Pan-cancer proteogenomics expands the landscape of therapeutic targets.

Fewer than 200 proteins are targeted by cancer drugs approved by the Food and Drug Administration (FDA). We integrate Clinical Proteomic Tumor Analysis Consortium (CPTAC) proteogenomics data from 1,043 patients across 10 cancer types with additional public datasets to identify potential therapeutic targets. Pan-cancer analysis of 2,863 druggable proteins reveals a wide abundance range and identifies biological factors that affect mRNA-protein correlation. Integration of proteomic data from tumors and genetic screen data from cell lines identifies protein overexpression- or hyperactivation-driven druggable dependencies, enabling accurate predictions of effective drug targets. Proteogenomic identification of synthetic lethality provides a strategy to target tumor suppressor gene loss. Combining proteogenomic analysis and MHC binding prediction prioritizes mutant KRAS peptides as promising public neoantigens. Computational identification of shared tumor-associated antigens followed by experimental confirmation nominates peptides as immunotherapy targets. These analyses, summarized at https://targets.linkedomics.org, form a comprehensive landscape of protein and peptide targets for companion diagnostics, drug repurposing, and therapy development.

PKMYT1 is a Marker of Treatment Response and a Therapeutic Target for CDK4/6 Inhibitor-Resistance in ER+ Breast Cancer.

Endocrine therapies (ET) with CDK4/6 inhibition are the standard treatment for estrogen receptor-α-positive (ER+) breast cancer, however drug resistance is common. In this study, proteogenomic analyses of 22 ER+ breast cancer patient-derived xenografts (PDXs) demonstrated that PKMYT1, a WEE1 homolog, is estradiol (E2) regulated in E2-dependent PDXs and constitutively expressed when growth is E2-independent. In clinical samples, high PKMYT1 mRNA levels associated with resistance to both ET and CDK4/6 inhibition. The PKMYT1 inhibitor lunresertib (RP-6306) with gemcitabine selectively and synergistically reduced the viability of ET and palbociclib-resistant ER+ breast cancer cells without functional p53. In vitro the combination increased DNA damage and apoptosis. In palbociclib-resistant, TP53 mutant PDX organoids and xenografts, RP-6306 with low-dose gemcitabine induced greater tumor volume reduction compared to treatment with either single agent. Our study demonstrates the clinical potential of RP-6306 in combination with gemcitabine for ET and CDK4/6 inhibitor resistant TP53 mutant ER+ breast cancer.

Deep Learning Prediction Boosts Phosphoproteomics-Based Discoveries Through Improved Phosphopeptide Identification.

Shotgun phosphoproteomics enables high-throughput analysis of phosphopeptides in biological samples. One of the primary challenges associated with this technology is the relatively low rate of phosphopeptide identification during data analysis. This limitation hampers the full realization of the potential offered by shotgun phosphoproteomics. Here we present DeepRescore2, a computational workflow that leverages deep learning-based retention time and fragment ion intensity predictions to improve phosphopeptide identification and phosphosite localization. Using a state-of-the-art computational workflow as a benchmark, DeepRescore2 increases the number of correctly identified peptide-spectrum matches by 17% in a synthetic dataset and identifies 19% to 46% more phosphopeptides in biological datasets. In a liver cancer dataset, 30% of the significantly altered phosphosites between tumor and normal tissues and 60% of the prognosis-associated phosphosites identified from DeepRescore2-processed data could not be identified based on the state-of-the-art workflow. Notably, DeepRescore2-processed data uniquely identifies EGFR hyperactivation as a new target in poor-prognosis liver cancer, which is validated experimentally. Integration of deep learning prediction in DeepRescore2 improves phosphopeptide identification and facilitates biological discoveries.

A proteogenomics data-driven knowledge base of human cancer.

By combining mass-spectrometry-based proteomics and phosphoproteomics with genomics, epi-genomics, and transcriptomics, proteogenomics provides comprehensive molecular characterization of cancer. Using this approach, the Clinical Proteomic Tumor Analysis Consortium (CPTAC) has characterized over 1,000 primary tumors spanning 10 cancer types, many with matched normal tissues. Here, we present LinkedOmicsKB, a proteogenomics data-driven knowledge base that makes consistently processed and systematically precomputed CPTAC pan-cancer proteogenomics data available to the public through ∼40,000 gene-, protein-, mutation-, and phenotype-centric web pages. Visualization techniques facilitate efficient exploration and reasoning of complex, interconnected data. Using three case studies, we illustrate the practical utility of LinkedOmicsKB in providing new insights into genes, phosphorylation sites, somatic mutations, and cancer phenotypes. With precomputed results of 19,701 coding genes, 125,969 phosphosites, and 256 genotypes and phenotypes, LinkedOmicsKB provides a comprehensive resource to accelerate proteogenomics data-driven discoveries to improve our understanding and treatment of human cancer. A record of this paper's transparent peer review process is included in the supplemental information.

Kinase Inhibitor Pulldown Assay Identifies a Chemotherapy Response Signature in Triple-negative Breast Cancer Based on Purine-binding Proteins.

Triple-negative breast cancer (TNBC) constitutes 10%-15% of all breast tumors. The current standard of care is multiagent chemotherapy, which is effective in only a subset of patients. The original objective of this study was to deploy a mass spectrometry (MS)-based kinase inhibitor pulldown assay (KIPA) to identify kinases elevated in non-pCR (pathologic complete response) cases for therapeutic targeting. Frozen optimal cutting temperature compound-embedded core needle biopsies were obtained from 43 patients with TNBC before docetaxel- and carboplatin-based neoadjuvant chemotherapy. KIPA was applied to the native tumor lysates that were extracted from samples with high tumor content. Seven percent of all identified proteins were kinases, and none were significantly associated with lack of pCR. However, among a large population of "off-target" purine-binding proteins (PBP) identified, seven were enriched in pCR-associated samples (P < 0.01). In orthogonal mRNA-based TNBC datasets, this seven-gene "PBP signature" was associated with chemotherapy sensitivity and favorable clinical outcomes. Functional annotation demonstrated IFN gamma response, nuclear import of DNA repair proteins, and cell death associations. Comparisons with standard tandem mass tagged-based discovery proteomics performed on the same samples demonstrated that KIPA-nominated pCR biomarkers were unique to the platform. KIPA is a novel biomarker discovery tool with unexpected utility for the identification of PBPs related to cytotoxic drug response. The PBP signature has the potential to contribute to clinical trials designed to either escalate or de-escalate therapy based on pCR probability. Significance: The identification of pretreatment predictive biomarkers for pCR in response to neoadjuvant chemotherapy would advance precision treatment for TNBC. To complement standard proteogenomic discovery profiling, a KIPA was deployed and unexpectedly identified a seven-member non-kinase PBP pCR-associated signature. Individual members served diverse pathways including IFN gamma response, nuclear import of DNA repair proteins, and cell death.

Proteogenomic data and resources for pan-cancer analysis.

The National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) investigates tumors from a proteogenomic perspective, creating rich multi-omics datasets connecting genomic aberrations to cancer phenotypes. To facilitate pan-cancer investigations, we have generated harmonized genomic, transcriptomic, proteomic, and clinical data for >1000 tumors in 10 cohorts to create a cohesive and powerful dataset for scientific discovery. We outline efforts by the CPTAC pan-cancer working group in data harmonization, data dissemination, and computational resources for aiding biological discoveries. We also discuss challenges for multi-omics data integration and analysis, specifically the unique challenges of working with both nucleotide sequencing and mass spectrometry proteomics data.

Proteogenomic insights suggest druggable pathways in endometrial carcinoma.

We characterized a prospective endometrial carcinoma (EC) cohort containing 138 tumors and 20 enriched normal tissues using 10 different omics platforms. Targeted quantitation of two peptides can predict antigen processing and presentation machinery activity, and may inform patient selection for immunotherapy. Association analysis between MYC activity and metformin treatment in both patients and cell lines suggests a potential role for metformin treatment in non-diabetic patients with elevated MYC activity. PIK3R1 in-frame indels are associated with elevated AKT phosphorylation and increased sensitivity to AKT inhibitors. CTNNB1 hotspot mutations are concentrated near phosphorylation sites mediating pS45-induced degradation of ß-catenin, which may render Wnt-FZD antagonists ineffective. Deep learning accurately predicts EC subtypes and mutations from histopathology images, which may be useful for rapid diagnosis. Overall, this study identified molecular and imaging markers that can be further investigated to guide patient stratification for more precise treatment of EC.

Proteogenomic Approaches for the Identification of NF1/Neurofibromin-depleted Estrogen Receptor-positive Breast Cancers for Targeted Treatment.

NF1 is a key tumor suppressor that represses both RAS and estrogen receptor-α (ER) signaling in breast cancer. Blocking both pathways by fulvestrant (F), a selective ER degrader, together with binimetinib (B), a MEK inhibitor, promotes tumor regression in NF1-depleted ER+ models. We aimed to establish approaches to determine how NF1 protein levels impact B+F treatment response to improve our ability to identify B+F sensitive tumors. We examined a panel of ER+ patient-derived xenograft (PDX) models by DNA and mRNA sequencing and found that more than half of these models carried an NF1 shallow deletion and generally have low mRNA levels. Consistent with RAS and ER activation, RET and MEK levels in NF1-depleted tumors were elevated when profiled by mass spectrometry (MS) after kinase inhibitor bead pulldown. MS showed that NF1 can also directly and selectively bind to palbociclib-conjugated beads, aiding quantification. An IHC assay was also established to measure NF1, but the MS-based approach was more quantitative. Combined IHC and MS analysis defined a threshold of NF1 protein loss in ER+ breast PDX, below which tumors regressed upon treatment with B+F. These results suggest that we now have a MS-verified NF1 IHC assay that can be used for patient selection as a complement to somatic genomic analysis. Significance: A major challenge for targeting the consequence of tumor suppressor disruption is the accurate assessment of protein functional inactivation. NF1 can repress both RAS and ER signaling, and a ComboMATCH trial is underway to treat the patients with binimetinib and fulvestrant. Herein we report a MS-verified NF1 IHC assay that can determine a threshold for NF1 loss to predict treatment response. These approaches may be used to identify and expand the eligible patient population.

Molecular portraits of cell cycle checkpoint kinases in cancer evolution, progression, and treatment responsiveness.

Cell cycle dysregulation is prerequisite for cancer formation. However, it is unknown whether the mode of dysregulation affects disease characteristics. Here, we conduct comprehensive analyses of cell cycle checkpoint dysregulation using patient data and experimental investigations. We find that ATM mutation predisposes the diagnosis of primary estrogen receptor (ER)+/human epidermal growth factor (HER)2- cancer in older women. Conversely, CHK2 dysregulation induces formation of metastatic, premenopausal ER+/HER2- breast cancer (P = 0.001) that is treatment-resistant (HR = 6.15, P = 0.01). Lastly, while mutations in ATR alone are rare, ATR/TP53 co-mutation is 12-fold enriched over expected in ER+/HER2- disease (P = 0.002) and associates with metastatic progression (HR = 2.01, P = 0.006). Concordantly, ATR dysregulation induces metastatic phenotypes in TP53 mutant, not wild-type, cells. Overall, we identify mode of cell cycle dysregulation as a distinct event that determines subtype, metastatic potential, and treatment responsiveness, providing rationale for reconsidering diagnostic classification through the lens of the mode of cell cycle dysregulation..

The Breast Cancer Proteome and Precision Oncology.

The goal of precision oncology is to translate the molecular features of cancer into predictive and prognostic tests that can be used to individualize treatment leading to improved outcomes and decreased toxicity. Success for this strategy in breast cancer is exemplified by efficacy of trastuzumab in tumors overexpressing ERBB2 and endocrine therapy for tumors that are estrogen receptor positive. However, other effective treatments, including chemotherapy, immune checkpoint inhibitors, and CDK4/6 inhibitors are not associated with strong predictive biomarkers. Proteomics promises another tier of information that, when added to genomic and transcriptomic features (proteogenomics), may create new opportunities to improve both treatment precision and therapeutic hypotheses. Here, we review both mass spectrometry-based and antibody-dependent proteomics as complementary approaches. We highlight how these methods have contributed toward a more complete understanding of breast cancer and describe the potential to guide diagnosis and treatment more accurately.

Kinome Reprogramming Is a Targetable Vulnerability in ESR1 Fusion-Driven Breast Cancer.

Transcriptionally active ESR1 fusions (ESR1-TAF) are a potent cause of breast cancer endocrine therapy (ET) resistance. ESR1-TAFs are not directly druggable because the C-terminal estrogen/anti-estrogen-binding domain is replaced with translocated in-frame partner gene sequences that confer constitutive transactivation. To discover alternative treatments, a mass spectrometry (MS)-based kinase inhibitor pulldown assay (KIPA) was deployed to identify druggable kinases that are upregulated by diverse ESR1-TAFs. Subsequent explorations of drug sensitivity validated RET kinase as a common therapeutic vulnerability despite remarkable ESR1-TAF C-terminal sequence and structural diversity. Organoids and xenografts from a pan-ET-resistant patient-derived xenograft model that harbors the ESR1-e6>YAP1 TAF were concordantly inhibited by the selective RET inhibitor pralsetinib to a similar extent as the CDK4/6 inhibitor palbociclib. Together, these findings provide preclinical rationale for clinical evaluation of RET inhibition for the treatment of ESR1-TAF-driven ET-resistant breast cancer. SIGNIFICANCE: Kinome analysis of ESR1 translocated and mutated breast tumors using drug bead-based mass spectrometry followed by drug-sensitivity studies nominates RET as a therapeutic target. See related commentary by Wu and Subbiah, p. 3159.

Identifying biomarkers of differential chemotherapy response in TNBC patient-derived xenografts with a CTD/WGCNA approach.

Although systemic chemotherapy remains the standard of care for TNBC, even combination chemotherapy is often ineffective. The identification of biomarkers for differential chemotherapy response would allow for the selection of responsive patients, thus maximizing efficacy and minimizing toxicities. Here, we leverage TNBC PDXs to identify biomarkers of response. To demonstrate their ability to function as a preclinical cohort, PDXs were characterized using DNA sequencing, transcriptomics, and proteomics to show consistency with clinical samples. We then developed a network-based approach (CTD/WGCNA) to identify biomarkers of response to carboplatin (MSI1, TMSB15A, ARHGDIB, GGT1, SV2A, SEC14L2, SERPINI1, ADAMTS20, DGKQ) and docetaxel (c, MAGED4, CERS1, ST8SIA2, KIF24, PARPBP). CTD/WGCNA multigene biomarkers are predictive in PDX datasets (RNAseq and Affymetrix) for both taxane- (docetaxel or paclitaxel) and platinum-based (carboplatin or cisplatin) response, thereby demonstrating cross-expression platform and cross-drug class robustness. These biomarkers were also predictive in clinical datasets, thus demonstrating translational potential.

Deep learning prediction boosts phosphoproteomics-based discoveries through improved phosphopeptide identification.

Shotgun phosphoproteomics enables high-throughput analysis of phosphopeptides in biological samples, but low phosphopeptide identification rate in data analysis limits the potential of this technology. Here we present DeepRescore2, a computational workflow that leverages deep learning-based retention time and fragment ion intensity predictions to improve phosphopeptide identification and phosphosite localization. Using a state-of-the-art computational workflow as a benchmark, DeepRescore2 increases the number of correctly identified peptide-spectrum matches by 17% in a synthetic dataset and identifies 19%-46% more phosphopeptides in biological datasets. In a liver cancer dataset, 30% of the significantly altered phosphosites between tumor and normal tissues and 60% of the prognosis-associated phosphosites identified from DeepRescore2-processed data could not be identified based on the state-of-the-art workflow. Notably, DeepRescore2-processed data uniquely identifies EGFR hyperactivation as a new target in poor-prognosis liver cancer, which is validated experimentally. Integration of deep learning prediction in DeepRescore2 improves phosphopeptide identification and facilitates biological discoveries.

Elevated NRAS expression during DCIS is a potential driver for progression to basal-like properties and local invasiveness.

BACKGROUND: Ductal carcinoma in situ (DCIS) is the most common type of in situ premalignant breast cancers. What drives DCIS to invasive breast cancer is unclear. Basal-like invasive breast cancers are aggressive. We have previously shown that NRAS is highly expressed selectively in basal-like subtypes of invasive breast cancers and can promote their growth and progression. In this study, we investigated whether NRAS expression at the DCIS stage can control transition from luminal DCIS to basal-like invasive breast cancers. METHODS: Wilcoxon rank-sum test was performed to assess expression of NRAS in DCIS compared to invasive breast tumors in patients. NRAS mRNA levels were also determined by fluorescence in situ hybridization in patient tumor microarrays (TMAs) with concurrent normal, DCIS, and invasive breast cancer, and association of NRAS mRNA levels with DCIS and invasive breast cancer was assessed by paired Wilcoxon signed-rank test. Pearson's correlation was calculated between NRAS mRNA levels and basal biomarkers in the TMAs, as well as in patient datasets. RNA-seq data were generated in cell lines, and unsupervised hierarchical clustering was performed after combining with RNA-seq data from a previously published patient cohort. RESULTS: Invasive breast cancers showed higher NRAS mRNA levels compared to DCIS samples. These NRAShigh lesions were also enriched with basal-like features, such as basal gene expression signatures, lower ER, and higher p53 protein and Ki67 levels. We have shown previously that NRAS drives aggressive features in DCIS-like and basal-like SUM102PT cells. Here, we found that NRAS-silencing induced a shift to a luminal gene expression pattern. Conversely, NRAS overexpression in the luminal DCIS SUM225 cells induced a basal-like gene expression pattern, as well as an epithelial-to-mesenchymal transition signature. Furthermore, these cells formed disorganized mammospheres containing cell masses with an apparent reduction in adhesion. CONCLUSIONS: These data suggest that elevated NRAS levels in DCIS are not only a marker but can also control the emergence of basal-like features leading to more aggressive tumor activity, thus supporting the therapeutic hypothesis that targeting NRAS and/or downstream pathways may block disease progression for a subset of DCIS patients with high NRAS.

Epithelial immunomodulation by aerosolized Toll-like receptor agonists prevents allergic inflammation in airway mucosa in mice.

Allergic asthma is a chronic inflammatory respiratory disease associated with eosinophilic infiltration, increased mucus production, airway hyperresponsiveness, and airway remodeling. Epidemiologic data reveal that the prevalence of allergic sensitization and associated diseases has increased in the twentieth century. This has been hypothesized to be partly due to reduced contact with microbial organisms (the hygiene hypothesis) in industrialized society. Airway epithelial cells, once considered a static physical barrier between the body and the external world, are now widely recognized as immunologically active cells that can initiate, maintain, and restrain inflammatory responses, such as those that mediate allergic disease. Airway epithelial cells can sense allergens via expression of myriad Toll-like receptors (TLRs) and other pattern-recognition receptors. We sought to determine whether the innate immune response stimulated by a combination of Pam2CSK4 ("Pam2", TLR2/6 ligand) and a class C oligodeoxynucleotide ODN362 ("ODN", TLR9 ligand), when delivered together by aerosol ("Pam2ODN"), can modulate the allergic immune response to allergens. Treatment with Pam2ODN 7 days before sensitization to House Dust Mite (HDM) extract resulted in a strong reduction in eosinophilic and lymphocytic inflammation. This Pam2ODN immunomodulatory effect was also seen using Ovalbumin (OVA) and A. oryzae (Ao) mouse models. The immunomodulatory effect was observed as much as 30 days before sensitization to HDM, but ineffective just 2 days after sensitization, suggesting that Pam2ODN immunomodulation lowers the allergic responsiveness of the lung, and reduces the likelihood of inappropriate sensitization to aeroallergens. Furthermore, Pam2 and ODN cooperated synergistically suggesting that this treatment is superior to any single agonist in the setting of allergen immunotherapy.

Proteogenomic Markers of Chemotherapy Resistance and Response in Triple-Negative Breast Cancer.

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.

LINC00355 regulates p27KIP expression by binding to MENIN to induce proliferation in late-stage relapse breast cancer.

Late-stage relapse (LSR) in patients with breast cancer (BC) occurs more than five years and up to 10 years after initial treatment and has less than 30% 5-year relative survival rate. Long non-coding RNAs (lncRNAs) play important roles in BC yet have not been studied in LSR BC. Here, we identify 1127 lncRNAs differentially expressed in LSR BC via transcriptome sequencing and analysis of 72 early-stage and 24 LSR BC patient tumors. Decreasing expression of the most up-regulated lncRNA, LINC00355, in BC and MCF7 long-term estrogen deprived cell lines decreases cellular invasion and proliferation. Subsequent mechanistic studies show that LINC00355 binds to MENIN and changes occupancy at the CDKN1B promoter to decrease p27Kip. In summary, this is a key study discovering lncRNAs in LSR BC and LINC00355 association with epigenetic regulation and proliferation in BC.

Transcriptional Reprogramming Differentiates Active from Inactive ESR1 Fusions in Endocrine Therapy-Refractory Metastatic Breast Cancer.

Genomic analysis has recently identified multiple ESR1 gene translocations in estrogen receptor alpha-positive (ERα+) metastatic breast cancer (MBC) that encode chimeric proteins whereby the ESR1 ligand binding domain (LBD) is replaced by C-terminal sequences from many different gene partners. Here we functionally screened 15 ESR1 fusions and identified 10 that promoted estradiol-independent cell growth, motility, invasion, epithelial-to-mesenchymal transition, and resistance to fulvestrant. RNA sequencing identified a gene expression pattern specific to functionally active ESR1 gene fusions that was subsequently reduced to a diagnostic 24-gene signature. This signature was further examined in 20 ERα+ patient-derived xenografts and in 55 ERα+ MBC samples. The 24-gene signature successfully identified cases harboring ESR1 gene fusions and also accurately diagnosed the presence of activating ESR1 LBD point mutations. Therefore, the 24-gene signature represents an efficient approach to screening samples for the presence of diverse somatic ESR1 mutations and translocations that drive endocrine treatment failure in MBC. SIGNIFICANCE: This study identifies a gene signature diagnostic for functional ESR1 fusions that drive poor outcome in advanced breast cancer, which could also help guide precision medicine approaches in patients harboring ESR1 mutations.

A proteogenomic portrait of lung squamous cell carcinoma.

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.

Proteogenomics drives therapeutic hypothesis generation for precision oncology.

Precision oncology has largely been driven by genomic profiling, but success so far has been limited. By combining genomic and proteomic analyses of tumours, proteogenomics holds promise in providing deeper mechanistic insights and generating therapeutic hypotheses to better match patients to targeted treatments than analysing each 'ome in isolation.