Onco-proteogenomics: cancer proteomics joins forces with genomics

Proteogenomics from a bioinformatics angle: A growing field

Proteogenomics is a research area that combines areas as proteomics and genomics in a multi-omics setup using both mass spectrometry and high-throughput sequencing technologies. Currently, the main goals of the field are to aid genome annotation or to unravel the proteome complexity. Mass spectrometry based identifications of matching or homologues peptides can further refine gene models. Also, the identification of novel proteoforms is also made possible based on detection of novel translation initiation sites (cognate or near-cognate), novel transcript isoforms, sequence variation or novel (small) open reading frames in intergenic or un-translated genic regions by analyzing high-throughput sequencing data from RNAseq or ribosome profiling experiments. Other proteogenomics studies using a combination of proteomics and genomics techniques focus on antibody sequencing, the identification of immunogenic peptides or venom peptides. Over the years, a growing amount of bioinformatics tools and databases became available to help streamlining these cross-omics studies. Some of these solutions only help in specific steps of the proteogenomics studies, e.g. building custom sequence databases (based on next generation sequencing output) for mass spectrometry fragmentation spectrum matching. Over the last few years a handful integrative tools also became available that can execute complete proteogenomics analyses. Some of these are presented as stand-alone solutions, whereas others are implemented in a web-based framework such as Galaxy. In this review we aimed at sketching a comprehensive overview of all the bioinformatics solutions that are available for this growing research area. © 2015 Wiley Periodicals, Inc. Mass Spec Rev 36:584-599, 2017.

Variant peptide detection utilizing mass spectrometry: laying the foundations for proteogenomic identification and validation

BACKGROUND

Proteogenomics is an emerging field at the intersection of genomics and proteomics. Many variant peptides corresponding to single nucleotide variations (SNVs) are associated with specific diseases. The aim of this study was to demonstrate the feasibility of proteogenomic-based variant peptide detection in disease models and clinical specimens.

METHODS

We sought to detect p53 single amino acid variant (SAAV) peptides in breast cancer tumor samples that have been previously subjected to sequencing analysis. Initially, two cancer cell lines having a cellular tumor antigen p53 (TP53) mutation and one wild type for TP53 were analyzed by selected reaction monitoring (SRM) assays as controls. One pool of wild type and one pool of mutated for TP53 cytosolic extracts were assayed with a shotgun proteogenomic workflow. Furthermore, 18 individual samples having a mutation in TP53 were assayed by SRM.

RESULTS

Two mutant p53 peptides were successfully detected in two cancer cell lines as expected from their DNA sequence. Wild type p53 peptides were detected in both cytosolic pools, however, none of the mutant p53 peptides were identified. Mutations at the protein level were detected in two cytosolic extracts and whole tumor lysates from the same patients by SRM analysis. Six thousand and six hundred and twenty eight non-redundant proteins were identified in the two cytosolic pools, thus greatly improving a previously reported cytosolic proteome.

CONCLUSIONS

In the current study we show the great potential of using proteogenomics for the direct identification of cancer-associated mutations in clinical samples and we discuss current limitations and future perspectives.

Proteogenomic analysis reveals alternative splicing and translation as part of the abscisic acid response in Arabidopsis seedlings

In eukaryotes, mechanisms such as alternative splicing (AS) and alternative translation initiation (ATI) contribute to organismal protein diversity. Specifically, splicing factors play crucial roles in responses to environment and development cues; however, the underlying mechanisms are not well investigated in plants. Here, we report the parallel employment of short-read RNA sequencing, single molecule long-read sequencing and proteomic identification to unravel AS isoforms and previously unannotated proteins in response to abscisic acid (ABA) treatment. Combining the data from the two sequencing methods, approximately 83.4% of intron-containing genes were alternatively spliced. Two AS types, which are referred to as alternative first exon (AFE) and alternative last exon (ALE), were more abundant than intron retention (IR); however, by contrast to AS events detected under normal conditions, differentially expressed AS isoforms were more likely to be translated. ABA extensively affects the AS pattern, indicated by the increasing number of non-conventional splicing sites. This work also identified thousands of unannotated peptides and proteins by ATI based on mass spectrometry and a virtual peptide library deduced from both strands of coding regions within the Arabidopsis genome. The results enhance our understanding of AS and alternative translation mechanisms under normal conditions, and in response to ABA treatment.

Detecting protein variants by mass spectrometry: a comprehensive study in cancer cell-lines

Background

Onco-proteogenomics aims to understand how changes in a cancer’s genome influences its proteome. One challenge in integrating these molecular data is the identification of aberrant protein products from mass-spectrometry (MS) datasets, as traditional proteomic analyses only identify proteins from a reference sequence database.

Methods

We established proteomic workflows to detect peptide variants within MS datasets. We used a combination of publicly available population variants (dbSNP and UniProt) and somatic variations in cancer (COSMIC) along with sample-specific genomic and transcriptomic data to examine proteome variation within and across 59 cancer cell-lines.

Results

We developed a set of recommendations for the detection of variants using three search algorithms, a split target-decoy approach for FDR estimation, and multiple post-search filters. We examined 7.3 million unique variant tryptic peptides not found within any reference proteome and identified 4771 mutations corresponding to somatic and germline deviations from reference proteomes in 2200 genes among the NCI60 cell-line proteomes.

Conclusions

We discuss in detail the technical and computational challenges in identifying variant peptides by MS and show that uncovering these variants allows the identification of druggable mutations within important cancer genes.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-017-0454-9) contains supplementary material, which is available to authorized users.

Methods, Tools and Current Perspectives in Proteogenomics

With combined technological advancements in high-throughput next-generation sequencing and deep mass spectrometry-based proteomics, proteogenomics, i.e. the integrative analysis of proteomic and genomic data, has emerged as a new research field. Early efforts in the field were focused on improving protein identification using sample-specific genomic and transcriptomic sequencing data. More recently, integrative analysis of quantitative measurements from genomic and proteomic studies have identified novel insights into gene expression regulation, cell signaling, and disease. Many methods and tools have been developed or adapted to enable an array of integrative proteogenomic approaches and in this article, we systematically classify published methods and tools into four major categories, (1) Sequence-centric proteogenomics; (2) Analysis of proteogenomic relationships; (3) Integrative modeling of proteogenomic data; and (4) Data sharing and visualization. We provide a comprehensive review of methods and available tools in each category and highlight their typical applications.

Harnessing the power of proteomics for identification of oncogenic, druggable signalling pathways in cancer

INTRODUCTION

Genomic and transcriptomic profiling of tumours has revolutionised our understanding of cancer. However, the majority of tumours possess multiple mutations, and determining which oncogene, or even which pathway, to target is difficult. Proteomics is emerging as a powerful approach to identify the functionally important pathways driving these cancers, and how they can be targeted therapeutically. Areas covered: The authors provide a technical overview of mass spectrometry based approaches for proteomic profiling, and review the current and emerging strategies available for the identification of dysregulated networks, pathways, and drug targets in cancer cells, with a key focus on the ability to profile cancer kinomes. The potential applications of mass spectrometry in the clinic are also highlighted. Expert opinion: The addition of proteomic information to genomic platforms - 'proteogenomics' - is providing unparalleled insight in cancer cell biology. Application of improved mass spectrometry technology and methodology, in particular the ability to analyse post-translational modifications (the PTMome), is providing a more complete picture of the dysregulated networks in cancer, and uncovering novel therapeutic targets. While the application of proteomics to discovery research will continue to rise, improved workflow standardisation and reproducibility is required before mass spectrometry can enter routine clinical use.

Proteogenomic studies on cancer drug resistance: towards biomarker discovery and target identification

INTRODUCTION

Chemoresistance is a major obstacle for current cancer treatment. Proteogenomics is a powerful multi-omics research field that uses customized protein sequence databases generated by genomic and transcriptomic information to identify novel genes (e.g. noncoding, mutation and fusion genes) from mass spectrometry-based proteomic data. By identifying aberrations that are differentially expressed between tumor and normal pairs, this approach can also be applied to validate protein variants in cancer, which may reveal the response to drug treatment. Areas covered: In this review, we will present recent advances in proteogenomic investigations of cancer drug resistance with an emphasis on integrative proteogenomic pipelines and the biomarker discovery which contributes to achieving the goal of using precision/personalized medicine for cancer treatment. Expert commentary: The discovery and comprehensive understanding of potential biomarkers help identify the cohort of patients who may benefit from particular treatments, and will assist real-time clinical decision-making to maximize therapeutic efficacy and minimize adverse effects. With the development of MS-based proteomics and NGS-based sequencing, a growing number of proteogenomic tools are being developed specifically to investigate cancer drug resistance.

Single Amino Acid Variant Profiles of Subpopulations in the MCF-7 Breast Cancer Cell Line

Cancers are initiated and developed from a small population of stem-like cells termed cancer stem cells (CSCs). There is heterogeneity among this CSC population that leads to multiple subpopulations with their own distinct biological features and protein expression. The protein expression and function may be impacted by amino acid variants that can occur largely due to single nucleotide changes. We have thus performed proteomic analysis of breast CSC subpopulations by mass spectrometry to study the presence of single amino acid variants (SAAVs) and their relation to breast cancer. We have used CSC markers to isolate pure breast CSC subpopulation fractions (ALDH+ and CD44+/CD24- cell populations) and the mature luminal cells (CD49f-EpCAM+) from the MCF-7 breast cancer cell line. By searching the Swiss-CanSAAVs database, 374 unique SAAVs were identified in total, where 27 are cancer-related SAAVs. 135 unique SAAVs were found in the CSC population compared with the mature luminal cells. The distribution of SAAVs detected in MCF-7 cells was compared with those predicted from the Swiss-CanSAAVs database, where we found distinct differences in the numbers of SAAVs detected relative to that expected from the Swiss-CanSAAVs database for several of the amino acids.

Analysis of Major Histocompatibility Complex (MHC) Immunopeptidomes Using Mass Spectrometry

The myriad of peptides presented at the cell surface by class I and class II major histocompatibility complex (MHC) molecules are referred to as the immunopeptidome and are of great importance for basic and translational science. For basic science, the immunopeptidome is a critical component for understanding the immune system; for translational science, exact knowledge of the immunopeptidome can directly fuel and guide the development of next-generation vaccines and immunotherapies against autoimmunity, infectious diseases, and cancers. In this mini-review, we summarize established isolation techniques as well as emerging mass spectrometry-based platforms (i.e. SWATH-MS) to identify and quantify MHC-associated peptides. We also highlight selected biological applications and discuss important current technical limitations that need to be solved to accelerate the development of this field.

A proteogenomic approach for protein-level evidence of genomic variants in cancer cells

Variations in protein coding sequence may sometimes play important roles in cancer development. However, since variants may not express into proteins due to various cellular quality control systems, it is important to get protein-level evidence of the genomic variations. We present a proteogenomic strategy getting protein-level evidence of genomic variants, which we call sequential targeted LC-MS/MS based on prediction of peptide pI and Retention time (STaLPIR). Our approach shows improved peptide identification, and has the potential for the unbiased analysis of variant sequence as well as corresponding reference sequence. Integrated analysis of DNA, mRNA and protein suggests that protein expression level of the nonsynonymous variant is regulated either before or after translation, according to influence of the variant on protein function. In conclusion, our data provides an excellent approach getting direct evidence for the expression of variant protein forms from genome sequence data.

Detection of Isoforms Differing by a Single Charge Unit in Individual Cells

To measure protein isoforms in individual mammalian cells, we report single-cell resolution isoelectric focusing (scIEF) and high-selectivity immunoprobing. Microfluidic design and photoactivatable materials establish the tunable pH gradients required by IEF and precisely control the transport and handling of each 17-pL cell lysate during analysis. The scIEF assay resolves protein isoforms with resolution down to a single-charge unit, including both endogenous cytoplasmic and nuclear proteins from individual mammalian cells.

Emerging power of proteomics for delineation of intrinsic tumor subtypes and resistance mechanisms to anti-cancer therapies

INTRODUCTION

Despite extreme genetic heterogeneity, tumors often show similar alterations in the expression, stability, and activation of proteins important in oncogenic signaling pathways. Thus, classifying tumor samples according to shared proteomic features may help facilitate the identification of cancer subtypes predictive of therapeutic responses and prognostic for patient outcomes. Meanwhile, understanding mechanisms of intrinsic and acquired resistance to anti-cancer therapies at the protein level may prove crucial to devising reversal strategies. Areas covered: Herein, we review recent advances in quantitative proteomic technology and their applications in studies to identify intrinsic tumor subtypes of various tumors, to illuminate mechanistic aspects of pharmacological and oncogenic adaptations, and to highlight interaction targets for anti-cancer compounds and cancer-addicted proteins. Expert commentary: Quantitative proteomic technologies are being successfully employed to classify tumor samples into distinct intrinsic subtypes, to improve existing DNA/RNA based classification methods, and to evaluate the activation status of key signaling pathways.

Targeted proteomics identifies liquid-biopsy signatures for extracapsular prostate cancer

Biomarkers are rapidly gaining importance in personalized medicine. Although numerous molecular signatures have been developed over the past decade, there is a lack of overlap and many biomarkers fail to validate in independent patient cohorts and hence are not useful for clinical application. For these reasons, identification of novel and robust biomarkers remains a formidable challenge. We combine targeted proteomics with computational biology to discover robust proteomic signatures for prostate cancer. Quantitative proteomics conducted in expressed prostatic secretions from men with extraprostatic and organ-confined prostate cancers identified 133 differentially expressed proteins. Using synthetic peptides, we evaluate them by targeted proteomics in a 74-patient cohort of expressed prostatic secretions in urine. We quantify a panel of 34 candidates in an independent 207-patient cohort. We apply machine-learning approaches to develop clinical predictive models for prostate cancer diagnosis and prognosis. Our results demonstrate that computationally guided proteomics can discover highly accurate non-invasive biomarkers.

Proteomic technologies are capable of identifying thousands of proteins in biological samples, but biomarker applications are lagging. Here the authors use Multiple Reaction Monitoring Mass Spectrometry to delineate peptide signatures that accurately distinguish between defined prostate cancer patient risk groups.

Threonine versus isothreonine in synthetic peptides analyzed by high-resolution liquid chromatography/tandem mass spectrometry

RATIONALE

One of the problems in proteogenomic research aimed at identification of variant peptides is the presence of peptides with amino acid isomers of different origin in the analyzed samples. Among the most challenging examples are peptides with threonine and isothreonine (homoserine) in their sequences. Indeed, the latter residue may appear in vitro as a methionine substitution during sample preparation for shotgun proteome analysis. Yet, this substitution of Met to isoThr is not encoded genetically and should be unambiguously distinguished from, e.g., point mutations in proteins that result in Met conversion to Thr.

METHODS

In this work we compared tandem mass (MS/MS) spectra produced by an Orbitrap mass spectrometer of Thr- and isoThr-containing tryptic peptides and found a distinctive feature in their collisionally activated fragmentation patterns.

RESULTS

Up to 84% of MS/MS spectra for peptides containing isoThr residues have been positively specified. We also studied the differences in retention times for peptides containing Thr isoforms that can be further used for their distinction.

CONCLUSIONS

Threonine can be distinguished from isothreonine by its retention time and HCD fragmentation pattern, specifically relative intensity of the bn - product ion, which can be further used in proteomic research. Copyright © 2016 John Wiley & Sons, Ltd.

Proteogenomics: Integrating Next-Generation Sequencing and Mass Spectrometry to Characterize Human Proteomic Variation

Mass spectrometry-based proteomics has emerged as the leading method for detection, quantification, and characterization of proteins. Nearly all proteomic workflows rely on proteomic databases to identify peptides and proteins, but these databases typically contain a generic set of proteins that lack variations unique to a given sample, precluding their detection. Fortunately, proteogenomics enables the detection of such proteomic variations and can be defined, broadly, as the use of nucleotide sequences to generate candidate protein sequences for mass spectrometry database searching. Proteogenomics is experiencing heightened significance due to two developments: (a) advances in DNA sequencing technologies that have made complete sequencing of human genomes and transcriptomes routine, and (b) the unveiling of the tremendous complexity of the human proteome as expressed at the levels of genes, cells, tissues, individuals, and populations. We review here the field of human proteogenomics, with an emphasis on its history, current implementations, the types of proteomic variations it reveals, and several important applications.

Noninvasive strategies for breast cancer early detection

Breast cancer screening and presurgical diagnosis are currently based on mammography, ultrasound and more sensitive imaging technologies; however, noninvasive biomarkers represent both a challenge and an opportunity for early detection of cancer. An extensive number of potential breast cancer biomarkers have been discovered by microarray hybridization or sequencing of circulating DNA, noncoding RNA and blood cell RNA; multiplex analysis of immune-related molecules and mass spectrometry-based approaches for high-throughput detection of protein, endogenous peptides, circulating and volatile metabolites. However, their medical relevance and their translation to clinics remain to be exploited. Once they will be fully validated, cancer biomarkers, used in combination with the current and emerging imaging technologies, represent an avenue to a personalized breast cancer diagnosis.

Three-layered proteomic characterization of a novel ACTN4 mutation unravels its pathogenic potential in FSGS

Genetic diseases constitute the most important cause for end-stage renal disease in children and adolescents. Mutations in the ACTN4 gene, encoding the actin-binding protein α-actinin-4, are a rare cause of autosomal dominant familial focal segmental glomerulosclerosis (FSGS). Here, we report the identification of a novel, disease-causing ACTN4 mutation (p.G195D, de novo) in a sporadic case of childhood FSGS using next generation sequencing. Proteome analysis by quantitative mass spectrometry (MS) of patient-derived urinary epithelial cells indicated that ACTN4 levels were significantly decreased when compared with healthy controls. By resolving the peptide bearing the mutated residue, we could proof that the mutant protein is less abundant when compared with the wild-type protein. Further analyses revealed that the decreased stability of p.G195D is associated with increased ubiquitylation in the vicinity of the mutation site. We next defined the ACTN4 interactome, which was predominantly composed of cytoskeletal modulators and LIM domain-containing proteins. Interestingly, this entire group of proteins, including several highly specific ACTN4 interactors, was globally decreased in the patient-derived cells. Taken together, these data suggest a mechanistic link between ACTN4 instability and proteome perturbations of the ACTN4 interactome. Our findings advance the understanding of dominant effects exerted by ACTN4 mutations in FSGS. This study illustrates the potential of genomics and complementary, high-resolution proteomics analyses to study the pathogenicity of rare gene variants.

Global proteogenomic analysis of human MHC class I-associated peptides derived from non-canonical reading frames

In view of recent reports documenting pervasive translation outside of canonical protein-coding sequences, we wished to determine the proportion of major histocompatibility complex (MHC) class I-associated peptides (MAPs) derived from non-canonical reading frames. Here we perform proteogenomic analyses of MAPs eluted from human B cells using high-throughput mass spectrometry to probe the six-frame translation of the B-cell transcriptome. We report that ∼10% of MAPs originate from allegedly noncoding genomic sequences or exonic out-of-frame translation. The biogenesis and properties of these ‘cryptic MAPs' differ from those of conventional MAPs. Cryptic MAPs come from very short proteins with atypical C termini, and are coded by transcripts bearing long 3′UTRs enriched in destabilizing elements. Relative to conventional MAPs, cryptic MAPs display different MHC class I-binding preferences and harbour more genomic polymorphisms, some of which are immunogenic. Cryptic MAPs increase the complexity of the MAP repertoire and enhance the scope of CD8 T-cell immunosurveillance.

Cryptic translation of the 'non-coding' genome is increasingly recognised, however its biological significance remains unclear. Laumont et al. employ proteogenomic techniques to map the human immunoproteome, and find that approximately 10% of MHC class I-associated peptides are cryptic.

Proteogenomic Analysis of Single Amino Acid Polymorphisms in Cancer Research

The integration of genomics and proteomics has led to the emergence of proteogenomics, a field of research successfully applied to the characterization of cancer samples. The diagnosis, prognosis and response to therapy of cancer patients will largely benefit from the identification of mutations present in their genome. The current state of the art of high throughput experiments for genome-wide detection of somatic mutations in cancer samples has allowed the development of projects such as the TCGA, in which hundreds of cancer genomes have been sequenced. This huge amount of data can be used to generate protein sequence databases in which each entry corresponds to a mutated peptide associated with certain cancer types. In this chapter, we describe a bioinformatics workflow for creating these databases and detecting mutated peptides in cancer samples from proteomic shotgun experiments. The performance of the proposed method has been evaluated using publicly available datasets from four cancer cell lines.

proBAMsuite, a Bioinformatics Framework for Genome-Based Representation and Analysis of Proteomics Data

To facilitate genome-based representation and analysis of proteomics data, we developed a new bioinformatics framework, proBAMsuite, in which a central component is the protein BAM (proBAM) file format for organizing peptide spectrum matches (PSMs)¹ within the context of the genome. proBAMsuite also includes two R packages, proBAMr and proBAMtools, for generating and analyzing proBAM files, respectively. Applying proBAMsuite to three recently published proteomics datasets, we demonstrated its utility in facilitating efficient genome-based sharing, interpretation, and integration of proteomics data. First, the interpretation of proteomics data is significantly enhanced with the rich genomic annotation information. Second, PSMs can be easily reannotated using user-specified gene annotation schemes and assembled into both protein and gene identifications. Third, using the genome as a common reference, proBAMsuite facilitates seamless proteomics and proteogenomics data integration. Finally, proBAM files can be readily visualized in genome browsers and thus bring proteomics data analysis to a general audience beyond the proteomics community. Results from this study establish proBAMsuite as a useful bioinformatics framework for proteomics and proteogenomics research.

Appraisal of the Missing Proteins Based on the mRNAs Bound to Ribosomes

Considering the technical limitations of mass spectrometry in protein identification, the mRNAs bound to ribosomes (RNC-mRNA) are assumed to reflect the mRNAs participating in the translational process. The RNC-mRNA data are reasoned to be useful for appraising the missing proteins. A set of the multiomics data including free-mRNAs, RNC-mRNAs, and proteomes was acquired from three liver cancer cell lines. On the basis of the missing proteins in neXtProt (release 2014-09-19), the bioinformatics analysis was carried out in three phases: (1) finding how many neXtProt missing proteins have or do not have RNA-seq and/or MS/MS evidence, (2) analyzing specific physicochemical and biological properties of the missing proteins that lack both RNA-seq and MS/MS evidence, and (3) analyzing the combined properties of these missing proteins. Total of 1501 missing proteins were found by neither RNC-mRNA nor MS/MS in the three liver cancer cell lines. For these missing proteins, some are expected higher hydrophobicity, unsuitable detection, or sensory functions as properties at the protein level, while some are predicted to have nonexpressing chromatin structures on the corresponding gene level. With further integrated analysis, we could attribute 93% of them (1391/1501) to these causal factors, which result in the expression products scarcely detected by RNA-seq or MS/MS.

Proteogenomic Study beyond Chromosome 9: New Insight into Expressed Variant Proteome and Transcriptome in Human Lung Adenocarcinoma Tissues

This is a report of a human proteome project (HPP) related to chromosome 9 (Chr 9). To reveal missing proteins and undiscovered features in proteogenomes, both LC-MS/MS analysis and next-generation RNA sequencing (RNA-seq)-based identification and characterization were conducted on five pairs of lung adenocarcinoma tumors and adjacent nontumor tissues. Before our previous Chromosome-Centric Human Proteome Project (C-HPP) special issue, there were 170 remaining missing proteins on Chr 9 (neXtProt 2013.09.26 rel.); 133 remain at present (neXtProt 2015.04.28 rel.). In the proteomics study, we found two missing protein candidates that require follow-up work and one unrevealed protein across all chromosomes. RNA-seq analysis detected RNA expression for four nonsynonymous (NS) single nucleotide polymorphisms (SNPs) (in CDH17, HIST1H1T, SAPCD2, and ZNF695) and three synonymous SNPs (in CDH17, CST1, and HNF1A) in all five tumor tissues but not in any of the adjacent normal tissues. By constructing a cancer patient sample-specific protein database based on individual RNA-seq data and by searching the proteomics data from the same sample, we identified four missense mutations in four genes (LTF, HDLBP, TF, and HBD). Two of these mutations were found in tumor samples but not in paired normal tissues. In summary, our proteogenomic study of human primary lung tumor tissues detected additional and revealed novel missense mutations and synonymous SNP signatures, some of which are specific to lung cancers. Data from mass spectrometry have been deposited in the ProteomeXchange with the identifier PXD002523.

Reconciling proteomics with next generation sequencing

Both genomics and proteomics technologies have matured in the last decade to a level where they are able to deliver system-wide data on the qualitative and quantitative abundance of their respective molecular entities, that is DNA/RNA and proteins. A next logical step is the collective use of these technologies, ideally gathering data on matching samples. The first large scale so-called proteogenomics studies are emerging, and display the benefits each of these layers of analysis has on the other layers to together generate more meaningful insight into the connection between the phenotype/physiology and genotype of the system under study. Here we review a selected number of these studies, highlighting what they can uniquely deliver. We also discuss the future potential and remaining challenges, from a somewhat proteome biased perspective.

A Pilot Proteogenomic Study with Data Integration Identifies MCT1 and GLUT1 as Prognostic Markers in Lung Adenocarcinoma

We performed a pilot proteogenomic study to compare lung adenocarcinoma to lung squamous cell carcinoma using quantitative proteomics (6-plex TMT) combined with a customized Affymetrix GeneChip. Using MaxQuant software, we identified 51,001 unique peptides that mapped to 7,241 unique proteins and from these identified 6,373 genes with matching protein expression for further analysis. We found a minor correlation between gene expression and protein expression; both datasets were able to independently recapitulate known differences between the adenocarcinoma and squamous cell carcinoma subtypes. We found 565 proteins and 629 genes to be differentially expressed between adenocarcinoma and squamous cell carcinoma, with 113 of these consistently differentially expressed at both the gene and protein levels. We then compared our results to published adenocarcinoma versus squamous cell carcinoma proteomic data that we also processed with MaxQuant. We selected two proteins consistently overexpressed in squamous cell carcinoma in all studies, MCT1 (SLC16A1) and GLUT1 (SLC2A1), for further investigation. We found differential expression of these same proteins at the gene level in our study as well as in other public gene expression datasets. These findings combined with survival analysis of public datasets suggest that MCT1 and GLUT1 may be potential prognostic markers in adenocarcinoma and druggable targets in squamous cell carcinoma. Data are available via ProteomeXchange with identifier PXD002622.

Matching Kidneys and Urines: Establishing Noninvasive Surrogates of Intrarenal Events in Primary Glomerulonephritis

Kidney biopsy is the gold standard procedure for providing diagnostic and prognostic information for patients with glomerular-based diseases, however, the utility of this procedure for assessing longitudinal disease activity is limited. The intense search for noninvasive biomarkers of kidney disease activity and injury is driven in large part by the inherent risks of the kidney biopsy procedure and limited information derived from the morphologic description of biopsy findings. Furthermore, gaps in our understanding of the core intrarenal molecular processes underlying the development and progression of glomerular-based diseases has limited the development of effective targeted therapy. In this review, we discuss the potential utility of molecular analysis of the urine to provide a dynamic window into intrarenal molecular and morphologic responses. We focus on molecular analysis of the urine to identify noninvasive surrogate markers of kidney responses, with the goal of using these biomarkers as more sensitive indicators of progression and tissue-level responses to therapeutic interventions in patients with primary glomerulonephritis.

The role of clinical genomic testing in diagnosis and discovery of pathogenic mutations

Next-generation sequencing in clinical practice allows for a critical review of the literature to evaluate disease relatedness of specific genes and pathogenicity of individual mutations, while providing an important discovery tool for new disease genes and disease-causing mutations. Data obtained from large panels, whole exome or whole genome sequencing, performed for constitutional or cancer cases, need to be managed in a transparent, yet powerful analytical framework. Assessment of reported pathogenic potential of a variant or disease association of a gene requires careful consideration of population allele frequency, variant data from parents, and precise, yet concise phenotypic description of the entire family and other individuals or families that have the same variant. The full potential for discovery can only be realized if there is data sharing between clinicians performing the interpretation worldwide and structural biologists, analytical chemists and cell biologists interested and knowledgeable of the structure and function of the genes involved.

Onco-proteogenomics identifies urinary S100A9 and GRN as potential combinatorial biomarkers for early diagnosis of hepatocellular carcinoma

Hepatocellular carcinoma (HCC), the major type of liver cancer, is among the most lethal cancers owing to its aggressive nature and frequently late detection. Therefore, the possibility to identify early diagnostic markers could be of significant benefit. Urine has especially become one of the most attractive body fluids in biomarker discovery as it can be obtained non-invasively in large quantities and is stable as compared with other body fluids. To identify potential protein biomarker for early diagnosis of HCC, we explored protein expression profiles in urine from HCC patients and normal controls (n = 44) by shotgun proteomics using nano-liquid chromatography coupled tandem mass spectrometry (nanoLC–MS/MS) and stable isotope dimethyl labeling. We have systematically mapped 91 proteins with differential expressions (p < 0.05), which included 8 down-regulated microtubule proteins and 83 up-regulated proteins involved in signal and inflammation response. Further integrated proteogenomic approach composed of proteomic, genomic and transcriptomic analysis identified that S100A9 and GRN were co-amplified (p < 0.001) and co-expressed (p < 0.01) in HCC tumors and urine samples. In addition, the amplifications of S100A9 or GRN were found to be associated with poor survival in HCC patients, and their co-amplification was also prognosed worse overall survival than individual ones. Our results suggest that urinary S100A9 and GRN as potential combinatorial biomarkers can be applied to early diagnosis of hepatocellular carcinoma and highlight the utility of onco-proteogenomics for identifying protein markers that can be applied to disease-oriented translational medicine.

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An integrated proteogenomic analysis is applied to identify biomarkers for HCC.

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Genomic amplifications of S100A9 and GRN co-occur in tumors from HCC patients.

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S100A9 and GRN are co-expressed in tumor and urine samples from HCC patients.

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Amplifications of S100A9 and GRN are associated with poor survival of HCC patients.

Proteogenomics: emergence and promise

Proteogenomics, or the integration of proteomics with genomics and transcriptomics, is emerging as the next step towards a unified understanding of cellular functions. Looking globally and simultaneously at gene structure, RNA expression, protein synthesis and post-translational modifications have become technically feasible and offer a new perspective to molecular processes. Recent publications have highlighted the value of proteogenomics in oncology for defining the molecular signature of human tumors, and translation to other areas of biomedicine and life sciences is anticipated. This mini-review will discuss recent developments, challenges and perspectives in proteogenomics.

Proteogenomics: concepts, applications and computational strategies

Proteogenomics is an area of research at the interface of proteomics and genomics. In this approach, customized protein sequence databases generated using genomic and transcriptomic information are used to help identify novel peptides (not present in reference protein sequence databases) from mass spectrometry-based proteomic data; in turn, the proteomic data can be used to provide protein-level evidence of gene expression and to help refine gene models. In recent years, owing to the emergence of new sequencing technologies such as RNA-seq and dramatic improvements in the depth and throughput of mass spectrometry-based proteomics, the pace of proteogenomic research has greatly accelerated. Here I review the current state of proteogenomic methods and applications, including computational strategies for building and using customized protein sequence databases. I also draw attention to the challenge of false positive identifications in proteogenomics and provide guidelines for analyzing the data and reporting the results of proteogenomic studies.

Integrated omic analysis of oropharyngeal carcinomas reveals human papillomavirus (HPV)-dependent regulation of the activator protein 1 (AP-1) pathway

HPV-positive oropharyngeal carcinoma (OPC) patients have superior outcomes relative to HPV-negative patients, but the underlying mechanisms remain poorly understood. We conducted a proteomic investigation of HPV-positive (n = 27) and HPV-negative (n = 26) formalin-fixed paraffin-embedded OPC biopsies to acquire insights into the biological pathways that correlate with clinical behavior. Among the 2,633 proteins identified, 174 were differentially abundant. These were enriched for proteins related to cell cycle, DNA replication, apoptosis, and immune response. The differential abundances of cortactin and methylthioadenosine phosphorylase were validated by immunohistochemistry in an independent cohort of 29 OPC samples (p = 0.023 and p = 0.009, respectively). An additional 1,124 proteins were independently corroborated through comparison to a published proteomic dataset of OPC. Furthermore, utilizing the Cancer Genome Atlas, we conducted an integrated investigation of OPC, attributing mechanisms underlying differential protein abundances to alterations in mutation, copy number, methylation, and mRNA profiles. A key finding of this integration was the identification of elevated cortactin oncoprotein levels in HPV-negative OPCs. These proteins might contribute to reduced survival in these patients via their established role in radiation resistance. Through interrogation of Cancer Genome Atlas data, we demonstrated that activation of the β1-integrin/FAK/cortactin/JNK1 signaling axis and associated differential regulation of activator protein 1 transcription factor target genes are plausible consequences of elevated cortactin protein levels.