Translation of non-canonical open reading frames as a cancer cell survival mechanism in childhood medulloblastoma.

A hallmark of high-risk childhood medulloblastoma is the dysregulation of RNA translation. Currently, it is unknown whether medulloblastoma dysregulates the translation of putatively oncogenic non-canonical open reading frames (ORFs). To address this question, we performed ribosome profiling of 32 medulloblastoma tissues and cell lines and observed widespread non-canonical ORF translation. We then developed a stepwise approach using multiple CRISPR-Cas9 screens to elucidate non-canonical ORFs and putative microproteins implicated in medulloblastoma cell survival. We determined that multiple lncRNA-ORFs and upstream ORFs (uORFs) exhibited selective functionality independent of main coding sequences. A microprotein encoded by one of these ORFs, ASNSD1-uORF or ASDURF, was upregulated, associated with MYC-family oncogenes, and promoted medulloblastoma cell survival through engagement with the prefoldin-like chaperone complex. Our findings underscore the fundamental importance of non-canonical ORF translation in medulloblastoma and provide a rationale to include these ORFs in future studies seeking to define new cancer targets.

The HLA-II immunopeptidome of SARS-CoV-2.

Targeted synthetic vaccines have the potential to transform our response to viral outbreaks, yet the design of these vaccines requires a comprehensive knowledge of viral immunogens. Here, we report severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) peptides that are naturally processed and loaded onto human leukocyte antigen-II (HLA-II) complexes in infected cells. We identify over 500 unique viral peptides from canonical proteins as well as from overlapping internal open reading frames. Most HLA-II peptides colocalize with known CD4+ T cell epitopes in coronavirus disease 2019 patients, including 2 reported immunodominant regions in the SARS-CoV-2 membrane protein. Overall, our analyses show that HLA-I and HLA-II pathways target distinct viral proteins, with the structural proteins accounting for most of the HLA-II peptidome and nonstructural and noncanonical proteins accounting for the majority of the HLA-I peptidome. These findings highlight the need for a vaccine design that incorporates multiple viral elements harboring CD4+ and CD8+ T cell epitopes to maximize vaccine effectiveness.

Pan-viral ORFs discovery using Massively Parallel Ribosome Profiling.

Unveiling the complete proteome of viruses is crucial to our understanding of the viral life cycle and interaction with the host. We developed Massively Parallel Ribosome Profiling (MPRP) to experimentally determine open reading frames (ORFs) in 20,170 designed oligonucleotides across 679 human-associated viral genomes. We identified 5,381 ORFs, including 4,208 non-canonical ORFs, and show successful detection of both annotated coding sequences (CDSs) and reported non-canonical ORFs. By examining immunopeptidome datasets of infected cells, we found class I human leukocyte antigen (HLA-I) peptides originating from non-canonical ORFs identified through MPRP. By inspecting ribosome occupancies on the 5'UTR and CDS regions of annotated viral genes, we identified hundreds of upstream ORFs (uORFs) that negatively regulate the synthesis of canonical viral proteins. The unprecedented source of viral ORFs across a wide range of viral families, including highly pathogenic viruses, expands the repertoire of vaccine targets and exposes new cis-regulatory sequences in viral genomes.

Pan-cancer proteogenomics connects oncogenic drivers to functional states.

Cancer driver events refer to key genetic aberrations that drive oncogenesis; however, their exact molecular mechanisms remain insufficiently understood. Here, our multi-omics pan-cancer analysis uncovers insights into the impacts of cancer drivers by identifying their significant cis-effects and distal trans-effects quantified at the RNA, protein, and phosphoprotein levels. Salient observations include the association of point mutations and copy-number alterations with the rewiring of protein interaction networks, and notably, most cancer genes converge toward similar molecular states denoted by sequence-based kinase activity profiles. A correlation between predicted neoantigen burden and measured T cell infiltration suggests potential vulnerabilities for immunotherapies. Patterns of cancer hallmarks vary by polygenic protein abundance ranging from uniform to heterogeneous. Overall, our work demonstrates the value of comprehensive proteogenomics in understanding the functional states of oncogenic drivers and their links to cancer development, surpassing the limitations of studying individual cancer types.

Pan-cancer analysis of post-translational modifications reveals shared patterns of protein regulation.

Post-translational modifications (PTMs) play key roles in regulating cell signaling and physiology in both normal and cancer cells. Advances in mass spectrometry enable high-throughput, accurate, and sensitive measurement of PTM levels to better understand their role, prevalence, and crosstalk. Here, we analyze the largest collection of proteogenomics data from 1,110 patients with PTM profiles across 11 cancer types (10 from the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium [CPTAC]). Our study reveals pan-cancer patterns of changes in protein acetylation and phosphorylation involved in hallmark cancer processes. These patterns revealed subsets of tumors, from different cancer types, including those with dysregulated DNA repair driven by phosphorylation, altered metabolic regulation associated with immune response driven by acetylation, affected kinase specificity by crosstalk between acetylation and phosphorylation, and modified histone regulation. Overall, this resource highlights the rich biology governed by PTMs and exposes potential new therapeutic avenues.

What Can Ribo-Seq, Immunopeptidomics, and Proteomics Tell Us About the Noncanonical Proteome?

Ribosome profiling (Ribo-Seq) has proven transformative for our understanding of the human genome and proteome by illuminating thousands of noncanonical sites of ribosome translation outside the currently annotated coding sequences (CDSs). A conservative estimate suggests that at least 7000 noncanonical ORFs are translated, which, at first glance, has the potential to expand the number of human protein CDSs by 30%, from ∼19,500 annotated CDSs to over 26,000 annotated CDSs. Yet, additional scrutiny of these ORFs has raised numerous questions about what fraction of them truly produce a protein product and what fraction of those can be understood as proteins according to conventional understanding of the term. Adding further complication is the fact that published estimates of noncanonical ORFs vary widely by around 30-fold, from several thousand to several hundred thousand. The summation of this research has left the genomics and proteomics communities both excited by the prospect of new coding regions in the human genome but searching for guidance on how to proceed. Here, we discuss the current state of noncanonical ORF research, databases, and interpretation, focusing on how to assess whether a given ORF can be said to be "protein coding."

The HLA-II immunopeptidome of SARS-CoV-2.

Targeted synthetic vaccines have the potential to transform our response to viral outbreaks; yet the design of these vaccines requires a comprehensive knowledge of viral immunogens, including T-cell epitopes. Having previously mapped the SARS-CoV-2 HLA-I landscape, here we report viral peptides that are naturally processed and loaded onto HLA-II complexes in infected cells. We identified over 500 unique viral peptides from canonical proteins, as well as from overlapping internal open reading frames (ORFs), revealing, for the first time, the contribution of internal ORFs to the HLA-II peptide repertoire. Most HLA-II peptides co-localized with the known CD4+ T cell epitopes in COVID-19 patients. We also observed that two reported immunodominant regions in the SARS-CoV-2 membrane protein are formed at the level of HLA-II presentation. Overall, our analyses show that HLA-I and HLA-II pathways target distinct viral proteins, with the structural proteins accounting for most of the HLA-II peptidome and non-structural and non-canonical proteins accounting for the majority of the HLA-I peptidome. These findings highlight the need for a vaccine design that incorporates multiple viral elements harboring CD4+ and CD8+ T cell epitopes to maximize the vaccine effectiveness.

What can Ribo-seq and proteomics tell us about the non-canonical proteome?

Ribosome profiling (Ribo-seq) has proven transformative for our understanding of the human genome and proteome by illuminating thousands of non-canonical sites of ribosome translation outside of the currently annotated coding sequences (CDSs). A conservative estimate suggests that at least 7,000 non-canonical open reading frames (ORFs) are translated, which, at first glance, has the potential to expand the number of human protein-coding sequences by 30%, from ∼19,500 annotated CDSs to over 26,000. Yet, additional scrutiny of these ORFs has raised numerous questions about what fraction of them truly produce a protein product and what fraction of those can be understood as proteins according to conventional understanding of the term. Adding further complication is the fact that published estimates of non-canonical ORFs vary widely by around 30-fold, from several thousand to several hundred thousand. The summation of this research has left the genomics and proteomics communities both excited by the prospect of new coding regions in the human genome, but searching for guidance on how to proceed. Here, we discuss the current state of non-canonical ORF research, databases, and interpretation, focusing on how to assess whether a given ORF can be said to be "protein-coding". In brief: The human genome encodes thousands of non-canonical open reading frames (ORFs) in addition to protein-coding genes. As a nascent field, many questions remain regarding non-canonical ORFs. How many exist? Do they encode proteins? What level of evidence is needed for their verification? Central to these debates has been the advent of ribosome profiling (Ribo-seq) as a method to discern genome-wide ribosome occupancy, and immunopeptidomics as a method to detect peptides that are processed and presented by MHC molecules and not observed in traditional proteomics experiments. This article provides a synthesis of the current state of non-canonical ORF research and proposes standards for their future investigation and reporting. Highlights: Combined use of Ribo-seq and proteomics-based methods enables optimal confidence in detecting non-canonical ORFs and their protein products.Ribo-seq can provide more sensitive detection of non-canonical ORFs, but data quality and analytical pipelines will impact results.Non-canonical ORF catalogs are diverse and span both high-stringency and low-stringency ORF nominations.A framework for standardized non-canonical ORF evidence will advance the research field.

HLA-II immunopeptidome profiling and deep learning reveal features of antigenicity to inform antigen discovery.

CD4+ T cell responses are exquisitely antigen specific and directed toward peptide epitopes displayed by human leukocyte antigen class II (HLA-II) on antigen-presenting cells. Underrepresentation of diverse alleles in ligand databases and an incomplete understanding of factors affecting antigen presentation in vivo have limited progress in defining principles of peptide immunogenicity. Here, we employed monoallelic immunopeptidomics to identify 358,024 HLA-II binders, with a particular focus on HLA-DQ and HLA-DP. We uncovered peptide-binding patterns across a spectrum of binding affinities and enrichment of structural antigen features. These aspects underpinned the development of context-aware predictor of T cell antigens (CAPTAn), a deep learning model that predicts peptide antigens based on their affinity to HLA-II and full sequence of their source proteins. CAPTAn was instrumental in discovering prevalent T cell epitopes from bacteria in the human microbiome and a pan-variant epitope from SARS-CoV-2. Together CAPTAn and associated datasets present a resource for antigen discovery and the unraveling genetic associations of HLA alleles with immunopathologies.

Sensitive, High-Throughput HLA-I and HLA-II Immunopeptidomics Using Parallel Accumulation-Serial Fragmentation Mass Spectrometry.

Comprehensive and in-depth identification of the human leukocyte antigen class I (HLA-I) and class II (HLA-II) tumor immunopeptidome can inform the development of cancer immunotherapies. Mass spectrometry (MS) is a powerful technology for direct identification of HLA peptides from patient-derived tumor samples or cell lines. However, achieving sufficient coverage to detect rare and clinically relevant antigens requires highly sensitive MS-based acquisition methods and large amounts of sample. While immunopeptidome depth can be increased by off-line fractionation prior to MS, its use is impractical when analyzing limited amounts of primary tissue biopsies. To address this challenge, we developed and applied a high-throughput, sensitive, and single-shot MS-based immunopeptidomics workflow that leverages trapped ion mobility time-of-flight MS on the Bruker timsTOF single-cell proteomics system (SCP). We demonstrate greater than twofold improved coverage of HLA immunopeptidomes relative to prior methods with up to 15,000 distinct HLA-I and HLA-II peptides from 4e7 cells. Our optimized single-shot MS acquisition method on the timsTOF SCP maintains high coverage, eliminates the need for off-line fractionation, and reduces input requirements to as few as 1e6 A375 cells for >800 distinct HLA-I peptides. This depth is sufficient to identify HLA-I peptides derived from cancer-testis antigen and noncanonical proteins. We also apply our optimized single-shot SCP acquisition methods to tumor-derived samples, enabling sensitive, high-throughput, and reproducible immunopeptidome profiling with detection of clinically relevant peptides from less than 4e7 cells or 15 mg wet weight tissue.

Translation of non-canonical open reading frames as a cancer cell survival mechanism in childhood medulloblastoma.

A hallmark of high-risk childhood medulloblastoma is the dysregulation of RNA translation. Currently, it is unknown whether medulloblastoma dysregulates the translation of putatively oncogenic non-canonical open reading frames. To address this question, we performed ribosome profiling of 32 medulloblastoma tissues and cell lines and observed widespread non-canonical ORF translation. We then developed a step-wise approach to employ multiple CRISPR-Cas9 screens to elucidate functional non-canonical ORFs implicated in medulloblastoma cell survival. We determined that multiple lncRNA-ORFs and upstream open reading frames (uORFs) exhibited selective functionality independent of the main coding sequence. One of these, ASNSD1-uORF or ASDURF, was upregulated, associated with the MYC family oncogenes, and was required for medulloblastoma cell survival through engagement with the prefoldin-like chaperone complex. Our findings underscore the fundamental importance of non-canonical ORF translation in medulloblastoma and provide a rationale to include these ORFs in future cancer genomics studies seeking to define new cancer targets.

Proteomic Profiling of Extracellular Matrix Components from Patient Metastases Identifies Consistently Elevated Proteins for Developing Nanobodies That Target Primary Tumors and Metastases.

Metastases are hard to detect and treat, and they cause most cancer-related deaths. The relative lack of therapies targeting metastases represents a major unmet clinical need. The extracellular matrix (ECM) forms a major component of the tumor microenvironment in both primary and metastatic tumors, and certain ECM proteins can be selectively and abundantly expressed in tumors. Nanobodies against ECM proteins that show selective abundance in metastases have the potential to be used as vehicles for delivery of imaging and therapeutic cargoes. Here, we describe a strategy to develop phage-display libraries of nanobodies against ECM proteins expressed in human metastases, using entire ECM-enriched preparations from triple-negative breast cancer (TNBC) and colorectal cancer metastases to different organs as immunogens. In parallel, LC-MS/MS-based proteomics were used to define a metastasis-associated ECM signature shared by metastases from TNBC and colorectal cancer, and this conserved set of ECM proteins was selectively elevated in other tumors. As proof of concept, selective and high-affinity nanobodies were isolated against an example protein from this signature, tenascin-C (TNC), known to be abundant in many tumor types and to play a role in metastasis. TNC was abundantly expressed in patient metastases and widely expressed across diverse metastatic sites originating from several primary tumor types. Immuno-PET/CT showed that anti-TNC nanobodies bind TNBC tumors and metastases with excellent specificity. We propose that such generic nanobodies against tumors and metastases are promising cancer-agnostic tools for delivery of therapeutics to tumor and metastatic ECM. SIGNIFICANCE: Nanobodies specific for extracellular matrix markers commonly expressed in primary tumors and metastases are promising agents for noninvasive detection of tumors and metastases and potential tools for targeted therapy.

Workflow enabling deepscale immunopeptidome, proteome, ubiquitylome, phosphoproteome, and acetylome analyses of sample-limited tissues.

Serial multi-omic analysis of proteome, phosphoproteome, and acetylome provides insights into changes in protein expression, cell signaling, cross-talk and epigenetic pathways involved in disease pathology and treatment. However, ubiquitylome and HLA peptidome data collection used to understand protein degradation and antigen presentation have not together been serialized, and instead require separate samples for parallel processing using distinct protocols. Here we present MONTE, a highly sensitive multi-omic native tissue enrichment workflow, that enables serial, deep-scale analysis of HLA-I and HLA-II immunopeptidome, ubiquitylome, proteome, phosphoproteome, and acetylome from the same tissue sample. We demonstrate that the depth of coverage and quantitative precision of each 'ome is not compromised by serialization, and the addition of HLA immunopeptidomics enables the identification of peptides derived from cancer/testis antigens and patient specific neoantigens. We evaluate the technical feasibility of the MONTE workflow using a small cohort of patient lung adenocarcinoma tumors.

Sensitive, high-throughput HLA-I and HLA-II immunopeptidomics using parallel accumulation-serial fragmentation mass spectrometry.

Comprehensive, in-depth identification of the human leukocyte antigen HLA-I and HLA-II tumor immunopeptidome can inform the development of cancer immunotherapies. Mass spectrometry (MS) is powerful technology for direct identification of HLA peptides from patient derived tumor samples or cell lines. However, achieving sufficient coverage to detect rare, clinically relevant antigens requires highly sensitive MS-based acquisition methods and large amounts of sample. While immunopeptidome depth can be increased by off-line fractionation prior to MS, its use is impractical when analyzing limited amounts of primary tissue biopsies. To address this challenge, we developed and applied a high throughput, sensitive, single-shot MS-based immunopeptidomics workflow that leverages trapped ion mobility time-of-flight mass spectrometry on the Bruker timsTOF SCP. We demonstrate >2-fold improved coverage of HLA immunopeptidomes relative to prior methods with up to 15,000 distinct HLA-I and HLA-II peptides from 4e7 cells. Our optimized single-shot MS acquisition method on the timsTOF SCP maintains high coverage, eliminates the need for off-line fractionation and reduces input requirements to as few as 1e6 A375 cells for > 800 distinct HLA-I peptides. This depth is sufficient to identify HLA-I peptides derived from cancer-testis antigen, and novel/unannotated open reading frames. We also apply our optimized single-shot SCP acquisition methods to tumor derived samples, enabling sensitive, high throughput and reproducible immunopeptidome profiling with detection of clinically relevant peptides from less than 4e7 cells or 15 mg wet weight tissue.

Reversal of viral and epigenetic HLA class I repression in Merkel cell carcinoma.

Cancers avoid immune surveillance through an array of mechanisms, including perturbation of HLA class I antigen presentation. Merkel cell carcinoma (MCC) is an aggressive, HLA-I-low, neuroendocrine carcinoma of the skin often caused by the Merkel cell polyomavirus (MCPyV). Through the characterization of 11 newly generated MCC patient-derived cell lines, we identified transcriptional suppression of several class I antigen presentation genes. To systematically identify regulators of HLA-I loss in MCC, we performed parallel, genome-scale, gain- and loss-of-function screens in a patient-derived MCPyV-positive cell line and identified MYCL and the non-canonical Polycomb repressive complex 1.1 (PRC1.1) as HLA-I repressors. We observed physical interaction of MYCL with the MCPyV small T viral antigen, supporting a mechanism of virally mediated HLA-I suppression. We further identify the PRC1.1 component USP7 as a pharmacologic target to restore HLA-I expression in MCC.

Cancer proteogenomics: current impact and future prospects.

Genomic analyses in cancer have been enormously impactful, leading to the identification of driver mutations and development of targeted therapies. But the functions of the vast majority of somatic mutations and copy number variants in tumours remain unknown, and the causes of resistance to targeted therapies and methods to overcome them are poorly defined. Recent improvements in mass spectrometry-based proteomics now enable direct examination of the consequences of genomic aberrations, providing deep and quantitative characterization of tumour tissues. Integration of proteins and their post-translational modifications with genomic, epigenomic and transcriptomic data constitutes the new field of proteogenomics, and is already leading to new biological and diagnostic knowledge with the potential to improve our understanding of malignant transformation and therapeutic outcomes. In this Review we describe recent developments in proteogenomics and key findings from the proteogenomic analysis of a wide range of cancers. Considerations relevant to the selection and use of samples for proteogenomics and the current technologies used to generate, analyse and integrate proteomic with genomic data are described. Applications of proteogenomics in translational studies and immuno-oncology are rapidly emerging, and the prospect for their full integration into therapeutic trials and clinical care seems bright.

TAILS Identifies Candidate Substrates and Biomarkers of ADAMTS7, a Therapeutic Protease Target in Coronary Artery Disease.

Loss-of-function mutations in the secreted enzyme ADAMTS7 (a disintegrin and metalloproteinase with thrombospondin motifs 7) are associated with protection for coronary artery disease. ADAMTS7 catalytic inhibition has been proposed as a therapeutic strategy for treating coronary artery disease; however, the lack of an endogenous substrate has hindered the development of activity-based biomarkers. To identify ADAMTS7 extracellular substrates and their cleavage sites relevant to vascular disease, we used TAILS (terminal amine isotopic labeling of substrates), a method for identifying protease-generated neo-N termini. We compared the secreted proteome of vascular smooth muscle and endothelial cells expressing either full-length mouse ADAMTS7 WT, catalytic mutant ADAMTS7 E373Q, or a control luciferase adenovirus. Significantly enriched N-terminal cleavage sites in ADAMTS7 WT samples were compared to the negative control conditions and filtered for stringency, resulting in catalogs of high confidence candidate ADAMTS7 cleavage sites from our three independent TAILS experiments. Within the overlap of these discovery sets, we identified 24 unique cleavage sites from 16 protein substrates, including cleavage sites in EFEMP1 (EGF-containing fibulin-like extracellular matrix protein 1/Fibulin-3). The ADAMTS7 TAILS preference for EFEMP1 cleavage at the amino acids 123.124 over the adjacent 124.125 site was validated using both endogenous EFEMP1 and purified EFEMP1 in a binary in vitro cleavage assay. Collectively, our TAILS discovery experiments have uncovered hundreds of potential substrates and cleavage sites to explore disease-related biological substrates and facilitate activity-based ADAMTS7 biomarker development.

Navigating Critical Challenges Associated with Immunopeptidomics-Based Detection of Proteasomal Spliced Peptide Candidates.

Within the tumor immunology community, the topic of proteasomal spliced peptides (PSP) has generated a great deal of controversy. In the earliest reports, careful biological validation led to the conclusion that proteasome-catalyzed peptide splicing was a rare event. To date, six PSPs have been validated biologically. However, the advent of algorithms to identify candidate PSPs in mass spectrometry data challenged this notion, with several studies concluding that the frequency of spliced peptides binding to MHC class I was quite high. Since this time, much debate has centered around the methodologies used in these studies. Several reanalyses of data from these studies have led to questions about the validity of the conclusions. Furthermore, the biological and technical validation that should be necessary for verifying PSP assignments was often lacking. It has been suggested therefore that the research community should unite around a common set of standards for validating candidate PSPs. In this review, we propose and highlight the necessary steps for validation of proteasomal splicing at both the mass spectrometry and biological levels. We hope that these guidelines will serve as a foundation for critical assessment of results from proteasomal splicing studies.

Unannotated proteins expand the MHC-I-restricted immunopeptidome in cancer.

Tumor-associated epitopes presented on MHC-I that can activate the immune system against cancer cells are typically identified from annotated protein-coding regions of the genome, but whether peptides originating from novel or unannotated open reading frames (nuORFs) can contribute to antitumor immune responses remains unclear. Here we show that peptides originating from nuORFs detected by ribosome profiling of malignant and healthy samples can be displayed on MHC-I of cancer cells, acting as additional sources of cancer antigens. We constructed a high-confidence database of translated nuORFs across tissues (nuORFdb) and used it to detect 3,555 translated nuORFs from MHC-I immunopeptidome mass spectrometry analysis, including peptides that result from somatic mutations in nuORFs of cancer samples as well as tumor-specific nuORFs translated in melanoma, chronic lymphocytic leukemia and glioblastoma. NuORFs are an unexplored pool of MHC-I-presented, tumor-specific peptides with potential as immunotherapy targets.

Optimized Liquid and Gas Phase Fractionation Increases HLA-Peptidome Coverage for Primary Cell and Tissue Samples.

MS is the most effective method to directly identify peptides presented on human leukocyte antigen (HLA) molecules. However, current standard approaches often use 500 million or more cells as input to achieve high coverage of the immunopeptidome, and therefore, these methods are not compatible with the often limited amounts of tissue available from clinical tumor samples. Here, we evaluated microscaled basic reversed-phase fractionation to separate HLA peptide samples offline followed by ion mobility coupled to LC-MS/MS for analysis. The combination of these two separation methods enabled identification of 20% to 50% more peptides compared with samples analyzed without either prior fractionation or use of ion mobility alone. We demonstrate coverage of HLA immunopeptidomes with up to 8107 distinct peptides starting with as few as 100 million cells. The increased sensitivity obtained using our methods can provide data useful to improve HLA-binding prediction algorithms as well as to enable detection of clinically relevant epitopes such as neoantigens.