Surface Protein Abundance Research Articles

Imputing abundance of over 2500 surface proteins from single-cell transcriptomes with context-agnostic zero-shot deep ensembles.

Cell surface proteins serve as primary drug targets and cell identity markers. The emergence of techniques like CITE-seq has enabled simultaneous quantification of surface protein abundance and transcript expression for multimodal data analysis within individual cells. The published data have been utilized to train machine learning models for predicting surface protein abundance based solely from transcript expression. However, the small scale of proteins predicted and the poor generalization ability for these computational approaches across diverse contexts, such as different tissues or disease states, impede their widespread adoption. Here we propose SPIDER (surface protein prediction using deep ensembles from single-cell RNA-seq), a context-agnostic zero-shot deep ensemble model, which enables the large-scale prediction of cell surface protein abundance and generalizes better to various contexts. Comprehensive benchmarking shows that SPIDER outperforms other state-of-the-art methods. Using the predicted surface abundance of >2500 proteins from single-cell transcriptomes, we demonstrate the broad applications of SPIDER including cell type annotation, biomarker/target identification, and cell-cell interaction analysis in hepatocellular carcinoma and colorectal cancer.

Abstract 906: Large-scale surface protein abundance prediction from single-cell transcriptomes with a zero-shot model and its applications to cancer research

Abstract We develop SPIDER, a zero-shot model which can predict the abundance for a large scale (>2,000) of surface proteins from single-cell transcriptomes in various contexts. This is to overcome the challenges in current single-cell protein abundance quantification tools (e.g., flow cytometry, CITE-seq) and computational models (e.g., totalVI, Seurat, cTPnet), where routinely only <300 surface proteins can be quantified or predicted. Comprehensive benchmarking on four external validation sets shows that the prediction accuracy of SPIDER outperforms other state-of-the-art methods including Seurat, totalVI, and cTPnet, with a prediction accuracy elevated as much as 42.5% in new contexts. We further conduct case studies in cancers by applying SPIDER to predict the abundance of >2,500 surface proteins on scRNA-seq datasets of hepatocellular carcinoma (HCC) and colorectal cancer liver metastasis (RCRLM), where the predicted surface protein abundance data is analyzed to demonstrate the broad downstream applications of SPIDER including facilitating cell type annotation, disease biomarker/target identification, and cell-cell interaction (CCI) inference. For instance, in these datasets we find that SPIDER can reveal (novel) disease biomarkers that are overlooked by solely observing transcript expression, such as CD44/PIK3IP1 in hepatocytes/γδ2 T cells as a positive/negative HCC biomarker, demonstrating the capability of SPIDER to compensate for the limitations in the RNA modality. In conclusion, we propose the SPIDER model which can provide valuable information on the abundance of a large scale of surfaceomes in single cells, and can be further used to gain new insights into cancers via promoting cell type annotation, disease biomarker/target identification, and CCI inference. Citation Format: Ruoqiao Chen, Bin Chen, Jiayu Zhou. Large-scale surface protein abundance prediction from single-cell transcriptomes with a zero-shot model and its applications to cancer research [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 906.

Abstract 1854: Plasma proteomic biomarkers identify non-responders and reveal biological insights about the tumor microenvironment in melanoma patients after PD1 blockade

Abstract Most patients treated with immune checkpoint blockade (ICB) do not have durable treatment responses, stressing a critical need to identify early non-invasive biomarkers of response. Circulating biomarkers provide easy access for serial monitoring and can provide insight on the mechanisms of response to ICB. We performed plasma proteomics (~3000 proteins) on 250 metastatic melanoma patients before and on ICB treatment and performed analysis to identify response- and time point-associated peripheral protein biomarkers. We next generated patient-matched peripheral blood lymphocyte (PBL) surface proteomic data and tumor sample bulk transcriptomic data to build a multimodal dataset. We leveraged these data to train machine learning models for unsupervised analysis and to classify treatment responses and learned features predictive of survival and patient endotypes. Using univariate linear models to predict protein abundance based on time point, response and the interaction term for time point and response, we identified 343 time point-associated proteins, 141 response-associated proteins, and one interaction-associated protein. We further investigated response-related proteins by fitting multivariate logistic regression and cox proportional hazards regression models to further investigate the ability of these proteins to predict response, progression-free survival (PFS), and overall survival (OS) and identify those proteins that are most important in predicting response. We calculated covarying modules of proteins that were found to be either response-associated or time point-associated to discover underlying biological networks at different treatment time points and disease response states. Additionally, we leveraged these modules to endotype patients and identified trends in protein modules across patient subsets, including modules related to biological processes such as immune infiltration, apoptosis, metaplasia and cell adhesion. To understand the contribution of different compartments on plasma proteomic abundance, we studied correlations of plasma protein abundance with PBL surface protein abundance and tumor bulk mRNA-sequencing expression. Finally, we integrated our data modalities and fit various statistical models to evaluate the relative importance of plasma protein biomarkers in predicting response compared to PBL surface proteins and tumor bulk mRNA-sequencing expression. Together, these data represent one of the deepest peripheral biomarker studies using paired samples in melanoma patients treated with anti-PD1 therapy. Citation Format: Samuel J. Wright, Deepika Yeramosu, Milan Parikh, Marijana Rucevic, Ngan Nguyen, Russell W. Jenkins, Keith T. Flaherty, Nir Hacohen, Genevieve M. Boland, Arnav Mehta. Plasma proteomic biomarkers identify non-responders and reveal biological insights about the tumor microenvironment in melanoma patients after PD1 blockade [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 1854.

Lipoteichoic Acids Are Essential for Pneumococcal Colonization and Membrane Integrity.

The hydrophilic, polymeric chain of the lipoteichoic acid (LTA) of the Gram-positive pathobiont Streptococcus pneumoniae is covalently linked to the glycosylglycerolipid α-<sc>d</sc>-glucopyranosyl-(1,3)-diacylglycerol by the LTA ligase TacL, leading to its fixation in the cytoplasmic membrane. Pneumococcal LTA, sharing identical repeating units with the wall teichoic acids (WTA), is dispensable for normal growth but required for full virulence in invasive infections. Mutants deficient in TacL and complemented strains constructed were tested for their growth, resistance against oxidative stress, and susceptibility against antimicrobial peptides. Further, the membrane fluidity of pneumococci, their capability to adhere to lung epithelial cells, and virulence in a Galleria mellonella as well as intranasal mouse infection model were assessed. In the present study, we indicate that LTA is already indispensable for pneumococcal adherence to human nasopharyngeal cells and colonization in an intranasal mouse infection model. Mutants deficient for TacL did not show morphological defects. However, our analysis of pneumococcal membranes in different serotypes showed an altered membrane fluidity and surface protein abundance of lipoproteins in mutants deficient for LTA but not WTA. These mutants had a decreased membrane fluidity, exhibited higher amounts of lipoproteins, and showed an increased susceptibility to antimicrobial peptides. In complemented mutant strains, this defect was fully restored. Taken together, LTA is crucial for colonization and required to effectively protect pneumococci from innate immune defence mechanisms by maintaining the membrane integrity.

Single-Cell-Multiomics Demonstrates Molecular Efficacy of a Clinical Lentiviral Vector for Gene Therapy of RPS19-Deficient Diamond-Blackfan Anemia

It has previously been demonstrated that lentiviral vector-mediated gene transfer of human codon-optimized RPS19 to hematopoietic stem cells by the gene therapy vector (CLIN-LV-EFS-coRPS19-PRE*) corrects the anemic phenotype of RPS19-deficient mice, supporting development of this vector for clinical gene therapy of RPS19-deficient Diamond-Blackfan Anemia (DBA). In this study, we evaluate the molecular efficacy of CLIN-LV-EFS-coRPS19-PRE* in CD34+ cells from RPS19-deficient DBA patients. CD34+ cells from two healthy donors and three DBA patients with confirmed heterozygous mutations in RPS19 were transduced using GMP-like reagents according to a protocol developed for clinical use. To evaluate the therapeutic effect of RPS19-gene transfer, transduced (GT) and untransduced (Mock) CD34+ cells were cultured under conditions supporting erythroid and myeloid progenitor proliferation and differentiation. In order to obtain a comprehensive molecular characterization of the therapeutic mechanisms, cells from day 9 of culture were subjected to CITE-seq (cellular indexing of transcriptomes and epitopes by sequencing) analysis, a single-cell-multiomics method combining the advantages of FACS and scRNA-Seq for simultaneously analyzing transcriptomes alongside cell surface protein abundance at the single cell level. In GT cells, transduced (coRPS19-positive) cells were identified based on transgene expression. At day 9, 84-91% of erythroid progenitors in the GT-DBA samples expressed the coRPS19-transgene, while the frequency in myeloid progenitors was 29-56%. To reveal the molecular therapeutic effect of GT, we compared coRPS19-positive cells to Mock-treated cells from the same individuals. Expression of coRPS19 in DBA erythroid progenitor cells led to a significant induction of genes associated with terminal erythropoiesis (HEMGN, HBB, AHSP, EPB42 and GYPA) and down-regulation of genes associated with apoptosis and p53 activation (BAX, MDM2, ZMAT3 and MIR34AHG). GT also induced up-regulation of the large non-coding RNA LINC01133 and down-regulation of XACT. Interestingly, the two most-significantly changed genes in coRPS19-positive erythroid cells in all DBA samples were RPL22L1 and CD70. RPL22L1 is an RNA-binding component of the 60S ribosomal subunit that regulates pre-mRNA splicing but is not required for global cap-dependent translation. CD70 mRNA and protein exclusively expressed in erythroid progenitors in Mock-treated DBA samples and down-regulated in coRPS19-positive cells. CD70 is thus a new potential marker of DBA erythroid progenitor cells with a possible role in DBA pathogenesis. Myeloid progenitor DBA cells primarily responded to GT by up-regulation of ribosomal protein genes, suggesting RPS19-deficiency in myeloid progenitors leads to reduced ribosome biogenesis without the nucleolar stress observed in erythroid cells. To summarize, GT-induced changes in gene and protein expression agree with restoration of healthy ribosome biogenesis and elimination of nucleolar stress-induced p53 activation in erythroid progenitor cells responsible for the DBA phenotype, demonstrating molecular efficacy of CLIN-LV-EFS-coRPS19-PRE* supporting development for clinical gene therapy. In addition, the GT-induced reversal of RPS19-deficiency reveals several genes with potential relevance in DBA diagnostics and pathogenesis, such as RPL22L1 and CD70 for further investigation.

STREAK: A supervised cell surface receptor abundance estimation strategy for single cell RNA-sequencing data using feature selection and thresholded gene set scoring.

The accurate estimation of cell surface receptor abundance for single cell transcriptomics data is important for the tasks of cell type and phenotype categorization and cell-cell interaction quantification. We previously developed an unsupervised receptor abundance estimation technique named SPECK (Surface Protein abundance Estimation using CKmeans-based clustered thresholding) to address the challenges associated with accurate abundance estimation. In that paper, we concluded that SPECK results in improved concordance with Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) data relative to comparative unsupervised abundance estimation techniques using only single-cell RNA-sequencing (scRNA-seq) data. In this paper, we outline a new supervised receptor abundance estimation method called STREAK (gene Set Testing-based Receptor abundance Estimation using Adjusted distances and cKmeans thresholding) that leverages associations learned from joint scRNA-seq/CITE-seq training data and a thresholded gene set scoring mechanism to estimate receptor abundance for scRNA-seq target data. We evaluate STREAK relative to both unsupervised and supervised receptor abundance estimation techniques using two evaluation approaches on six joint scRNA-seq/CITE-seq datasets that represent four human and mouse tissue types. We conclude that STREAK outperforms other abundance estimation strategies and provides a more biologically interpretable and transparent statistical model.

Dynamic thresholding and tissue dissociation optimization for CITE-seq identifies differential surface protein abundance in metastatic melanoma

Multi-omics profiling by CITE-seq bridges the RNA-protein gap in single-cell analysis but has been largely applied to liquid biopsies. Applying CITE-seq to clinically relevant solid biopsies to characterize healthy tissue and the tumor microenvironment is an essential next step in single-cell translational studies. In this study, gating of cell populations based on their transcriptome signatures for use in cell type-specific ridge plots allowed identification of positive antibody signals and setting of manual thresholds. Next, we compare five skin dissociation protocols by taking into account dissociation efficiency, captured cell type heterogeneity and recovered surface proteome. To assess the effect of enzymatic digestion on transcriptome and epitope expression in immune cell populations, we analyze peripheral blood mononuclear cells (PBMCs) with and without dissociation. To further assess the RNA-protein gap, RNA-protein we perform codetection and correlation analyses on thresholded protein values. Finally, in a proof-of-concept study, using protein abundance analysis on selected surface markers in a cohort of healthy skin, primary, and metastatic melanoma we identify CD56 surface marker expression on metastatic melanoma cells, which was further confirmed by multiplex immunohistochemistry. This work provides practical guidelines for processing and analysis of clinically relevant solid tissue biopsies for biomarker discovery.

SPECK: an unsupervised learning approach for cell surface receptor abundance estimation for single-cell RNA-sequencing data.

The rapid development of single-cell transcriptomics has revolutionized the study of complex tissues. Single-cell RNA-sequencing (scRNA-seq) can profile tens-of-thousands of dissociated cells from a tissue sample, enabling researchers to identify cell types, phenotypes and interactions that control tissue structure and function. A key requirement of these applications is the accurate estimation of cell surface protein abundance. Although technologies to directly quantify surface proteins are available, these data are uncommon and limited to proteins with available antibodies. While supervised methods that are trained on Cellular Indexing of Transcriptomes and Epitopes by Sequencing data can provide the best performance, these training data are limited by available antibodies and may not exist for the tissue under investigation. In the absence of protein measurements, researchers must estimate receptor abundance from scRNA-seq data. Therefore, we developed a new unsupervised method for receptor abundance estimation using scRNA-seq data called SPECK (Surface Protein abundance Estimation using CKmeans-based clustered thresholding) and primarily evaluated its performance against unsupervised approaches for at least 25 human receptors and multiple tissue types. This analysis reveals that techniques based on a thresholded reduced rank reconstruction of scRNA-seq data are effective for receptor abundance estimation, with SPECK providing the best overall performance. SPECK is freely available at https://CRAN.R-project.org/package=SPECK. Supplementary data are available at Bioinformatics Advances online.

Circulating monocytes associated with anti-PD-1 resistance in human biliary cancer induce Tcell paralysis.

Suppressive myeloid cells can contribute to immunotherapy resistance, but their role in response to checkpoint inhibition (CPI) in anti-PD-1 refractory cancers, such as biliary tract cancer (BTC), remains elusive. We use multiplexed single-cell transcriptomic and epitope sequencing to profile greater than 200,000 peripheral blood mononuclear cells from advanced BTC patients (n= 9) and matched healthy donors (n= 8). Following anti-PD-1 treatment, CD14+ monocytes expressing high levels of immunosuppressive cytokines and chemotactic molecules (CD14CTX) increase in the circulation of patients with BTC tumors that are CPI resistant. CD14CTX can directly suppress CD4+ Tcells and induce SOCS3 expression in CD4+ Tcells, rendering them functionally unresponsive. The CD14CTX gene signature associates with worse survival in patients with BTC as well as in other anti-PD-1 refractory cancers. These results demonstrate that monocytes arising after anti-PD-1 treatment can induce Tcell paralysis as a distinct mode of tumor-mediated immunosuppression leading to CPI resistance.

SECANT: a biology-guided semi-supervised method for clustering, classification, and annotation of single-cell multi-omics.

The recent advance of single cell sequencing (scRNA-seq) technology such as Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) allows researchers to quantify cell surface protein abundance and RNA expression simultaneously at single cell resolution. Although CITE-seq and other similar technologies have gained enormous popularity, novel methods for analyzing this type of single cell multi-omics data are in urgent need. A limited number of available tools utilize data-driven approach, which may undermine the biological importance of surface protein data. In this study, we developed SECANT, a biology-guided SEmi-supervised method for Clustering, classification, and ANnoTation of single-cell multi-omics. SECANT is used to analyze CITE-seq data, or jointly analyze CITE-seq and scRNA-seq data. The novelties of SECANT include (1) using confident cell type label identified from surface protein data as guidance for cell clustering, (2) providing general annotation of confident cell types for each cell cluster, (3) utilizing cells with uncertain or missing cell type label to increase performance, and (4) accurate prediction of confident cell types for scRNA-seq data. Besides, as a model-based approach, SECANT can quantify the uncertainty of the results through easily interpretable posterior probability, and our framework can be potentially extended to handle other types of multi-omics data. We successfully demonstrated the validity and advantages of SECANT via simulation studies and analysis of public and in-house datasets from multiple tissues. We believe this new method will be complementary to existing tools for characterizing novel cell types and make new biological discoveries using single-cell multi-omics data.

Regulation of electrogenic Na+ /HCO3- cotransporter 1 (NBCe1) function and its dependence on m-TOR mediated phosphorylation of Ser245.

Astrocytes are pivotal responders to alterations of extracellular pH, primarily by regulation of their principal acid-base transporter, the membrane-bound electrogenic Na+ /bicarbonate cotransporter 1 (NBCe1). Here, we describe amammalian target of rapamycin (mTOR)-dependent and NBCe1-mediated astroglial response to extracellular acidosis. Using primary mouse cortical astrocytes, we investigated the effect of long-term extracellular metabolic acidosis on regulation of NBCe1 and elucidated the underlying molecular mechanisms by immunoblotting, biotinylation of surface proteins, intracellular H+ recording using the H+ -sensitive dye 2',7'-bis-(carboxyethyl)-5-(and-6)-carboxyfluorescein, and phosphoproteomic analysis. The results showed significant increase of NBCe1-mediated recovery of intracellular pH from acidification in WT astrocytes, but not in cortical astrocytes from NBCe1-deficient mice. Acidosis-induced upregulation of NBCe1 activity was prevented following inhibition of mTOR signaling by rapamycin. Yet, during acidosis or following exposure of astrocytes to rapamycin, surface protein abundance of NBCe1 remained -unchanged. Mutational analysis in HeLa cells suggested that NBCe1 activity was dependent on phosphorylation state of Ser245 , a residue conserved in all NBCe1 variants. Moreover, phosphorylation state of Ser245 is regulated by mTOR and is inversely correlated with NBCe1 transport activity. Our results identify pSer245 as a novel regulator of NBCe1 functional expression. We propose that context-dependent and mTOR-mediated multisite phosphorylation of serine residues of NBCe1 is likely to be a potent mechanism contributing to the response of astrocytes to acid/base challenges during pathophysiological conditions.

Ensemble learning models that predict surface protein abundance from single-cell multimodal omics data

Single-cell protein abundance is a fundamental type of information to characterize cell states. Due to high cost and technical barriers, however, direct quantification of proteins is difficult. Single-cell RNA sequencing (scRNA-seq) data, serving as a cost-effective substitute of single-cell proteomics, may not accurately reflect protein expression levels due to measurement error, noise, post-transcriptional and translational regulation, etc. The recently emerging single-cell multimodal omics data, e.g. CITE-seq and REAP-seq, can simultaneously profile RNA and protein abundances in single cells, providing labeled data for predictive modeling in a supervised learning framework. Deep neural network-based transfer learning method has been applied to imputation of surface protein abundances from single-cell transcriptomic data. However, it is unclear if the artificial neural network is the best model, and it is desirable to improve the prediction performance (e.g. accuracy, interpretability) of machine learning models. In this paper, we compared several tree-based ensemble learning methods with neural network models, and found that ensemble learning often performed better than neural network, and Random Forest (RF) performed the best overall. Moreover, we used the feature importance scores from RF to interpret biological mechanisms underlying the prediction. Our study demonstrates the effectiveness of ensemble learning for reliable protein abundances prediction using single-cell multimodal omics data, and paves the way for knowledge discovery by mining single-cell multi-omics data in large scale.

Profiling the Cell Surface Landscape of Proteasome Inhibitor-Treated and -Resistant Multiple Myeloma to Guide Immunotherapy Targeting, Diagnosis, and Biology

Introduction: Proteasome inhibitor (PI) resistance remains a major clinical challenge in multiple myeloma (MM). As the proteasome plays a central role in cellular protein homeostasis, we hypothesized both PI treatment and resistance might rewire protein transport and recycling pathways, thereby leading to broad changes in cell surface protein expression. Defining the cell surface proteome has become increasingly important in MM to quantify immunotherapy target expression, identify potential biomarkers of drug response or resistance, and reveal proteins mediating interaction with the tumor microenvironment. Unbiased mass spectrometry approaches allow for profiling of hundreds of surface proteins simultaneously, allowing for novel protein discovery and extending beyond the limits of flow or mass cytometry. Methods: PI-resistant cells derived from cell lines AMO-1, L363, and RPMI-8226, and ARH-77 were grown in 90nM Bortezomib or Carfilzomib as previously described (Soriano et al, Leukemia (2016)). N-glycoproteomics was performed in triplicate on PI-resistant and parental lines. Briefly, glycosylated cell surface proteins were biotinylated prior to enrichment with Neutravidin beads and downstream LC-MS analysis. For perturbation studies, AMO-1 and RPMI-8226 cells were treated for 48 hours with Bortezomib, Lenalidomide, or CB-5083 prior to N-glycoproteomics. Flow cytometry was performed to validate surface protein abundance changes in PI-resistant lines, as well as with MM cell lines and ex vivo MM patient bone marrow mononuclear cells (PBMCs) treated with bortezomib and lenalidomide. For ex vivo MM patient cell surface proteomics, plasma cells were isolated from PBMCs with anti-CD138 magnetic beads prior to N-glycoproteomic analysis. Mass spectrometry data was analyzed in Maxquant and statistical analysis was performed in Perseus and R. Results: Supporting our hypothesis, we found that the MM surfaceome is broadly re-wired in the PI-resistant state. Using N-glycoproteomics, we identified EVI2B, CD53, CD50, and ITGB7 as downregulated and CD151, CD10, and SERC3 as upregulated in PI-resistant myeloma cell lines (up and downregulated defined as at least a +/- log2 fold change of 1.5 and p &lt; .05). Notably, ITGB7 is under development as an immunotherapy target for MM (Hosen et al, Nat Med (2017)). To understand how surface remodeling in the PI-resistant state compares with short-term exposure to PI, we treated MM cells with bortezomib and again found broad surfaceome remodeling, including loss of immunotherapy target BCMA. We identified GITR, CD53, and ITGB7, among other proteins, as downregulated in both bortezomib-exposed and PI-resistant MM, suggesting that expression of proteins involved in therapeutic resistance might be rapidly modulated in initial cycles of therapy. To compare surfaceome changes identified in PI-exposure and resistance and those seen under anti-MM therapeutics with alternative mechanisms of action, we performed N-glycoproteomics on MM cells treated with the clinical immunomodulating agent Lenalidomide and the cytotoxic p97/VCP inhibitor CB-5083. Interestingly, we found MUC1, a proposed myeloma oncoprotein, to be upregulated under lenalidomide in AMO-1 but downregulated in the PI-resistant state. We validated surface protein abundance changes in PI-resistant MM cell lines and drug-treated MM cell lines or ex-vivo MM patient samples by flow cytometry. For additional validation, we are currently optimizing methods for N-glycoproteomics on CD138+ cells isolated from primary MM patient bone marrow aspirates. Initial results suggest several markers identified in cell lines are also highly expressed in primary PI-resistant MM. Conclusions: Unbiased N-glycoproteomics reveals broad cell surface protein remodeling in PI-resistant MM. Short-term treatment with bortezomib or lenalidomide also leads to significant surface alterations, with differential expression of proteins including MUC1, GITR, and BCMA potentially playing a role in drug resistance and informing rational combination with immunotherapies. Disclosures Martin: Roche and Juno: Consultancy; Amgen, Sanofi, Seattle Genetics: Research Funding. Wong:Celgene: Research Funding; Janssen: Research Funding; Roche: Research Funding; Fortis: Research Funding. Wiita:Indapta Therapeutics: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Protocol Intelligence: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; UCSF: Patents &amp; Royalties.

Abstract 4546: Functional cell surface proteomics of acute myeloid leukemia enables predictive modeling of antibody-drug conjugate cytotoxicity

Abstract Acute myeloid leukemia (AML) remains a significant unmet medical need. The development of targeted therapies for AML is challenging due to the heterogeneity of immunological and genetic disease subtypes and physiological similarities shared by blasts and normal myeloid progenitor cells. The identification of abundant cell surface markers on AML blasts with limited expression on bone marrow progenitors could drive the development of novel antibody-drug conjugates (ADCs) and other antibody-based therapeutics with improved therapeutic windows. Here we used hydrazide chemistry and mass spectrometry-based proteomics to profile the cell surface landscape of 95 primary blast samples from the Beat AML research consortium and normal bone marrow progenitor cells from healthy donors. We measured the abundance of over 5,000 proteins with high reproducibility (R2 = 0.89) and coverage of known cell surface markers (240/371). We also identified more than 2,500 N-glycosylation sites, many of which are predicted by canonical sequence motifs but have not been demonstrated experimentally. These findings thereby expand our knowledge of both the AML and normal hematological cell surface proteome. Additionally, we used pulsed stable isotope labeling coupled with the aforementioned hydrazide method to assess global surface protein abundance and turnover dynamics in a panel of malignant cell lines. We integrated our cell surface characterization with Beat AML’s mRNA expression, mutation, and AML subtype classification data to prioritize proteins for further exploration as candidate targets for ADC therapeutics. We generated a library of ADCs using monoclonal antibodies conjugated with Auristatin T, a highly potent membrane impermeable microtubule inhibitor, and measured ADC cytotoxic activity on a panel of AML cell lines as well as primary bone marrow progenitor cells. We used these integrated datasets to more precisely refine AML subtypes and identify patient populations that could benefit from novel AML ADCs. We report the preclinical validation of CD317 as a proof-of-concept for the utility of cell surface proteomics to identify novel targets for ADC therapeutics. Citation Format: Robert Lawrence, Robert Thurman, Travis Biechele, Cristina Tognon, Samantha Savage, Anna Reister Schultz, Jeffrey Tyner, William Arthur. Functional cell surface proteomics of acute myeloid leukemia enables predictive modeling of antibody-drug conjugate cytotoxicity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4546.

Up-regulation of hERG K⁺ channels by B-RAF.

Human ether-a-go-go related-gene K+ channels (hERG) participate in the regulation of tumor cell proliferation and apoptosis. HERG channel activity is up-regulated by growth factors. Kinases sensitive to growth factor signaling include the serine/threonine protein kinase B-RAF. The present study thus explored whether B-RAF influences hERG channel expression and activity. To this end, hERG channels were expressed in Xenopus oocytes with or without wild-type B-RAF, hERG channel activity was determined utilizing dual-electrode voltage clamp and hERG protein abundance in the cell membrane was analyzed utilizing confocal microscopy as well as chemiluminescence. Moreover, in rhabdomyosarcoma RD cells the effect of B-RAF inhibitor PLX-4720 on hERG-mediated current was quantified by whole-cell patch clamp and hERG cell surface protein abundance by utilizing biotinylation of cell surface proteins as well as flow cytometry. As a result, co-expression of wild-type B-RAF in hERG-expressing Xenopus oocytes significantly increased hERG channel activity and hERG channel protein abundance in the cell membrane. Treatment for 24 hours of B-RAF and hERG-expressing Xenopus oocytes with B-RAF inhibitor PLX-4720 (10 µM) significantly decreased hERG-mediated current and hERG cell surface expression. Similarly, in rhabdomyosarcoma RD cells, treatment for 24 hours with B-RAF inhibitor PLX-4720 significantly decreased hERG cell membrane protein abundance and hERG-mediated current. In conclusion, B-RAF is a powerful regulator of hERG channel activity and cell surface hERG protein abundance.

Upregulation of the Na+-Coupled Phosphate Cotransporters NaPi-IIa and NaPi-IIb by B-RAF

B-RAF, a serine/threonine protein kinase, contributes to signaling of insulin-like growth factor IGF1. Effects of IGF1 include stimulation of proximal renal tubular phosphate transport, accomplished in large part by Na⁺-coupled phosphate cotransporter NaPi-IIa. The related Na⁺-coupled phosphate cotransporter NaPi-IIb accomplishes phosphate transport in intestine and tumor cells. The present study explored whether B-RAF influences protein abundance and/or activity of type II Na⁺-coupled phosphate cotransporters NaPi-IIa and NaPi-IIb. cRNA encoding wild-type NaPi-IIa and wild-type NaPi-IIb was injected into Xenopus oocytes with or without additional injection of cRNA encoding wild-type B-RAF, and electrogenic phosphate transport determined by dual-electrode voltage clamp. NaPi-IIa protein abundance in Xenopus oocyte cell membrane was visualized by confocal microscopy and quantified by chemiluminescence. Moreover, in HEK293 cells, the effect of B-RAF inhibitor PLX-4720 on NaPi-IIa cell surface protein abundance was quantified utilizing biotinylation of cell surface proteins and western blotting. In NaPi-IIa-expressing Xenopus oocytes, but not in oocytes injected with water, addition of phosphate to extracellular bath generated a current (I P), which was significantly increased following coexpression of B-RAF. According to kinetic analysis, coexpression of B-RAF enhanced the maximal IP. Coexpression of B-RAF further enhanced NaPi-IIa protein abundance in the Xenopus oocyte cell membrane. Treatment of HEK293 cells for 24 h with PLX-4720 significantly decreased NaPi-IIa cell membrane protein abundance. Coexpression of B-RAF, further significantly increased IP in NaPi-IIb-expressing Xenopus oocytes. Again, B-RAF coexpression enhanced the maximal IP. In conclusion, B-RAF is a powerful stimulator of the renal and intestinal type II Na⁺-coupled phosphate cotransporters NaPi-IIa and NaPi-IIb, respectively.

Up-Regulation of Amino Acid Transporter SLC6A19 Activity and Surface Protein Abundance by PKB/Akt and PIKfyve

Background: The amino acid transporter B0AT1 (SLC6A19) accomplishes concentrative cellular uptake of neutral amino acids. SLC6A19 is stimulated by serum- &amp; glucocorticoid-inducible kinase (SGK) isoforms. SGKs are related to PKB/Akt isoforms, which also stimulate several amino acid transporters. PKB/Akt modulates glucose transport in part by phosphorylating and thus activating phosphatidylinositol-3-phosphate-5-kinase (PIKfyve), which fosters carrier protein insertion into the cell membrane. The present study explored whether PKB/Akt and/or PIKfyve stimulate SLC6A19. Methods: SLC6A19 was expressed in Xenopus oocytes with or without wild-type PKB/Akt or inactive ^T308A/S473APKB/Akt without or with additional expression of wild-type PIKfyve or PKB/Akt-resistant ^S318APIKfyve. Electrogenic amino acid transport was determined by dual electrode voltage clamping. Results: In SLC6A19-expressing oocytes but not in water-injected oocytes, the addition of the neutral amino acid L-leucine (2 mM) to the bath generated a current (I_le), which was significantly increased following coexpression of PKB/Akt, but not by coexpression of ^T308A/S473APKB/Akt. The effect of PKB/Akt was augmented by additional coexpression of PIKfyve but not of ^S318APIKfyve. Coexpression of PKB/Akt enhanced the maximal transport rate without significantly modifying the affinity of the carrier. The decline of I_le following inhibition of carrier insertion by brefeldin A (5 µM) was similar in the absence and presence of PKB/Akt indicating that PKB/Akt stimulated carrier insertion into rather than inhibiting carrier retrieval from the cell membrane. Conclusion: PKB/Akt up-regulates SLC6A19 activity, which may foster amino acid uptake into PKB/Akt-expressing epithelial and tumor cells.

The Calcineurin Homologous Protein-1 Increases Na+/H+-Exchanger 3 Trafficking via Ezrin Phosphorylation

The Na(+)/H(+)-exchanger 3 (NHE3) is essential for regulation of Na(+) transport in the renal and intestinal epithelium. Although changes in cell surface abundance control NHE3 function, the molecular signals that regulate NHE3 surface expression are not well defined. We found that overexpression of the calcineurin homologous protein-1 (CHP1) in opossum kidney cells increased NHE3 transport activity, surface protein abundance, and ezrin phosphorylation. CHP1 knockdown by small interfering RNA had the opposite effects. Overexpression of wild-type ezrin increased both NHE3 transport activity and surface protein abundance, confirming that NHE3 is downstream of ezrin. Expression of a pseudophosphorylated ezrin enhanced these effects, whereas expression of an ezrin variant that could not be phosphorylated prevented the downstream effects on NHE3. Furthermore, CHP1 knockdown reversed the activation of NHE3 by wild-type ezrin but not by the pseudophosphorylated ezrin. Taken together, these results demonstrate that CHP1 increases NHE3 abundance and constitutive function in a manner dependent on ezrin phosphorylation.

Receptors Respond to Metabolic Flux

Many cell surface proteins, including receptors, are N-glycosylated as they transit through the biosynthetic pathway. Proteins containing branched structures with poly-N-acetyllactosamine show a higher affinity for galectins, cell-surface proteins that bind glycosylated proteins, than proteins with less branched structures. The galectin interaction appears to limit endocytosis of the receptors. The formation of branched structures requires the intermediate formed by the activity of N-acetylglycosaminyltransferase V (Mgat5), which is dependent on the concentration of UDP-N-acetylglucosamine (UDP-GlcNAc), a product of the hexosamine pathway. Lau et al . combined mathematical modeling of glycosylation branching and the effects of this on the cell-surface expression and signaling of various types of receptors. They found that supplementation of Mgat5 –/– cells with GlcNAc increased the interaction of epidermal growth factor receptors (EGFRs) and transforming growth factor-β receptors (TGFβRs) with galectin and increased their abundance at the cell surface. Surprisingly, the EGFR with eight N-glycosylation sites showed a hyberbolic increase in responsiveness (phosphorylation of its downstream effector ERK), whereas TGFβR (with only one or two glycosylation sites) showed a sigmoid or switchlike increase in responsiveness (nuclear translocation of its downstream effector Smad proteins) when the Mgat5 –/– cells were supplemented with increasing concentrations of GlcNAc. Based on this observation, the authors were able to create a mathematical model that described the dependency of cell surface protein abundance on glycosylation state and metabolic flux through the hexosamine pathway. Receptors that promote proliferation generally have higher numbers of glycosylation sites than those that promote growth arrest and differentiation, and these proliferation-inducing receptors show a hyperbolic increase in responsiveness to increased flux through the hexosamine pathway. Modeling suggested that the switch-like behavior of the growth-arresting receptors allows them to be ultrasensitive to metabolic state. Stanley discusses the implications and next steps for testing this model. K. S. Lau, E. A. Partridge, A. Grigorian, C. I. Silvescu, V. N. Reinhold, M. Demetriou, J. W. Dennis, Complex N-glycan number and degree of branching cooperate to regulate cell proliferation and differentiation. Cell 129 , 123-134 (2007). [Online Journal] P. Stanley, A method to the madness of N-glycan complexity? Cell 129 , 27-29 (2007). [Online Journal]