation of distinct DNA repair pathways and cell death mechanisms during the drug treatment time course, providing new insights into the molecular mechanisms underlying the cellular response to DNA damage.

Anti-Inflammatory and Cancer-Preventive Potential of Chamomile (Matricaria chamomilla L.): A Comprehensive In Silico and In Vitro Study.

Chamomile tea, renowned for its exquisite taste, has been appreciated for centuries not only for its flavor but also for its myriad health benefits. In this study, we investigated the preventive potential of chamomile (Matricaria chamomilla L.) towards cancer by focusing on its anti-inflammatory activity. A virtual drug screening of 212 phytochemicals from chamomile revealed β-amyrin, β-eudesmol, β-sitosterol, apigenin, daucosterol, and myricetin as potent NF-κB inhibitors. The in silico results were verified through microscale thermophoresis, reporter cell line experiments, and flow cytometric determination of reactive oxygen species and mitochondrial membrane potential. An oncobiogram generated through comparison of 91 anticancer agents with known modes of action using the NCI tumor cell line panel revealed significant relationships of cytotoxic chamomile compounds, lupeol, and quercetin to microtubule inhibitors. This hypothesis was verified by confocal microscopy using α-tubulin-GFP-transfected U2OS cells and molecular docking of lupeol and quercetin to tubulins. Both compounds induced G2/M cell cycle arrest and necrosis rather than apoptosis. Interestingly, lupeol and quercetin were not involved in major mechanisms of resistance to established anticancer drugs (ABC transporters, TP53, or EGFR). Performing hierarchical cluster analyses of proteomic expression data of the NCI cell line panel identified two sets of 40 proteins determining sensitivity and resistance to lupeol and quercetin, further pointing to the multi-specific nature of chamomile compounds. Furthermore, lupeol, quercetin, and β-amyrin inhibited the mRNA expression of the proinflammatory cytokines IL-1β and IL6 in NF-κB reporter cells (HEK-Blue Null1). Moreover, Kaplan-Meier-based survival analyses with NF-κB as the target protein of these compounds were performed by mining the TCGA-based KM-Plotter repository with 7489 cancer patients. Renal clear cell carcinomas (grade 3, low mutational rate, low neoantigen load) were significantly associated with shorter survival of patients, indicating that these subgroups of tumors might benefit from NF-κB inhibition by chamomile compounds. This study revealed the potential of chamomile, positioning it as a promising preventive agent against inflammation and cancer. Further research and clinical studies are recommended.

Exploratory drug discovery in breast cancer patients: A multimodal deep learning approach to identify novel drug candidates targeting RTK signaling

Breast cancer, a highly formidable and diverse malignancy predominantly affecting women globally, poses a significant threat due to its intricate genetic variability, rendering it challenging to diagnose accurately. Various therapies such as immunotherapy, radiotherapy, and diverse chemotherapy approaches like drug repurposing and combination therapy are widely used depending on cancer subtype and metastasis severity. Our study revolves around an innovative drug discovery strategy targeting potential drug candidates specific to RTK signalling, a prominently targeted receptor class in cancer. To accomplish this, we have developed a multimodal deep neural network (MM-DNN) based QSAR model integrating omics datasets to elucidate genomic, proteomic expression data, and drug responses, validated rigorously. The results showcase an R2 value of 0.917 and an RMSE value of 0.312, affirming the model's commendable predictive capabilities. Structural analogs of drug molecules specific to RTK signalling were sourced from the PubChem database, followed by meticulous screening to eliminate dissimilar compounds. Leveraging the MM-DNN-based QSAR model, we predicted the biological activity of these molecules, subsequently clustering them into three distinct groups. Feature importance analysis was performed. Consequently, we successfully identified prime drug candidates tailored for each potential downstream regulatory protein within the RTK signalling pathway. This method makes the early stages of drug development faster by removing inactive compounds, providing a hopeful path in combating breast cancer.

ShinyExprPortal: a configurable 'shiny' portal for sharing analysis of molecular expression data.

The scale of omics research presents many obstacles to full sharing and access to analysis results. Current publication models impose limits on the number of pages and figures, requiring careful preparation and selection of content. At the same time, depositing data in open repositories significantly shifts the burden of access and reproduction to readers, who may include people who are not programmers or analysts. We introduce shinyExprPortal, an R package that implements omics web portals with minimal coding effort. The portals allow exploration of transcriptomic or proteomic expression data and phenotypes, showcasing results of various types of analysis including differential expression, co-expression and pathways analysis. The integration with bioinformatics workflows enables researchers to focus on their results and share findings using interactive and publication-quality plots. The shinyExprPortal package is available to download and install from CRAN and https://github.com/C4TB/shinyExprPortal.

Abstract 888: Uncovering clinically relevant omics signatures from pan-cancer imaging and multi-omics data integration

Abstract Despite advancements in deep learning for histopathology, integrating these insights with multi-omics data to uncover clinically relevant omics pathway-level signatures remains a challenge. Our study addresses this gap by applying unsupervised learning techniques on pan-cancer multi-omics data, leveraging 3,080 Hematoxylin and Eosin (H&E) images from 1,010 patients in Clinical Proteomic Tumor Analysis Consortium (CPTAC) to uncover omics pathway-level signatures that drive discernable morphology phenotypes at the tissue level. First, imaging models were trained to predict clinical and mutation outcomes, and thereafter, integrated with transcriptomic and proteomic expression data using sparse canonical correlation analysis. Our findings reveal that images of TP53 mutated samples exhibited increased nuclear size and dense lymphoplasmacytic infiltration. These morphological changes correlated with neutrophil and macrophage signaling at the proteomic level, and IL-1 mediated signaling at the transcriptomic level, highlighting the complementary perspectives different omics can provide. To further elucidate immune interactions, we applied multi-omics deconvolution to identify 7 immune subtypes, characterizing each with gene set enrichment, germline DNA variations, and kinase activations. We show that imaging models trained to predict these subtypes differentiate tissue morphologies corresponding to immune enrichment (AUROC: 0.84). To further confirm the robustness of our approach, we trained models to predict co-regulated proteomic modules clustered by independent component analysis. We demonstrate a signature in pan-squamous tumors, consisting of T cell markers and interferon-gamma response proteins like CD4, CD48, and GBP5, which imaging models can successfully predict from histopathology. Manual review from pathologists confirmed lymphocytic T-cell density as a differentiator in samples with the highest and lowest T-cell signaling. Together, these approaches demonstrate that genomic characteristics discovered via unbiased mining of pan-cancer multi-omics data manifest as quantitative imaging phenotypes. These results underscore the potential of multi-omics and digital pathology to integratively uncover and confirm new cancer biology. Our ongoing work will explore predicting drug response and survival from morphology patterns related to multi-omics signatures. Citation Format: Joshua Wang, Runyu Hong, Jimin Tan, Wenke Liu, David Fenyȯ. Uncovering clinically relevant omics signatures from pan-cancer imaging and multi-omics data integration [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 888.

A Bioconductor workflow for processing, evaluating, and interpreting expression proteomics data.

Background: Expression proteomics involves the global evaluation of protein abundances within a system. In turn, differential expression analysis can be used to investigate changes in protein abundance upon perturbation to such a system. Methods: Here, we provide a workflow for the processing, analysis and interpretation of quantitative mass spectrometry-based expression proteomics data. This workflow utilizes open-source R software packages from the Bioconductor project and guides users end-to-end and step-by-step through every stage of the analyses. As a use-case we generated expression proteomics data from HEK293 cells with and without a treatment. Of note, the experiment included cellular proteins labelled using tandem mass tag (TMT) technology and secreted proteins quantified using label-free quantitation (LFQ). Results: The workflow explains the software infrastructure before focusing on data import, pre-processing and quality control. This is done individually for TMT and LFQ datasets. The application of statistical differential expression analysis is demonstrated, followed by interpretation via gene ontology enrichment analysis. Conclusions: A comprehensive workflow for the processing, analysis and interpretation of expression proteomics is presented. The workflow is a valuable resource for the proteomics community and specifically beginners who are at least familiar with R who wish to understand and make data-driven decisions with regards to their analyses.

A bioinformatic investigation of proteasome and autophagy expression in the central nervous system

The ubiquitin proteasome system (UPS) and autophagy lysosome pathway (ALP) are crucial in the control of protein quality. However, data regarding the relative significance of UPS and ALP in the central nervous system (CNS) are limited. In the present study, using publicly available data, we computed the quantitative expression status of UPS- and ALP-related genes and their products in the CNS as compared with that in other tissues and cells. We obtained human and mouse gene expression datasets from the reference expression dataset (RefEx) and Genevestigator (a tool for handling curated transcriptomic data from public repositories) as well as human proteomics data from the proteomics database (ProteomicsDB). The expression levels of genes and proteins in four categories—ubiquitin, proteasome, autophagy, and lysosome––in the cells and tissues were assessed. Perturbation of the gene expression by drugs was also analyzed for the four categories. Compared with that for ubiquitin, autophagy, and lysosome, gene expression for proteasome was consistently lower in the CNS of mice but was more pronounced in humans. Neural stem cells and neurons showed low proteasome gene expression as compared with embryonic stem cells. Proteomic analyses, however, did not show trends similar to those observed in the gene expression analyses. Perturbation analyses revealed that azithromycin and vitamin D3 upregulated the expression of both UPS and ALP. Gene and proteomic expression data could offer a fresh perspective on CNS pathophysiology. Our results indicate that disproportional expression of UPS and ALP might affect CNS disorders and that this imbalance might be redressed by several therapeutic candidates.

Incorporating Mitochondrial Gene Expression Changes Within a Testable Mathematical Model for Alzheimer's Disease: Stress Response Modulation Predicts Potential Therapeutic Targets.

Alzheimer's disease is a specific form of dementia characterized by the aggregation of amyloid-β plaques and tau tangles. New research has found that the formation of these aggregates occurs after dysregulation of cellular respiration and the production of radical oxygen species. Proteomic data shows that these changes are also related to unique gene expression patterns. This study is designed to incorporate both proteomic and gene expression data into a testable mathematical model for AD. Manipulation of this new model allows the identification of potential therapeutic targets for AD. We investigate the impact of these findings on new therapeutic targets via metabolic flux analysis of sirtuin stress response pathways while also highlighting the importance of metabolic enzyme activity in maintaining proper respiratory activity. Our results indicate that protective changes in SIRT1 and AMPK expression are potential avenues for therapeutics. Combining our mitochondrial gene expression analyses with available protein data allowed the construction of a new mathematical model for AD that provides a useful approach to test the efficacy of potential AD therapeutic targets.

Better Agreement of Human Transcriptomic and Proteomic Cancer Expression Data at the Molecular Pathway Activation Level.

Previously, we have shown that the aggregation of RNA-level gene expression profiles into quantitative molecular pathway activation metrics results in lesser batch effects and better agreement between different experimental platforms. Here, we investigate whether pathway level of data analysis provides any advantage when comparing transcriptomic and proteomic data. We compare the paired proteomic and transcriptomic gene expression and pathway activation profiles obtained for the same human cancer biosamples in The Cancer Genome Atlas (TCGA) and the NCI Clinical Proteomic Tumor Analysis Consortium (CPTAC) projects, for a total of 755 samples of glioblastoma, breast, liver, lung, ovarian, pancreatic, and uterine cancers. In a CPTAC assay, expression levels of 15,112 protein-coding genes were profiled using the Thermo QE series of mass spectrometers. In TCGA, RNA expression levels of the same genes were obtained using the Illumina HiSeq 4000 engine for the same biosamples. At the gene level, absolute gene expression values are compared, whereas pathway-grade comparisons are made between the pathway activation levels (PALs) calculated using average sample-normalized transcriptomic and proteomic profiles. We observed remarkably different average correlations between the primary RNA- and protein expression data for different cancer types: Spearman Rho between 0.017 (p = 1.7 × 10−13) and 0.27 (p < 2.2 × 10−16). However, at the pathway level we detected overall statistically significantly higher correlations: averaged Rho between 0.022 (p < 2.2 × 10−16) and 0.56 (p < 2.2 × 10−16). Thus, we conclude that data analysis at the PAL-level yields results of a greater similarity when comparing high-throughput RNA and protein expression profiles.

Computed tomography-based radiomics decodes prognostic and molecular differences in interstitial lung disease related to systemic sclerosis.

BackgroundRadiomic features calculated from routine medical images show great potential for personalised medicine in cancer. Patients with systemic sclerosis (SSc), a rare, multiorgan autoimmune disorder, have a similarly poor prognosis due to interstitial lung disease (ILD). Here, our objectives were to explore computed tomography (CT)-based high-dimensional image analysis (“radiomics”) for disease characterisation, risk stratification and relaying information on lung pathophysiology in SSc-ILD.MethodsWe investigated two independent, prospectively followed SSc-ILD cohorts (Zurich, derivation cohort, n=90; Oslo, validation cohort, n=66). For every subject, we defined 1355 robust radiomic features from standard-of-care CT images. We performed unsupervised clustering to identify and characterise imaging-based patient clusters. A clinically applicable prognostic quantitative radiomic risk score (qRISSc) for progression-free survival (PFS) was derived from radiomic profiles using supervised analysis. The biological basis of qRISSc was assessed in a cross-species approach by correlation with lung proteomic, histological and gene expression data derived from mice with bleomycin-induced lung fibrosis.ResultsRadiomic profiling identified two clinically and prognostically distinct SSc-ILD patient clusters. To evaluate the clinical applicability, we derived and externally validated a binary, quantitative radiomic risk score (qRISSc) composed of 26 features that accurately predicted PFS and significantly improved upon clinical risk stratification parameters in multivariable Cox regression analyses in the pooled cohorts. A high qRISSc score, which identifies patients at risk for progression, was reverse translatable from human to experimental ILD and correlated with fibrotic pathway activation.ConclusionsRadiomics-based risk stratification using routine CT images provides complementary phenotypic, clinical and prognostic information significantly impacting clinical decision making in SSc-ILD.

In silico Approach for Validating and Unveiling New Applications for Prognostic Biomarkers of Endometrial Cancer.

Simple SummaryEndometrial cancer (EC) mortality is directly associated with the presence of poor prognostic factors. Molecular prognostic factors have been identified, but none are used in clinical practice due to lack of validation studies. This study aims to validate a set of 255 prognostic biomarkers previously identified in an extensive literature review and explore new prognostic applications by analyzing them in The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) databases. A total of 30 biomarkers were validated and associated to a histological type (n = 15), histological grade (n = 6), FIGO stage (n = 1), molecular classification (n = 16), overall survival (n = 11), and recurrence-free survival (n = 5). Our results encourage further studies of understudied biomarkers such as TPX2, and validates already broadly studied biomarkers such as MSH6, MSH2, or L1CAM, among others. Finally, our results present a significant step to advance the quest for biomarkers to accurately assess the risk of EC patients.Endometrial cancer (EC) mortality is directly associated with the presence of prognostic factors. Current stratification systems are not accurate enough to predict the outcome of patients. Therefore, identifying more accurate prognostic EC biomarkers is crucial. We aimed to validate 255 prognostic biomarkers identified in multiple studies and explore their prognostic application by analyzing them in TCGA and CPTAC datasets. We analyzed the mRNA and proteomic expression data to assess the statistical prognostic performance of the 255 proteins. Significant biomarkers related to overall survival (OS) and recurrence-free survival (RFS) were combined and signatures generated. A total of 30 biomarkers were associated either to one or more of the following prognostic factors: histological type (n = 15), histological grade (n = 6), FIGO stage (n = 1), molecular classification (n = 16), or they were associated to OS (n = 11), and RFS (n = 5). A prognostic signature composed of 11 proteins increased the accuracy to predict OS (AUC = 0.827). The study validates and identifies new potential applications of 30 proteins as prognostic biomarkers and suggests to further study under-studied biomarkers such as TPX2, and confirms already used biomarkers such as MSH6, MSH2, or L1CAM. These results are expected to advance the quest for biomarkers to accurately assess the risk of EC patients.

Data, Reagents, Assays and Merits of Proteomics for SARS-CoV-2 Research and Testing

As the COVID-19 pandemic continues to spread, thousands of scientists around the globe have changed research direction to understand better how the virus works and to find out how it may be tackled. The number of manuscripts on preprint servers is soaring and peer-reviewed publications using MS-based proteomics are beginning to emerge. To facilitate proteomic research on SARS-CoV-2, the virus that causes COVID-19, this report presents deep-scale proteomes (10,000 proteins; >130,000 peptides) of common cell line models, notably Vero E6, Calu-3, Caco-2, and ACE2-A549 that characterize their protein expression profiles including viral entry factors such as ACE2 or TMPRSS2. Using the 9 kDa protein SRP9 and the breast cancer oncogene BRCA1 as examples, we show how the proteome expression data can be used to refine the annotation of protein-coding regions of the African green monkey and the Vero cell line genomes. Monitoring changes of the proteome on viral infection revealed widespread expression changes including transcriptional regulators, protease inhibitors, and proteins involved in innate immunity. Based on a library of 98 stable-isotope labeled synthetic peptides representing 11 SARS-CoV-2 proteins, we developed PRM (parallel reaction monitoring) assays for nano-flow and micro-flow LC-MS/MS. We assessed the merits of these PRM assays using supernatants of virus-infected Vero E6 cells and challenged the assays by analyzing two diagnostic cohorts of 24 (+30) SARS-CoV-2 positive and 28 (+9) negative cases. In light of the results obtained and including recent publications or manuscripts on preprint servers, we critically discuss the merits of MS-based proteomics for SARS-CoV-2 research and testing.

Integration of postmortem amygdala expression profiling, GWAS, and functional cell culture assays: neuroticism-associated synaptic vesicle glycoprotein 2A (SV2A) gene is regulated by miR-133a and miR-218

Recent genome-wide studies have begun to identify gene variants, expression profiles, and regulators associated with neuroticism, anxiety disorders, and depression. We conducted a set of experimental cell culture studies of gene regulation by micro RNAs (miRNAs), based on genome-wide transcriptome, proteome, and miRNA expression data from twenty postmortem samples of lateral amygdala from donors with known neuroticism scores. Using Ingenuity Pathway Analysis and TargetScan, we identified a list of mRNA–protein–miRNA sets whose expression patterns were consistent with miRNA-based translational repression, as a function of trait anxiety. Here, we focused on one gene from that list, which is of particular translational significance in Psychiatry: synaptic vesicle glycoprotein 2A (SV2A) is the binding site of the anticonvulsant drug levetiracetam ((S)-α-Ethyl-2-oxo-1-pyrrolidineacetamide), which has shown promise in anxiety disorder treatments. We confirmed that SV2A is associated with neuroticism or anxiety using an original GWAS of a community cohort (N = 1,706), and cross-referencing a published GWAS of multiple cohorts (Ns ranging from 340,569 to 390,278). Postmortem amygdala expression profiling implicated three putative regulatory miRNAs to target SV2A: miR-133a, miR-138, and miR-218. Moving from association to experimental causal testing in cell culture, we used a luciferase assay to demonstrate that miR-133a and miR-218, but not miR-138, significantly decreased relative luciferase activity from the SV2A dual-luciferase construct. In human neuroblastoma cells, transfection with miR-133a and miR-218 reduced both endogenous SV2A mRNA and protein levels, confirming miRNA targeting of the SV2A gene. This study illustrates the utility of combining postmortem gene expression data with GWAS to guide experimental cell culture assays examining gene regulatory mechanisms that may contribute to complex human traits. Identifying specific molecular mechanisms of gene regulation may be useful for future clinical applications in anxiety disorders or other forms of psychopathology.

Integrative Proteomic and MicroRNA Analysis: Insights Into Mechanisms of Eyestalk Ablation-Induced Ovarian Maturation in the Swimming Crab Portunus trituberculatus.

Eyestalk ablation is the most common method to induce ovarian maturation in decapod crustacean aquaculture, but it jeopardizes broodstock survival and larvae production. It is important to understand the molecular basis underlying the maturation triggered by ablation and thereby develop an alternative measure for maturation manipulation. In this study, we investigate alterations of ovarian proteome and miRNA profile after ablation in a commercially important marine crab Portunus trituberculatus. Quantitative proteomic analysis using iTRAQ reveals that 163 proteins are differentially expressed following ablation, and modulation of methyl farnesoate metabolism and activation of calcium signaling may play important roles in the ovarian maturation induced by ablation. miRNA expression profiling identifies 31 miRNAs that show statistically significant changes. Integration of miRNA and proteome expression data with miRNA target prediction algorithms generates a potential regulatory network consisting of 26 miRNAs and 30 proteins linked by 71 possible functional associations. The miRNA-protein network analysis suggests that miRNAs are involved in promoting ovarian maturation by controlling expression of proteins related to methyl farnesoate synthesis, calcium signals, and energy metabolism. Experimental validation and temporal expression analysis indicate multiple miRNAs can act synergistically to regulate expression of Farnesoic acid O-methyltransferase and Calmodulin. Our findings provide new insights for elucidating the mechanisms underlying eyestalk ablation-induced ovarian maturation and could be useful for devising an alternative technique for manipulating reproduction in P. trituberculatus and other decapods.

Abstract 6280: CDK4/6 inhibitor-mediatated radiosensitization of estrogen receptor positive breast cancer

Abstract Purpose: Although cyclin-dependent kinase 4 and 6 (CDK4/6) inhibitors like palbociclib, ribociclib, and abemaciclib have improved progression free survival in patients with metastatic, estrogen receptor positive (ER+) breast cancer, acquired resistance to these drugs limits their efficacy. Despite promising new studies defining the utility of CDK4/6 inhibitors in the upfront, non-metastatic setting, there is limited data available on the effects of concurrent CDK4/6 inhibition and radiation (RT). Methods: Transcriptomic and proteomic expression data was used to quantify changes in RNA and protein expression in ER+ breast cancer cell lines (MCF-7, T47D) after short term (16 hour) CDK4/6 inhibition. Proliferation assays were used to determine the half-maximal inhibitory concentration (IC50) of palbociclib, ribociclib, and abemaciclib. Clonogenic survival assays were performed to calculate the radiation enhancement ratio (rER) and the surviving fraction at 2 Gy for each treatment. Homologous recombination (HR) proficiency was assessed using RAD51 and γH2AX foci formation and a pYFP reporter was used to assess non-homologous end joining (NHEJ) efficiency. Western blots were used to quantify protein expression. MCF-7 xenografts were used to study the efficacy of combination (palbociclib + RT) therapy in vivo. MCF-7 and T47D cell lines with acquired resistance to CDK4/6 inhibition (IC50 &amp;gt;1uM) were used for comparison in all assays. Results: Transcriptomic and proteomic analyses identified changes in expression of DNA damage response mediators and cell cycle machinery with short term CDK4/6 inhibition. Palbociclib significantly radiosensitized ER+ cell lines at concentrations at or below the IC50 value in clonogenic survival assays (MCF-7 rER: 1.22-1.52, T47D rER: 1.23-1.50) and led to a decrease in the surviving fraction of cells at 2 Gy (p &amp;lt; 0.001). Similar results were observed in ribociclib- (rER: 1.08 - 1.68) and abemaciclib-treated (rER: 1.19 - 2.05) cells. MCF-7 and T47D cells treated with CDK4/6 inhibition and RT showed a decrease in RAD51 foci formation, suggesting a decrease in HR efficiency (p &amp;lt; 0.001). However, CDK4/6 inhibition did not affect NHEJ efficiency (p &amp;gt; 0.05). CDK4/6 inhibition + RT led to a decrease in expression of protein expression of HR meditators like p-CHK1 but did not affect phosphorylation of NHEJ proteins like pKu80/pKu70. Cells with acquired resistance to CDK4/6 inhibition did not demonstrate radiosensitization (MCF-7 rER: 0.93 - 1.03, T47D rER: 0.96 - 1.11) or changes in RAD51 foci formation with combination treatment. Conclusions: Our data suggests that CDK4/6 inhibitor-mediated radiosensitization may be effective in ER+ breast cancers prior to the development of CDK4/6 inhibitor resistance. These studies provide preclinical rationale to test CDK4/6 inhibition + RT in women with locally-advanced ER+ breast cancer at high risk for locoregional recurrence. Citation Format: Andrea M. Pesch, Nicole Hirsh, Benjamin C. Chandler, Anna R. Michmerhuizen, Cassandra L. Ritter, Marlie Androsiglio, Kari Wilder-Romans, Meilan Liu, Christina L. Gersch, Jose M. Larios, James M. Rae, Corey W. Speers. CDK4/6 inhibitor-mediatated radiosensitization of estrogen receptor positive breast cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 6280.

Using the Gibbs Function as a Measure of Human Brain Development Trends from Fetal Stage to Advanced Age.

We propose to use a Gibbs free energy function as a measure of the human brain development. We adopt this approach to the development of the human brain over the human lifespan: from a prenatal stage to advanced age. We used proteomic expression data with the Gibbs free energy to quantify human brain’s protein–protein interaction networks. The data, obtained from BioGRID, comprised tissue samples from the 16 main brain areas, at different ages, of 57 post-mortem human brains. We found a consistent functional dependence of the Gibbs free energies on age for most of the areas and both sexes. A significant upward trend in the Gibbs function was found during the fetal stages, which is followed by a sharp drop at birth with a subsequent period of relative stability and a final upward trend toward advanced age. We interpret these data in terms of structure formation followed by its stabilization and eventual deterioration. Furthermore, gender data analysis has uncovered the existence of functional differences, showing male Gibbs function values lower than female at prenatal and neonatal ages, which become higher at ages 8 to 40 and finally converging at late adulthood with the corresponding female Gibbs functions.

Integrated Multi-Level Omics to Characterize Bortezomib Resistance in Multiple Myeloma

Introduction The use of proteasome inhibitors (PIs), such as bortezomib (BTZ), in multiple myeloma (MM) has markedly increased the survival of newly diagnosed patients. Although advancements in therapeutic regimens in the past decade have improved prognosis, we lack knowledge of the mechanisms that lead to drug resistance. To assess the contributors to BTZ-resistance, we integrated steady-state metabolomics, proteomics and gene expression from two naïve and BTZ-resistant cell line models. In addition, gene expression associated with ex vivo PI resistance has been analyzed. Potential predictive biomarkers of PI-resistance and novel targets for combination therapy will be investigated. Methods Parental cell lines, RPMI 8226 and U266, were acquired from ATCC. 8226-B25 and U266-PSR (kind gift from Dr. S. Grant) BTZ-resistant derivatives were selected from their respective parental naïve cell lines by chronic drug exposure. Untargeted metabolomics, activity-based protein profiling (ABPP), and expression proteomics data were acquired using liquid chromatography-mass spectrometry. Gene expression profiles of both cell lines and ex vivo patient specimens were derived from RNAseq. Metabolomics and proteomics data were normalized with iterative rank order normalization. Significantly different genes, proteins, and metabolites were integrated for pathway mapping and identification of biomarkers for PI resistance. Results Consistent with previous findings, kynurenine, a product of tryptophan catabolism, is significantly altered in both of our cell line models. In the 8226 and 8226-B25 pair, PI resistance was associated with increased kynurenine and positively correlated with TDO2 and IDO1 overexpression consistent with published literature (Li et al. Nature Medicine, 2019, 25, 850-60). As expected, PSMB2, a subunit of the proteasome, is overexpressed and has a higher activity in both 8226-B25 and U266-PSR in the ABPP and expression proteomics, and higher expression in 8226-B25 RNAseq data. PSMB2 is also overexpressed and significant in the RNAseq patient data, increasingly from newly diagnosed/pre-treatment to early relapse (p-value 2E-4) and late relapse (p-value 0.0052). In addition, CD38 is an enzyme responsible for conversion of NAD+ to nicotinamide and ADP-ribose. It has increased expression in MM cells and is significantly downregulated in ABPP (log2 ratio -4.25, p-value 2E-13), expression proteomics (log2 ratio -2.5), and RNAseq (log2 ratio -2.6, p-value 5E-6) in the 8226-B25 BTZ-resistant cells. In the steady-state metabolomics of the 8226-B25 cells, ADP-ribose (log2 ratio 4.11, p-value 2E-5) is the most upregulated known metabolite. This change suggests a downstream result of resistance within this interaction and a potential biomarker of PI resistance. However, gene expression of CD38 in patient samples was relatively unchanged. CD38 was not detected in the U266-PSR proteomics or RNAseq data and ADP-ribose (log2 ratio -0.63, p-value 0.06) was not significantly altered, suggesting a different mechanism of resistance in this cell line. Conclusions Though common mechanisms of PI resistance were identified, our data clearly show that BTZ-resistance arises by heterogeneous means in the two cell line models, promoting the need for biomarkers that can determine resistance and predict response in individual patients (or cohorts). Decreased expression of CD38 in 8226-B25 could elucidate mechanisms of PI resistance and immune response evasion strategies of MM cells. Further investigation of CD38 expression as a BTZ-resistance biomarker could lead to improving combination therapies with monoclonal antibodies, such as daratumumab, and PIs in newly diagnosed MM patients by predicting response prior to treatment. Further examination of ADP-ribose metabolism may lead to the mechanism of synergy between PARP inhibitors and proteasome inhibitors. Ultimately, we plan to integrate and utilize these multi-omics approaches in patient specimens and improve MM patient care by identifying PI resistance biomarkers to predict patient response. Disclosures Shain: Adaptive Biotechnologies: Consultancy; Janssen: Membership on an entity's Board of Directors or advisory committees; AbbVie: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees; Sanofi Genzyme: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees.

Unique Regulatory Pathways Dictate the Translational Program in Hematopoietic Stem and Progenitor Cells

Prior studies in numerous biological systems have shown that alterations in mRNA expression frequently fail to predict changes in protein expression. This may be due to many regulatory mechanisms that occur post-transcriptionally including mRNA recruitment to ribosomes, translational initiation, ribosome processivity, and protein stability, among others. Indeed, several examples of selective translation of mRNAs has been described both in malignant and normal cells. To determine the extent and potential impact of translational reprogramming on early hematopoietic development, we performed an integrated analysis of total RNA, polysome RNA, and whole proteome data generated from HSC-enriched LSK (Lin-Sca-1+c-Kit+) and MP (Lin-Sca-1-c-Kit+) cells from mouse. Our studies revealed that although LSK cells show lower global translation than MPs, they exhibited significantly higher translational efficiency (TE = polysome/total RNA abundance) of mRNAs supporting processes required for HSC maintenance (e.g. glycolysis, fatty acid metabolism, oxidative phosphorylation, mTOR signaling) (Fig 1A). Additionally, integrated analysis of proteomic and RNA expression data showed that, TE changes better predicted protein expression changes for these pathways, than total RNA expression (Fig1B). Biochemical characterization of MP cells revealed markedly decreased mTOR protein expression and signaling in MP cells, especially in GMP and MEP. This is mediated through proteasomal degradation of mTOR protein. An E3 ligase prediction algorithm, identified c-Cbl as a potential candidate, targeting mTOR, which was confirmed by demonstrating the aberrant expression of mTOR in MPs in c-Cbl KO mice. In vitro and in vivo mTOR inhibition studies confirm that the MPN-like phenotype of c-Cbl KO mice, is due to aberrant activation of mTOR signaling in committed myeloid progenitors. Intriguingly, despite decreased expression of mTOR protein in MP cells, 4E-BP1, a known target of mTOR, was still phosphorylated at Ser-65- a critical step for initiating cap-dependent translation. Through a combination of prediction algorithms and candidate gene experimental approaches, we show that the critical phosphorylation event at Ser-65 is mediated by , as immunoprecipitation studies show physical association between CDK1 and 4E-BP, and pharmacological inhibition of CDK1 activity, reduced 4E-BP P-Ser-65 levels. Overall, our data provide the first comprehensive characterization of the translatome in early hematopoiesis and demonstrated that the LSK to MP transition is characterized by significant translational reprogramming. This is, in part, mediated by the activation of a unique, mTOR-independent pathway to activate cap-dependent translation through the concerted action of c-Cbl and CDK1 to induce degradation of mTOR and phosphorylate 4E-BP to activation translation, respectively. Abrogation of the downregulation of mTOR signaling in myeloid progenitors, results in expansions of numerous myeloid lineages including neutrophils, monocytes and platelets (Fig 1C). Thus, our studies demonstrate the importance of proper translational reprogramming in early hematopoiesis. Figure legend. (A) Heatmap showing pathways significantly enriched in LSK and or MP cells based on TE. (B) Comparison of TE to protein expression in LSK cells for genes involved in the indicated biological processes (Blue dots: mRNAs that showed an anticorrelation between total RNA and protein expression; Red dots: mRNAs that showed a positive correlation between total RNA and protein expression). (C) Model for translational reprogramming in early hematopoiesis. Despite lower rates of global translation, LSK cells show preferential translation of mRNAs sensitive to mTOR inhibition and required for HSC maintenance. In contrast, in highly translating MP cells, loss of mTOR expression is mediated by the E3 ubiquitin ligase c-Cbl. When c-Cbl is deleted and mTOR protein is aberrantly expressed, this results in increased mature myeloid output. In the absence of mTOR, eIF4E-cap-dependent translation is maintained through the action of CDK1, which phosphorylates the S65 residue of 4E-BP1 to release eIF4E. Figure Disclosures No relevant conflicts of interest to declare.