Proteogenomic Pipeline Research Articles

A comprehensive proteogenomic pipeline for neoantigen discovery to advance personalized cancer immunotherapy.

The accurate identification and prioritization of antigenic peptides is crucial for the development of personalized cancer immunotherapies. Publicly available pipelines to predict clinical neoantigens do not allow direct integration of mass spectrometry immunopeptidomics data, which can uncover antigenic peptides derived from various canonical and noncanonical sources. To address this, we present an end-to-end clinical proteogenomic pipeline, called NeoDisc, that combines state-of-the-art publicly available and in-house software for immunopeptidomics, genomics and transcriptomics with in silico tools for the identification, prediction and prioritization of tumor-specific and immunogenic antigens from multiple sources, including neoantigens, viral antigens, high-confidence tumor-specific antigens and tumor-specific noncanonical antigens. We demonstrate the superiority of NeoDisc in accurately prioritizing immunogenic neoantigens over recent prioritization pipelines. We showcase the various features offered by NeoDisc that enable both rule-based and machine-learning approaches for personalized antigen discovery and neoantigen cancer vaccine design. Additionally, we demonstrate how NeoDisc's multiomics integration identifies defects in the cellular antigen presentation machinery, which influence the heterogeneous tumor antigenic landscape.

A Proteogenomic Pipeline for the Analysis of Protein Biosynthesis Errors in the Human Pathogen Candida albicans

Candida albicans is a diploid pathogen known for its ability to live as a commensal fungus in healthy individuals but causing both superficial infections and disseminated candidiasis in immunocompromised patients where it is associated with high morbidity and mortality. Its success in colonizing the human host is attributed to a wide range of virulence traits that modulate interactions between the host and the pathogen, such as optimal growth rate at 37°C, the ability to switch between yeast and hyphal forms, and a remarkable genomic and phenotypic plasticity. A fascinating aspect of its biology is a prominent heterogeneous proteome that arises from frequent genomic rearrangements, high allelic variation, and high levels of amino acid misincorporations in proteins. This leads to increased morphological and physiological phenotypic diversity of high adaptive potential, but the scope of such protein mistranslation is poorly understood due to technical difficulties in detecting and quantifying amino acid misincorporation events in complex protein samples. We have developed and optimized mass spectrometry and bioinformatics pipelines capable of identifying rare amino acid misincorporation events at the proteome level. We have also analyzed the proteomic profile of an engineered C.albicans strain that exhibits high level of leucine misincorporation at protein CUG sites and employed an invivo quantitative gain-of-function fluorescence reporter system to validate our LC-MS/MS data. C.albicans misincorporates amino acids above the background level at protein sites of diverse codons, particularly at CUG, confirming our previous data on the quantification of leucine incorporation at single CUG sites of recombinant reporter proteins, but increasing misincorporation of Leucine at these sites does not alter the translational fidelity of the other codons. These findings indicate that the C.albicans statistical proteome exceeds prior estimates, suggesting that its highly plastic phenome may also be modulated by environmental factors due to translational ambiguity.

Proteogenomics in Nephrology: A New Frontier in Nephrological Research.

Proteogenomics represents a transformative intersection in nephrology, uniting genomics, transcriptomics, and proteomics to unravel the molecular intricacies of kidney diseases. This review encapsulates the methodological essence of proteogenomics and its profound implications in chronic kidney disease (CKD) research. We explore the proteogenomic pipeline, highlighting the integrated analysis of genomic, transcriptomic, and proteomic data and its pivotal role in enhancing our understanding of kidney pathologies. Through case studies, we showcase the application of proteogenomics in clear cell renal cell carcinoma (ccRCC) and Autosomal Recessive Polycystic Kidney Disease (ARPKD), emphasizing its potential in personalized treatment strategies and biomarker discovery. The review also addresses the challenges in proteogenomic analysis, including data integration complexities and bioinformatics limitations, and proposes solutions for advancing the field. Ultimately, this review underscores the prospective future of proteogenomics in nephrology, particularly in advancing personalized medicine and providing novel therapeutic insights.

Abstract A038: Non-canonical MHC class I-associated antigens in pancreatic cancer

Abstract Translation of the ostensibly non-coding genome in cancer can generate novel peptides capable of presentation by major histocompatibility complex class I (MHC-I, HLA-I in humans). These non-canonical peptide sources can broaden the landscape of potentially targetable antigens in low-to-intermediate mutational burden malignancies, typified by pancreatic ductal adenocarcinoma (PDA). However, non-canonical MHC-I-associated peptides (ncMAPs) have yet to be explored in pancreatic cancer. We purified the malignant compartment from low tumor cellularity PDA specimens using patient-derived organoids (PDOs) and developed a personalized proteogenomics pipeline coupled with high-depth immunopeptidomics to deeply characterize the repertoire of HLA:peptide targets presented specifically on malignant cells. We demonstrate that ncMAPs are abundant on PDA and predominate over mutation-derived neoepitopes in the detectable immunopeptidome. While emerging evidence in other tumor types has implicated dysregulated translation of non-canonical open reading frames (ncORFs), it is currently unknown to what extent these translation products are truly cancer-restricted and how effectively the resulting ncMAPs can elicit a cytolytic T lymphocyte (CTL) response. To investigate the cancer-specificity of non-canonical peptides, we developed a translation-centric analysis pipeline that examines ncORF expression across a range of healthy tissues, including healthy thymus. We provide evidence that ~30% of ncMAPs exhibit cancer-restricted translation patterns, and a substantial subset of these are shared among pancreatic cancer patients with the appropriate HLA. To investigate immunogenicity, we employed a highly sensitive ex vivo platform to prime and expand antigen-specific T cells. We demonstrate that PDA-restricted ncMAPs are highly immunogenic, on par with or even exceeding the immunogenicity observed with mutation-derived peptides or tumor-associated antigens assayed using the same platform. These findings uncover a critical role for dysregulated translation in pancreatic cancer as a potential source for recurrent cancer-restricted epitopes capable of recognition by cytotoxic T cells. We envision that this novel class of antigens will accelerate ongoing efforts to treat pancreatic cancer patients with vaccines and cell-based therapies. Citation Format: Zackery A. Ely, Zachary J. Kulstad, Eva K. Verzani, Jennifer G. Abelin, Sudarsana Addepalli, Karl R. Clauser, Marta Casarrubios, Kevin S. Kapner, Miles P. Agus, Connor J. Hennessey, Sine R. Hadrup, Susan Klaeger, Jennifer Su, Alex M. Jaeger, Brian M. Wolpin, Srivatsan Raghavan, Philip D. Greenberg, Andrew J. Aguirre, Steven A. Carr, Tyler Jacks, William A. Freed-Pastor. Non-canonical MHC class I-associated antigens in pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr A038.

Retention Time and Fragmentation Predictors Increase Confidence in Identification of Common Variant Peptides.

Precision medicine focuses on adapting care to the individual profile of patients, for example, accounting for their unique genetic makeup. Being able to account for the effect of genetic variation on the proteome holds great promise toward this goal. However, identifying the protein products of genetic variation using mass spectrometry has proven very challenging. Here we show that the identification of variant peptides can be improved by the integration of retention time and fragmentation predictors into a unified proteogenomic pipeline. By combining these intrinsic peptide characteristics using the search-engine post-processor Percolator, we demonstrate improved discrimination power between correct and incorrect peptide-spectrum matches. Our results demonstrate that the drop in performance that is induced when expanding a protein sequence database can be compensated, hence enabling efficient identification of genetic variation products in proteomics data. We anticipate that this enhancement of proteogenomic pipelines can provide a more refined picture of the unique proteome of patients and thereby contribute to improving patient care.

Detection of Known and Novel Small Proteins in Pseudomonas stutzeri Using a Combination of Bottom-Up and Digest-Free Proteomics and Proteogenomics.

Small proteins of around 50 aa in length have been largely overlooked in genetic and biochemical assays due to the inherent challenges with detecting and characterizing them. Recent discoveries of their critical roles in many biological processes have led to an increased recognition of the importance of small proteins for basic research and as potential new drug targets. One example is CcoM, a 36 aa subunit of the cbb3-type oxidase that plays an essential role in adaptation to oxygen-limited conditions in Pseudomonas stutzeri (P. stutzeri), a model for the clinically relevant, opportunistic pathogen Pseudomonas aeruginosa. However, as no comprehensive data were available in P. stutzeri, we devised an integrated, generic approach to study small proteins more systematically. Using the first complete genome as basis, we conducted bottom-up proteomics analyses and established a digest-free, direct-sequencing proteomics approach to study cells grown under aerobic and oxygen-limiting conditions. Finally, we also applied a proteogenomics pipeline to identify missed protein-coding genes. Overall, we identified 2921 known and 29 novel proteins, many of which were differentially regulated. Among 176 small proteins 16 were novel. Direct sequencing, featuring a specialized precursor acquisition scheme, exhibited advantages in the detection of small proteins with higher (up to 100%) sequence coverage and more spectral counts, including sequences with high proline content. Three novel small proteins, uniquely identified by direct sequencing and not conserved beyond P. stutzeri, were predicted to form an operon with a conserved protein and may represent de novo genes. These data demonstrate the power of this combined approach to study small proteins in P. stutzeri and show its potential for other prokaryotes.

The Immunopeptidome from a Genomic Perspective: Establishing the Noncanonical Landscape of MHC Class I-Associated Peptides.

Tumor antigens can emerge through multiple mechanisms, including translation of noncoding genomic regions. This noncanonical category of tumor antigens has recently gained attention; however, our understanding of how they recur within and between cancer types is still in its infancy. Therefore, we developed a proteogenomic pipeline based on deep learning de novo mass spectrometry (MS) to enable the discovery of noncanonical MHC class I-associated peptides (ncMAP) from noncoding regions. Considering that the emergence of tumor antigens can also involve posttranslational modifications (PTM), we included an open search component in our pipeline. Leveraging the wealth of MS-based immunopeptidomics, we analyzed data from 26 MHC class I immunopeptidomic studies across 11 different cancer types. We validated the de novo identified ncMAPs, along with the most abundant PTMs, using spectral matching and controlled their FDR to 1%. The noncanonical presentation appeared to be 5 times enriched for the A03 HLA supertype, with a projected population coverage of 55%. The data reveal an atlas of 8,601 ncMAPs with varying levels of cancer selectivity and suggest 17 cancer-selective ncMAPs as attractive therapeutic targets according to a stringent cutoff. In summary, the combination of the open-source pipeline and the atlas of ncMAPs reported herein could facilitate the identification and screening of ncMAPs as targets for T-cell therapies or vaccine development.

P400: PROTEOGENOMIC CHARACTERIZATION OF 5-AZACYTIDINE EFFECTS ON ACUTE MYELOID LEUKEMIA IMMUNOPEPTIDOME

Background: Hypomethylating agents like 5-azacytidine (AZA) are licensed for the treatment of acute myeloid leukemia (AML) patients ineligible for allogeneic hematopoietic cell transplantation. While previous reports suggested that AZA promotes the recognition of AML blasts by cytotoxic T cells, the mechanism behind this improved recognition is not fully understood. Specifically, AZA is assumed to promote the expression of transcripts repressed by genomic methylation, namely cancer-testis antigens (CTA) and endogenous retroelements (ERE), resulting in the presentation of immunogenic MHC-I-associated peptides (MAPs, collectively referred to as the immunopeptidome) derived from the translation of these transcripts. However, the presentation of such MAPs after AZA treatment has not been firmly demonstrated thus far. Aims: Our study aims to characterize how AZA treatment shapes the identity of MAPs presented by AML cells. Methods: Four different AML cell lines, THP-1, OCI-AML3, SKM-1, and MOLM-13 were treated with a non-cytotoxic dose of AZA for 3 days. Their transcriptome has been characterized by high-coverage RNA sequencing (RNA-seq) and their immunopeptidome by shotgun mass spectrometry (MS). To identify MAPs deriving from unannotated genomic regions, we have designed a cutting-edge proteogenomic pipeline using the RNA-seq data to build personalized MS databases that enabled the identification of non-canonical MAPs such as ERE-derived MAPs. Results: Paired transcriptomic comparisons between treated and untreated cells showed that AZA induces a large-scale gene upregulation (87% differentially-expressed transcripts were upregulated). Among them, 38% were EREs and 6% were CTAs, suggesting that AZA-induced MAPs have greater chances of deriving from EREs than from CTAs. However, we could not identify a single ERE-derived upregulated MAP among the immunopeptidome of AZA-treated AML cells while multiple CTA-derived MAPs (0.4 - 0.7% of the upregulated MAPs) were presented at greater levels by AZA. Because a GO-term analysis of the upregulated protein-coding genes evidenced a robust innate immune response in AZA-treated cells, we conclude that AZA-induced enhanced CTL recognition is more dependent on CTA- than on ERE-derived MAP presentation and that ERE enhanced expression could rather trigger the observed innate immune response. An in-depth analysis of the immunopeptidome showed that MAPs having an altered presentation following AZA treatment derived only partially from transcripts whose expression was affected by AZA. The analysis of the other AZA-induced MAPs showed that they resulted preferentially from the activity of the constitutive proteasome (vs the immunoproteasome) and derived preferentially from proteins having less aromatic residues (and therefore more prone to misfolding). Because the degradation of misfolded proteins by the constitutive proteasome can indicate that AZA induces an unfolded protein response, we have examined whether AZA stimulates autophagy, a process frequently triggered in association with such stress. Accordingly, flow cytometry assays on AML cells treated with AZA for 24h evidenced a robust autophagy induction. Summary/Conclusion: Altogether our results show that AZA promotes the presentation of CTA-derived rather than ERE-derived MAPs and that autophagy induction could enable the survival of AML cells to AZA-induced proteotoxic stress. Our findings suggest that autophagy inhibitors could synergize with AZA in AML therapy.

Single-cell RNA-seq-based proteogenomics identifies glioblastoma-specific transposable elements encoding HLA-I-presented peptides.

We analyze transposable elements (TEs) in glioblastoma (GBM) patients using a proteogenomic pipeline that combines single-cell transcriptomics, bulk RNA sequencing (RNA-seq) samples from tumors and healthy-tissue cohorts, and immunopeptidomic samples. We thus identify 370 human leukocyte antigen (HLA)-I-bound peptides encoded by TEs differentially expressed in GBM. Some of the peptides are encoded by repeat sequences from intact open reading frames (ORFs) present in up to several hundred TEs from recent long interspersed nuclear element (LINE)-1, long terminal repeat (LTR), and SVA subfamilies. Other HLA-I-bound peptides are encoded by single copies of TEs from old subfamilies that are expressed recurrently in GBM tumors and not expressed, or very infrequently and at low levels, in healthy tissues (including brain). These peptide-coding, GBM-specific, highly recurrent TEs represent potential tumor-specific targets for cancer immunotherapies.

µProteInS-a proteogenomics pipeline for finding novel bacterial microproteins encoded by small ORFs.

Genome annotation pipelines traditionally exclude open reading frames (ORFs) shorter than 100 codons to avoid false identifications. However, studies have been showing that these may encode functional microproteins with meaningful biological roles. We developed µProteInS, a proteogenomics pipeline that combines genomics, transcriptomics and proteomics to identify novel microproteins in bacteria. Our pipeline employs a model to filter out low confidence spectra, to avoid the need for manually inspecting Mass Spectrometry data. It also overcomes the shortcomings of traditional approaches that usually exclude overlapping genes, leaderless transcripts and non-conserved sequences, characteristics that are common among small ORFs (smORFs) and hamper their identification. µProteInS is implemented in Python 3.8 within an Ubuntu 20.04 environment. It is an open-source software distributed under the GNU General Public License v3, available as a command-line tool. It can be downloaded at https://github.com/Eduardo-vsouza/uproteins and either installed from source or executed as a Docker image. Supplementary data are available at Bioinformatics online.

Proteogenomic discovery of sORF-encoded peptides associated with bacterial virulence in Yersinia pestis

Plague caused by Yersinia pestis is one of the deadliest diseases. However, many molecular mechanisms of bacterial virulence remain unclear. This study engaged in the discovery of small open reading frame (sORF)-encoded peptides (SEPs) in Y. pestis. An integrated proteogenomic pipeline was established, and an atlas containing 76 SEPs was described. Bioinformatic analysis indicated that 20% of these SEPs were secreted or localized to the transmembrane and that 33% contained functional domains. Two SEPs, named SEPs-yp1 and -yp2 and encoded in noncoding regions, were selected by comparative peptidomics analysis under host-specific environments and high-salinity stress. They displayed important roles in the regulation of antiphagocytic capability in a thorough functional assay. Remarkable attenuation of virulence in mice was observed in the SEP-deleted mutants. Further global proteomic analysis indicated that SEPs-yp1 and -yp2 affected the bacterial metabolic pathways, and SEP-yp1 was associated with the bacterial virulence by modulating the expression of key virulence factors of the Yersinia type III secretion system. Our study provides a rich resource for research on Y. pestis and plague, and the findings on SEP-yp1 and SEP-yp2 shed light on the molecular mechanism of bacterial virulence.

Gene Validation and Remodelling Using Proteogenomics of Phytophthora cinnamomi, the Causal Agent of Dieback.

Phytophthora cinnamomi is a pathogenic oomycete that causes plant dieback disease across a range of natural ecosystems and in many agriculturally important crops on a global scale. An annotated draft genome sequence is publicly available (JGI Mycocosm) and suggests 26,131 gene models. In this study, soluble mycelial, extracellular (secretome), and zoospore proteins of P. cinnamomi were exploited to refine the genome by correcting gene annotations and discovering novel genes. By implementing the diverse set of sub-proteomes into a generated proteogenomics pipeline, we were able to improve the P. cinnamomi genome annotation. Liquid chromatography mass spectrometry was used to obtain high confidence peptides with spectral matching to both the annotated genome and a generated 6-frame translation. Two thousand seven hundred sixty-four annotations from the draft genome were confirmed by spectral matching. Using a proteogenomic pipeline, mass spectra were used to edit the P. cinnamomi genome and allowed identification of 23 new gene models and 60 edited gene features using high confidence peptides obtained by mass spectrometry, suggesting a rate of incorrect annotations of 3% of the detectable proteome. The novel features were further validated by total peptide support, alongside functional analysis including the use of Gene Ontology and functional domain identification. We demonstrated the use of spectral data in combination with our proteogenomics pipeline can be used to improve the genome annotation of important plant diseases and identify missed genes. This study presents the first use of spectral data to edit and manually annotate an oomycete pathogen.

Abstract GS2-05: Microscaled proteogenomic methods for precision oncology

Abstract Cancer proteogenomics combines genomics, transcriptomics and mass spectrometry-based proteomics to gain insights into cancer biology and treatment responsiveness. While proteogenomics analyses have already shown great potential to deepen our understanding of cancer tissue complexity and signaling, how a patient’s tumor changes upon treatment has largely been the province of genomics. This is due to technical difficulties associated with doing proteogenomic analysis on clinic-derived core-needle biopsies. To address this critical need, we have developed a “microscaled” proteogenomics approach for tumor-rich OCT-embedded core needle biopsies. Tissue-sparing specimen processing (“Biopsy Trifecta EXTraction”, BioTExt) and microscaled proteomics (MiProt) methodologies allowed generation of deep-scale proteogenomics datasets, with copy number and transcript information for >20,000 genes and mass spectrometry-based identification and quantification of nearly all expressed proteins in a tumor (>10,000 proteins) and more than >20,000 phosphosites starting with just 25 micrograms of protein per sample. In order to understand the capabilities and limitations our our approach relative to more conventional deepscale proteomics requiring >10X more starting material, we compared preclinical patient derived xenograft (PDX) models at conventional scale with data obtained by core-needle biopsy of the same tissues. Comparable depth and biological insights were obtained from the cores relative to surgically resected tumors. As a proof-of-concept for implementation in clinical trials, we applied microscaled proteogenomic methods to a small-scale clinical study where biopsies were accrued from patients with ERBB2+ locally advanced breast cancer before and 48 to 72 hours after the first dose of neoadjuvant Trastuzumab-based chemotherapy. Multi-omics comparisons were conducted between samples associated with residual disease versus samples associated with complete pathological response. Integrative, microscaled proteogenomic analyses efficiently diagnosed the molecular bases of diverse candidate treatment resistance mechanisms including: 1) absence of ERBB2 amplification (false-ERBB2+); 2) insufficient ERBB2 activity for therapeutic sensitivity despite ERBB2 amplification (pseudo-ERBB2+); 3) resistance features in true-ERBB2+ cases including androgen receptor signaling, mucin expression and an inactive immune microenvironment; 4) lack of acute phospho-ERBB2 down-regulation in non-pCR cases. In summary, we have developed a robust proteogenomics pipeline well suited for large-scale cancer clinical studies to identify potential resistance mechanism in patients. We conclude that microscaled cancer proteogenomics could improve diagnostic precision in the clinical setting. Citation Format: Shankha Satpathy, Eric Jaehnig, Krug Karsten, Beom-Jun Kim, Alexander Saltzman, Doug Chan, Kimberly Holloway, Meenakshi Anurag, Chen Huang, Purba Singh, Ari Gao, Noel Namai, Yongchao Dou, Bo Wen, Suhas Vasaikar, David Mutch, Mark Watson, Cynthia Ma, Foluso Ademuyiwa, Mothaffar Rimawi, Jeremy Hoog, Samuel Jacobs, Anna Malovannaya, Terry Hyslop, D.R Mani, Charles Perou, George Miles, Bing Zhang, Michael Gillette, Steven Carr, Matthew Ellis. Microscaled proteogenomic methods for precision oncology [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr GS2-05.

SAAVpedia: Identification, Functional Annotation, and Retrieval of Single Amino Acid Variants for Proteogenomic Interpretation.

Next-generation genome sequencing has enabled the discovery of numerous disease- or drug-response-associated nonsynonymous single nucleotide variants (nsSNVs) that alter the amino acid sequences of a protein. Although several studies have attempted to characterize pathogenic nsSNVs, few have been confirmed as single amino acid variants (SAAVs) at the protein level. Here we developed the SAAVpedia platform to identify, annotate, and retrieve pathogenic SAAV candidates from proteomic and genomic data. The platform consists of four modules: SAAVidentifier, SAAVannotator, SNV/SAAVretriever, and SAAVvisualizer. The SAAVidentifier provides a reference database containing 18 206 090 SAAVs and performs the identification and quality assessment of SAAVs. The SAAVannotator provides functional annotation with biological, clinical, and pharmacological information for the interpretation of condition-specific SAAVs. The SNV/SAAVretriever module enables bidirectional navigation between relevant SAAVs and nsSNVs with diverse genomic and proteomic data. SAAVvisualizer provides various statistical plots based on functional annotations of detected SAAVs. To demonstrate the utility of SAAVpedia, the proteogenomic pipeline with protein-protein interaction network analysis was applied to proteomic data from breast cancer and glioblastoma patients. We identified 1326 and 12 breast-cancer- and glioblastoma-related genes that contained one or more SAAVs, including BRCA2 and FAM49B, respectively. SAAVpedia is a suitable platform for confirming whether a genomic variant is maintained in an amino acid sequence. Furthermore, as a result of the SAAV discovery of these positive controls, the SAAVpedia could play a key role in the protein functional study for the Human Proteome Project (HPP).

Strategies to boost archaea sulfolobus solfataricus P2 proteome coverage and predict new genes

Archaea are well-known for their ability to adapt to harsh environments, which are too severe for the growth of bacteria and Eukaryotes. So far, the studies of Archaea proteomics haven’t achieved satisfying proteome coverage yet. In this work, S. solfataricus P2, a typical model Archaea was chosen for proteogenomic analysis to further characterize the proteome. Among 2938 predicted protein-coding genes, 2122 proteins (>72%) were identified. Furthermore, 48 peptides out of 549 genome search-specific peptides were mapped to regions of the genome where no genes were annotated. 31 previously unidentified protein-coding regions were matched by these 48 peptides using FgenesB and 7 of these regions were assigned by at least two unique genome search-specific peptides. Findings in this work suggest the effectiveness of proteogenomics pipeline in expanding the depth of proteome and genome annotation.

Genome Annotation of a Model Diatom Phaeodactylum tricornutum Using an Integrated Proteogenomic Pipeline

Diatoms comprise a diverse and ecologically important group of eukaryotic phytoplankton that significantly contributes to marine primary production and global carbon cycling. Phaeodactylum tricornutum is commonly used as a model organism for studying diatom biology. Although its genome was sequenced in 2008, a high-quality genome annotation is still not available for this diatom. Here we report the development of an integrated proteogenomic pipeline and its application for improved annotation of P. tricornutum genome using mass spectrometry (MS)-based proteomics data. Our proteogenomic analysis unambiguously identified approximately 8300 genes and revealed 606 novel proteins, 506 revised genes, 94 splice variants, 58 single amino acid variants, and a holistic view of post-translational modifications in P. tricornutum. We experimentally confirmed a subset of novel events and obtained MS evidence for more than 200 micropeptides in P. tricornutum. These findings expand the genomic landscape of P. tricornutum and provide a rich resource for the study of diatom biology. The proteogenomic pipeline we developed in this study is applicable to any sequenced eukaryote and thus represents a significant contribution to the toolset for eukaryotic proteogenomic analysis. The pipeline and its source code are freely available at https://sourceforge.net/projects/gapeproteogenomic.

Abstract 2278: Scalability and reliability improvements to the Iso-Seq analysis pipeline enables higher throughput sequencing of full-length cancer transcripts

Abstract The characterization of gene expression profiles via transcriptome sequencing has proven to be an important tool for characterizing how genomic rearrangements in cancer affect the biological pathways involved in cancer progression and treatment response. More recently, better resolution of transcript isoforms has shown that this additional level of information can be useful in stratifying patients into cancer subtypes with different outcomes and responses to treatment. The Iso-Seq protocol developed at PacBio is uniquely able to deliver full-length, single molecule cDNA sequences, allowing for the unambiguous, direct determination of splice variants. This has led to the identification of novel cancer biomarker candidates and has yielded new insights into gene fusion events that contribute to cancer progression.1,2,3 Recent improvements to the Iso-Seq bioinformatics pipeline increases the speed and scalability of data analysis while boosting the reliability of isoform detection and cross-platform usability. Here we report evaluation of Iso-Seq runs of human UHRR samples with spiked-in synthetic RNA controls on the Sequel System and show that the new pipeline recovers both more human and synthetic isoforms while reducing the number of false positives. We also share the results of sequencing the well-characterized HCC-1954 breast cancer and matched normal cell lines, which will be made publicly available. Combined with the recent simplification of the Iso-Seq sample preparation protocol, the new analysis pipeline completes a streamlined workflow for revealing the most comprehensive picture of transcriptomes at the throughput needed to characterize cancer samples. 1. Kohli M, et. al. (2017) Androgen Receptor Variant AR-V9 Is Coexpressed with AR-V7 in Prostate Cancer Metastases and Predicts Abiraterone Resistance. Clin Cancer Res. ePub ahead of print. doi: 10.1158/1078-0432.CCR-17-0017 2. Komor MA, et. al. (2017) Identification of differentially expressed splice variants by the proteogenomic pipeline Splicify. Mol Cell Prot. ePub ahead of print. doi: 10.1074/mcp.TIR117.000056 3. Weirather JL, et. al. (2015) Characterization of fusion genes and the significantly expressed fusion isoforms in breast cancer by hybrid sequencing. Nucleic Acids Res, 43 (18), e116. Citation Format: Elizabeth Tseng, Tyson Clark, Meredith H. Ashby. Scalability and reliability improvements to the Iso-Seq analysis pipeline enables higher throughput sequencing of full-length cancer transcripts [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2278.

Identification of a Novel Splice Variant of Neural Cell Adhesion Molecule in Glioblastoma Through Proteogenomics Analysis.

Splice variants are known to be important in the pathophysiology of tumors, including the brain cancers. We applied a proteogenomics pipeline to identify splice variants in glioblastoma (GBM, grade IV glioma), a highly malignant brain tumor, using in-house generated mass spectrometric proteomic data and public domain RNASeq dataset. Our analysis led to the identification of a novel exon that maps to the long isoform of Neural cell adhesion molecule 1 (NCAM1), expressed on the surface of glial cells and neurons, important for cell adhesion and cell signaling. The presence of the novel exon is supported with the identification of five peptides spanning it. Additional peptides were also detected in sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) gel separated proteins from GBM patient tissue, underscoring the presence of the novel peptides in the intact brain protein. The novel exon was detected in the RNASeq dataset in 18 of 25 GBM samples and separately validated in additional 10 GBM tumor tissues using quantitative real-time-polymerase chain reaction (qRT-PCR). Both transcriptomic and proteomic data indicate downregulation of NCAM1, including the novel variant, in GBM. Domain analysis of the novel NCAM1 sequence indicates that the insertion of the novel exon contributes extra low-complexity region in the protein that may be important for protein-protein interactions and hence for cell signaling associated with tumor development. Taken together, the novel NCAM1 variant reported in this study exemplifies the importance of future multiomics research and systems biology applications in GBM.

The influence of transcript assembly on the proteogenomics discovery of microproteins

Proteogenomics methods have identified many non-annotated protein-coding genes in the human genome. Many of the newly discovered protein-coding genes encode peptides and small proteins, referred to collectively as microproteins. Microproteins are produced through ribosome translation of small open reading frames (smORFs). The discovery of many smORFs reveals a blind spot in traditional gene-finding algorithms for these genes. Biological studies have found roles for microproteins in cell biology and physiology, and the potential that there exists additional bioactive microproteins drives the interest in detection and discovery of these molecules. A key step in any proteogenomics workflow is the assembly of RNA-Seq data into likely mRNA transcripts that are then used to create a searchable protein database. Here we demonstrate that specific features of the assembled transcriptome impact microprotein detection by shotgun proteomics. By tailoring transcript assembly for downstream mass spectrometry searching, we show that we can detect more than double the number of high-quality microprotein candidates and introduce a novel open-source mRNA assembler for proteogenomics (MAPS) that incorporates all of these features. By integrating our specialized assembler, MAPS, and a popular generalized assembler into our proteogenomics pipeline, we detect 45 novel human microproteins from a high quality proteogenomics dataset of a human cell line. We then characterize the features of the novel microproteins, identifying two classes of microproteins. Our work highlights the importance of specialized transcriptome assembly upstream of proteomics validation when searching for short and potentially rare and poorly conserved proteins.

Identification of Differentially Expressed Splice Variants by the Proteogenomic Pipeline Splicify

Proteogenomics, i.e. comprehensive integration of genomics and proteomics data, is a powerful approach identifying novel protein biomarkers. This is especially the case for proteins that differ structurally between disease and control conditions. As tumor development is associated with aberrant splicing, we focus on this rich source of cancer specific biomarkers. To this end, we developed a proteogenomic pipeline, Splicify, which can detect differentially expressed protein isoforms. Splicify is based on integrating RNA massive parallel sequencing data and tandem mass spectrometry proteomics data to identify protein isoforms resulting from differential splicing between two conditions. Proof of concept was obtained by applying Splicify to RNA sequencing and mass spectrometry data obtained from colorectal cancer cell line SW480, before and after siRNA-mediated downmodulation of the splicing factors SF3B1 and SRSF1. These analyses revealed 2172 and 149 differentially expressed isoforms, respectively, with peptide confirmation upon knock-down of SF3B1 and SRSF1 compared with their controls. Splice variants identified included RAC1, OSBPL3, MKI67, and SYK. One additional sample was analyzed by PacBio Iso-Seq full-length transcript sequencing after SF3B1 downmodulation. This analysis verified the alternative splicing identified by Splicify and in addition identified novel splicing events that were not represented in the human reference genome annotation. Therefore, Splicify offers a validated proteogenomic data analysis pipeline for identification of disease specific protein biomarkers resulting from mRNA alternative splicing. Splicify is publicly available on GitHub (https://github.com/NKI-TGO/SPLICIFY) and suitable to address basic research questions using pre-clinical model systems as well as translational research questions using patient-derived samples, e.g. allowing to identify clinically relevant biomarkers.