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Microscaled proteogenomics was deployed to probe the molecular basis for differential response to neoadjuvant carboplatin and docetaxel combination chemotherapy for triple-negative breast cancer (TNBC). Proteomic analyses of pretreatment patient biopsies uniquely revealed metabolic pathways, including oxidative phosphorylation, adipogenesis, and fatty acid metabolism, that were associated with resistance. Both proteomics and transcriptomics revealed that sensitivity was marked by elevation of DNA repair, E2F targets, G2–M checkpoint, interferon-gamma signaling, and immune-checkpoint components. Proteogenomic analyses of somatic copy-number aberrations identified a resistance-associated 19q13.31–33 deletion where LIG1, POLD1, and XRCC1 are located. In orthogonal datasets, LIG1 (DNA ligase I) gene deletion and/or low mRNA expression levels were associated with lack of pathologic complete response, higher chromosomal instability index (CIN), and poor prognosis in TNBC, as well as carboplatin-selective resistance in TNBC preclinical models. Hemizygous loss of LIG1 was also associated with higher CIN and poor prognosis in other cancer types, demonstrating broader clinical implications. Significance: Proteogenomic analysis of triple-negative breast tumors revealed a complex landscape of chemotherapy response associations, including a 19q13.31–33 somatic deletion encoding genes serving lagging-strand DNA synthesis (LIG1, POLD1, and XRCC1), that correlate with lack of pathologic response, carboplatin-selective resistance, and, in pan-cancer studies, poor prognosis and CIN. This article is highlighted in the In This Issue feature, p. 2483

Microscaled proteogenomics was deployed to probe the molecular basis for differential response to neoadjuvant carboplatin and docetaxel combination chemotherapy for triple-negative breast cancer (TNBC). Proteomic analyses of pretreatment patient biopsies uniquely revealed metabolic pathways, including oxidative phosphorylation, adipogenesis, and fatty acid metabolism, that were associated with resistance. Both proteomics and transcriptomics revealed that sensitivity was marked by elevation of DNA repair, E2F targets, G2–M checkpoint, interferon-gamma signaling, and immune-checkpoint components. Proteogenomic analyses of somatic copy-number aberrations identified a resistance-associated 19q13.31–33 deletion where LIG1, POLD1, and XRCC1 are located. In orthogonal datasets, LIG1 (DNA ligase I) gene deletion and/or low mRNA expression levels were associated with lack of pathologic complete response, higher chromosomal instability index (CIN), and poor prognosis in TNBC, as well as carboplatin-selective resistance in TNBC preclinical models. Hemizygous loss of LIG1 was also associated with higher CIN and poor prognosis in other cancer types, demonstrating broader clinical implications.Significance:Proteogenomic analysis of triple-negative breast tumors revealed a complex landscape of chemotherapy response associations, including a 19q13.31–33 somatic deletion encoding genes serving lagging-strand DNA synthesis (LIG1, POLD1, and XRCC1), that correlate with lack of pathologic response, carboplatin-selective resistance, and, in pan-cancer studies, poor prognosis and CIN.This article is highlighted in the In This Issue feature, p. 2483

Supplementary Figure from Proteogenomic Markers of Chemotherapy Resistance and Response in Triple-Negative Breast Cancer

Supplementary Table from Proteogenomic Markers of Chemotherapy Resistance and Response in Triple-Negative Breast Cancer

Pharmacodynamic changes (pathways)

Response markers

Extended Materials and Methods with in depth details about experimental procedures and protocols

Pharmacodynamic markers

mRNA isoforms

Supplementary tables 1 and 2; Clinical information about the patients that the PDX models were established from and pathways that are regulated in the sensitive tumors after buparlisib treatment, respectively.

Response markers (pathways)

Activation of PI3K signaling is frequently observed in triple-negative breast cancer (TNBC), yet PI3K inhibitors have shown limited clinical activity. To investigate intrinsic and adaptive mechanisms of resistance, we analyzed a panel of patient-derived xenograft models of TNBC with varying responsiveness to buparlisib, a pan-PI3K inhibitor. In a subset of patient-derived xenografts, resistance was associated with incomplete inhibition of PI3K signaling and upregulated MAPK/MEK signaling in response to buparlisib. Outlier phosphoproteome and kinome analyses identified novel candidates functionally important to buparlisib resistance, including NEK9 and MAP2K4. Knockdown of NEK9 or MAP2K4 reduced both baseline and feedback MAPK/MEK signaling and showed synthetic lethality with buparlisib in vitro. A complex in/del frameshift in PIK3CA decreased sensitivity to buparlisib via NEK9/MAP2K4–dependent mechanisms. In summary, our study supports a role for NEK9 and MAP2K4 in mediating buparlisib resistance and demonstrates the value of unbiased omic analyses in uncovering resistance mechanisms to targeted therapy.Significance: Integrative phosphoproteogenomic analysis is used to determine intrinsic resistance mechanisms of triple-negative breast tumors to PI3K inhibition. Cancer Res; 78(10); 2732–46. ©2018 AACR.

Supplementary Figures 1-10 supporting the study Mundt et al., "Proteomic profiling of PI3K inhibitor resistance"

STING is an intracellular sensor of cyclic di-nucleotides involved in response to pathogen- or self-derived DNA that induces protective immunity, or if dysregulated, autoimmunity. STING trafficking is tightly linked to its activity. We aimed to systematically characterize genes regulating STING trafficking and to define their impact on STING responses. Based on proximity-ligation proteomics and genetic screens, an ESCRT complex containing HGS, VPS37A and UBAP1 was found to be required for STING degradation and signaling shutdown. Analogous to phosphorylated STING creating a platform for IRF3 recruitment, oligomerization-driven STING ubiquitination by UBE2N formed a platform for ESCRT recruitment at the endosome, responsible for STING signaling shutdown. A UBAP1 mutant that underlies human spastic paraplegia and disrupts ESCRT function led to STING-dependent type I IFN responses at the steady-state, defining ESCRT as a homeostatic regulator of STING signaling.

a) Many cancer drivers are considered “undruggable” and without targeted treatments because they lack binding sites for conventional small molecules. Here, we introduce The FRONTIER™ Platform applying machine learning (ML), chemoproteomics and covalent chemistry to identify binding sites and cell-active covalent fragments across the human proteome, including against most cancer drivers and previously “undruggable” targets. Molecules tested in functional assays are active and can serve as starting points for new drug discovery initiatives. b) We are using mass spectrometry and data analysis workflows to perform high-throughput chemoproteomic profiling experiments. These experiments identify hits across the proteome using different cancer-relevant cell backgrounds and characterize binding sites for drug discovery. Customized ML algorithms using chemoproteomic, genomic and structural data to characterize and prioritize identified binding sites for covalent drug discovery. The performance of the platform allows the profiling of thousands of compounds from a custom-built library that has been optimized by ML for covalent fragment-based drug discovery. The nature of the fragment hits and the ability to map them to and focus on preferred binding sites for covalent drugs enables accelerated lead generation. c) We show details of the platform highlighting library design concepts and the hotspot map with binding site prioritization algorithms. We show coverage and applicability across important cancer target classes and signaling pathways. Covalent fragment hits have been identified for multiple difficult cancer targets, including KRAS, p53, STAT3, KEAP1, PTPN11 and others. The platform also identifies ligands for novel allosteric binding sites in established oncology targets such as CDK4, PI3KCA and BTK. We will highlight how discovered ligands show functional activity in orthogonal assays, demonstrating their fitness for drug discovery campaigns and target validation experiments. d) Undruggable targets across a variety of cancer target classes have become druggable. Citation Format: Johannes C. Hermann, Robert Everley, Laura Marholz, Matthew Berberich, Tzu-Yi Yang, Yu-Hsin Chao, Abduselam K. Awol, Michael Shaghafi, Han Yoon, Rohan Varma, Reed Stein, Karsten Krug, Emily Lachtara, Daniel Erlanson, Chris Varma, Kevin R. Webster. Combining chemoproteomics with machine learning identifies functionally active covalent fragments for hard-to-drug cancer drivers. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5333.

Multiple challenges remain in Data-Independent Acquisition (DIA) data analysis, like confidently identifying peptides, defining integration boundaries, removing interferences, and controlling false discovery rates. In practice, a visual inspection of the signals is still required, which is impractical with large datasets. We developed Avant-garde as a tool to refine DIA (and PRM) data. Avant-garde uses a novel data-driven scoring strategy; signals are refined by learning from the data itself, using all measurements in all samples to achieve the best optimization. We evaluated Avant-garde’s performance with benchmarking DIA datasets. We showed that it can determine the quantitative suitability of a peptide peak, and reaches the same levels of selectivity, accuracy, and reproducibility as manual validation. Avant-garde is complementary to existing DIA analysis engines and aims to establish a strong foundation for subsequent analysis of quantitative MS data., Editorial summary: A computational tool, Avant-garde, automates refinement of data-independent acquisition mass spectrometry-based quantitative proteomics data.

Cancer proteogenomics promises new insights into cancer biology and treatment efficacy by integrating genomics, transcriptomics and protein profiling including modifications by mass spectrometry (MS). A critical limitation is sample input requirements that exceed many sources of clinically important material. Here we report a proteogenomics approach for core biopsies using tissue-sparing specimen processing and microscaled proteomics. As a demonstration, we analyze core needle biopsies from ERBB2 positive breast cancers before and 48–72 h after initiating neoadjuvant trastuzumab-based chemotherapy. We show greater suppression of ERBB2 protein and both ERBB2 and mTOR target phosphosite levels in cases associated with pathological complete response, and identify potential causes of treatment resistance including the absence of ERBB2 amplification, insufficient ERBB2 activity for therapeutic sensitivity despite ERBB2 amplification, and candidate resistance mechanisms including androgen receptor signaling, mucin overexpression and an inactive immune microenvironment. The clinical utility and discovery potential of proteogenomics at biopsy-scale warrants further investigation., Connecting genomics and proteomics allows the development of more efficient and specific treatments for cancer. Here, the authors develop proteogenomic methods to defining cancer signaling in-vivo starting from core needle biopsies and with application to a HER2 breast cancer focused clinical trial.

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.

Clear cell renal cell carcinomas (ccRCCs) represent ∼75% of RCC cases and account for most RCC-associated deaths. Inter- and intratumoral heterogeneity (ITH) results in varying prognosis and treatment outcomes. To obtain the most comprehensive profile of ccRCC, we perform integrative histopathologic, proteogenomic, and metabolomic analyses on 305 ccRCC tumor segments and 166 paired adjacent normal tissues from 213 cases. Combining histologic and molecular profiles reveals ITH in 90% of ccRCCs, with 50% demonstrating immune signature heterogeneity. High tumor grade, along with BAP1 mutation, genome instability, increased hypermethylation, and a specific protein glycosylation signature define a high-risk disease subset, where UCHL1 expression displays prognostic value. Single-nuclei RNA sequencing of the adverse sarcomatoid and rhabdoid phenotypes uncover gene signatures and potential insights into tumor evolution. In vitro cell line studies confirm the potential of inhibiting identified phosphoproteome targets. This study molecularly stratifies aggressive histopathologic subtypes that may inform more effective treatment strategies.

Homeostatic synaptic plasticity requires widespread remodeling of synaptic signaling and scaffolding networks, but the role of post-translational modifications in this process has not been systematically studied. Using deep-scale quantitative analysis of the phosphoproteome in mouse neocortical neurons, we found widespread and temporally complex changes during synaptic scaling up and down. We observed 424 bidirectionally modulated phosphosites that were strongly enriched for synapse-associated proteins, including S1539 in the autism spectrum disorder-associated synaptic scaffold protein Shank3. Using a parallel proteomic analysis performed on Shank3 isolated from rat neocortical neurons by immunoaffinity, we identified two sites that were persistently hypophosphorylated during scaling up and transiently hyperphosphorylated during scaling down: one (rat S1615) that corresponded to S1539 in mouse, and a second highly conserved site, rat S1586. The phosphorylation status of these sites modified the synaptic localization of Shank3 during scaling protocols, and dephosphorylation of these sites via PP2A activity was essential for the maintenance of synaptic scaling up. Finally, phosphomimetic mutations at these sites prevented scaling up but not down, while phosphodeficient mutations prevented scaling down but not up. These mutations did not impact baseline synaptic strength, indicating that they gate, rather than drive, the induction of synaptic scaling. Thus, an activity-dependent switch between hypo- and hyperphosphorylation at S1586 and S1615 of Shank3 enables scaling up or down, respectively. Collectively, our data show that activity-dependent phosphoproteome dynamics are important for the functional reconfiguration of synaptic scaffolds and can bias synapses toward upward or downward homeostatic plasticity.

Summary Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with poor patient survival. Toward understanding the underlying molecular alterations that drive PDAC oncogenesis, we conducted comprehensive proteogenomic analysis of 140 pancreatic cancers, 67 normal adjacent tissues, and 9 normal pancreatic ductal tissues. Proteomic, phosphoproteomic, and glycoproteomic analyses were used to characterize proteins and their modifications. In addition, whole-genome sequencing, whole-exome sequencing, methylation, RNA sequencing (RNA-seq), and microRNA sequencing (miRNA-seq) were performed on the same tissues to facilitate an integrated proteogenomic analysis and determine the impact of genomic alterations on protein expression, signaling pathways, and post-translational modifications. To ensure robust downstream analyses, tumor neoplastic cellularity was assessed via multiple orthogonal strategies using molecular features and verified via pathological estimation of tumor cellularity based on histological review. This integrated proteogenomic characterization of PDAC will serve as a valuable resource for the community, paving the way for early detection and identification of novel therapeutic targets.

Reliable methods to quantify dynamic signaling changes across diverse pathways are needed to better understand the effects of disease and drug treatment in cells and tissues but are presently lacking. Here, we present SigPath, a targeted mass spectrometry (MS) assay that measures 284 phosphosites in 200 phosphoproteins of biological interest. SigPath probes a broad swath of signaling biology with high throughput and quantitative precision. We applied the assay to investigate changes in phospho‐signaling in drug‐treated cancer cell lines, breast cancer preclinical models, and human medulloblastoma tumors. In addition to validating previous findings, SigPath detected and quantified a large number of differentially regulated phosphosites newly associated with disease models and human tumors at baseline or with drug perturbation. Our results highlight the potential of SigPath to monitor phosphoproteomic signaling events and to nominate mechanistic hypotheses regarding oncogenesis, response, and resistance to therapy., SigPath is a targeted, quantitative mass spectrometry assay that measures 284 phosphosites spanning 200 phosphoproteins with high throughput and quantitative precision across a broad swath of signaling biology of known interest.

Introduction: There are no robust molecular predictors for response of TNBC to chemotherapy. Microscaled proteogenomics (PMC6985126) was therefore applied to biopsies from TNBC patients undergoing neoadjuvant carboplatin/docetaxel. Approach: Sufficient tumor-rich tumor core biopsies were accrued from 59 patients. A second biopsy was collected from 16 patients on day 3. Study endpoints were pathological complete response (pCR) and residual cancer burden (RCB). Analyses included exome-based mutational signatures, RNA-based TNBC subtyping, immune cell infiltrate scores, and multi-gene proliferation scores, protein and phosphoprotein-based stimulatory and inhibitory immunomodulators. Single-sample Gene Set Enrichment Analysis (ssGSEA) was used for gene set and pathway scoring. Non-parametric tests and outlier analyses were applied to identify differential genes, proteins and pathways. Results: Most cases were PAM50 basal-like and all Lehmann TNBC subtypes were represented. BRCA1/2 and PALB2 homologous recombination (HRD) mutations were observed in 7 cases. Neither subtype, HRD mutation nor HRD mutation signature associated with pCR, but higher mismatch repair defect signature associated with higher RCB. Comparison of baseline pCR vs non-pCR cases showed higher ssGSEA scores for metabolic pathways including oxidative phosphorylation, fatty-acid metabolism, and glycolysis in the non-pCR cases at the protein but not at mRNA level. Non-pCR cases were associated with chromosomal deletions in chemokine receptors, JAK2, and PD-L1, lower PD-L1 protein levels, and lower immune activation. Consistently, ssGSEA scores for interferon alpha and gamma response pathways were higher in pCR cases. Phospho-PD-L1 levels were anti-correlated with developmental pathways. Matched comparisons of baseline and on-treatment samples revealed increase in proteins involved in cell cycle, DNA replication, and mismatch repair following treatment. Metabolic proteins were also upregulated following treatment, while complement activation, immune, and cell adhesion-related proteins were downregulated. Conclusion: Microscaled proteogenomic analysis revealed a wealth of biological features associated with chemotherapy resistance beyond immune response markers, including metabolic features that are only present at the protein level. These data suggests that the development of a microscaled proteogenomic chemotherapy response predictor is a feasible objective for future studies. Citation Format: Meenakshi Anurag, Eric Jaehnig, Shankha Satpathy, Karsten krug, Jonathan T. Lei, Yongchao Dou, Beom-Jun Kim, Cathy M. Sullivan, D. R. Mani, Erik J. Bergstrom, Gloria V. Echeverria, Ian S. Hagemann, Kristen Otte, Henry Rodriguez, Ana I. Robles, Michael T. Lewis, Michael Gillette, Bing Zhang, Mothaffar F. Rimawi, Steven Carr, Foluso O. Ademuyiwa, Matthew J. Ellis. Molecular dissection of chemotherapy response in triple negative breast cancer (TNBC) using microscaled proteogenomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 18.

Proteogenomics involves the integrative analysis of genomic, transcriptomic, proteomic and post-translational modification data produced by next-generation sequencing and mass spectrometry-based proteomics. Several publications by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and others have highlighted the impact of proteogenomics in enabling deeper insight into the biology of cancer and identification of potential drug targets. In order to encapsulate the complex data processing required for proteogenomics, and provide a simple interface to deploy a range of algorithms developed for data analysis, we have developed PANOPLY—a cloud-based platform for automated and reproducible proteogenomic data analysis. A wide array of algorithms have been implemented, and we highlight the application of PANOPLY to the analysis of cancer proteogenomic data.

Introduction: Cytotoxic chemotherapy for sporadic Triple Negative Breast Cancer (TNBC) remains the standard of care and the recent approval for adjuvant PD1 therapy is not biomarker guided. Pathological complete response (pCR) is often not achieved and portends poor survival. Predictive markers for individual drugs have proven elusive. Approach: Microscaled proteogenomics (MPG) was applied to snap-frozen TNBC clinical trial core needle biopsies obtained before treatment with carboplatin and docetaxel (WashU: NCT201404107 and BCM: NCT02544987). Clinical endpoints for discovery analysis were pathological complete response (pCR) and residual cancer burden (RCB). Standard non-parametric statistical tests were employed to identify proteogenomic features associated with these endpoints. Results: Copy number aberrations (CNA) are a recurrent feature of TNBC and a potential driver of chemotherapy sensitivity. We therefore sought CNA with concordant changes at the mRNA and protein levels that also associate with pCR status. Genes located within a recurrent interstitial deletion at chromosomal location 19q13.3 were the most significantly down-regulated at mRNA and protein level in chemotherapy resistant cases. 19q13.3 encodes multiple DNA damage response (DDR) genes; however, only LIG1, a DNA ligase required for lagging strand synthesis and DNA repair, showed concordant changes at both the mRNA and protein level. In multiple independent TNBC data sets, LIG1 deletion was associated lack of pCR and poor metastasis-free survival. Additionally in the BrighTNess TNBC trial lower LIG1 mRNA levels were associated with increased chemotherapy resistance in the carboplatin containing arms (no pCR and residual cancer burden I-III; p=0.0008 and 0.003 respectively). In PDX-derived short-term cultures and PDXs treated with docetaxel or carboplatin, a specific association of carboplatin resistance with LIG1 deletion was observed. LIG1 depleted-tumors did not harbor elevated scores for homologous recombination defect signature, suggesting LIG1 loss is an orthogonal pathway for TNBC pathogenesis The high chromosomal instability index in LIG1 deletion tumors in our TNBC study was robustly reproduced in multiple datasets (including TCGA-BRCA ; Metastatic breast cancer project). LIG1 copy number deletion was also associated with poor progression free survival, and high chromosomal instability in multiple other cancers (including TCGA-​UCEC HR=2.23, TCGA-HNSC HR=1.46, TCGA-PRAD HR=2.07, TCGA- COAD HR=1.75 and TCGA-KIRP HR=4.00). Conclusion: Deletion of LIG1 is associated with chromosomal instability in TNBC and occurs in tumors without genomic evidence for defects in homologous recombination. Other clinical features of LIG1 deleted TNBC and how LIG1 loss may cause chromosomal instability and tumorigenesis will be discussed. Citation Format: Meenakshi Anurag, Eric Jaehnig, Jonathan Lei, Beom-Jun Kim, Anh Minh Tran Huynh, Yongchao Dou, Tanmayi Vashist, Erik Bergstrom, Xuxu Gou, Viktoriya Korchina, Donna Marie Muzny, Kristen Otte, Harshavardhan Doddapaneni, Lacey Dobrolecki, Gloria Vittone Echeverria, Bora Lim, Mothaffar Rimawi, Karsten Krug, Ian Hageman, Henry Rodriguez, Ana I. Robles, Tara Hiltke, Kent Osborne, Michael Gillette, George Miles, Steven Carr, Michael T Lewis, Bing Zhang, Foluso Ademuyiwa, Shankha Satpathy, Matthew J. Ellis. LIG1 deletion predicts chemotherapy resistance, chromosomal instability, and poor prognosis in triple negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1010.

In the 20 years since the completion of the Human Genome Project, cancer biology remains rooted in the assumption that the human genome encodes ~20,000 protein-coding genes. Yet, I and others have shown that thousands of “non-canonical” open reading frames (ncORFs) populate the human genome, potentially representing a dramatic expansion of the cancer proteome. Despite their abundance, little is known about the role of ncORFs as cancer driver genes. We developed functional genomics approaches to pursue this question across human cancers. To determine whether ncORFs represent biologically active proteins, we experimentally interrogated 553 candidates selected from ncORF datasets. Of these, 257 (46%) showed evidence of stable protein expression using multiple assays, and 401 (72%) induced gene expression changes when expressed in cancer cell lines. The bioactivity of ncORFs was dependent on their ability to translate a protein: mutation of the ORF start codon prevented induction of gene expression changes observed with the wild type ncORF in 48 of 51 (94%) cases. Using custom CRISPR/Cas9 knock-out screens targeting >2,000 ncORFs in 20 cancer cell lines, we found that genomic knock-out of approximately 10% of ncORFs induced viability defects in cancer cells. We focused on two candidates for functional studies. In breast cancer, we described G029442 - renamed glycine-rich extracellular protein-1 (GREP1) - as a secreted protein that is highly expressed and prognostic for poor patient outcomes. Knock-out of GREP1 in 263 cancer cell lines showed preferential essentiality in breast cancer-derived lines. The secretome of GREP1-expressing cells has an increased abundance of the oncogenic cytokine GDF15, and GDF15 supplementation mitigated the growth-inhibitory effect of GREP1 knockout. In medulloblastoma, we found that MYC-driven medulloblastoma cells are enriched for bioactive upstream ORFs (uORFs) that are encoded within the 5’ untranslated regions of mRNAs. We validated the ASNSD1 uORF as a top genetic vulnerability in multiple models of medulloblastoma, and its overexpression is sufficient to increase neural stem cell proliferation. Mechanistically, ASNSD1 uORF promotes a MYC-associated cellular program and interacts with the multiprotein prefoldin complex, which is required for tumors to maintain post-transcriptional regulation. Our work supports a generalizable principle that ncORFs commonly encode biologically-active proteins in diverse malignancies. Ongoing investigation of ncORFs therefore represents a new frontier in cancer research with the potential to define the next generation of therapeutic target genes. Citation Format: John R. Prensner, Ian Yannuzzi, Karl Clauser, Karsten Krug, Oana Enache, Adam Brown, Amy Goodale, David E. Root, Pratiti Bandopadhayay, Todd Golub. Non-canonical proteins are cancer cell vulnerabilities in diverse malignancies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3624.

Glioblastoma (GBM) is the most aggressive nervous system cancer. Understanding its molecular pathogenesis is crucial to improving diagnosis and treatment. Integrated analysis of genomic, proteomic, post-translational modification and metabolomic data on 99 treatment-naive GBMs provides insights to GBM biology. We identify key phosphorylation events (e.g., phosphorylated PTPN11 and PLCG1) as potential switches mediating oncogenic pathway activation, as well as potential targets for EGFR-, TP53-, and RB1-altered tumors. Immune subtypes with distinct immune cell types are discovered using bulk omics methodologies, validated by snRNA-seq, and correlated with specific expression and histone acetylation patterns. Histone H2B acetylation in classical-like and immune-low GBM is driven largely by BRDs, CREBBP, and EP300. Integrated metabolomic and proteomic data identify specific lipid distributions across subtypes and distinct global metabolic changes in IDH-mutated tumors. This work highlights biological relationships that could contribute to stratification of GBM patients for more effective treatment. Wang et al. perform integrated proteogenomic analysis of adult glioblastoma (GBM), including metabolomics, lipidomics, and single nuclei RNA-Seq, revealing insights into the immune landscape of GBM, cell-specific nature of EMT signatures, histone acetylation in classical GBM, and the existence of signaling hubs which could provide therapeutic vulnerabilities.

Reliable methods to quantify dynamic signaling changes across diverse pathways are needed to better understand the effects of disease and drug-treatment in cells and tissues but are presently lacking. Here we present SigPath, a targeted mass spectrometry (MS) assay that measures 284 phosphosites in 200 phosphoproteins of biological interest. SigPath probes a broad swath of signaling biology with high throughput and quantitative precision. We applied the assay to investigate changes in phospho-signaling in drug-treated cancer cell lines, breast cancer preclinical models and human medulloblastoma tumors. In addition to validating previous findings, SigPath detected and quantified a large number of differentially regulated phosphosites newly associated with disease models and human tumors at baseline or with drug perturbation. Our results highlight the potential of SigPath to monitor phosphoproteomic signaling events and to nominate mechanistic hypotheses regarding oncogenesis, response and resistance to therapy.

Lung squamous cell carcinoma (LSCC) remains a leading cause of cancer death with few therapeutic options. We characterized the proteogenomic landscape of LSCC, providing a deeper exposition of LSCC biology with potential therapeutic implications. We identify NSD3 as an alternative driver in FGFR1-amplified tumors and low-p63 tumors overexpressing the therapeutic target survivin. SOX2 is considered undruggable, but our analyses provide rationale for exploring chromatin modifiers such as LSD1 and EZH2 to target SOX2-overexpressing tumors. Our data support complex regulation of metabolic pathways by crosstalk between post-translational modifications including ubiquitylation. Numerous immune-related proteogenomic observations suggest directions for further investigation. Proteogenomic dissection of CDKN2A mutations argue for more nuanced assessment of RB1 protein expression and phosphorylation before declaring CDK4/6 inhibition unsuccessful. Finally, triangulation between LSCC, LUAD, and HNSCC identified both unique and common therapeutic vulnerabilities. These observations and proteogenomics data resources may guide research into the biology and treatment of LSCC.

A key question in genome research is whether biologically active proteins are restricted to the ∼20,000 canonical, well-annotated genes, or rather extend to the many non-canonical open reading frames (ORFs) predicted by genomic analyses. To address this, we experimentally interrogated 553 ORFs nominated in ribosome profiling datasets. Of these 553 ORFs, 57 (10%) induced a viability defect when the endogenous ORF was knocked out using CRISPR/Cas9 in 8 human cancer cell lines, 257 (46%) showed evidence of protein translation when ectopically expressed in HEK293T cells, and 401 (73%) induced gene expression changes measured by transcriptional profiling following ectopic expression across 4 cell types. CRISPR tiling and start codon mutagenesis indicated that the biological effects of these non-canonical ORFs required their translation as opposed to RNA-mediated effects. We selected one of these ORFs,G029442--renamedGREP1(Glycine-Rich Extracellular Protein-1)--for further characterization. We found thatGREP1encodes a secreted protein highly expressed in breast cancer, and its knock-out in 263 cancer cell lines showed preferential essentiality in breast cancer derived lines. Analysis of the secretome of GREP1-expressing cells showed increased abundance of the oncogenic cytokine GDF15, and GDF15 supplementation mitigated the growth inhibitory effect ofGREP1knock-out. Taken together, these experiments suggest that the non-canonical ORFeome is surprisingly rich in biologically active proteins and potential cancer therapeutic targets deserving of further study.

Exercise provides a robust physiological stimulus that evokes cross-talk among multiple tissues that when repeated regularly (i.e., training) improves physiological capacity, benefits numerous organ systems, and decreases the risk for premature mortality. However, a gap remains in identifying the detailed molecular signals induced by exercise that benefits health and prevents disease. The Molecular Transducers of Physical Activity Consortium (MoTrPAC) was established to address this gap and generate a molecular map of exercise. Preclinical and clinical studies will examine the systemic effects of endurance and resistance exercise across a range of ages and fitness levels by molecular probing of multiple tissues before and after acute and chronic exercise. From this multi-omic and bioinformatic analysis, a molecular map of exercise will be established. Altogether, MoTrPAC will provide a public database that is expected to enhance our understanding of the health benefits of exercise and to provide insight into how physical activity mitigates disease.

Truncating mutations in the terminal exon of protein phosphatase Mg2+/Mn2+ 1D (PPM1D) have been identified in clonal hematopoiesis and myeloid neoplasms, with a striking enrichment in patients previously exposed to chemotherapy. In this study, we demonstrate that truncating PPM1D mutations confer a chemoresistance phenotype, resulting in the selective expansion of PPM1D-mutant hematopoietic cells in the presence of chemotherapy in vitro and in vivo. Clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated protein-9 nuclease mutational profiling of PPM1D in the presence of chemotherapy selected for the same exon 6 mutations identified in patient samples. These exon 6 mutations encode for a truncated protein that displays elevated expression and activity due to loss of a C-terminal degradation domain. Global phosphoproteomic profiling revealed altered phosphorylation of target proteins in the presence of the mutation, highlighting multiple pathways including the DNA damage response (DDR). In the presence of chemotherapy, PPM1D-mutant cells have an abrogated DDR resulting in altered cell cycle progression, decreased apoptosis, and reduced mitochondrial priming. We demonstrate that treatment with an allosteric, small molecule inhibitor of PPM1D reverts the phosphoproteomic, DDR, apoptotic, and mitochondrial priming changes observed in PPM1D-mutant cells. Finally, we show that the inhibitor preferentially kills PPM1D-mutant cells, sensitizes the cells to chemotherapy, and reverses the chemoresistance phenotype. These results provide an explanation for the enrichment of truncating PPM1D mutations in the blood of patients exposed to chemotherapy and in therapy-related myeloid neoplasms, and demonstrate that PPM1D can be a targeted in the prevention of clonal expansion of PPM1D-mutant cells and the treatment of PPM1D-mutant disease.

Here we present an optimized workflow for global proteome and phosphoproteome analysis of tissues or cell lines that uses isobaric tags (TMT (tandem mass tags)-10) for multiplexed analysis and relative quantification, and provides 3× higher throughput than iTRAQ (isobaric tags for absolute and relative quantification)-4-based methods with high intra- and inter-laboratory reproducibility. The workflow was systematically characterized and benchmarked across three independent laboratories using two distinct breast cancer subtypes from patient-derived xenograft models to enable assessment of proteome and phosphoproteome depth and quantitative reproducibility. Each plex consisted of ten samples, each being 300 μg of peptide derived from 0.88. The maximum deviation for the phosphoproteome coverage was 37,000 quantified phosphosites per sample and differential quantification correlations of r > 0.72. The full procedure, including sample processing and data generation, can be completed within 10 d for ten tissue samples, and 100 samples can be analyzed in −4 months using a single LC-MS/MS instrument. The high quality, depth, and reproducibility of the data obtained both within and across laboratories should enable new biological insights to be obtained from mass spectrometry-based proteomics analyses of cells and tissues together with proteogenomic data integration.

Pancreatic cancer is one of the deadliest cancers and the five-year survival rate is less than 10%. Pancreatic ductal adenocarcinoma (PDAC) represents more than 90% of all pancreatic malignancies, and is responsible for the majority of pancreatic cancer-related deaths. Towards understanding the underlying molecular alterations that drive PDAC oncogenesis and identify therapeutic targets for personalized treatments, we comprehensively characterized 140 pancreatic cancers and 67 normal adjacent tissues. To ensure robust, downstream analyses, tumor neoplastic cellularity was assessed via multiple, orthogonal strategies using molecular features, and verified via pathological estimation of tumor cellularity based on histological review to select tumors with sufficient tumor cellularity. We also included the analysis of 9 normal pancreatic ductal tissues. Proteomic, phosphoproteomic, and glycoproteomic analyses were used to characterize proteins and their modifications. In addition, whole genome sequencing, whole exome sequencing, methylation, RNA-seq, and miRNA-seq were performed on the same tissues to facilitate an integrated proteogenomic analysis and determine the impact of genomic alterations on protein expression, signaling pathways, and post-translational modifications. These characterizations revealed functional impacts of genomic and epigenomic alterations on proteins and protein modifications, delineated PDAC cell microenvironment compositions and the immune signatures for immunotherapy, also uncovered putative kinase inhibitors that could be tested for therapy. This integrated proteogenomic characterization of PDAC will serve as a valuable resource for the community, paving the way for early detection and identification of novel therapeutic targets. Citation Format: Liwei Cao, Chen Huang, Daniel Cui Zhou, Yingwei Hu, Mamie Lih, Sara R. Savage, Karsten Krug, David J. Clark, Michael Schnaubelt, Lijun Chen, Felipe da Veiga Leprevost, Rodrigo Vargas Eguez, Alexey I. Nesvizhskii, D.R. Mani, Gilbert S. Omenn, Emily S. Boja, Mehdi Mesri, Ana I. Robles, Henry Rodriguez, Oliver F. Bathe, Daniel W. Chan, Ralph H. Hruban, Li Ding, Bing Zhang, Hui Zhang, Clinical Proteomic Tumor Analysis Consortium. Proteogenomic characterizations of pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the AACR Virtual Special Conference on Pancreatic Cancer; 2021 Sep 29-30. Philadelphia (PA): AACR; Cancer Res 2021;81(22 Suppl):Abstract nr IA-003.

Post-translational modifications (PTMs) (e.g., ubiquitylation, phosphorylation, acetylation) have been studied for their key role in cell signaling and in regulating cell physiology. Advances in mass spectrometry now enable measuring PTMs and studying their role and prevalence in cancer. Although most studies have focused on a single PTM and its impact on downstream signaling in a single cancer type, understanding PTM-driven commonalities, disparities, and their crosstalk across cancer types will be critical in understanding fundamental oncogenic principles governed by PTMs. Thus, we aim to understand the underlying patterns of PTMs in molecular signaling pathways shared across multiple cancer types by studying changes in protein acetylation and phosphorylation within the largest collection of in-depth genomic, transcriptomic, proteomic, and PTM profiles compiled from 11 cancer types generated across the CPTAC projects.We used matrix factorization methods to extract expression signatures jointly from transcriptomic, proteomic, and PTM data across multiple cancer types. Preliminary analysis of 6 tumor types identified 18 pan-cancer, multi-omic expression signatures. Characterization of these signatures using ranked gene-set enrichment analysis (GSEA) and site-specific PTM signature enrichment analysis (PTM-SEA) highlighted pathways including DNA damage response, immune inactivation/deactivation, tumor invasiveness, and metabolic pathways. To investigate the effect of driver alterations on PPIs, we (i) compute rank correlations of pan-cancer mutational signatures to both expression levels and our multi-omic expression signatures, and (ii) map PTMs and mutations to available crystal structures. We additionally investigate mutations that recur across cancer types (e.g., TP53, PIK3CA, CTNNB1) and impact expression profiles of downstream PTMs. We further connect pan-cancer phosphoproteomic data to a library of biochemical specificities of the human kinome using a novel, unbiased computational platform that builds on extensive and experimentally validated kinase-substrate relationships. We identified activated and deactivated kinases/acetylases based on their altered PTM sites within and across the multi-omic subtypes and compared the effects on different targets. We also characterize the crosstalk between acetylation and phosphorylation across cancer, and, systematically identify acetylation events that prime and enhance kinase activity. Finally, we define kinases, acetylases, and PTM outliers as potential druggable targets (using The Drug Gene Interaction Database and DepMap). Overall, this study presents the landscape of PTMs across cancer and will serve as a community resource to promote a deeper understanding of PTM-governed processes leading to cancer progression, with the potential to improve current therapies or help design new treatment approaches against cancer. Citation Format: Yifat Geffen, Shankara Anand, Yo Akiyama, Tommer M. Yaron, Alexander Kerelsky, Jared L. Johnson, Karsten Krug, David Heiman, Shankha Satpathy, Karl Clauser, Michael Gillette, D. R. Mani, Chet Birger, Steven Carr, Lewis C. Cantley, Francois Aguet, Gad Getz, Oncogenic Drivers and Pathways Group, Clinical Proteomic Tumor Analysis Consortium (CPTAC). Patterns and regulation of post translational modifications in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 16.

Although purification of biotinylated molecules is highly efficient, identifying specific sites of biotinylation remains challenging. We show that anti-biotin antibodies enable unprecedented enrichment of biotinylated peptides from complex peptide mixtures. Live-cell proximity labeling using APEX peroxidase followed by anti-biotin enrichment and mass spectrometry yielded over 1,600 biotinylation sites on hundreds of proteins, an increase of more than 30-fold in the number of biotinylation sites identified compared to streptavidin-based enrichment of proteins.

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.