43 results on '"Dattatreya Mellacheruvu"'
Search Results
2. 79 Extensively validated HLA LOH algorithm demonstrates an association between HLA LOH and genomic instability
- Author
-
Simo Zhang, Gabor Bartha, Sean Boyle, John West, Richard Chen, Dattatreya Mellacheruvu, Rachel Pyke, Charles Abbott, Eric Levy, Michael Snyder, Lee McDaniel, and Steven Dea
- Subjects
Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,RC254-282 - Published
- 2021
- Full Text
- View/download PDF
3. 57 Precision neoantigen discovery using novel algorithms and expanded HLA-ligandome datasets
- Author
-
Sean Boyle, John West, Richard Chen, Dattatreya Mellacheruvu, Rachel Pyke, Charles Abbott, Nick Phillips, Sejal Desai, and Rena McClory
- Subjects
Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,RC254-282 - Published
- 2020
- Full Text
- View/download PDF
4. Large-Scale Analysis of Kinase Signaling in Yeast Pseudohyphal Development Identifies Regulation of Ribonucleoprotein Granules.
- Author
-
Christian A Shively, Hye Kyong Kweon, Kaitlyn L Norman, Dattatreya Mellacheruvu, Tao Xu, Daniel T Sheidy, Craig J Dobry, Ivan Sabath, Eric E P Cosky, Elizabeth J Tran, Alexey Nesvizhskii, Philip C Andrews, and Anuj Kumar
- Subjects
Genetics ,QH426-470 - Abstract
Yeast pseudohyphal filamentation is a stress-responsive growth transition relevant to processes required for virulence in pathogenic fungi. Pseudohyphal growth is controlled through a regulatory network encompassing conserved MAPK (Ste20p, Ste11p, Ste7p, Kss1p, and Fus3p), protein kinase A (Tpk2p), Elm1p, and Snf1p kinase pathways; however, the scope of these pathways is not fully understood. Here, we implemented quantitative phosphoproteomics to identify each of these signaling networks, generating a kinase-dead mutant in filamentous S. cerevisiae and surveying for differential phosphorylation. By this approach, we identified 439 phosphoproteins dependent upon pseudohyphal growth kinases. We report novel phosphorylation sites in 543 peptides, including phosphorylated residues in Ras2p and Flo8p required for wild-type filamentous growth. Phosphoproteins in these kinase signaling networks were enriched for ribonucleoprotein (RNP) granule components, and we observe co-localization of Kss1p, Fus3p, Ste20p, and Tpk2p with the RNP component Igo1p. These kinases localize in puncta with GFP-visualized mRNA, and KSS1 is required for wild-type levels of mRNA localization in RNPs. Kss1p pathway activity is reduced in lsm1Δ/Δ and pat1Δ/Δ strains, and these genes encoding P-body proteins are epistatic to STE7. The P-body protein Dhh1p is also required for hyphal development in Candida albicans. Collectively, this study presents a wealth of data identifying the yeast phosphoproteome in pseudohyphal growth and regulatory interrelationships between pseudohyphal growth kinases and RNPs.
- Published
- 2015
- Full Text
- View/download PDF
5. The yeast Sks1p kinase signaling network regulates pseudohyphal growth and glucose response.
- Author
-
Cole Johnson, Hye Kyong Kweon, Daniel Sheidy, Christian A Shively, Dattatreya Mellacheruvu, Alexey I Nesvizhskii, Philip C Andrews, and Anuj Kumar
- Subjects
Genetics ,QH426-470 - Abstract
The yeast Saccharomyces cerevisiae undergoes a dramatic growth transition from its unicellular form to a filamentous state, marked by the formation of pseudohyphal filaments of elongated and connected cells. Yeast pseudohyphal growth is regulated by signaling pathways responsive to reductions in the availability of nitrogen and glucose, but the molecular link between pseudohyphal filamentation and glucose signaling is not fully understood. Here, we identify the glucose-responsive Sks1p kinase as a signaling protein required for pseudohyphal growth induced by nitrogen limitation and coupled nitrogen/glucose limitation. To identify the Sks1p signaling network, we applied mass spectrometry-based quantitative phosphoproteomics, profiling over 900 phosphosites for phosphorylation changes dependent upon Sks1p kinase activity. From this analysis, we report a set of novel phosphorylation sites and highlight Sks1p-dependent phosphorylation in Bud6p, Itr1p, Lrg1p, Npr3p, and Pda1p. In particular, we analyzed the Y309 and S313 phosphosites in the pyruvate dehydrogenase subunit Pda1p; these residues are required for pseudohyphal growth, and Y309A mutants exhibit phenotypes indicative of impaired aerobic respiration and decreased mitochondrial number. Epistasis studies place SKS1 downstream of the G-protein coupled receptor GPR1 and the G-protein RAS2 but upstream of or at the level of cAMP-dependent PKA. The pseudohyphal growth and glucose signaling transcription factors Flo8p, Mss11p, and Rgt1p are required to achieve wild-type SKS1 transcript levels. SKS1 is conserved, and deletion of the SKS1 ortholog SHA3 in the pathogenic fungus Candida albicans results in abnormal colony morphology. Collectively, these results identify Sks1p as an important regulator of filamentation and glucose signaling, with additional relevance towards understanding stress-responsive signaling in C. albicans.
- Published
- 2014
- Full Text
- View/download PDF
6. Withdrawal of ‘Precision Neoantigen Discovery Using Large-scale Immunopeptidomes and Composite Modeling of MHC Peptide Presentation’
- Author
-
Rachel Marty Pyke, Dattatreya Mellacheruvu, Steven Dea, Charles W. Abbott, Simo V. Zhang, Nick A. Phillips, Jason Harris, Gabor Bartha, Sejal Desai, Rena McClory, John West, Michael P. Snyder, Richard Chen, and Sean Michael Boyle
- Subjects
Withdrawal Notice ,Molecular Biology ,Biochemistry ,Analytical Chemistry - Published
- 2023
7. Supplementary Data from Prediction of Immunotherapy Response in Melanoma through Combined Modeling of Neoantigen Burden and Immune-Related Resistance Mechanisms
- Author
-
Richard Chen, Sekwon Jang, Michael P. Snyder, Rena McClory, Jason Harris, Gabor Bartha, Pamela Milani, Zeid M. Rusan, Rose Santiago, Mengyao Tan, Simo V. Zhang, Dattatreya Mellacheruvu, Fábio C.P. Navarro, Eric Levy, Lee D. McDaniel, Rachel Marty Pyke, Sean M. Boyle, and Charles W. Abbott
- Abstract
Supplementary Data from Prediction of Immunotherapy Response in Melanoma through Combined Modeling of Neoantigen Burden and Immune-Related Resistance Mechanisms
- Published
- 2023
- Full Text
- View/download PDF
8. Supplementary Table from Prediction of Immunotherapy Response in Melanoma through Combined Modeling of Neoantigen Burden and Immune-Related Resistance Mechanisms
- Author
-
Richard Chen, Sekwon Jang, Michael P. Snyder, Rena McClory, Jason Harris, Gabor Bartha, Pamela Milani, Zeid M. Rusan, Rose Santiago, Mengyao Tan, Simo V. Zhang, Dattatreya Mellacheruvu, Fábio C.P. Navarro, Eric Levy, Lee D. McDaniel, Rachel Marty Pyke, Sean M. Boyle, and Charles W. Abbott
- Abstract
Supplementary Table from Prediction of Immunotherapy Response in Melanoma through Combined Modeling of Neoantigen Burden and Immune-Related Resistance Mechanisms
- Published
- 2023
- Full Text
- View/download PDF
9. Data from Prediction of Immunotherapy Response in Melanoma through Combined Modeling of Neoantigen Burden and Immune-Related Resistance Mechanisms
- Author
-
Richard Chen, Sekwon Jang, Michael P. Snyder, Rena McClory, Jason Harris, Gabor Bartha, Pamela Milani, Zeid M. Rusan, Rose Santiago, Mengyao Tan, Simo V. Zhang, Dattatreya Mellacheruvu, Fábio C.P. Navarro, Eric Levy, Lee D. McDaniel, Rachel Marty Pyke, Sean M. Boyle, and Charles W. Abbott
- Abstract
Purpose:While immune checkpoint blockade (ICB) has become a pillar of cancer treatment, biomarkers that consistently predict patient response remain elusive due to the complex mechanisms driving immune response to tumors. We hypothesized that a multi-dimensional approach modeling both tumor and immune-related molecular mechanisms would better predict ICB response than simpler mutation-focused biomarkers, such as tumor mutational burden (TMB).Experimental Design:Tumors from a cohort of patients with late-stage melanoma (n = 51) were profiled using an immune-enhanced exome and transcriptome platform. We demonstrate increasing predictive power with deeper modeling of neoantigens and immune-related resistance mechanisms to ICB.Results:Our neoantigen burden score, which integrates both exome and transcriptome features, more significantly stratified responders and nonresponders (P = 0.016) than TMB alone (P = 0.049). Extension of this model to include immune-related resistance mechanisms affecting the antigen presentation machinery, such as HLA allele-specific LOH, resulted in a composite neoantigen presentation score (NEOPS) that demonstrated further increased association with therapy response (P = 0.002).Conclusions:NEOPS proved the statistically strongest biomarker compared with all single-gene biomarkers, expression signatures, and TMB biomarkers evaluated in this cohort. Subsequent confirmation of these findings in an independent cohort of patients (n = 110) suggests that NEOPS is a robust, novel biomarker of ICB response in melanoma.
- Published
- 2023
- Full Text
- View/download PDF
10. Supplementary Figure from Prediction of Immunotherapy Response in Melanoma through Combined Modeling of Neoantigen Burden and Immune-Related Resistance Mechanisms
- Author
-
Richard Chen, Sekwon Jang, Michael P. Snyder, Rena McClory, Jason Harris, Gabor Bartha, Pamela Milani, Zeid M. Rusan, Rose Santiago, Mengyao Tan, Simo V. Zhang, Dattatreya Mellacheruvu, Fábio C.P. Navarro, Eric Levy, Lee D. McDaniel, Rachel Marty Pyke, Sean M. Boyle, and Charles W. Abbott
- Abstract
Supplementary Figure from Prediction of Immunotherapy Response in Melanoma through Combined Modeling of Neoantigen Burden and Immune-Related Resistance Mechanisms
- Published
- 2023
- Full Text
- View/download PDF
11. Prediction of Immunotherapy Response in Melanoma through Combined Modeling of Neoantigen Burden and Immune-Related Resistance Mechanisms
- Author
-
Gabor Bartha, Jason B. Harris, Simo V. Zhang, Sean Michael Boyle, Michael Snyder, Rena McClory, Pamela Milani, Fabio C. P. Navarro, Rachel Marty Pyke, Eric Levy, Richard Chen, Zeid M. Rusan, Rose Santiago, Lee D. McDaniel, Mengyao Tan, Charles Abbott, Sekwon Jang, and Dattatreya Mellacheruvu
- Subjects
Oncology ,Cancer Research ,medicine.medical_specialty ,business.industry ,medicine.medical_treatment ,Melanoma ,Models, Immunological ,Immunotherapy ,Human leukocyte antigen ,medicine.disease ,Immune checkpoint ,Transcriptome ,Treatment Outcome ,Immune system ,Drug Resistance, Neoplasm ,Internal medicine ,Humans ,Medicine ,Biomarker (medicine) ,business ,Exome ,Forecasting - Abstract
Purpose: While immune checkpoint blockade (ICB) has become a pillar of cancer treatment, biomarkers that consistently predict patient response remain elusive due to the complex mechanisms driving immune response to tumors. We hypothesized that a multi-dimensional approach modeling both tumor and immune-related molecular mechanisms would better predict ICB response than simpler mutation-focused biomarkers, such as tumor mutational burden (TMB). Experimental Design: Tumors from a cohort of patients with late-stage melanoma (n = 51) were profiled using an immune-enhanced exome and transcriptome platform. We demonstrate increasing predictive power with deeper modeling of neoantigens and immune-related resistance mechanisms to ICB. Results: Our neoantigen burden score, which integrates both exome and transcriptome features, more significantly stratified responders and nonresponders (P = 0.016) than TMB alone (P = 0.049). Extension of this model to include immune-related resistance mechanisms affecting the antigen presentation machinery, such as HLA allele-specific LOH, resulted in a composite neoantigen presentation score (NEOPS) that demonstrated further increased association with therapy response (P = 0.002). Conclusions: NEOPS proved the statistically strongest biomarker compared with all single-gene biomarkers, expression signatures, and TMB biomarkers evaluated in this cohort. Subsequent confirmation of these findings in an independent cohort of patients (n = 110) suggests that NEOPS is a robust, novel biomarker of ICB response in melanoma.
- Published
- 2021
- Full Text
- View/download PDF
12. Kir2.1 Interactome Mapping Uncovers PKP4 as a Modulator of the Kir2.1-Regulated Inward Rectifier Potassium Currents
- Author
-
Venkatesha Basrur, Rork Kuick, José Jalife, Guadalupe Guerrero-Serna, Justin Yoon, Alexey I. Nesvizhskii, Jean François Rual, Sung Soo Park, Daniela Ponce-Balbuena, Dattatreya Mellacheruvu, Kevin P. Conlon, National Institutes of Health (Estados Unidos), NIH - National Heart, Lung, and Blood Institute (NHLBI) (Estados Unidos), NIH - National Institute of General Medical Sciences (NIGMS) (Estados Unidos), NIH - National Cancer Institute (NCI) (Estados Unidos), University of Michigan Rogel Cancer Center (Estados Unidos), National Institutes of Health (United States), National Heart, Lung, and Blood Institute (United States), National Institute of General Medical Sciences (United States), National Cancer Institute (United States), and University of Michigan Rogel Cancer Center (United States)
- Subjects
Patch-Clamp Techniques ,Utrophin ,macromolecular complex analysis ,Kir2.1 ,cardiovascular function or biology ,Regulator ,Action Potentials ,Computational biology ,Biology ,Biochemistry ,Interactome ,Analytical Chemistry ,Protein–protein interaction ,03 medical and health sciences ,Somatomedins ,Tandem Mass Spectrometry ,cardiovascular disease ,inward rectifier potassium current ,Humans ,Myocytes, Cardiac ,Protein Interaction Maps ,Potassium Channels, Inwardly Rectifying ,BioID ,Molecular Biology ,mass spectrometry ,PKP4 ,030304 developmental biology ,Andersen Syndrome ,Membrane potential ,0303 health sciences ,Inward-rectifier potassium ion channel ,Research ,030302 biochemistry & molecular biology ,Desmosomes ,Protein-protein interactions ,Protein Transport ,HEK293 Cells ,Mutation ,Potassium ,cardiovascular system ,Signal transduction ,Lysosomes ,Plakophilins ,cardiomyopathy ,Function (biology) ,Chromatography, Liquid ,Molecular Chaperones ,Signal Transduction - Abstract
A comprehensive map of the Kir2.1 interactome was generated using the proximity-labeling approach BioID. The map encompasses 218 interactions, the vast majority of which are novel, and explores the variations in the interactome profiles of Kir2.1WT versus Kir2.1Δ314-315, a trafficking deficient ATS1 mutant, thus uncovering molecular mechanisms whose malfunctions may underlie ATS1 disease. PKP4, one of the BioID interactors, is validated as a modulator of Kir2.1-controlled inward rectifier potassium currents., Graphical Abstract Highlights • Generation using BioID of a map of the Kir2.1 interactome with 218 interactions. • Identification of Kir2.1WT- versus Kir2.1Δ314-315-preferred interactors. • Identification of the desmosome protein PKP4 as a new modulator of IKir2.1 currents., Kir2.1, a strong inward rectifier potassium channel encoded by the KCNJ2 gene, is a key regulator of the resting membrane potential of the cardiomyocyte and plays an important role in controlling ventricular excitation and action potential duration in the human heart. Mutations in KCNJ2 result in inheritable cardiac diseases in humans, e.g. the type-1 Andersen-Tawil syndrome (ATS1). Understanding the molecular mechanisms that govern the regulation of inward rectifier potassium currents by Kir2.1 in both normal and disease contexts should help uncover novel targets for therapeutic intervention in ATS1 and other Kir2.1-associated channelopathies. The information available to date on protein-protein interactions involving Kir2.1 channels remains limited. Additional efforts are necessary to provide a comprehensive map of the Kir2.1 interactome. Here we describe the generation of a comprehensive map of the Kir2.1 interactome using the proximity-labeling approach BioID. Most of the 218 high-confidence Kir2.1 channel interactions we identified are novel and encompass various molecular mechanisms of Kir2.1 function, ranging from intracellular trafficking to cross-talk with the insulin-like growth factor receptor signaling pathway, as well as lysosomal degradation. Our map also explores the variations in the interactome profiles of Kir2.1WTversus Kir2.1Δ314-315, a trafficking deficient ATS1 mutant, thus uncovering molecular mechanisms whose malfunctions may underlie ATS1 disease. Finally, using patch-clamp analysis, we validate the functional relevance of PKP4, one of our top BioID interactors, to the modulation of Kir2.1-controlled inward rectifier potassium currents. Our results validate the power of our BioID approach in identifying functionally relevant Kir2.1 interactors and underline the value of our Kir2.1 interactome as a repository for numerous novel biological hypotheses on Kir2.1 and Kir2.1-associated diseases.
- Published
- 2020
- Full Text
- View/download PDF
13. RAD51AP1 regulates ALT-HDR through chromatin-directed homeostasis of TERRA
- Author
-
Nicole Kaminski, Anne R. Wondisford, Youngho Kwon, Michelle Lee Lynskey, Ragini Bhargava, Jonathan Barroso-González, Laura García-Expósito, Boxue He, Meng Xu, Dattatreya Mellacheruvu, Simon C. Watkins, Mauro Modesti, Kyle M. Miller, Alexey I. Nesvizhskii, Huaiying Zhang, Patrick Sung, Roderick J. O’Sullivan, University of Pittsburgh (PITT), Pennsylvania Commonwealth System of Higher Education (PCSHE), University of Texas Health Science Center, The University of Texas Health Science Center at Houston (UTHealth), Central South University [Changsha], Carnegie Mellon University [Pittsburgh] (CMU), University of Michigan Medical School [Ann Arbor], University of Michigan [Ann Arbor], University of Michigan System-University of Michigan System, Centre de Recherche en Cancérologie de Marseille (CRCM), Aix Marseille Université (AMU)-Institut Paoli-Calmettes, Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institut Paoli-Calmettes, Fédération nationale des Centres de lutte contre le Cancer (FNCLCC), University of Texas at Austin [Austin], University of Michigan System, and Modesti, Mauro
- Subjects
Proteomics ,ALT ,[SDV]Life Sciences [q-bio] ,RAD51AP1 ,Telomere Homeostasis ,TERRA ,Cell Biology ,Telomere ,Chromatin ,[SDV] Life Sciences [q-bio] ,homology-directed repair ,cancer ,Homeostasis ,RNA, Long Noncoding ,transcription ,Molecular Biology - Abstract
International audience; Alternative lengthening of telomeres (ALT) is a homology-directed repair (HDR) mechanism of telomere elongation that controls proliferation in subsets of aggressive cancer. Recent studies have revealed that telomere repeat-containing RNA (TERRA) promotes ALT-associated HDR (ALT-HDR). Here, we report that RAD51AP1, a crucial ALT factor, interacts with TERRA and utilizes it to generate D- and R-loop HR intermediates. We also show that RAD51AP1 binds to and might stabilize TERRA-containing R-loops as RAD51AP1 depletion reduces R-loop formation at telomere DNA breaks. Proteomic analyses uncover a role for RAD51AP1-mediated TERRA R-loop homeostasis in a mechanism of chromatin-directed suppression of TERRA and prevention of transcription-replication collisions (TRCs) during ALT-HDR. Intriguingly, we find that both TERRA binding and this non-canonical function of RAD51AP1 require its intrinsic SUMO-SIM regulatory axis. These findings provide insights into the multi-contextual functions of RAD51AP1 within the ALT mechanism and regulation of TERRA.
- Published
- 2022
- Full Text
- View/download PDF
14. Precision Neoantigen Discovery Using Large-Scale Immunopeptidomes and Composite Modeling of MHC Peptide Presentation
- Author
-
Simo V. Zhang, Gabor Bartha, Rachel Marty Pyke, Richard Chen, Sean Michael Boyle, Jason Harris, John A. West, Michael Snyder, Sejal Desai, Rena McClory, Charles Abbott, Dattatreya Mellacheruvu, Nick A. Phillips, and Steven Dea
- Subjects
NMDP, National Marrow Donor Program ,False discovery rate ,Proteome ,Computer science ,Biochemistry ,Immunoproteomics ,Epitope ,Analytical Chemistry ,immunology ,Major Histocompatibility Complex ,SHERPA, Systematic HLA Epitope Ranking Pan Algorithm ,Immune Epitope Database and Analysis Resource ,GFP, green fluorescent protein ,next generation sequencing ,Antigen Presentation ,0303 health sciences ,biology ,Antigen processing ,030302 biochemistry & molecular biology ,Technological Innovation and Resources ,immunopeptidomics ,G, gene propensity (model feature) ,ELISA, enzyme-linked immunosorbent assay ,B, binding pocket (model feature) ,machine learning ,P, peptide (model feature) ,H, hotspot score (model feature) ,F, flanking regions (model feature) ,cancer vaccines ,Algorithms ,L, peptide length (model feature) ,FDR, false discovery rate ,T, protein abundance as measured by TPM (model feature) ,Decision tree ,Computational biology ,Human leukocyte antigen ,Major histocompatibility complex ,Cell Line ,03 medical and health sciences ,TPM, transcripts per million ,Special Issue: Immunopeptidomics ,Antigens, Neoplasm ,cancer ,MHC, major histocompatibility complex ,Humans ,Gene ,Molecular Biology ,030304 developmental biology ,ATCC, American Type Culture Collection ,IEDB, Immune Epitope Database and Analysis Resource ,pMHC, major histocompatibility complex-peptide ,HLA, human leukocyte antigen ,Research ,Models, Theoretical ,neoantigen prediction ,IMGT, International ImMunoGeneTics Information System ,LOO, leave one out model ,biology.protein ,Gradient boosting ,MHC ,LC-MS/MS, liquid chromatography with tandem mass spectrometry ,Peptides ,Transcriptome ,P-models, primary models ,neoantigens - Abstract
Major histocompatibility complex (MHC)-bound peptides that originate from tumor-specific genetic alterations, known as neoantigens, are an important class of anticancer therapeutic targets. Accurately predicting peptide presentation by MHC complexes is a key aspect of discovering therapeutically relevant neoantigens. Technological improvements in mass-spectrometry-based immunopeptidomics and advanced modeling techniques have vastly improved MHC presentation prediction over the past two decades. However, improvement in the sensitivity and specificity of prediction algorithms is needed for clinical applications such as the development of personalized cancer vaccines, the discovery of biomarkers for response to checkpoint blockade, and the quantification of autoimmune risk in gene therapies. Toward this end, we generated allele-specific immunopeptidomics data using 25 monoallelic cell lines and created Systematic HLA Epitope Ranking Pan Algorithm (SHERPA), a pan-allelic MHC-peptide algorithm for predicting MHC-peptide binding and presentation. In contrast to previously published large-scale monoallelic data, we used an HLA-null K562 parental cell line and a stable transfection of HLA alleles to better emulate native presentation. Our dataset includes five previously unprofiled alleles that expand MHC-binding pocket diversity in the training data and extend allelic coverage in under profiled populations. To improve generalizability, SHERPA systematically integrates 128 monoallelic and 384 multiallelic samples with publicly available immunoproteomics data and binding assay data. Using this dataset, we developed two features that empirically estimate the propensities of genes and specific regions within gene bodies to engender immunopeptides to represent antigen processing. Using a composite model constructed with gradient boosting decision trees, multiallelic deconvolution, and 2.15 million peptides encompassing 167 alleles, we achieved a 1.44-fold improvement of positive predictive value compared with existing tools when evaluated on independent monoallelic datasets and a 1.15-fold improvement when evaluating on tumor samples. With a high degree of accuracy, SHERPA has the potential to enable precision neoantigen discovery for future clinical applications., Graphical Abstract, Highlights • Generated 25 stably transfected monoallelic cell lines and applied immunopeptidomics. • Harmonized 512 public immunopeptidomic samples through systematic reprocessing. • Developed pan-allele MHC-binding algorithm (SHERPA) utilizing 167 human HLA alleles. • SHERPA demonstrates up to 1.44-fold increased precision over competing algorithms., In Brief Accurately identifying neoantigens is critical for many clinical applications. We generated immunopeptidomics data from 25 stably transfected monoallelic cell lines. Then, we systematically reprocessed a large corpus of public data to improve major histocompatibility complex (MHC) binding pocket diversity and to empirically learn the rules of antigen presentation. In applying these datasets, we trained SHERPA, an MHC binding and presentation prediction algorithm. SHERPA improves performance compared with existing tools by 1.44-fold in held-out monoallelic data and 1.11-fold for immunogenic epitopes.
- Published
- 2023
- Full Text
- View/download PDF
15. 79 Extensively validated HLA LOH algorithm demonstrates an association between HLA LOH and genomic instability
- Author
-
Steven Dea, Simo V. Zhang, Gabor Bartha, Sean Michael Boyle, Dattatreya Mellacheruvu, Michael Snyder, Charles Abbott, Lee McDaniel, Richard Chen, John A. West, Rachel Marty Pyke, and Eric Levy
- Subjects
Pharmacology ,Genome instability ,Genetics ,Cancer Research ,Oncology ,Association (object-oriented programming) ,Immunology ,Molecular Medicine ,Immunology and Allergy ,Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,Human leukocyte antigen ,Biology ,RC254-282 - Abstract
BackgroundHuman Leukocyte Antigen (HLA) genes are critical for the presentation of neoantigens to the immune system by cancer cells. Deletion of HLA alleles, known as HLA loss of heterozygosity (LOH), has been highlighted as a key immune escape mechanism. Validated algorithms to detect HLA LOH from sequencing data are critical for exploring the biological impact of HLA LOH and assessing its utility as a clinical biomarker.MethodsWe developed DASH (Deletion of Allele-Specific HLAs), a machine learning algorithm trained on data from 279 patients on the ImmunoID NeXT Platform using features that account for probe capture variability between alleles and incorporate information from the regions flanking each HLA gene. To understand the contribution of boosted sequencing in the HLA region of the ImmunoID NeXT Platform, we performed an in silico downsampling analysis. To assess DASH’s performance at variable tumor purities and HLA LOH subclonalities we identified three tumor-normal cell lines with HLA LOH and created in silico mixtures. Furthermore, after designing patient-specific primers for 21 patients that target specific alleles, we applied digital PCR (dPCR) to validate the HLA allele copy number status of the patients. Finally, we applied DASH to 611 patients spanning 15 tumor types.ResultsIn cross validation analyses across patient samples, DASH achieved 98.7% specificity and 92.9% sensitivity while LOHHLA, a widely used algorithm, only reached 94.3% and 78.8%, respectively (figure 1). Downsampling analyses demonstrated that DASH benefits significantly from the boosted HLA sequencing on the ImmunoID NeXT Platform, dropping 0.06 in F-score after downsampling to the sequencing depth of other exome platforms. In cell line mixture analyses, DASH demonstrates greater than 99% specificity across all tumor purity and sub-clonality levels and greater than 98% sensitivity for above 27% tumor purity. Moreover, DASH demonstrated 100% sensitivity and specificity in dPCR experiments across 21 tumor samples with stable controls. We applied DASH to a large pan-cancer cohort and found that 18% of patients had HLA LOH (figure 2). We identified strong associations between HLA LOH and genomic instability. Moreover, we demonstrated relationships between HLA LOH and markers of immune pressure, such as a correlation with CD274 (PD-1) expression and allele-specific neoantigen enrichment for deleted HLA alleles.ConclusionsDASH, a highly sensitive HLA LOH algorithm that has been extensively validated using cross validation, in silico downsampling, cell line mixtures and dPCR, has demonstrated the widespread impact of HLA LOH in a large pan-cancer cohort.Abstract 79 Figure 1Bar plots showing the sensitivity and specificities scores across ImmunoID NeXT cross validation samples between LOHHLA (blue) and DASH (green).Abstract 79 Figure 2Bar plots denoting the number of patients and the frequency of HLA LOH in each tumor type cohort. 95% confidence intervals are shown with the thin dark grey bars. Only cohorts with at least 10 patients are shown
- Published
- 2021
16. Allele-specific RNA interference prevents neuropathy in Charcot-Marie-Tooth disease type 2D mouse models
- Author
-
Kathryn H. Morelli, Laurie B. Griffin, Allison M. Fowler, Timothy J. Hines, James R. Lupski, Lindsay M. Wallace, Samuel G. Kocen, Scott Q. Harper, Jacob O. Kitzman, Ryuichi Takase, Stephanie N. Oprescu, Alexey I. Nesvizhskii, Rebecca Meyer-Schuman, Nettie K. Pyne, Pedro Mancias, Robert W. Burgess, Dattatreya Mellacheruvu, Ya-Ming Hou, Emily Spaulding, Anthony Antonellis, Na Wei, Xiang-Lei Yang, and Ian J. Butler
- Subjects
Glycine-tRNA Ligase ,0301 basic medicine ,Genetic enhancement ,Mutant ,Charcot-Marie-Tooth Disease Type 2D ,Mice ,03 medical and health sciences ,0302 clinical medicine ,Charcot-Marie-Tooth Disease ,RNA interference ,Mutant protein ,Animals ,Humans ,Medicine ,Allele ,Alleles ,Gene knockdown ,business.industry ,Genetic Therapy ,General Medicine ,medicine.disease ,Disease Models, Animal ,HEK293 Cells ,030104 developmental biology ,Peripheral neuropathy ,030220 oncology & carcinogenesis ,Mutation ,Cancer research ,RNA Interference ,business ,Research Article - Abstract
Gene therapy approaches are being deployed to treat recessive genetic disorders by restoring the expression of mutated genes. However, the feasibility of these approaches for dominantly inherited diseases — where treatment may require reduction in the expression of a toxic mutant protein resulting from a gain-of-function allele — is unclear. Here we show the efficacy of allele-specific RNAi as a potential therapy for Charcot-Marie-Tooth disease type 2D (CMT2D), caused by dominant mutations in glycyl-tRNA synthetase (GARS). A de novo mutation in GARS was identified in a patient with a severe peripheral neuropathy, and a mouse model precisely recreating the mutation was produced. These mice developed a neuropathy by 3–4 weeks of age, validating the pathogenicity of the mutation. RNAi sequences targeting mutant GARS mRNA, but not wild-type, were optimized and then packaged into AAV9 for in vivo delivery. This almost completely prevented the neuropathy in mice treated at birth. Delaying treatment until after disease onset showed modest benefit, though this effect decreased the longer treatment was delayed. These outcomes were reproduced in a second mouse model of CMT2D using a vector specifically targeting that allele. The effects were dose dependent, and persisted for at least 1 year. Our findings demonstrate the feasibility of AAV9-mediated allele-specific knockdown and provide proof of concept for gene therapy approaches for dominant neuromuscular diseases.
- Published
- 2019
- Full Text
- View/download PDF
17. Validated machine learning algorithm with sub-clonal sensitivity reveals widespread pan-cancer human leukocyte antigen loss of heterozygosity
- Author
-
Snyder Mm, Simo V. Zhang, Dattatreya Mellacheruvu, Sean Michael Boyle, McDaniel L, Gabor Bartha, John A. West, Rachel Marty Pyke, Richard Chen, Charles Abbott, Steven Dea, and Eric Levy
- Subjects
Pan cancer ,business.industry ,medicine.medical_treatment ,Human leukocyte antigen ,Immunotherapy ,Biology ,Machine learning ,computer.software_genre ,Loss of heterozygosity ,stomatognathic diseases ,Immune recognition ,Cancer cell ,medicine ,Digital polymerase chain reaction ,Artificial intelligence ,Allele ,business ,neoplasms ,computer ,Algorithm - Abstract
Human leukocyte antigen loss of heterozygosity (HLA LOH) allows cancer cells to escape immune recognition by deleting HLA alleles, causing the suppressed presentation of tumor neoantigens that would otherwise bind to them. Despite its importance in immunotherapy response, few methods exist to detect HLA LOH, and their accuracy is not well understood. Here, we develop DASH (Deletion of Allele-Specific HLAs), a novel machine learning-based algorithm to detect HLA LOH from paired tumor-normal sequencing data. Through validation with cell line mixtures and patient-specific digital PCR, we demonstrate increased sensitivity compared to previously published tools and pave the way for clinical utility. Using DASH on 611 patients across 15 tumor types, we found that 18% of patients had HLA LOH. Moreover, we show inflated HLA LOH rates compared to genome-wide LOH and correlations between CD274 (PD-L1) expression and MSI status, suggesting the HLA LOH is a key immune resistance strategy.
- Published
- 2021
- Full Text
- View/download PDF
18. 73 Orthogonally and functionally validated algorithm for detecting HLA loss of heterozygosity
- Author
-
Sean Michael Boyle, Simo V. Zhang, John A. West, Dattatreya Mellacheruvu, Manjula Chinnappa, Gabor Bartha, Charles Abbott, Richard Chen, Rachel Marty Pyke, Eric Levy, John Lyle, and Devayani Bhave
- Subjects
0301 basic medicine ,medicine.medical_treatment ,Human leukocyte antigen ,Immunotherapy ,Biology ,lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,lcsh:RC254-282 ,Loss of heterozygosity ,03 medical and health sciences ,030104 developmental biology ,0302 clinical medicine ,030220 oncology & carcinogenesis ,Dash ,medicine ,Biomarker (medicine) ,Digital polymerase chain reaction ,Allele ,Algorithm ,Exome sequencing - Abstract
Background Human leukocyte antigen (HLA) genes facilitate communication between tumor cells and the immune system through the cell surface presentation of a diverse set of peptides. HLA loss of heterozygosity (LOH) has been associated with reduced immune pressure on neoantigens and impaired response to checkpoint blockade immunotherapy. Although HLA LOH is emerging as a key biomarker for response to immunotherapy, few tools exist to detect HLA LOH. Moreover, the accuracy of these tools is not well understood due to lack of orthogonal validation approaches. Here, we briefly describe DASH (Deletion of Allele-Specific HLAs), an algorithm to detect HLA LOH from exome sequencing data, and present a three-pronged validation approach to assess its performance. Methods In-silico evaluation of the limit of detection (LOD) of DASH was performed by deeply sequencing a tumor-normal paired cell line with HLA LOH and mixing reads at different proportions to simulate variable tumor purity and clonality. Direct genomic validation was performed using digital PCR (dPCR) with allele-specific primers targeting both predicted kept and lost alleles in ten patient samples and one cell line. Quantitative immunopeptidomics was performed to compare peptides presented by HLA alleles in tumor cells and adjacent normal cells. The relative increase or decrease of peptide presentation per allele was estimated by predicting the binding of each peptide to the patient-specific alleles. Results DASH is a machine learning model built upon the HLA-enhanced ImmunoID NeXT Platform®. We validated the performance of DASH using three orthogonal approaches to better understand the factors driving sensitivity and specificity of the algorithm. Evaluation using cell line mixtures that simulate LOH at various dilutions helped establish the LOD of DASH. For fully clonal tumors, DASH had 100% sensitivity at all tumor purity levels above 8% and 100% specificity at tumor purity levels higher than 24%. Patient-specific and allele-specific dPCR assays provided sensitive, direct evidence of HLA LOH. All samples predicted to have HLA LOH by DASH with high confidence were confirmed by dPCR. Finally, a quantitative immunopeptidomics experiment in one patient with HLA LOH revealed a large decrease in the peptides presented by deleted alleles, revealing the functional implications of HLA LOH. Conclusions HLA LOH detection methods need to be rigorously validated in order to be used as a clinical biomarker. Here, we introduced three methods to assess performance, demonstrated the strong predictive power of DASH, and highlighted the need to consider tumor purity in such assessments.
- Published
- 2020
19. Philosopher: a versatile toolkit for shotgun proteomics data analysis
- Author
-
Andy T. Kong, Avinash Kumar Shanmugam, Dattatreya Mellacheruvu, Dmitry M. Avtonomov, Felipe da Veiga Leprevost, Sarah E. Haynes, Hui Yin Chang, and Alexey I. Nesvizhskii
- Subjects
Data Analysis ,Proteomics ,Proteomics methods ,Extramural ,Computer science ,MEDLINE ,Computational Biology ,Cell Biology ,Computational biology ,Biochemistry ,Article ,Shotgun proteomics ,Databases, Protein ,Molecular Biology ,Software ,Biotechnology - Published
- 2020
20. PAF1 complex interactions with SETDB1 mediate promoter H3K9 methylation and transcriptional repression ofHoxa9andMeis1in acute myeloid leukemia
- Author
-
James Ropa, Wei Chen, Zhiling Chen, Andrew G. Muntean, Venkatesha Basrur, Lili Zhao, Nirmalya Saha, Justin Serio, Dattatreya Mellacheruvu, and Alexey I. Nesvizhskii
- Subjects
0301 basic medicine ,Regulation of gene expression ,biology ,Myeloid leukemia ,RNA polymerase II ,Fusion protein ,Protein–protein interaction ,Cell biology ,03 medical and health sciences ,Histone H3 ,030104 developmental biology ,Oncology ,Transcription (biology) ,hemic and lymphatic diseases ,biology.protein ,Epigenetics ,neoplasms - Abstract
The Polymerase Associated Factor 1 complex (PAF1c) is an epigenetic co-modifying complex that directly contacts RNA polymerase II (RNAPII) and several epigenetic regulating proteins. Mutations, overexpression and loss of expression of subunits of the PAF1c are observed in various forms of cancer suggesting proper regulation is needed for cellular development. However, the biochemical interactions with the PAF1c that allow dynamic gene regulation are unclear. We and others have shown that the PAF1c makes a direct interaction with MLL fusion proteins, which are potent oncogenic drivers of acute myeloid leukemia (AML). This interaction is critical for the maintenance of MLL translocation driven AML by targeting MLL fusion proteins to the target genes Meis1 and Hoxa9. Here, we use a proteomics approach to identify protein-protein interactions with the PAF1c subunit CDC73 that regulate the function of the PAF1c. We identified a novel interaction with a histone H3 lysine 9 (H3K9) methyltransferase protein, SETDB1. This interaction is stabilized with a mutant CDC73 that is incapable of supporting AML cell growth. Importantly, transcription of Meis1 and Hoxa9 is reduced and promoter H3K9 trimethylation (H3K9me3) increased by overexpression of SETDB1 or stabilization of the PAF1c-SETDB1 interaction in AML cells. These findings were corroborated in human AML patients where increased SETDB1 expression was associated with reduced HOXA9 and MEIS1. To our knowledge, this is the first proteomics approach to search for CDC73 protein-protein interactions in AML, and demonstrates that the PAF1c may play a role in H3K9me3-mediated transcriptional repression in AML.
- Published
- 2018
- Full Text
- View/download PDF
21. MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry–based proteomics
- Author
-
Felipe da Veiga Leprevost, Andy T. Kong, Dattatreya Mellacheruvu, Dmitry M. Avtonomov, and Alexey I. Nesvizhskii
- Subjects
0301 basic medicine ,False discovery rate ,030102 biochemistry & molecular biology ,Computer science ,Search engine indexing ,Cell Biology ,Computational biology ,Tandem mass spectrometry ,Proteomics ,Mass spectrometry ,Bioinformatics ,Biochemistry ,03 medical and health sciences ,Identification (information) ,030104 developmental biology ,Proteome ,Database search engine ,Molecular Biology ,Biotechnology - Abstract
There is a need to better understand and handle the 'dark matter' of proteomics-the vast diversity of post-translational and chemical modifications that are unaccounted in a typical mass spectrometry-based analysis and thus remain unidentified. We present a fragment-ion indexing method, and its implementation in peptide identification tool MSFragger, that enables a more than 100-fold improvement in speed over most existing proteome database search tools. Using several large proteomic data sets, we demonstrate how MSFragger empowers the open database search concept for comprehensive identification of peptides and all their modified forms, uncovering dramatic differences in modification rates across experimental samples and conditions. We further illustrate its utility using protein-RNA cross-linked peptide data and using affinity purification experiments where we observe, on average, a 300% increase in the number of identified spectra for enriched proteins. We also discuss the benefits of open searching for improved false discovery rate estimation in proteomics.
- Published
- 2017
- Full Text
- View/download PDF
22. Abstract 1898: Accurate modeling of antigen processing and MHC peptide presentation using large-scale immunopeptidomes and a novel machine learning framework
- Author
-
Sejal Desai, Dattatreya Mellacheruvu, John A. West, Sean Michael Boyle, Charles Abbott, Steven Dea, Rena McClory, Rachel Marty Pyke, Richard Chen, Nicholas S. Phillips, Steven L. C. Ketelaars, and Pia Kvistborg
- Subjects
Cancer Research ,Scale (ratio) ,biology ,Computer science ,business.industry ,Antigen processing ,media_common.quotation_subject ,Machine learning ,computer.software_genre ,Major histocompatibility complex ,Presentation ,Oncology ,biology.protein ,Artificial intelligence ,business ,computer ,media_common - Abstract
Neoantigens, which are antigens specific to cancer cells, can be harnessed to develop precision immunotherapies, such as personalized cancer vaccines, and prognostic biomarkers for checkpoint blockade inhibition. Next generation sequencing technologies have enabled comprehensive profiling of putative neoantigens by interrogating the tumor exome and transcriptome, but accurate prediction of peptides presented by MHC complexes remains a significant challenge. We present here Systematic HLA Epitope Ranking Pan Algorithm (SHERPA™) that addresses this critical need. SHERPA comprises highly sensitive, accurate and pan-allelic MHC-peptide (MHCp) binding and presentation prediction models, that were built using a multi-pronged strategy. First, we generated a large-scale, high-quality HLA ligandome using approximately 75 stably transfected mono-allelic K562 cell lines. Intact MHCp complexes were immunoprecipitated using W6/32 antibody and profiled using LC/MS-MS. Second, we trained models that predict both MHCp binding and presentation. Briefly, MHCp binding was modeled using the amino acid sequences of the ligand and the binding pocket of the cognate allele. MHCp presentation, which encompasses in vivo antigen processing, was modeled using multiple features including the expression level of the source protein, proteasomal cleavage, and two novel features representing presentation propensities of genes and regions within gene bodies. Third, we expanded the scale and scope of our in-house dataset using a large curated repository of publicly available mono- and multi-allelic datasets resulting in > 160 alleles and > 1.6 million peptides. Integrating data from diverse cell line and tissue types improved the generalizability of our models, a critically important aspect when applying our models to patient samples. Finally, we implemented a model-based deconvolution of multi-allelic datasets to generate pseudo mono-allelic data, and developed an integrative machine learning architecture to model our expanded HLA-ligandome. We evaluated the performance of our binding and prediction models on 10% held-out mono-allelic test data from multiple cell line sources. The precision at various recall values of both binding and prediction models was markedly higher than NetMHCPan 4.0, and the positive predictive values were 0.59 and 0.73 respectively, significantly higher compared to NetMHCpan 4.0 (PPV = 0.38). Additionally, a strong concordance of raw and predicted motifs for alleles excluded from training data indicated a robust pan-allelic performance. When evaluated on 12 tissue samples profiled in-house, the SHERPA presentation model had a consistently high recall (90%) compared to NetMHCpan 4.0 (63%). This trend holds true on external immunopeptidomics datasets from tumor samples. In summary, SHERPA enables precision neoantigen discovery. Citation Format: Rachel Marty Pyke, Dattatreya Mellacheruvu, Steven Dea, Charles Abbott, Nick Phillips, Sejal Desai, Rena McClory, Steven Ketelaars, Pia Kvistborg, John West, Richard Chen, Sean Michael Boyle. Accurate modeling of antigen processing and MHC peptide presentation using large-scale immunopeptidomes and a novel machine learning framework [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1898.
- Published
- 2021
- Full Text
- View/download PDF
23. Abstract 2512: Pan-cancer characterization of the tumor and immune microenvironment facilitates identification of cancer-specific biological signatures
- Author
-
Simo V. Zhang, Rena McClory, John West, Sean Michael Boyle, Dattatreya Mellacheruvu, Richard Chen, Charles Abbott, Eric Levy, Rachel Marty Pyke, and Mengyao Tan
- Subjects
Cancer Research ,Oncology ,Pan cancer ,Immune microenvironment ,medicine ,Cancer research ,Cancer ,Identification (biology) ,Biology ,medicine.disease - Abstract
Background: A better understanding of the characteristics of cancer across different indications is required to drive the development of personalized treatments, inform therapy decisions, and improve outcomes. Integrating data from the tumor and the immune system can enable the identification of comprehensive biological signatures and composite biomarkers for the improved stratification of responders/progressors. Here, we describe a pan cancer study, including an enhanced whole-exome and transcriptome sequencing approach, across over 500 samples representing 13 tumor types, analyzed at high depth using the ImmunoID NeXT platform. Methods: We sequenced paired tumor-normal samples on the ImmunoID NeXT platform, an enhanced exome/transcriptome-based diagnostic platform that can simultaneously profile the tumor and immune microenvironment from a single FFPE sample, across all of the approximately 20,000 genes. For each sample, we analyzed a broad set of features focused on both the tumor and immune system. From DNA, we profiled small variants, CNAs, MSI status, oncoviruses, HLA LOH, and neoantigens. From RNA, we profiled gene expression, small variants, fusions, TILs, TCR, BCR, and immune signatures. Integrated analyses assessing the impact of each feature, both within and across tumor types, were performed across the cohort. Results: Through immunogenomic analysis we identified striking differences in both tumor and TME profiles across cancer types. In addition to mutation and neoantigen burden, by. we also computed a composite neoantigen score for each sample, which we have shown in a separate melanoma study can be a stronger predictor of response to immunotherapy. The composite neoantigen score integrates neoantigen prediction with mechanisms of tumor escape that can affect neoantigen presentation, providing a more accurate model of the antigen presentation biology. We also looked at the distribution of HLA LOH using our DASH algorithm and found differences in LOH frequency between tumor types. For example, we found HLA LOH to be five timesmore common in lung cancer than breast cancer. Further, we profiled immune gene signatures, including Gejewski and Ribas signatures, highlighting varied immune activation across cancer types. Analysis of somatic alterations in pathways controlling cell growth, PI3K/AKT signaling, apoptosis, and other canonical pathways revealed malignancy-specific alteration frequencies. The varying frequency, and combination of these alterations is indicative of a complex hierarchy of cross-talk between pathways, which operates in a cancer specific manner. Conclusions: We performed a broad integrated analysis of the tumor and immune microenvironment for over 500 samples across 13 different tumor types using the ImmunoID NeXT platform. This comprehensive profiling revealed significant differences between cancer types beyond mutational burden, including neoantigen burden, immune microenvironment differences, and incidence of putative tumor escape mechanisms Citation Format: Sean Michael Boyle, Charles Abbott, Eric Levy, Rachel Marty Pyke, Dattatreya Mellacheruvu, Simo Zhang, Mengyao Tan, Rena McClory, John West, Richard Chen. Pan-cancer characterization of the tumor and immune microenvironment facilitates identification of cancer-specific biological signatures [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 2512.
- Published
- 2020
- Full Text
- View/download PDF
24. Abstract 2085: Precision neoantigen discovery using a pan-allelic machine learning model for enabling the development of composite biomarkers and personalized immunotherapy
- Author
-
Rachel Marty Pyke, Richard Chen, Rena McClory, Sean Michael Boyle, John West, Charles Abbott, Dattatreya Mellacheruvu, and Nick A. Phillips
- Subjects
Cancer Research ,Oncology ,Computer science ,business.industry ,medicine.medical_treatment ,medicine ,Immunotherapy ,Artificial intelligence ,business ,Machine learning ,computer.software_genre ,computer - Abstract
Background: Technologies for neoantigen discovery are critical for developing more advanced, composite biomarkers for immunotherapy as well as personalized cancer therapies. Precision neoantigen discovery entails comprehensive detection of tumor specific genomic variants and accurate prediction of MHC presentation of epitopes originating from such variants. Here we present our pan-allelic machine learning model for predicting MHC class I presentation and identifying potentially immunogenic patient-specific neoantigens. We then apply our predictions to develop composite biomarkers that can stratify patients by response to immunotherapy. Methods: Mono-allelic cell lines were generated by transfecting a single allele of interest into HLA-null K562 cell lines. Immunoprecipitation mass spectrometry (IPMS) was performed as follows: W6/32 antibody was used for immunoprecipitation of HLA complexes, followed by elution of bound peptides and identification using liquid chromatography-mass spectrometry. Machine learning models were implemented to predict neoantigen presentation. Our prediction model, integrated into the ImmunoID NeXT platform, was used with other genomic features to generate composite neoantigen-based biomarkers. Results: We generated a high quality and unambiguous immunopeptidomics training dataset by performing IPMS on ~60 mono-allelic cell lines, with ongoing efforts to expand it to ~100 alleles. These alleles, selected to optimize both allelic diversity and population coverage, enable accurate and comprehensive modeling of MHC ligand processing and presentation. Our advanced prediction model combines multiple modelling strategies, including deep neural networks, convolutional neural networks and gradient boosted decision trees. New features that model antigen-processing were implemented using large scale public and private datasets to improve presentation specificity (60% PPV in dataset containing 999-fold decoys). As a result, our pan-allelic model has significantly higher specificity across a range of sensitivity values in comparison to NetMHCPan 4.0 and generalizes to both trained and untrained alleles. Our comprehensive validation strategy includes: evaluation of overall performance of the model on an independent, multi-allelic immunopeptidomics dataset generated from tumor samples; validation of top ranking tumor specific neoantigens nominated using an integrated patient-centric model that incorporates HLA loss of heterozygosity using targeted proteomics (Parallel Reaction Monitoring); and evaluation of our model's utility to predict neoantigens that drive immunogenic responses to tumors. Finally, a composite neoantigen-based biomarker score calculated using our model stratifies patients by response to immunotherapy. Conclusions: In summary, we present here a pan-allelic MHC presentation prediction model trained on a large mono-allelic data set and evaluated using tumor samples and known immunogenic peptides. Through integration with ImmunoID NeXT, our platform enables precision neoantigen discovery by comprehensively surveying neoantigens and accurately predicting MHC presentation. These methods can significantly enhance the creation of composite biomarkers and applications in personalized immunotherapy. Citation Format: DATTATREYA MELLACHERUVU, Rachel Marty Pyke, Charles Abbott, Nick Phillips, Rena McClory, John West, Richard Chen, Sean Michael Boyle. Precision neoantigen discovery using a pan-allelic machine learning model for enabling the development of composite biomarkers and personalized immunotherapy [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 2085.
- Published
- 2020
- Full Text
- View/download PDF
25. Functions of the COPII gene paralogs SEC23A and SEC23B are interchangeable in vivo
- Author
-
Geoffrey G. Hesketh, Anne-Claude Gingras, Rami Khoriaty, Guojing Zhu, Mark J. Hoenerhoff, Elizabeth J. Adams, Daniel J. Klionsky, Thomas L. Saunders, Jordan A. Shavit, Bin Zhang, Alexey I. Nesvizhskii, Lesley Everett, Beth McGee, Angela C. Weyand, David Ginsburg, Amélie Bernard, Dattatreya Mellacheruvu, Shandong University, Life Sciences Institute and Department of Molecular, Cellular, and Developmental Biology and Biological Chemistry, University of Michigan [Ann Arbor], and University of Michigan System-University of Michigan System
- Subjects
0301 basic medicine ,Erythrocytes ,Transgene ,[SDV]Life Sciences [q-bio] ,Vesicular Transport Proteins ,Locus (genetics) ,03 medical and health sciences ,Species Specificity ,Bone Marrow ,Humans ,COPII ,Gene ,Zebrafish ,ComputingMilieux_MISCELLANEOUS ,Anemia, Dyserythropoietic, Congenital ,Multidisciplinary ,biology ,SEC23A ,biology.organism_classification ,Phenotype ,Cell biology ,Complementation ,030104 developmental biology ,HEK293 Cells ,PNAS Plus ,Gene Expression Regulation ,Multiprotein Complexes ,COP-Coated Vesicles - Abstract
Approximately one-third of the mammalian proteome is transported from the endoplasmic reticulum-to-Golgi via COPII-coated vesicles. SEC23, a core component of coat protein-complex II (COPII), is encoded by two paralogous genes in vertebrates (Sec23a and Sec23b). In humans, SEC23B deficiency results in congenital dyserythropoietic anemia type-II (CDAII), while SEC23A deficiency results in a skeletal phenotype (with normal red blood cells). These distinct clinical disorders, together with previous biochemical studies, suggest unique functions for SEC23A and SEC23B. Here we show indistinguishable intracellular protein interactomes for human SEC23A and SEC23B, complementation of yeast Sec23 by both human and murine SEC23A/B, and rescue of the lethality of sec23b deficiency in zebrafish by a sec23a-expressing transgene. We next demonstrate that a Sec23a coding sequence inserted into the murine Sec23b locus completely rescues the lethal SEC23B-deficient pancreatic phenotype. We show that SEC23B is the predominantly expressed paralog in human bone marrow, but not in the mouse, with the reciprocal pattern observed in the pancreas. Taken together, these data demonstrate an equivalent function for SEC23A/B, with evolutionary shifts in the transcription program likely accounting for the distinct phenotypes of SEC23A/B deficiency within and across species, a paradigm potentially applicable to other sets of paralogous genes. These findings also suggest that enhanced erythroid expression of the normal SEC23A gene could offer an effective therapeutic approach for CDAII patients.
- Published
- 2018
- Full Text
- View/download PDF
26. Targeting the MLL complex in castration-resistant prostate cancer
- Author
-
John R. Prensner, Yashar S. Niknafs, Arul M. Chinnaiyan, Nallasivam Palanisamy, Alexey I. Nesvizhskii, Yi-Mi Wu, Dattatreya Mellacheruvu, Xiaojun Jing, Matthew K. Iyer, Pranathi Meda Krishnamurthy, Rohit Malik, Lakshmi P. Kunju, Yuanyuan Qiao, Saravana M. Dhanasekaran, Rachell Stender, Tomasz Cierpicki, Anastasia K. Yocum, Dmitry Borkin, Xuhong Cao, Felix Y. Feng, Amjad Khan, Marcin Cieślik, Xia Jiang, Jolanta Grembecka, Xiaoju Wang, June Escara-Wilke, and Irfan A. Asangani
- Subjects
medicine.drug_class ,Cancer ,General Medicine ,Biology ,Androgen ,medicine.disease ,Bioinformatics ,General Biochemistry, Genetics and Molecular Biology ,Androgen receptor ,Leukemia ,Prostate cancer ,hemic and lymphatic diseases ,medicine ,Cancer research ,Myeloid-Lymphoid Leukemia Protein ,Neoplasm ,Signal transduction ,neoplasms - Abstract
Resistance to androgen deprivation therapies and increased androgen receptor (AR) activity are major drivers of castration-resistant prostate cancer (CRPC). Although prior work has focused on targeting AR directly, co-activators of AR signaling, which may represent new therapeutic targets, are relatively underexplored. Here we demonstrate that the mixed-lineage leukemia protein (MLL) complex, a well-known driver of MLL fusion-positive leukemia, acts as a co-activator of AR signaling. AR directly interacts with the MLL complex via the menin-MLL subunit. Menin expression is higher in CRPC than in both hormone-naive prostate cancer and benign prostate tissue, and high menin expression correlates with poor overall survival of individuals diagnosed with prostate cancer. Treatment with a small-molecule inhibitor of menin-MLL interaction blocks AR signaling and inhibits the growth of castration-resistant tumors in vivo in mice. Taken together, this work identifies the MLL complex as a crucial co-activator of AR and a potential therapeutic target in advanced prostate cancer.
- Published
- 2015
- Full Text
- View/download PDF
27. Proteomic profiling of naïve multiple myeloma patient plasma cells identifies pathways associated with favourable response to bortezomib-based treatment regimens
- Author
-
Shaun Rosebeck, Dominik Dytfeld, Malathi Kandarpa, Samuel L. Volchenboum, Dattatreya Mellacheruvu, Jagoda Jasielec, Lambert Ngoka, Anoop Mayampurath, Alexey I. Nesvizhskii, Mattina Alonge, Paul G. Richardson, Arun Sreekumar, and Andrzej Jakubowiak
- Subjects
Adult ,Proteomics ,Oncology ,medicine.medical_specialty ,Plasma Cells ,Bioinformatics ,Dexamethasone ,Polyethylene Glycols ,Bortezomib ,hemic and lymphatic diseases ,Internal medicine ,Antineoplastic Combined Chemotherapy Protocols ,medicine ,Humans ,Precision Medicine ,Lenalidomide ,Multiple myeloma ,Aged ,Very Good Partial Response ,business.industry ,Acute-phase protein ,Hematology ,Middle Aged ,medicine.disease ,Boronic Acids ,Thalidomide ,Regimen ,Doxorubicin ,Pyrazines ,Personalized medicine ,Multiple Myeloma ,business ,medicine.drug - Abstract
Toward our goal of personalized medicine, we comprehensively profiled pre-treatment malignant plasma cells from multiple myeloma patients and prospectively identified pathways predictive of favourable response to bortezomib-based treatment regimens. We utilized two complementary quantitative proteomics platforms to identify differentially-regulated proteins indicative of at least a very good partial response (VGPR) or complete response/near complete response (CR/nCR) to two treatment regimens containing either bortezomib, liposomal doxorubicin and dexamethasone (VDD), or lenalidomide, bortezomib and dexamethasone (RVD). Our results suggest enrichment of 'universal response' pathways that are common to both treatment regimens and are probable predictors of favourable response to bortezomib, including a subset of endoplasmic reticulum stress pathways. The data also implicate pathways unique to each regimen that may predict sensitivity to DNA-damaging agents, such as mitochondrial dysfunction, and immunomodulatory drugs, which was associated with acute phase response signalling. Overall, we identified patterns of tumour characteristics that may predict response to bortezomib-based regimens and their components. These results provide a rationale for further evaluation of the protein profiles identified herein for targeted selection of anti-myeloma therapy to increase the likelihood of improved treatment outcome of patients with newly-diagnosed myeloma.
- Published
- 2015
- Full Text
- View/download PDF
28. HSC70 is a chaperone for wild-type and mutant cardiac myosin binding protein C
- Author
-
Vi T. Tang, Jason E. Gestwicki, Alexey I. Nesvizhskii, Amelia A. Glazier, Hao Shao, Neha Hafeez, Jaime Yob, Venkatesha Basrur, Lap Man Lee, Adam S. Helms, Dattatreya Mellacheruvu, and Sharlene M. Day
- Subjects
0301 basic medicine ,Sarcomeres ,Proteasome Endopeptidase Complex ,animal structures ,Immunoprecipitation ,Mutant ,macromolecular substances ,Haploinsufficiency ,Ventricular Septum ,03 medical and health sciences ,Animals ,Humans ,Cell Nucleus ,biology ,Chemistry ,Binding protein ,Myocardium ,Protein turnover ,Wild type ,HSC70 Heat-Shock Proteins ,General Medicine ,Cardiomyopathy, Hypertrophic ,Hsp70 ,Cell biology ,Acetylcysteine ,Rats ,030104 developmental biology ,Proteostasis ,HEK293 Cells ,Animals, Newborn ,Chaperone (protein) ,Gene Knockdown Techniques ,Proteolysis ,biology.protein ,Carrier Proteins ,Proteasome Inhibitors ,Research Article - Abstract
Cardiac myosin binding protein C (MYBPC3) is the most commonly mutated gene associated with hypertrophic cardiomyopathy (HCM). Haploinsufficiency of full-length MYBPC3 and disruption of proteostasis have both been proposed as central to HCM disease pathogenesis. Discriminating the relative contributions of these 2 mechanisms requires fundamental knowledge of how turnover of WT and mutant MYBPC3 proteins is regulated. We expressed several disease-causing mutations in MYBPC3 in primary neonatal rat ventricular cardiomyocytes. In contrast to WT MYBPC3, mutant proteins showed reduced expression and failed to localize to the sarcomere. In an unbiased coimmunoprecipitation/mass spectrometry screen, we identified HSP70-family chaperones as interactors of both WT and mutant MYBPC3. Heat shock cognate 70 kDa (HSC70) was the most abundant chaperone interactor. Knockdown of HSC70 significantly slowed degradation of both WT and mutant MYBPC3, while pharmacologic activation of HSC70 and HSP70 accelerated degradation. HSC70 was expressed in discrete striations in the sarcomere. Expression of mutant MYBPC3 did not affect HSC70 localization, nor did it induce a protein folding stress response or ubiquitin proteasome dysfunction. Together these data suggest that WT and mutant MYBPC3 proteins are clients for HSC70, and that the HSC70 chaperone system plays a major role in regulating MYBPC3 protein turnover.
- Published
- 2018
29. PAF1 complex interactions with SETDB1 mediate promoter H3K9 methylation and transcriptional repression of
- Author
-
James, Ropa, Nirmalya, Saha, Zhiling, Chen, Justin, Serio, Wei, Chen, Dattatreya, Mellacheruvu, Lili, Zhao, Venkatesha, Basrur, Alexey I, Nesvizhskii, and Andrew G, Muntean
- Subjects
H3K9 methyltransferase ,protein-protein interaction ,hemic and lymphatic diseases ,leukemia ,polymerase associated factor complex ,transcription ,neoplasms ,Research Paper - Abstract
The Polymerase Associated Factor 1 complex (PAF1c) is an epigenetic co-modifying complex that directly contacts RNA polymerase II (RNAPII) and several epigenetic regulating proteins. Mutations, overexpression and loss of expression of subunits of the PAF1c are observed in various forms of cancer suggesting proper regulation is needed for cellular development. However, the biochemical interactions with the PAF1c that allow dynamic gene regulation are unclear. We and others have shown that the PAF1c makes a direct interaction with MLL fusion proteins, which are potent oncogenic drivers of acute myeloid leukemia (AML). This interaction is critical for the maintenance of MLL translocation driven AML by targeting MLL fusion proteins to the target genes Meis1 and Hoxa9. Here, we use a proteomics approach to identify protein-protein interactions with the PAF1c subunit CDC73 that regulate the function of the PAF1c. We identified a novel interaction with a histone H3 lysine 9 (H3K9) methyltransferase protein, SETDB1. This interaction is stabilized with a mutant CDC73 that is incapable of supporting AML cell growth. Importantly, transcription of Meis1 and Hoxa9 is reduced and promoter H3K9 trimethylation (H3K9me3) increased by overexpression of SETDB1 or stabilization of the PAF1c-SETDB1 interaction in AML cells. These findings were corroborated in human AML patients where increased SETDB1 expression was associated with reduced HOXA9 and MEIS1. To our knowledge, this is the first proteomics approach to search for CDC73 protein-protein interactions in AML, and demonstrates that the PAF1c may play a role in H3K9me3-mediated transcriptional repression in AML.
- Published
- 2017
30. The Function of the COPII Gene Paralogs SEC23A and SEC23B Are InterchangeableIn Vivo
- Author
-
Beth McGee, Angela C. Weyand, Thomas L. Saunders, Amélie Bernard, Elizabeth J. Adams, Anne-Claude Gingras, Jordan A. Shavit, Daniel J. Klionsky, Alexey I. Nesvizhskii, Guojing Zhu, Lesley Everett, Mark J. Hoenerhoff, Rami Khoriaty, David Ginsburg, Dattatreya Mellacheruvu, Geoffrey G. Hesketh, and Bin Zhang
- Subjects
Genetics ,0303 health sciences ,Locus (genetics) ,Biology ,SEC23A ,biology.organism_classification ,Phenotype ,03 medical and health sciences ,0302 clinical medicine ,Secretory protein ,Gene duplication ,Gene ,COPII ,Zebrafish ,030217 neurology & neurosurgery ,030304 developmental biology - Abstract
SEC23 is a core component of the coat protein-complex II (COPII)-coated vesicle, which mediates transport of secretory proteins from the endoplasmic reticulum (ER) to the Golgi1-3. Mammals express 2 paralogs for SEC23 (SEC23A and SEC23B). Though theSEC23gene duplication dates back >500 million years, both SEC23’s are ~85% identical at the amino acid sequence level. In humans, deficiency for SEC23A or SEC23B results in cranio-lenticulo-sutural dysplasia4or congenital dyserythropoietic anemia type II (CDAII), respectively5. The disparate human syndromes and reports of secretory cargos with apparent paralog-specific dependence6,7, suggest unique functions for the two SEC23 paralogs. Here we show indistinguishable intracellular interactomes for human SEC23A and SEC23B, complementation of yeast SEC23 by both human and murine SEC23A/B paralogs, and the rescue of lethality resulting fromSec23bdisruption in zebrafish by aSec23a-expressing transgene. Finally, we demonstrate that theSec23acoding sequence inserted into the endogenous murineSec23blocus fully rescues the mortality and severe pancreatic phenotype previously reported with SEC23B-deficiency in the mouse8-10. Taken together, these data indicate that the disparate phenotypes of SEC23A and SEC23B deficiency likely result from evolutionary shifts in gene expression program rather than differences in protein function, a paradigm likely applicable to other sets of paralogous genes. These findings also suggest the potential for increased expression of SEC23A as a novel therapeutic approach to the treatment of CDAII, with potential relevance to other disorders due to mutations in paralogous genes.
- Published
- 2017
- Full Text
- View/download PDF
31. Using MSFragger for ultrafast database searching
- Author
-
Andy T Kong, Felipe V. Leprevost, Dmitry M. Avtonomov, Dattatreya Mellacheruvu, and Alexey I. Nesvizhskii
- Subjects
0301 basic medicine ,03 medical and health sciences ,030104 developmental biology ,Database ,Computer science ,General Earth and Planetary Sciences ,computer.software_genre ,computer ,Ultrashort pulse ,General Environmental Science - Published
- 2017
- Full Text
- View/download PDF
32. Genetic Networks Inducing Invasive Growth in Saccharomyces cerevisiae Identified Through Systematic Genome-Wide Overexpression
- Author
-
Dattatreya Mellacheruvu, Anuj Kumar, Craig J. Dobry, Alexey I. Nesvizhskii, Christian A. Shively, and Matthew J. Eckwahl
- Subjects
Saccharomyces cerevisiae Proteins ,Transcription, Genetic ,MAP Kinase Signaling System ,Saccharomyces cerevisiae ,Hyphae ,Gene regulatory network ,Cyclin B ,Investigations ,Biology ,S Phase ,Pseudohyphal growth ,Genetics ,Gene Regulatory Networks ,Nuclear export signal ,Gene ,Epistasis, Genetic ,biology.organism_classification ,Phenotype ,Yeast ,biology.protein ,Genome, Fungal ,Mitogen-Activated Protein Kinases ,Gene Deletion - Abstract
The budding yeast Saccharomyces cerevisiae can respond to nutritional and environmental stress by implementing a morphogenetic program wherein cells elongate and interconnect, forming pseudohyphal filaments. This growth transition has been studied extensively as a model signaling system with similarity to processes of hyphal development that are linked with virulence in related fungal pathogens. Classic studies have identified core pseudohyphal growth signaling modules in yeast; however, the scope of regulatory networks that control yeast filamentation is broad and incompletely defined. Here, we address the genetic basis of yeast pseudohyphal growth by implementing a systematic analysis of 4909 genes for overexpression phenotypes in a filamentous strain of S. cerevisiae. Our results identify 551 genes conferring exaggerated invasive growth upon overexpression under normal vegetative growth conditions. This cohort includes 79 genes lacking previous phenotypic characterization. Pathway enrichment analysis of the gene set identifies networks mediating mitogen-activated protein kinase (MAPK) signaling and cell cycle progression. In particular, overexpression screening suggests that nuclear export of the osmoresponsive MAPK Hog1p may enhance pseudohyphal growth. The function of nuclear Hog1p is unclear from previous studies, but our analysis using a nuclear-depleted form of Hog1p is consistent with a role for nuclear Hog1p in repressing pseudohyphal growth. Through epistasis and deletion studies, we also identified genetic relationships with the G2 cyclin Clb2p and phenotypes in filamentation induced by S-phase arrest. In sum, this work presents a unique and informative resource toward understanding the breadth of genes and pathways that collectively constitute the molecular basis of filamentation.
- Published
- 2013
- Full Text
- View/download PDF
33. SAINT: probabilistic scoring of affinity purification–mass spectrometry data
- Author
-
Damian Fermin, Mike Tyers, Ashton Breitkreutz, Zhen Yuan Lin, Hyungwon Choi, Anne-Claude Gingras, Alexey I. Nesvizhskii, Dattatreya Mellacheruvu, Zhaohui S. Qin, and Brett Larsen
- Subjects
Computer science ,Bioinformatics ,Mass spectrometry ,Proteomics ,Biochemistry ,Interactome ,Article ,Chromatography, Affinity ,Mass Spectrometry ,03 medical and health sciences ,Affinity chromatography ,Protein Interaction Mapping ,Computer Simulation ,Molecular Biology ,Probability ,030304 developmental biology ,0303 health sciences ,business.industry ,Extramural ,030302 biochemistry & molecular biology ,Probabilistic logic ,Computational Biology ,Proteins ,Pattern recognition ,Cell Biology ,Proteins metabolism ,Artificial intelligence ,business ,Protein Binding ,Biotechnology - Abstract
We present SAINT (Significance Analysis of INTeractome), a computational tool that assigns confidence scores to protein-protein interaction data generated using affinity-purification coupled to mass spectrometry (AP-MS). The method utilizes label-free quantitative data and constructs separate distributions for true and false interactions to derive the probability of a bona fide protein-protein interaction. We demonstrate that SAINT is applicable to data of different scales and protein connectivity and allows for the transparent analysis of AP-MS data.
- Published
- 2010
- Full Text
- View/download PDF
34. Abstract 18826: Cardiac Myosin Binding Protein C Mutants Interact With and Cause Mislocalization of the Hsp70 Family Chaperones
- Author
-
Amelia A Glazier, Adam Helms, Jaime M Yob, Dattatreya Mellacheruvu, Vi Tang, Sarah Bartolone, Venkatesha Basrur, Alexey I Nesvizhskii, and Sharlene Day
- Subjects
Physiology (medical) ,Cardiology and Cardiovascular Medicine - Abstract
Introduction: Myosin binding protein C (MYBPC3) is the most frequently mutated gene in hypertrophic cardiomyopathy (HCM) and >90% of MYBPC3 mutations produce truncated proteins. Ubiquitin proteasome dysfunction has been observed in human HCM hearts with MYBPC3 mutations and in experimental models, but the mechanisms responsible are unknown. We hypothesize that interactions between truncated MYBPC3 and molecular chaperones could underlie proteasome dysfunction by exhausting a critical chaperone pool required to sustain normal cellular protein quality control. Methods and Results: FLAG-tagged wild-type and two mutant truncated MYBPC3 proteins of different lengths were expressed in neonatal rat cardiomyocytes via adenovirus. Spontaneous contractions per minute were markedly reduced in myocytes expressing mutant compared to wild-type MYBPC3 (non-transduced 49±8; WT 57±7; W1098* 25±3; I154Lfs*5 4±1; n=6, p Conclusions: These results suggest MYBPC3 is a client of Hsp70 chaperones and that mutant proteins may interfere with regular functions and localization of Hsp70 and Hsc70. Further experiments will determine if these interactions are important for maintaining cellular proteostasis in MYBPC3-mutant HCM.
- Published
- 2015
- Full Text
- View/download PDF
35. The yeast Sks1p kinase signaling network regulates pseudohyphal growth and glucose response
- Author
-
Philip C. Andrews, Daniel T. Sheidy, Cole Johnson, Anuj Kumar, Dattatreya Mellacheruvu, Christian A. Shively, Hye Kyong Kweon, and Alexey I. Nesvizhskii
- Subjects
Proteomics ,Cancer Research ,Saccharomyces cerevisiae Proteins ,lcsh:QH426-470 ,Nitrogen ,Saccharomyces cerevisiae ,Hyphae ,Yeast and Fungal Models ,Protein Serine-Threonine Kinases ,GPR1 ,03 medical and health sciences ,Pseudohyphal growth ,Model Organisms ,Genetics ,Ras2 ,Kinase activity ,Phosphorylation ,Molecular Biology ,Transcription factor ,Biology ,Genetics (clinical) ,Ecology, Evolution, Behavior and Systematics ,030304 developmental biology ,2. Zero hunger ,0303 health sciences ,Spectrometric Identification of Proteins ,biology ,030302 biochemistry & molecular biology ,Cell Differentiation ,Genomics ,biology.organism_classification ,Cell biology ,Functional Genomics ,lcsh:Genetics ,Glucose ,Biochemistry ,Signal transduction ,Signal Transduction ,Research Article - Abstract
The yeast Saccharomyces cerevisiae undergoes a dramatic growth transition from its unicellular form to a filamentous state, marked by the formation of pseudohyphal filaments of elongated and connected cells. Yeast pseudohyphal growth is regulated by signaling pathways responsive to reductions in the availability of nitrogen and glucose, but the molecular link between pseudohyphal filamentation and glucose signaling is not fully understood. Here, we identify the glucose-responsive Sks1p kinase as a signaling protein required for pseudohyphal growth induced by nitrogen limitation and coupled nitrogen/glucose limitation. To identify the Sks1p signaling network, we applied mass spectrometry-based quantitative phosphoproteomics, profiling over 900 phosphosites for phosphorylation changes dependent upon Sks1p kinase activity. From this analysis, we report a set of novel phosphorylation sites and highlight Sks1p-dependent phosphorylation in Bud6p, Itr1p, Lrg1p, Npr3p, and Pda1p. In particular, we analyzed the Y309 and S313 phosphosites in the pyruvate dehydrogenase subunit Pda1p; these residues are required for pseudohyphal growth, and Y309A mutants exhibit phenotypes indicative of impaired aerobic respiration and decreased mitochondrial number. Epistasis studies place SKS1 downstream of the G-protein coupled receptor GPR1 and the G-protein RAS2 but upstream of or at the level of cAMP-dependent PKA. The pseudohyphal growth and glucose signaling transcription factors Flo8p, Mss11p, and Rgt1p are required to achieve wild-type SKS1 transcript levels. SKS1 is conserved, and deletion of the SKS1 ortholog SHA3 in the pathogenic fungus Candida albicans results in abnormal colony morphology. Collectively, these results identify Sks1p as an important regulator of filamentation and glucose signaling, with additional relevance towards understanding stress-responsive signaling in C. albicans., Author Summary Eukaryotic cells respond to nutritional and environmental stress through complex regulatory programs controlling cell metabolism, growth, and morphology. In the budding yeast Saccharomyces cerevisiae, conditions of limited nitrogen and/or glucose can initiate a dramatic growth transition wherein the yeast cells form extended multicellular filaments resembling the true hyphal tubes of filamentous fungi. The formation of these pseudohyphal filaments is governed by core regulatory pathways that have been studied for decades; however, the mechanism by which these signaling systems are integrated is less well understood. We find that the protein kinase Sks1p contributes to the integration of signals for nitrogen and/or glucose limitation, resulting in pseudohyphal growth. We implemented a mass spectrometry-based approach to profile phosphorylation events across the proteome dependent upon Sks1p kinase activity and identified phosphorylation sites important for mitochondrial function and pseudohyphal growth. Our studies place Sks1p in the regulatory context of a well-known pseudohyphal growth signaling pathway. We further find that SKS1 is conserved and required for stress-responsive colony morphology in the principal opportunistic human fungal pathogen Candida albicans. Thus, Sks1p is part of the mechanism integrating glucose-responsive cell signaling and pseudohyphal growth, and its function is required for colony morphology linked with virulence in C. albicans.
- Published
- 2014
36. Analyzing protein-protein interactions from affinity purification-mass spectrometry data with SAINT
- Author
-
Mike Tyers, Guomin Liu, Hyungwon Choi, Dattatreya Mellacheruvu, Alexey I. Nesvizhskii, and Anne-Claude Gingras
- Subjects
Proteomics ,Computer science ,Quantitative proteomics ,computer.software_genre ,Bioinformatics ,Biochemistry ,Interactome ,Article ,Chromatography, Affinity ,Mass Spectrometry ,Software ,Structural Biology ,Protein Interaction Mapping ,Databases, Protein ,Graphical user interface ,Binding Sites ,business.industry ,Proteins ,Statistical model ,Visualization ,Virtual machine ,OS X ,Data mining ,business ,computer - Abstract
Significance Analysis of INTeractome (SAINT) is a software package for scoring protein-protein interactions based on label-free quantitative proteomics data (e.g. spectral count or intensity) in affinity purification – mass spectrometry (AP-MS) experiments. SAINT allows bench scientists to select bona fide interactions and remove non-specific interactions in an unbiased manner. However, there is no `one-size-fits-all' statistical model for every dataset, since the experimental design varies across studies. Key variables include the number of baits, the number of biological replicates per bait, and control purifications. Here we give a detailed account of input data format, control data, selection of high confidence interactions, and visualization of filtered data. We explain additional options for customizing the statistical model for optimal filtering in specific datasets. We also discuss a graphical user interface of SAINT in connection to the LIMS system ProHits which can be installed as a virtual machine on Mac OSX or PC Windows computers.
- Published
- 2012
37. Large-Scale Analysis of Kinase Signaling in Yeast Pseudohyphal Development Identifies Regulation of Ribonucleoprotein Granules
- Author
-
Ivan Sabath, Craig J. Dobry, Alexey I. Nesvizhskii, Christian A. Shively, Elizabeth J. Tran, Kaitlyn L. Norman, Eric E. P. Cosky, Hye Kyong Kweon, Tao Xu, Anuj Kumar, Philip C. Andrews, Daniel T. Sheidy, and Dattatreya Mellacheruvu
- Subjects
Cancer Research ,Saccharomyces cerevisiae Proteins ,lcsh:QH426-470 ,Saccharomyces cerevisiae ,Hyphae ,Biology ,03 medical and health sciences ,0302 clinical medicine ,Pseudohyphal growth ,Gene Expression Regulation, Fungal ,Candida albicans ,Genetics ,Phosphorylation ,Protein kinase A ,Molecular Biology ,Genetics (clinical) ,Ecology, Evolution, Behavior and Systematics ,030304 developmental biology ,Ribonucleoprotein ,2. Zero hunger ,0303 health sciences ,Kinase ,Phosphotransferases ,Phosphoproteomics ,biology.organism_classification ,Cell biology ,lcsh:Genetics ,Phenotype ,Ribonucleoproteins ,Biochemistry ,Signal transduction ,030217 neurology & neurosurgery ,Research Article ,Signal Transduction - Abstract
Yeast pseudohyphal filamentation is a stress-responsive growth transition relevant to processes required for virulence in pathogenic fungi. Pseudohyphal growth is controlled through a regulatory network encompassing conserved MAPK (Ste20p, Ste11p, Ste7p, Kss1p, and Fus3p), protein kinase A (Tpk2p), Elm1p, and Snf1p kinase pathways; however, the scope of these pathways is not fully understood. Here, we implemented quantitative phosphoproteomics to identify each of these signaling networks, generating a kinase-dead mutant in filamentous S. cerevisiae and surveying for differential phosphorylation. By this approach, we identified 439 phosphoproteins dependent upon pseudohyphal growth kinases. We report novel phosphorylation sites in 543 peptides, including phosphorylated residues in Ras2p and Flo8p required for wild-type filamentous growth. Phosphoproteins in these kinase signaling networks were enriched for ribonucleoprotein (RNP) granule components, and we observe co-localization of Kss1p, Fus3p, Ste20p, and Tpk2p with the RNP component Igo1p. These kinases localize in puncta with GFP-visualized mRNA, and KSS1 is required for wild-type levels of mRNA localization in RNPs. Kss1p pathway activity is reduced in lsm1Δ/Δ and pat1Δ/Δ strains, and these genes encoding P-body proteins are epistatic to STE7. The P-body protein Dhh1p is also required for hyphal development in Candida albicans. Collectively, this study presents a wealth of data identifying the yeast phosphoproteome in pseudohyphal growth and regulatory interrelationships between pseudohyphal growth kinases and RNPs., Author Summary Eukaryotic cells affect precise changes in shape and growth in response to environmental and nutritional stress, enabling cell survival and wild-type function. The single-celled budding yeast provides a striking example, undergoing a set of changes under conditions of nitrogen or glucose limitation resulting in the formation of extended cellular chains or filaments. Related filamentous growth transitions are required for virulence in pathogenic fungi and have been studied extensively; however, the full scope of signaling underlying the filamentous growth transition remains to be determined. Here, we used a combination of genetics and proteomics to identify proteins that undergo phosphorylation dependent upon kinases required for filamentous growth. Within this protein set, we identified novel sites of phosphorylation in the yeast proteome and extensive phosphorylation of mRNA-protein complexes regulating mRNA decay and translation. The data indicate an interrelationship between filamentous growth and these ubiquitously conserved sites of RNA regulation: the RNA-protein complexes are required for the filamentous growth transition, and a well studied filamentous growth signaling kinase is required for wild-type numbers of RNA-protein complexes. This interdependence is previously unappreciated, highlighting an additional level of translational control underlying this complex growth transition.
- Published
- 2015
- Full Text
- View/download PDF
38. Thioredoxin domain containing 5 (TXNDC5) as a marker of response in multiple myeloma – validation studies of proteomic profiling
- Author
-
Malathi Kandarpa, Andrzej Jakubowiak, Dattatreya Mellacheruvu, Dominik Dytfeld, Arun Sreekumar, Jagoda Jasielec, Mieczysław Komarnicki, Stephanie J Kraftson, Alexey I. Nesvizhskii, Paul G. Richardson, Mattina Alonge, Shaun Rosebeck, S. Subramani, Lambert Ngoka, and J.R. Strahler
- Subjects
Oncology ,Proteomic Profiling ,medicine ,Hematology ,Computational biology ,Thioredoxin ,Biology ,medicine.disease ,Molecular biology ,Multiple myeloma ,Domain (software engineering) - Published
- 2013
- Full Text
- View/download PDF
39. The CRAPome: a contaminant repository for affinity purification–mass spectrometry data
- Author
-
Ruedi Aebersold, Wade H. Dunham, Tony Pawson, Nicole St-Denis, Simon Hauri, Annie Bouchard, Daniel Durocher, Keiryn L. Bennett, Zhen Yuan Lin, Mihaela E. Sardiu, Anne-Claude Gingras, Rob M. Ewing, Damian Fermin, Vincentius A. Halim, Amber L. Couzens, Tuo Li, Shabaz Mohammed, Michael P. Washburn, Hyungwon Choi, Beatriz Gonzalez Badillo, Brian Raught, Nina C. Hubner, Marilyn Goudreault, Jacques Colinge, Zachary Wright, Dattatreya Mellacheruvu, Alexey I. Nesvizhskii, Ileana M. Cristea, Denis Faubert, Matthias Gstaiger, Abdallah Al-Hakim, Yana Miteva, Teck Yew Low, Giulio Superti-Furga, Richard D. Bagshaw, Benoit Coulombe, Jean-Philippe Lambert, and Albert J. R. Heck
- Subjects
Proteomics ,0303 health sciences ,Databases, Factual ,030302 biochemistry & molecular biology ,Proteins ,Cell Biology ,Computational biology ,Biology ,Mass spectrometry ,Biochemistry ,Interactome ,Molecular biology ,Epitope ,Chromatography, Affinity ,Mass Spectrometry ,Article ,03 medical and health sciences ,Affinity Reagent ,Affinity chromatography ,Protein Interaction Mapping ,Humans ,Molecular Biology ,030304 developmental biology ,Biotechnology - Abstract
Affinity purification coupled with mass spectrometry (AP-MS) is a widely used approach for the identification of protein-protein interactions. However, for any given protein of interest, determining which of the identified polypeptides represent bona fide interactors versus those that are background contaminants (for example, proteins that interact with the solid-phase support, affinity reagent or epitope tag) is a challenging task. The standard approach is to identify nonspecific interactions using one or more negative-control purifications, but many small-scale AP-MS studies do not capture a complete, accurate background protein set when available controls are limited. Fortunately, negative controls are largely bait independent. Hence, aggregating negative controls from multiple AP-MS studies can increase coverage and improve the characterization of background associated with a given experimental protocol. Here we present the contaminant repository for affinity purification (the CRAPome) and describe its use for scoring protein-protein interactions. The repository (currently available for Homo sapiens and Saccharomyces cerevisiae) and computational tools are freely accessible at http://www.crapome.org/. © 2013 Nature America, Inc. All rights reserved.
- Published
- 2013
40. Proteomic Profiling of Multiple Myeloma (MM) Cells Using iTRAQ and Label-Free Quantitative Proteomics for the Prediction of Complete or near Complete Response (CR/nCR) In Frontline Treatment with Lenalidomide, Bortezomib, and Dexamethasone
- Author
-
Alexey I. Nesvizhskii, Stephanie J Kraftson, John R. Strahler, Dominik Dytfeld, Malathi Kandarpa, Dattatreya Mellacheruvu, Lambert Ngoka, Andrzej Jakubowiak, Arun Sreekumar, Paul G. Richardson, and Suchitra Subramani
- Subjects
Oncology ,medicine.medical_specialty ,business.industry ,Bortezomib ,Proteomic Profiling ,Immunology ,Cell Biology ,Hematology ,medicine.disease ,Biochemistry ,Chemotherapy regimen ,Clinical trial ,Regimen ,Internal medicine ,Medicine ,business ,Multiple myeloma ,Dexamethasone ,medicine.drug ,Lenalidomide - Abstract
Abstract 618 Introduction: Despite an increased number of new treatments, multiple myeloma (MM) remains mostly incurable. There is emerging evidence that achieving complete or near complete response (CR/nCR), or at least a 90% reduction of the disease (≥VGPR), in response to initial treatment when MM is most sensitive to chemotherapy is associated with improved progression-free survival (PFS) and possibly overall survival (OS). However, even with the most active regimens, a majority of patients (pts) with newly diagnosed MM achieve less than CR/nCR to initial therapy. The objective of this study is to establish predictors of response and drug resistance by applying proteomic profiling of MM. Here we present the analysis of differential proteomic profiling of baseline plasma cells (PCs) from pts with MM predicting achievement of CR/nCR after completion of a course of first line treatment with lenalidomide (Revlimid®), bortezomib (Velcade®), and dexamethasone (RVD) regimen. Methods: After obtaining informed consent from pts, we performed quantitative proteomic analysis of PCs isolated from bone marrow of 16 pts with previously untreated MM enrolled at the University of Michigan site in the Phase II portion of the multi-site frontline RVD clinical trial. Eight of the analyzed pts achieved CR/nCR, while the remaining had a lesser response (6 VGPR, 2 PR i.e ≥50% but < 90% reduction of disease). We used two independent proteomic platforms: iTRAQ (Isobaric Tags for Relative and Absolute Quantitation) technique in 8-plex variant, as well as a label-free approach (LF) based on spectra counting. PCs were acquired from bone marrow aspiration and, thereafter, were enriched with a RosetteSep negative selection kit (StemCell Technologies). In iTRAQ experiments, proteins were processed with reagents according to the manufacturer's protocol (Applied Biosystems) followed by SCX fractionation and LC-MS/MS analysis (4800 Plus MALDI TOF/TOF). Peptides from the MM1S cell line were used as an internal reference. The data were analyzed using ProteinPilot software. For LF analysis, proteins were pre-fractionated before trypsin digestion on NuPage Bis-Tris-Gel and subsequently run on LC-ESI-MS/MS on a linear trap mass spectrometer (LTQ Orbitrap). Database search was carried out using X!Tandem followed by Trans-proteomic Pipeline (TPP). A 1.5-fold difference in expression in both platforms was used as a cut-off value. Results: A total of 926 proteins were identified with high confidence (FDR Conclusions: We analyzed proteomic characteristics of patient-derived MM cells using two independent proteomics platforms. As a proof of concept, analysis of PCs obtained from pts enrolled in the frontline RVD trial shows differential expression of 70 proteins in patients who achieved CR/nCR versus those with a lesser response. Consistent with our prior observations, differentially regulated proteins are involved in the c-Myc pathway, which has an established critical role in pathogenesis of MM. Validation studies of candidate proteins are in progress. This study was supported by a grant from the Multiple Myeloma Research Foundation. Disclosures: Off Label Use: Lenalidomide in combination with bortezomib and dexamethasone for the treatment of newly diagnosed Myeloma. Richardson:Millennium Pharmaceuticals, Inc.: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees; Johnson & Johnson: Membership on an entity's Board of Directors or advisory committees. Jakubowiak:Millennium Pharmaceuticals, Inc.: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Centocor Ortho Biotech: Consultancy, Honoraria; Exelixis: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Research Funding.
- Published
- 2010
- Full Text
- View/download PDF
41. Proteomic Signature Predicting Achievement of Very Good Partial Response In Patients with Multiple Myeloma Based On Complementary Label-Free and iTRAQ Quantitative Proteome Analysis
- Author
-
John R. Strahler, Dominik Dytfeld, Alexey I. Nesvizhskii, Lambert Ngoka, Malathi Kandarpa, Stephanie J Kraftson, Dattatreya Mellacheruvu, Suchitra Subramani, Andrzej Jakubowiak, and Arun Sreekumar
- Subjects
Oncology ,medicine.medical_specialty ,Proteomic Profile ,business.industry ,Bortezomib ,Immunology ,Quantitative proteomics ,Dose fractionation ,Cell Biology ,Hematology ,Bioinformatics ,medicine.disease ,Proteomics ,Biochemistry ,Internal medicine ,Proteome ,medicine ,Database search engine ,business ,Multiple myeloma ,medicine.drug - Abstract
Abstract 1902 Introduction: Multiple myeloma (MM) remains mostly incurable. Novel therapies have improved response rates, which are now reaching 100%. More importantly, number of recent studies showed that the depth of response, e.g. achievement of at least 90% reduction of the disease (≥VGPR) is associated with longer disease control. Therefore, improving VGPR rates and establishing predictors of VGPR to a given regimen may be an important clinical goal. High throughput quantitative proteomics may offer greater insight into the actual biology of the malignant cell than genome analysis and therefore, may be more useful in the development of personalized therapy. The objective of this study is to establish a proteomic signature predicting achievement of at least VGPR to initial treatment with bortezomib (Velcade®), pegylated liposomal doxorubicin, and dexamethasone (VDD). We previously reported preliminary proteomic profile of malignant plasma cells (PCs) obtained from a set of naïve MM pts enrolled in the VDD trial (Dytfeld et al., ASH 2009). Here we present the results of differential proteomic analysis of MM PCs of all available samples from the frontline VDD study (≥VGPR vs. Methods: PCs were acquired from pre-treatment bone marrow specimens after obtaining informed consent from patients (pts), and were thereafter enriched with a RosetteSep® negative selection kit. Quantitative proteomic analysis of PCs from 17 naïve pts with MM from the VDD study was performed using iTRAQ approach in 8-plex variant. To increase confidence of analysis, label-free quantitative proteomics (LF) based on spectra counting was conducted on PCs from 12 pts. In iTRAQ experiments, proteins were processed with reagents according to the manufacturer's protocol followed by SCX fractionation and LC-MS/MS analysis (4800 Plus MALDI TOF/TOF). Peptides from the MM1S cell line were used as a reference. The data were analyzed using ProteinPilot™. For LF analysis, proteins were fractionated before trypsin digestion on Bis-Tris-Gel and subsequently run on LC-ESI-MS/MS on a linear trap mass spectrometer (LTQ Orbitrap). A database search was carried out using X!Tandem followed by Trans-proteomic Pipeline. At least 1.5-fold difference in expression in both platforms was used as a cut-off value. To correlate proteomics with gene expression of dysregulated proteins of interest, mRNA levels were analyzed by quantitative real time PCR (RT-PCR). Validation of proteomic findings on proteins of interest was performed using Western Blot. Results: We identified a total of 894 proteins in 3 iTRAQ experiments with high confidence (FDR Conclusions: We present patient-derived proteomic characteristics of MM cells using two independent proteomic platforms. As a proof of concept, analysis of PCs obtained from pts enrolled in the frontline VDD study shows differential expression of 34 proteins in pts who achieved ≥VGPR vs. pts with This study was supported by a grant from the Multiple Myeloma Research Foundation. Disclosures: Jakubowiak: Millennium, Celgene, Bristol-Myers Squibb, Johnson & Johnson Ortho-Centocor: Honoraria; Millennium, Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Millennium, Celgene, Centocor-Ortho Biotech: Speakers Bureau.
- Published
- 2010
- Full Text
- View/download PDF
42. Proteomic Profiling of Multiple Myeloma Plasma Cells and Normal Plasma Cells Reveals Differential Expression of Clu1 and Basp1 Proteins
- Author
-
Alexey I. Nesvizhskii, Dominik Dytfeld, John R. Strahler, Dattatreya Mellacheruvu, Andrzej Jakubowiak, Malathi Kandarpa, Stephanie J Kraftson, and Suchitra Subramani
- Subjects
Plasma cell leukemia ,Pathology ,medicine.medical_specialty ,biology ,Clusterin ,business.industry ,Immunology ,Quantitative proteomics ,Cell Biology ,Hematology ,medicine.disease ,Proteomics ,Biochemistry ,Molecular biology ,medicine.anatomical_structure ,medicine ,biology.protein ,Bone marrow ,Antibody ,Precordial catch syndrome ,business ,Multiple myeloma - Abstract
Abstract 4034 Introduction: Multiple myeloma (MM) is a monoclonal gammopathy characterized by the uncontrolled proliferation of plasma cells (PCs). The lack of knowledge about MM cell biology compared to normal PCs is hindering the discovery of myeloma specific targeted therapeutics. Current therapeutics target broad cellular functions such as suppression of the bone marrow environment, myeloma cell proliferation and induction of apoptosis. The objective of our study was to determine biomarkers of the disease and identify new potential targets for future therapeutics, and therefore increase treatment options for MM. We utilized quantitative proteomics using an iTRAQ-based approach to identify biomarkers that can distinguish between MM and normal PCs. Methods: Tonsil tissues, removed from patients suffering from sleep apnea syndrome who consented for tissue repository, were the source of normal PCs. First, the tonsil cells were depleted of T-cells, granulocytes and macrophages using RosetteSep® antibody cocktail and, subsequently, CD138+ PCs were isolated by EasySep® magnetic bead selection. Bone marrow aspirates from MM patients who consented for IRB-approved MM repository protocol, were enriched for PCs with RosetteSep® antibody cocktail. PC percentage for purity assessment was performed by Wright-Giemsa staining of cytospin preparations. PCs (250,000) were lysed and proteomic profiles were generated by iTRAQ 4-plex methods where 2 tonsil PCs (TPC) and 2 MM plasma cells (MMPC) were in each 4-plex. After labeling with iTRAQ tags, the proteins were fractionated by cation exchange chromatography followed by LC-MS/MS analysis on a MALDI-TOF/TOF™ analyzer. The data were analyzed and quantification performed using ProteinPilot™ software. Real time PCR of cDNA from TPC and two independent MMPC samples was performed to validate the results. Results: We consistently obtained 100–250,000 normal PCs from each tonsil sample, at a purity of >80%. To obtain reliable data from proteomics we required >200,000 cells and therefore tonsil pools were utilized wherever necessary. Three types of MM patient samples were studied: newly diagnosed MM, relapsed MM and plasma cell leukemia. We detected and quantified 848 proteins with high confidence from three 4-plex iTRAQ experiments (FDR Conclusion: We have successfully isolated a sufficient number of PCs from tonsils to compare proteomic profiles of tonsilar PCs, from subjects without malignancy, with PCs from bone marrows of MM patients. Our analysis has identified clusterin and Basp1 as proteins that are differentially expressed between TPCs and MMPCs, and therefore might play a role in disease physiology. Regulatory pathways identified in this study might be candidates for myeloma-specific therapeutic intervention. This study was partly supported by a grant from the Multiple Myeloma Research Foundation. Disclosures: Jakubowiak: Millennium, Celgene, Bristol-Myers Squibb, Johnson & Johnson Ortho-Centocor: Honoraria; Millennium, Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Millennium, Celgene, Centocor-Ortho Biotech: Speakers Bureau.
- Published
- 2010
- Full Text
- View/download PDF
43. Proteomic Profiling of Multiple Myeloma Using iTRAQ Labeling Followed by Multidimensional Liquid Chromatography and Tandem Mass Spectrometry
- Author
-
Malathi Hari, John R. Strahler, Andrzej Jakubowiak, Dominik Dytfeld, Suchitra Subramani, Dattatreya Mellacheruvu, Alexey I. Nesvizhskii, and Arun Sreekumar
- Subjects
Oncology ,Very Good Partial Response ,medicine.medical_specialty ,Bortezomib ,Proteomic Profiling ,business.industry ,Immunology ,Cell Biology ,Hematology ,Tandem mass spectrometry ,medicine.disease ,Bioinformatics ,Biochemistry ,Internal medicine ,medicine ,Doxorubicin ,Precordial catch syndrome ,business ,Dexamethasone ,Multiple myeloma ,medicine.drug - Abstract
Abstract 4865 Introduction Despite an increased number of new treatments, multiple myeloma (MM) remains mostly incurable. There is emerging evidence that achieving at least 90% disease reduction (very good partial response, VGPR) is associated with improved control of disease (Jakubowiak et al, JCO, in press). However, even with the most active regimens, 40-60% of patients (pts) with newly diagnosed MM and the majority of pts with relapsed/refractory MM do not achieve VGPR. The objective of this study is to establish predictors of response and drug resistance by applying proteomic profiling of MM. Here we present the preliminary analysis of differential proteomic profiling of plasma cells (PC) from pts with MM. Methods We have performed proteomic analysis of PC from 5 pts with previously untreated MM enrolled in the frontline VDD study (combination treatment with bortezomib, pegylated doxorubicin, and dexamethasone) using iTRAQ (Isobaric Tags for Relative and Absolute Quantitation) technique in 8-plex variant. With regards to samples analyzed two pts showed at least VGPR (≥VGPR) and the rest exhibited less than VGPR ( Results We identified 426 proteins, of which 399 were quantified. Using a 2 fold expression threshold, 18 proteins were elevated in samples from pts ≥VGPR to VDD while 102 proteins were down regulated compared to the Conclusions We showed patient-derived proteomic characteristics in MM using 8-plex iTRAQ methodology. As a proof of concept, preliminary analysis of samples obtained from pts enrolled in frontline VDD shows differential expression of 120 proteins in patients who achieved ≥VGPR vs This study was supported by a grant from the Multiple Myeloma Research Foundation. Disclosures No relevant conflicts of interest to declare.
- Published
- 2009
- Full Text
- View/download PDF
Catalog
Discovery Service for Jio Institute Digital Library
For full access to our library's resources, please sign in.