Proteogenomic Characterization of Ovarian HGSC Implicates Mitotic Kinases, Replication Stress in Observed Chromosomal Instability

Dormant origin firing promotes head-on transcription-replication conflicts at transcription termination sites in response to BRCA2 deficiency

BRCA2 is a tumor suppressor protein responsible for safeguarding the cellular genome from replication stress and genotoxicity, but the specific mechanism(s) by which this is achieved to prevent early oncogenesis remains unclear. Here, we provide evidence that BRCA2 acts as a critical suppressor of head-on transcription-replication conflicts (HO-TRCs). Using Okazaki-fragment sequencing (Ok-seq) and computational analysis, we identified origins (dormant origins) that are activated near the transcription termination sites (TTS) of highly expressed, long genes in response to replication stress. Dormant origins are a source for HO-TRCs, and drug treatments that inhibit dormant origin firing led to a reduction in HO-TRCs, R-loop formation, and DNA damage. Using super-resolution microscopy, we showed that HO-TRC events track with elongating RNA polymerase II, but not with transcription initiation. Importantly, RNase H2 is recruited to sites of HO-TRCs in a BRCA2-dependent manner to help alleviate toxic R-loops associated with HO-TRCs. Collectively, our results provide a mechanistic basis for how BRCA2 shields against genomic instability by preventing HO-TRCs through both direct and indirect means occurring at predetermined genomic sites based on the pre-cancer transcriptome.

Integration of proteomics in the molecular tumor board

Cancer remains one of the most complex and challenging diseases in mankind. To address the need for a personalized treatment approach for particularly complex tumor cases, molecular tumor boards (MTBs) have been initiated. MTBs are interdisciplinary teams that perform in-depth molecular diagnostics to cooperatively and interdisciplinarily advise on the best therapeutic strategy. Current molecular diagnostics are routinely performed on the transcriptomic and genomic levels, aiming to identify tumor-driving mutations. However, these approaches can only partially capture the actual phenotype and the molecular key players of tumor growth and progression. Thus, direct investigation of the expressed proteins and activated signaling pathways provide valuable complementary information on the tumor-driving molecular characteristics of the tissue. Technological advancements in mass spectrometry-based proteomics enable the robust, rapid, and sensitive detection of thousands of proteins in minimal sample amounts, paving the way for clinical proteomics and the probing of oncogenic signaling activity. Therefore, proteomics is currently being integrated into molecular diagnostics within MTBs and holds promising potential in aiding tumor classification and identifying personalized treatment strategies. This review introduces MTBs and describes current clinical proteomics, its potential in precision oncology, and highlights the benefits of multi-omic data integration.

Molecular-Level Dysregulation of Insulin Pathways and Inflammatory Processes in Peripheral Blood Mononuclear Cells by Circadian Misalignment

Circadian misalignment due to night work has been associated with an elevated risk for chronic diseases. We investigated the effects of circadian misalignment using shotgun protein profiling of peripheral blood mononuclear cells taken from healthy humans during a constant routine protocol, which was conducted immediately after participants had been subjected to a 3-day simulated night shift schedule or a 3-day simulated day shift schedule. By comparing proteomic profiles between the simulated shift conditions, we identified proteins and pathways that are associated with the effects of circadian misalignment and observed that insulin regulation pathways and inflammation-related proteins displayed markedly different temporal patterns after simulated night shift. Further, by integrating the proteomic profiles with previously assessed metabolomic profiles in a network-based approach, we found key associations between circadian dysregulation of protein-level pathways and metabolites of interest in the context of chronic metabolic diseases. Endogenous circadian rhythms in circulating glucose and insulin differed between the simulated shift conditions. Overall, our results suggest that circadian misalignment is associated with a tug of war between central clock mechanisms controlling insulin secretion and peripheral clock mechanisms regulating insulin sensitivity, which may lead to adverse long-term outcomes such as diabetes and obesity. Our study provides a molecular-level mechanism linking circadian misalignment and adverse long-term health consequences of night work.

Sample-Wise and Gene-Wise Comparisons Confirm a Greater Similarity of RNA and Protein Expression Data at the Level of Molecular Pathways and Suggest an Approach for the Data Quality Check in High-Throughput Expression Databases

Identification of genes and molecular pathways with congruent profiles in the proteomic and transcriptomic datasets may result in the discovery of promising transcriptomic biomarkers that would be more relevant to phenotypic changes. In this study, we conducted comparative analysis of 943 paired RNA and proteomic profiles obtained for the same samples of seven human cancer types from The Cancer Genome Atlas (TCGA) and NCI Clinical Proteomic Tumor Analysis Consortium (CPTAC) [two major open human cancer proteomic and transcriptomic databases] that included 15,112 protein-coding genes and 1611 molecular pathways. Overall, our findings demonstrated statistically significant improvement of the congruence between RNA and proteomic profiles when performing analysis at the level of molecular pathways rather than at the level of individual gene products. Transition to the molecular pathway level of data analysis increased the correlation to 0.19-0.57 (Pearson) and 0.14-057 (Spearman), or 2-3-fold for some cancer types. Evaluating the gain of the correlation upon transition to the data analysis the pathway level can be used to refine the omics data by identifying outliers that can be excluded from the comparison of RNA and proteomic profiles. We suggest using sample- and gene-wise correlations for individual genes and molecular pathways as a measure of quality of RNA/protein paired molecular data. We also provide a database of human genes, molecular pathways, and samples related to the correlation between RNA and protein products to facilitate an exploration of new cancer transcriptomic biomarkers and molecular mechanisms at different levels of human gene expression.

Comprehensive multi-omics analysis reveals WEE1 as a synergistic lethal target with hyperthermia through CDK1 super-activation

Hyperthermic intraperitoneal chemotherapy's role in ovarian cancer remains controversial, hindered by limited understanding of hyperthermia-induced tumor cellular changes. This limits developing potent combinatory strategies anchored in hyperthermic intraperitoneal therapy (HIPET). Here, we perform a comprehensive multi-omics study on ovarian cancer cells under hyperthermia, unveiling a distinct molecular panorama, primarily characterized by rapid protein phosphorylation changes. Based on the phospho-signature, we pinpoint CDK1 kinase is hyperactivated during hyperthermia, influencing the global signaling landscape. We observe dynamic, reversible CDK1 activity, causing replication arrest and early mitotic entry post-hyperthermia. Subsequent drug screening shows WEE1 inhibition synergistically destroys cancer cells with hyperthermia. An in-house developed miniaturized device confirms hyperthermia and WEE1 inhibitor combination significantly reduces tumors in vivo. These findings offer additional insights into HIPET, detailing molecular mechanisms of hyperthermia and identifying precise drug combinations for targeted treatment. This research propels the concept of precise hyperthermic intraperitoneal therapy, highlighting its potential against ovarian cancer.

Mass Spectrometry-Based Proteogenomics: New Therapeutic Opportunities for Precision Medicine

Proteogenomics refers to the integration of comprehensive genomic, transcriptomic, and proteomic measurements from the same samples with the goal of fully understanding the regulatory processes converting genotypes to phenotypes, often with an emphasis on gaining a deeper understanding of disease processes. Although specific genetic mutations have long been known to drive the development of multiple cancers, gene mutations alone do not always predict prognosis or response to targeted therapy. The benefit of proteogenomics research is that information obtained from proteins and their corresponding pathways provides insight into therapeutic targets that can complement genomic information by providing an additional dimension regarding the underlying mechanisms and pathophysiology of tumors. This review describes the novel insights into tumor biology and drug resistance derived from proteogenomic analysis while highlighting the clinical potential of proteogenomic observations and advances in technique and analysis tools.

Rescue of p53 functions by in vitro-transcribed mRNA impedes the growth of high-grade serous ovarian cancer

BACKGROUND

The cellular tumor protein p53 (TP53) is a tumor suppressor gene that is frequently mutated in human cancers. Among various cancer types, the very aggressive high-grade serous ovarian carcinoma (HGSOC) exhibits the highest prevalence of TP53 mutations, present in >96% of cases. Despite intensive efforts to reactivate p53, no clinical drug has been approved to rescue p53 function. In this study, our primary objective was to administer in vitro-transcribed (IVT) wild-type (WT) p53-mRNA to HGSOC cell lines, primary cells, and orthotopic mouse models, with the aim of exploring its impact on inhibiting tumor growth and dissemination, both in vitro and in vivo.

METHODS

To restore the activity of p53, WT p53 was exogenously expressed in HGSOC cell lines using a mammalian vector system. Moreover, IVT WT p53 mRNA was delivered into different HGSOC model systems (primary cells and patient-derived organoids) using liposomes and studied for proliferation, cell cycle progression, apoptosis, colony formation, and chromosomal instability. Transcriptomic alterations induced by p53 mRNA were analyzed using RNA sequencing in OVCAR-8 and primary HGSOC cells, followed by ingenuity pathway analysis. In vivo effects on tumor growth and metastasis were studied using orthotopic xenografts and metastatic intraperitoneal mouse models.

RESULTS

Reactivation of the TP53 tumor suppressor gene was explored in different HGSOC model systems using newly designed IVT mRNA-based methods. The introduction of WT p53 mRNA triggered dose-dependent apoptosis, cell cycle arrest, and potent long-lasting inhibition of HGSOC cell proliferation. Transcriptome analysis of OVCAR-8 cells upon mRNA-based p53 reactivation revealed significant alterations in gene expression related to p53 signaling, such as apoptosis, cell cycle regulation, and DNA damage. Restoring p53 function concurrently reduces chromosomal instability within the HGSOC cells, underscoring its crucial contribution in safeguarding genomic integrity by moderating the baseline occurrence of double-strand breaks arising from replication stress. Furthermore, in various mouse models, treatment with p53 mRNA reduced tumor growth and inhibited tumor cell dissemination in the peritoneal cavity in a dose-dependent manner.

CONCLUSIONS

The IVT mRNA-based reactivation of p53 holds promise as a potential therapeutic strategy for HGSOC, providing valuable insights into the molecular mechanisms underlying p53 function and its relevance in ovarian cancer treatment.

Proteomic characterization of epithelial ovarian cancer delineates molecular signatures and therapeutic targets in distinct histological subtypes

Clear cell carcinoma (CCC), endometrioid carcinoma (EC), and serous carcinoma (SC) are the major histological subtypes of epithelial ovarian cancer (EOC), whose differences in carcinogenesis are still unclear. Here, we undertake comprehensive proteomic profiling of 80 CCC, 79 EC, 80 SC, and 30 control samples. Our analysis reveals the prognostic or diagnostic value of dysregulated proteins and phosphorylation sites in important pathways. Moreover, protein co-expression network not only provides comprehensive view of biological features of each histological subtype, but also indicates potential prognostic biomarkers and progression landmarks. Notably, EOC have strong inter-tumor heterogeneity, with significantly different clinical characteristics, proteomic patterns and signaling pathway disorders in CCC, EC, and SC. Finally, we infer MPP7 protein as potential therapeutic target for SC, whose biological functions are confirmed in SC cells. Our proteomic cohort provides valuable resources for understanding molecular mechanisms and developing treatment strategies of distinct histological subtypes.

HCK Promotes High-Grade Serous Ovarian Cancer Tumorigenesis through CD44 and NOTCH3 Signaling

High-grade serous ovarian cancer (HGSOC) is a highly aggressive and lethal subtype of ovarian cancer. While most patients initially respond to standard-of-care treatment, the majority will eventually relapse and succumb to their disease. Despite significant advances in our understanding of this disease, the mechanisms that govern the distinctions between HGSOC with good and poor prognosis remain unclear. In this study, we implemented a proteogenomic approach to analyze gene expression, proteomic and phosphoproteomic profiles of HGSOC tumor samples to identify molecular pathways that distinguish HGSOC tumors relative to clinical outcome. Our analyses identify significant upregulation of hematopoietic cell kinase (HCK) expression and signaling in poor prognostic HGSOC patient samples. Analyses of independent gene expression datasets and IHC of patient samples confirmed increased HCK signaling in tumors relative to normal fallopian or ovarian samples and demonstrated aberrant expression in tumor epithelial cells. Consistent with the association between HCK expression and tumor aggressiveness in patient samples, in vitro phenotypic studies showed that HCK can, in part, promote cell proliferation, colony formation, and invasive capacity of cell lines. Mechanistically, HCK mediates these phenotypes, partly through CD44 and NOTCH3-dependent signaling, and inhibiting CD44 or NOTCH3 activity, either genetically or through gamma-secretase inhibitors, can revert HCK-driven phenotypes.

IMPLICATIONS

Collectively, these studies establish that HCK acts as an oncogenic driver of HGSOC through aberrant activation of CD44 and NOTCH3 signaling and identifies this network as a potential therapeutic opportunity in a subset of patients with aggressive and recurrent HGSOC.

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.

ABPEPserver: a web application for documentation and analysis of substitutants

BACKGROUND

Cancer immunotherapy is implemented by identifying antigens that are presented on the cell surface of cancer cells and illicit T-cell response (Schumacher and Schreiber, Science 348:69-74, 2015; Waldman et al., Nat Rev Immunol 20:651-668, 2020; Zhang et al., Front Immunol 12:672,356, 2021b). Classical candidates of such antigens are the peptides resulting from genetic alterations and are named "neoantigen" (Schumacher and Schreiber, Science 348:69-74, 2015). Neoantigens have been widely catalogued across several human cancer types (Tan et al., Database (Oxford) 2020;2020b; Vigneron et al., Cancer Immun 13:15, 2013; Yi et al., iScience 24:103,107, 2021; Zhang et al., BMC Bioinformatics 22:40, 2021a). Recently, a new class of inducible antigens has been identified, namely Substitutants, that are produced as a result of aberrant protein translation (Pataskar et al., Nature 603:721-727, 2022). MAIN: Catalogues of Substitutant expression across human cancer types, their specificity and association to gene expression signatures remain elusive for the scientific community's access. As a solution, we present ABPEPserver, an online database and analytical platform that can visualize a large-scale tumour proteomics analysis of Substitutant expression across eight tumour types sourced from the CPTAC database (Edwards et al., J Proteome Res 14:2707-2713, 2015). Functionally, ABPEPserver offers the analysis of gene-association signatures of Substitutant peptides, a comparison of enrichment between tumour and tumour-adjacent normal tissues, and a list of peptides that serve as candidates for immunotherapy design. ABPEPserver will significantly enhance the exploration of aberrant protein production in human cancer, as exemplified in a case study.

CONCLUSION

ABPEPserver is designed on an R SHINY platform to catalogue Substitutant peptides in human cancer. The application is available at https://rhpc.nki.nl/sites/shiny/ABPEP/ . The code is available under GNU General public license from GitHub ( https://github.com/jasminesmn/ABPEPserver ).

Prognostic relevance of HRDness gene expression signature in ovarian high-grade serous carcinoma; JGOG3025-TR2 study

BACKGROUND

This study aimed to evaluate the homologous recombination repair pathway deficiency (HRD) in ovarian high-grade serous carcinoma (HGSC).

METHODS

In the ovarian cancer data from The Cancer Genome Atlas, we identified genes differentially expressed between tumours with and without HRD genomic scars and named these genes "HRDness signature". We performed SNP array, RNA sequencing, and methylation array analyses on 274 HGSC tumours for which targeted sequencing of 51 genes and clinical data were available to generate JGOG3025-TR2 dataset. The HRDness signature was tested on external datasets, including the JGOG3025-TR2 cohort, by computational scoring and machine-learning prediction.

RESULTS

High scores and positive predictions of the HRDness signature were significantly associated with BRCA alterations, genomic scar scores, and better survival. On the other hand, among cases with high scores and/or positive predictions, those with BRCA1 methylation showed poorer survival. In the JGOG3025-TR2 cohort, HRD status was significantly associated with the use of olaparib after relapse and progression-free survival after its initiation.

CONCLUSIONS

The HRDness gene expression signature is associated with a good prognosis, while BRCA1 methylation is associated with a poor prognosis. The newly generated JGOG3025-TR2 dataset will be useful in future HGSC studies.

Clinical Relevance of Mortalin in Ovarian Cancer Patients

Background: Ovarian cancer (OC) is the most lethal malignancy of the female reproductive tract. Consequently, a better understanding of the malignant features in OC is pertinent. Mortalin (mtHsp70/GRP75/PBP74/HSPA9/HSPA9B) promotes cancer development, progression, metastasis, and recurrence. Yet, there is no parallel evaluation and clinical relevance of mortalin in the peripheral and local tumor ecosystem in OC patients. Methods: A cohort of 92 pretreatment women was recruited, including 50 OC patients, 14 patients with benign ovarian tumors, and 28 healthy women. Blood plasma and ascites fluid-soluble mortalin concentrations were measured by ELISA. Mortalin protein levels in tissues and OC cells were analyzed using proteomic datasets. The gene expression profile of mortalin in ovarian tissues was evaluated through the analysis of RNAseq data. Kaplan-Meier analysis was used to demonstrate the prognostic relevance of mortalin. Results: First, we found upregulation of local mortalin in two different ecosystems, i.e., ascites and tumor tissues in human OC compared to control groups. Second, abundance expression of local tumor mortalin is associated with cancer-driven signaling pathways and worse clinical outcome. Third, high mortalin level in tumor tissues, but not in the blood plasma or ascites fluid, predicts worse patient prognosis. Conclusions: Our findings demonstrate a previously unknown mortalin profile in peripheral and local tumor ecosystem and its clinical relevance in OC. These novel findings may serve clinicians and investigators in the development of biomarker-based targeted therapeutics and immunotherapies.

OSppc: A web server for online survival analysis using proteome of pan-cancers

Prognostic biomarker, as a feasible and objective indicator, is valuable in the assessment of cancer risk. With the development of high-throughput sequencing technology, the screening of prognostic biomarkers has become easy, but it is difficult to screen prognostic markers based on proteomic data. In this study we developed a tool named Online consensus Survival analysis web server based on Proteome of Pan-cancers, abbreviated as OSppc, to evaluate the prognostic values of protein biomarkers. >8000 cancer cases with proteomic data, transcriptomic data and clinical follow-up information were collected from TCGA and CPTAC. 14,038 proteins (including proteins and their phosphorylated forms) analyzed by reverse-phase protein arrays and mass spectrometry in 33 types of cancers were collected. In OSppc, three analysis modules are provided, including Survival Analysis, Differential Analysis and Correlation Analysis. Survival analysis module exhibits HR with 95% CI and KM curves with log-rank p value of protein and mRNA levels of input genes. Differential analysis module shows the box plots of protein expression levels in different tissues. Correlation analysis module provides scatter plot with pearson's and spearman's correlation coefficient of the protein and its corresponding mRNA. OSppc can be accessed at http://bioinfo.henu.edu.cn/Protein/OSppc.html. SIGNIFICANCE: OSppc can analyze the association between protein, mRNA and prognosis, the correlation between proteome data and gene expression profiles, the differential expression of proteome data between subgroups such as normal and cancer as well. OSppc is registration-free and very valuable to evaluate the prognostic potency of protein of interests. OSppc is very valuable for researchers and clinicians to screen, develop and validate potential protein prognostic biomarkers in pan-cancers, and offers the opportunities to investigate the clinical important functional genes and therapeutic targets of cancers.

Evaluation of mitochondrial biogenesis and ROS generation in high-grade serous ovarian cancer

Introduction

Ovarian cancer is one of the leading causes of death for women with cancer worldwide. Energy requirements for tumor growth in epithelial high-grade serous ovarian cancer (HGSOC) are fulfilled by a combination of aerobic glycolysis and oxidative phosphorylation (OXPHOS). Although reduced OXPHOS activity has emerged as one of the significant contributors to tumor aggressiveness and chemoresistance, up-regulation of mitochondrial antioxidant capacity is required for matrix detachment and colonization into the peritoneal cavity to form malignant ascites in HGSOC patients. However, limited information is available about the mitochondrial biogenesis regulating OXPHOS capacity and generation of mitochondrial reactive oxygen species (mtROS) in HGSOC.

Methods

To evaluate the modulation of OXPHOS in HGSOC tumor samples and ovarian cancer cell lines, we performed proteomic analyses of proteins involved in mitochondrial energy metabolism and biogenesis and formation of mtROS by immunoblotting and flow cytometry, respectively.

Results and discussion

We determined that the increased steady-state expression levels of mitochondrial- and nuclear-encoded OXPHOS subunits were associated with increased mitochondrial biogenesis in HGSOC tumors and ovarian cancer cell lines. The more prominent increase in MT-COII expression was in agreement with significant increase in mitochondrial translation factors, TUFM and DARS2. On the other hand, the ovarian cancer cell lines with reduced OXPHOS subunit expression and mitochondrial translation generated the highest levels of mtROS and significantly reduced SOD2 expression. Evaluation of mitochondrial biogenesis suggested that therapies directed against mitochondrial targets, such as those involved in transcription and translation machineries, should be considered in addition to the conventional chemotherapies in HGSOC treatment.

Proteogenomic analysis of cancer aneuploidy and normal tissues reveals divergent modes of gene regulation across cellular pathways

How cells control gene expression is a fundamental question. The relative contribution of protein-level and RNA-level regulation to this process remains unclear. Here, we perform a proteogenomic analysis of tumors and untransformed cells containing somatic copy number alterations (SCNAs). By revealing how cells regulate RNA and protein abundances of genes with SCNAs, we provide insights into the rules of gene regulation. Protein complex genes have a strong protein-level regulation while non-complex genes have a strong RNA-level regulation. Notable exceptions are plasma membrane protein complex genes, which show a weak protein-level regulation and a stronger RNA-level regulation. Strikingly, we find a strong negative association between the degree of RNA-level and protein-level regulation across genes and cellular pathways. Moreover, genes participating in the same pathway show a similar degree of RNA- and protein-level regulation. Pathways including translation, splicing, RNA processing, and mitochondrial function show a stronger protein-level regulation while cell adhesion and migration pathways show a stronger RNA-level regulation. These results suggest that the evolution of gene regulation is shaped by functional constraints and that many cellular pathways tend to evolve one predominant mechanism of gene regulation at the protein level or at the RNA level.

DAGBagM: learning directed acyclic graphs of mixed variables with an application to identify protein biomarkers for treatment response in ovarian cancer

BACKGROUND

Applying directed acyclic graph (DAG) models to proteogenomic data has been shown effective for detecting causal biomarkers of complex diseases. However, there remain unsolved challenges in DAG learning to jointly model binary clinical outcome variables and continuous biomarker measurements.

RESULTS

In this paper, we propose a new tool, DAGBagM, to learn DAGs with both continuous and binary nodes. By using appropriate models, DAGBagM allows for either continuous or binary nodes to be parent or child nodes. It employs a bootstrap aggregating strategy to reduce false positives in edge inference. At the same time, the aggregation procedure provides a flexible framework to robustly incorporate prior information on edges.

CONCLUSIONS

Through extensive simulation experiments, we demonstrate that DAGBagM has superior performance compared to alternative strategies for modeling mixed types of nodes. In addition, DAGBagM is computationally more efficient than two competing methods. When applying DAGBagM to proteogenomic datasets from ovarian cancer studies, we identify potential protein biomarkers for platinum refractory/resistant response in ovarian cancer. DAGBagM is made available as a github repository at https://github.com/jie108/dagbagM .

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.

BRCA1 mutations in high-grade serous ovarian cancer are associated with proteomic changes in DNA repair, splicing, transcription regulation and signaling

Despite recent advances in the management of BRCA1 mutated high-grade serous ovarian cancer (HGSC), the physiology of these tumors remains poorly understood. Here we provide a comprehensive molecular understanding of the signaling processes that drive HGSC pathogenesis with the addition of valuable ubiquitination profiling, and their dependency on BRCA1 mutation-state directly in patient-derived tissues. Using a multilayered proteomic approach, we show the tight coordination between the ubiquitination and phosphorylation regulatory layers and their role in key cellular processes related to BRCA1-dependent HGSC pathogenesis. In addition, we identify key bridging proteins, kinase activity, and post-translational modifications responsible for molding distinct cancer phenotypes, thus providing new opportunities for therapeutic intervention, and ultimately advance towards a more personalized patient care.

Tryptophan depletion results in tryptophan-to-phenylalanine substitutants

Activated T cells secrete interferon-γ, which triggers intracellular tryptophan shortage by upregulating the indoleamine 2,3-dioxygenase 1 (IDO1) enzyme^1-4. Here we show that despite tryptophan depletion, in-frame protein synthesis continues across tryptophan codons. We identified tryptophan-to-phenylalanine codon reassignment (W>F) as the major event facilitating this process, and pinpointed tryptophanyl-tRNA synthetase (WARS1) as its source. We call these W>F peptides 'substitutants' to distinguish them from genetically encoded mutants. Using large-scale proteomics analyses, we demonstrate W>F substitutants to be highly abundant in multiple cancer types. W>F substitutants were enriched in tumours relative to matching adjacent normal tissues, and were associated with increased IDO1 expression, oncogenic signalling and the tumour-immune microenvironment. Functionally, W>F substitutants can impair protein activity, but also expand the landscape of antigens presented at the cell surface to activate T cell responses. Thus, substitutants are generated by an alternative decoding mechanism with potential effects on gene function and tumour immunoreactivity.

Better Agreement of Human Transcriptomic and Proteomic Cancer Expression Data at the Molecular Pathway Activation Level

Previously, we have shown that the aggregation of RNA-level gene expression profiles into quantitative molecular pathway activation metrics results in lesser batch effects and better agreement between different experimental platforms. Here, we investigate whether pathway level of data analysis provides any advantage when comparing transcriptomic and proteomic data. We compare the paired proteomic and transcriptomic gene expression and pathway activation profiles obtained for the same human cancer biosamples in The Cancer Genome Atlas (TCGA) and the NCI Clinical Proteomic Tumor Analysis Consortium (CPTAC) projects, for a total of 755 samples of glioblastoma, breast, liver, lung, ovarian, pancreatic, and uterine cancers. In a CPTAC assay, expression levels of 15,112 protein-coding genes were profiled using the Thermo QE series of mass spectrometers. In TCGA, RNA expression levels of the same genes were obtained using the Illumina HiSeq 4000 engine for the same biosamples. At the gene level, absolute gene expression values are compared, whereas pathway-grade comparisons are made between the pathway activation levels (PALs) calculated using average sample-normalized transcriptomic and proteomic profiles. We observed remarkably different average correlations between the primary RNA- and protein expression data for different cancer types: Spearman Rho between 0.017 (p = 1.7 × 10−13) and 0.27 (p < 2.2 × 10−16). However, at the pathway level we detected overall statistically significantly higher correlations: averaged Rho between 0.022 (p < 2.2 × 10−16) and 0.56 (p < 2.2 × 10−16). Thus, we conclude that data analysis at the PAL-level yields results of a greater similarity when comparing high-throughput RNA and protein expression profiles.

LINE-1 expression in cancer correlates with p53 mutation, copy number alteration, and S phase checkpoint

Retrotransposons are genomic DNA sequences that copy themselves to new genomic locations via RNA intermediates; LINE-1 is the only active and autonomous retrotransposon in the human genome. The mobility of LINE-1 is largely repressed in somatic tissues but is derepressed in many cancers, where LINE-1 retrotransposition is correlated with p53 mutation and copy number alteration (CNA). In cell lines, inducing LINE-1 expression can cause double-strand breaks (DSBs) and replication stress. Reanalyzing multiomic data from breast, ovarian, endometrial, and colon cancers, we confirmed correlations between LINE-1 expression, p53 mutation status, and CNA. We observed a consistent correlation between LINE-1 expression and the abundance of DNA replication complex components, indicating that LINE-1 may also induce replication stress in human tumors. In endometrial cancer, high-quality phosphoproteomic data allowed us to identify the DSB-induced ATM-MRN-SMC S phase checkpoint pathway as the primary DNA damage response (DDR) pathway associated with LINE-1 expression. Induction of LINE-1 expression in an in vitro model led to increased phosphorylation of MRN complex member RAD50, suggesting that LINE-1 directly activates this pathway.

Multi-modal Profiling of the Extracellular Matrix of Human Fallopian Tubes and Serous Tubal Intraepithelial Carcinomas

Recent evidence supports the fimbriae of the fallopian tube as one origin site for high-grade serous ovarian cancer (HGSOC). The progression of many solid tumors is accompanied by changes in the microenvironment, including alterations of the extracellular matrix (ECM). Therefore, we sought to determine the ECM composition of the benign fallopian tube and changes associated with serous tubal intraepithelial carcinomas (STICs), precursors of HGSOC. The ECM composition of benign human fallopian tube was first defined from a meta-analysis of published proteomic datasets that identified 190 ECM proteins. We then conducted de novo proteomics using ECM enrichment and identified 88 proteins, 7 of which were not identified in prior studies (COL2A1, COL4A5, COL16A1, elastin, LAMA5, annexin A2, and PAI1). To enable future in vitro studies, we investigated the levels and localization of ECM components included in tissue-engineered models (type I, III, and IV collagens, fibronectin, laminin, versican, perlecan, and hyaluronic acid) using multispectral immunohistochemical staining of fimbriae from patients with benign conditions or STICs. Quantification revealed an increase in stromal fibronectin and a decrease in epithelial versican in STICs. Our results provide an in-depth picture of the ECM in the benign fallopian tube and identified ECM changes that accompany STIC formation. (J Histochem Cytochem XX: XXX-XXX, XXXX).

Integrated weighted gene co-expression network analysis reveals biomarkers associated with prognosis of high-grade serous ovarian cancer

Background

Ovarian cancer is the gynecologic tumor with the highest fatality rate, and high‐grade serous ovarian cancer (HGSOC) is the most common and malignant type of ovarian cancer. One important reason for the poor prognosis of HGSOC is the lack of effective diagnostic and prognostic biomarkers. New biomarkers are necessary for the improvement of treatment strategies and to ensure appropriate healthcare decisions.

Methods

To construct the co‐expression network of HGSOC samples, we applied weighted gene co‐expression network analysis (WGCNA) to assess the proteomic data obtained from the Clinical Proteomic Tumor Analysis Consortium (CPTAC), and module‐trait relationship was then analyzed and plotted in a heatmap to choose key module associated with HGSOC. Subsequently, hub genes with high connectivity in key module were identified by Cytoscape software. Furthermore, the biomarkers were selected through survival analysis, followed by evaluation using the relative operating characteristic (ROC) analysis.

Results

A total of 9 modules were identified by WGCNA, and module‐trait analysis revealed that the brown module was significantly associated with HGSOC (cor = 0.7). Ten hub genes with the highest connectivity were selected by protein‐protein interaction analysis. After survival and ROC analysis, ALB, APOB and SERPINA1 were suggested to be the biomarkers, and their protein levels were positively correlated with HGSOC prognosis.

Conclusion

We conducted the first gene co‐expression analysis using proteomic data from HGSOC samples, and found that ALB, APOB and SERPINA1 had prognostic value, which might be applied for the treatment of HGSOC in the future.

The role of patient-derived ovarian cancer organoids in the study of PARP inhibitors sensitivity and resistance: from genomic analysis to functional testing

Epithelial ovarian cancer (EOC) harbors distinct genetic features such as homologous recombination repair (HRR) deficiency, and therefore may respond to poly ADP-ribose polymerase inhibitors (PARPi). Over the past few years, PARPi have been added to the standard of care for EOC patients in both front-line and recurrent settings. Next-generation sequencing (NGS) genomic analysis provides key information, allowing for the prediction of PARPi response in patients who are PARPi naïve. However, there are indeed some limitations in NGS analyses. A subset of patients can benefit from PARPi, despite the failed detection of the predictive biomarkers such as BRCA1/2 mutations or HRR deficiency. Moreover, in the recurrent setting, the sequencing of initial tumor does not allow for the detection of reversions or secondary mutations restoring proficient HRR and thus leading to PARPi resistance. Therefore, it becomes crucial to better screen patients who will likely benefit from PARPi treatment, especially those with prior receipt of maintenance PARPi therapy. Recently, patient-derived organoids (PDOs) have been regarded as a reliable preclinical platform with clonal heterogeneity and genetic features of original tumors. PDOs are found feasible for functional testing and interrogation of biomarkers for predicting response to PARPi in EOC. Hence, we review the strengths and limitations of various predictive biomarkers and highlight the role of patient-derived ovarian cancer organoids as functional assays in the study of PARPi response. It was found that a combination of NGS and functional assays using PDOs could enhance the efficient screening of EOC patients suitable for PARPi, thus prolonging their survival time.

Recent Developments and Applications of Quantitative Proteomics Strategies for High-Throughput Biomolecular Analyses in Cancer Research

Innovations in medical technology and dedicated focus from the scientific community have inspired numerous treatment strategies for benign and invasive cancers. While these improvements often lend themselves to more positive prognoses and greater patient longevity, means for early detection and severity stratification have failed to keep pace. Detection and validation of cancer-specific biomarkers hinges on the ability to identify subtype-specific phenotypic and proteomic alterations and the systematic screening of diverse patient groups. For this reason, clinical and scientific research settings rely on high throughput and high sensitivity mass spectrometry methods to discover and quantify unique molecular perturbations in cancer patients. Discussed within is an overview of quantitative proteomics strategies and a summary of recent applications that enable revealing potential biomarkers and treatment targets in prostate, ovarian, breast, and pancreatic cancer in a high throughput manner.

Mapping of m6A and Its Regulatory Targets in Prostate Cancer Reveals a METTL3-Low Induction of Therapy Resistance

Recent evidence has highlighted the role of N ⁶-methyladenosine (m⁶A) in the regulation of mRNA expression, stability, and translation, supporting a potential role for posttranscriptional regulation mediated by m⁶A in cancer. Here, we explore prostate cancer as an exemplar and demonstrate that low levels of N ⁶-adenosine-methyltransferase (METTL3) is associated with advanced metastatic disease. To investigate this relationship, we generated the first prostate m⁶A maps, and further examined how METTL3 regulates expression at the level of transcription, translation, and protein. Significantly, transcripts encoding extracellular matrix proteins are consistently upregulated with METTL3 knockdown. We also examined the relationship between METTL3 and androgen signaling and discovered the upregulation of a hepatocyte nuclear factor-driven gene signature that is associated with therapy resistance in prostate cancer. Significantly, METTL3 knockdown rendered the cells resistant to androgen receptor antagonists via an androgen receptor-independent mechanism driven by the upregulation of nuclear receptor NR5A2/LRH-1. IMPLICATIONS: These findings implicate changes in m⁶A as a mechanism for therapy resistance in metastatic prostate cancer.

Comprehending the Proteomic Landscape of Ovarian Cancer: A Road to the Discovery of Disease Biomarkers

Despite recent technological advancements allowing the characterization of cancers at a molecular level along with biomarkers for cancer diagnosis, the management of ovarian cancers (OC) remains challenging. Proteins assume functions encoded by the genome and the complete set of proteins, termed the proteome, reflects the health state. Comprehending the circulatory proteomic profiles for OC subtypes, therefore, has the potential to reveal biomarkers with clinical utility concerning early diagnosis or to predict response to specific therapies. Furthermore, characterization of the proteomic landscape of tumor-derived tissue, cell lines, and PDX models has led to the molecular stratification of patient groups, with implications for personalized therapy and management of drug resistance. Here, we review single and multiple marker panels that have been identified through proteomic investigations of patient sera, effusions, and other biospecimens. We discuss their clinical utility and implementation into clinical practice.

Basic and Preclinical Research for Personalized Medicine

Basic and preclinical research founded the progress of personalized medicine by providing a prodigious amount of integrated profiling data and by enabling the development of biomedical applications to be implemented in patient-centered care and cures. If the rapid development of genomics research boosted the birth of personalized medicine, further development in omics technologies has more recently improved our understanding of the functional genome and its relevance in profiling patients’ phenotypes and disorders. Concurrently, the rapid biotechnological advancement in diverse research areas enabled uncovering disease mechanisms and prompted the design of innovative biological treatments tailored to individual patient genotypes and phenotypes. Research in stem cells enabled clarifying their role in tissue degeneration and disease pathogenesis while providing novel tools toward the development of personalized regenerative medicine strategies. Meanwhile, the evolving field of integrated omics technologies ensured translating structural genomics information into actionable knowledge to trace detailed patients’ molecular signatures. Finally, neuroscience research provided invaluable models to identify preclinical stages of brain diseases. This review aims at discussing relevant milestones in the scientific progress of basic and preclinical research areas that have considerably contributed to the personalized medicine revolution by bridging the bench-to-bed gap, focusing on stem cells, omics technologies, and neuroscience fields as paradigms.

Proteomic Studies on the Management of High-Grade Serous Ovarian Cancer Patients: A Mini-Review

High-grade serous ovarian cancer (HGSC) remains the most common and deadly subtype of ovarian cancer. It is characterized by its late diagnosis and frequent relapse despite standardized treatment with cytoreductive surgery and platinum-based chemotherapy. The past decade has seen significant advances in the clinical management and molecular understanding of HGSC following the publication of the Cancer Genome Atlas (TCGA) researchers and the introduction of targeted therapies with anti-angiogenic drugs and poly(ADP-ribose) polymerase inhibitors in specific subgroups of patients. We provide a comprehensive review of HGSC, focusing on the most important molecular advances aimed at providing a better understanding of the disease and its response to treatment. We emphasize the role that proteomic technologies are now playing in these two aspects of the disease, through the identification of proteins and their post-translational modifications in ovarian cancer tumors. Finally, we highlight how the integration of proteomics with genomics, exemplified by the work performed by the Clinical Proteomic Tumor Analysis Consortium (CPTAC), can guide the development of new biomarkers and therapeutic targets.

The next horizon in precision oncology: Proteogenomics to inform cancer diagnosis and treatment

When it comes to precision oncology, proteogenomics may provide better prospects to the clinical characterization of tumors, help make a more accurate diagnosis of cancer, and improve treatment for patients with cancer. This perspective describes the significant contributions of The Cancer Genome Atlas and the Clinical Proteomic Tumor Analysis Consortium to precision oncology and makes the case that proteogenomics needs to be fully integrated into clinical trials and patient care in order for precision oncology to deliver the right cancer treatment to the right patient at the right dose and at the right time.

leapR: An R Package for Multiomic Pathway Analysis

A generalized goal of many high-throughput data studies is to identify functional mechanisms that underlie observed biological phenomena, whether they be disease outcomes or metabolic output. Increasingly, studies that rely on multiple sources of high-throughput data (genomic, transcriptomic, proteomic, metabolomic) are faced with a challenge of summarizing the data to generate testable hypotheses. However, this requires a time-consuming process to evaluate numerous statistical methods across numerous data sources. Here, we introduce the leapR package, a framework to rapidly assess biological pathway activity using diverse statistical tests and data sources, allowing facile integration of multisource data. The leapR package with a user manual and example workflow is available for download from GitHub (https://github.com/biodataganache/leapR).

Analysis of ovarian cancer cell secretome during epithelial to mesenchymal transition reveals a protein signature associated with advanced stages of ovarian tumors

Ovarian cancer (OvCA) is the most lethal neoplasia among gynecologic malignancies and faces high rates of new cases particularly in South America. In special, the High Grade Serous Ovarian Carcinoma (HGSC) presents very poor prognosis with deaths caused mainly by metastasis. Among several mechanisms involved in metastasis, the Epithelial to Mesenchymal Transition (EMT) molecular reprogramming represents a model for latest stages of cancer progression. EMT promotes important cellular changes in cellular adhesion and cell-cell communication, which particularly depends on the paracrine signaling from neighbor cells. Considering the importance of cellular communication during EMT and metastasis, here we analyzed the changes in the secretome of the ovarian cancer cell line Caov-3 induced to EMT by Epidermal Growth Factor (EGF). Using a combination of GEL-LC-MS/MS and stable isotopic metabolic labelling (SILAC), we identified up-regulated candidates during EMT as a starting point to identify relevant proteins for HGSC. Based on public databases, our candidate proteins were validated and prioritized for further analysis. Importantly, several of the protein candidates were associated with cellular vesicles, which are important to the cell-cell communication and metastasis. Furthermore, the association of candidate proteins with gene expression data uncovered a subset of proteins correlated with the mesenchymal subtype of ovarian cancer. Based on this relevant molecular signature for aggressive ovarian cancer, supported by protein and gene expression data, we developed a targeted proteomic method to evaluate individual OvCA clinical samples. The quantitative information obtained for 33 peptides, representative of 18 proteins, was able to segregate HGSC from other tumor types. Our study highlighted the richness of the secretome and EMT to reveal relevant proteins for HGSC, which could be used in further studies and larger patient cohorts as a potential stratification signature for ovarian cancer tumor that could guide clinical conduct for patient treatment.

Simplified and Unified Access to Cancer Proteogenomic Data

Comprehensive cancer data sets recently generated by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) offer great potential for advancing our understanding of how to combat cancer. These data sets include DNA, RNA, protein, and clinical characterization for tumor and normal samples from large cohorts of many different cancer types. The raw data are publicly available at various Cancer Research Data Commons. However, widespread reuse of these data sets is also facilitated by easy access to the processed quantitative data tables. We have created a data application programming interface (API) to distribute these processed tables, implemented as a Python package called cptac. We implement it such that users who prefer to work in R can easily use our package for data access and then transfer the data into R for analysis. Our package distributes the finalized processed CPTAC data sets in a consistent, up-to-date format. This consistency makes it easy to integrate the data with common graphing, statistical, and machine-learning packages for advanced analysis. Additionally, consistent formatting across all cancer types promotes the investigation of pan-cancer trends. The data API structure of directly streaming data within a programming environment enhances the reproducibility. Finally, with the accompanying tutorials, this package provides a novel resource for cancer research education. View the software documentation at https://paynelab.github.io/cptac/. View the GitHub repository at https://github.com/PayneLab/cptac.

Integrated Proteomic and Glycoproteomic Characterization of Human High-Grade Serous Ovarian Carcinoma

Many gene products exhibit great structural heterogeneity because of an array of modifications. These modifications are not directly encoded in the genomic template but often affect the functionality of proteins. Protein glycosylation plays a vital role in proper protein functions. However, the analysis of glycoproteins has been challenging compared with other protein modifications, such as phosphorylation. Here, we perform an integrated proteomic and glycoproteomic analysis of 83 prospectively collected high-grade serous ovarian carcinoma (HGSC) and 23 non-tumor tissues. Integration of the expression data from global proteomics and glycoproteomics reveals tumor-specific glycosylation, uncovers different glycosylation associated with three tumor clusters, and identifies glycosylation enzymes that were correlated with the altered glycosylation. In addition to providing a valuable resource, these results provide insights into the potential roles of glycosylation in the pathogenesis of HGSC, with the possibility of distinguishing pathological outcomes of ovarian tumors from non-tumors, as well as classifying tumor clusters.

A high-stringency blueprint of the human proteome

The Human Proteome Organization (HUPO) launched the Human Proteome Project (HPP) in 2010, creating an international framework for global collaboration, data sharing, quality assurance and enhancing accurate annotation of the genome-encoded proteome. During the subsequent decade, the HPP established collaborations, developed guidelines and metrics, and undertook reanalysis of previously deposited community data, continuously increasing the coverage of the human proteome. On the occasion of the HPP's tenth anniversary, we here report a 90.4% complete high-stringency human proteome blueprint. This knowledge is essential for discerning molecular processes in health and disease, as we demonstrate by highlighting potential roles the human proteome plays in our understanding, diagnosis and treatment of cancers, cardiovascular and infectious diseases.

ATM inhibition synergizes with fenofibrate in high grade serous ovarian cancer cells

While therapies targeting deficiencies in the homologous recombination (HR) pathway are emerging as the standard treatment for high grade serous ovarian cancer (HGSOC) patients, this strategy is limited to the ~50% of patients with a deficiency in this pathway. Therefore, patients with HR-proficient tumors are likely to be resistant to these therapies and require alternative strategies. We found that the HR gene Ataxia Telangiectasia Mutated (ATM) is wildtype and its activity is upregulated in HGSOC compared to normal fallopian tube tissue. Interestingly, multiple pathways related to metabolism are inversely correlated with ATM expression in HGSOC specimens, suggesting that combining ATM inhibition with metabolic drugs would be effective. Analysis of FDA-approved drugs from the Dependency Map demonstrated that ATM-low cells are more sensitive to fenofibrate, a PPARα agonist that affects multiple cellular metabolic pathways. Consistently, PPARα signaling is associated with ATM expression. We validated that combined inhibition of ATM and treatment with fenofibrate is synergistic in multiple HGSOC cell lines by inducing senescence. Together, our results suggest that metabolic changes induced by ATM inhibitors are a potential target for the treatment of HGSOC.