Cancer Proteomics Research Articles

Proteogenomic landscape of squamous cell lung cancer

How genomic and transcriptomic alterations affect the functional proteome in lung cancer is not fully understood. Here, we integrate DNA copy number, somatic mutations, RNA-sequencing, and expression proteomics in a cohort of 108 squamous cell lung cancer (SCC) patients. We identify three proteomic subtypes, two of which (Inflamed, Redox) comprise 87% of tumors. The Inflamed subtype is enriched with neutrophils, B-cells, and monocytes and expresses more PD-1. Redox tumours are enriched for oxidation-reduction and glutathione pathways and harbor more NFE2L2/KEAP1 alterations and copy gain in the 3q2 locus. Proteomic subtypes are not associated with patient survival. However, B-cell-rich tertiary lymph node structures, more common in Inflamed, are associated with better survival. We identify metabolic vulnerabilities (TP63, PSAT1, and TFRC) in Redox. Our work provides a powerful resource for lung SCC biology and suggests therapeutic opportunities based on redox metabolism and immune cell infiltrates.

Cancer Proteomics and the Elusive Diagnostic Biomarkers.

Despite progress in genomic and proteomic technology and applications, the validation of cancer biomarkers of use as clinical early detection diagnostics has remained elusive. As described in this brief viewpoint, there are now recognized to be many types of clinical biomarkers and proteomic analyses, particularly when combined with other 'omic analyses, have been effective in many such biomarker identifications. However, in the area of early diagnosis of cancers, the problems associated with the conversion from identification to diagnostic have largely not been overcome. Notably, the Clinical Proteomic Tumor Analysis Consortium (CPTAC) of the National Cancer Institute (NCI), has been particularly successful in refining the analytical steps needed to tackle this challenging issue and has provided positive insight into how to solve many of the underlying problems. The potential for developing clinical diagnostics for early detection of highly lethal cancers and possible new therapeutic strategies through proteomic analyses, as seen through these CPTAC successes, is more promising than ever.

TCPA v3.0: An Integrative Platform to Explore the Pan-Cancer Analysis of Functional Proteomic Data

Reverse-phase protein arrays represent a powerful functional proteomics approach to characterizing cell signaling pathways and understanding their effects on cancer development. Using this platform, we have characterized ∼8,000 patient samples of 32 cancer types through The Cancer Genome Atlas and built a widely used, open-access bioinformatic resource, The Cancer Proteome Atlas (TCPA). To maximize the utility of TCPA, we have developed a new module called "TCGA Pan-Cancer Analysis," which provides comprehensive protein-centric analyses that integrate protein expression data and other TCGA data across cancer types. We further demonstrate the value of this module by examining the correlations of RPPA proteins with significantly mutated genes, assessing the predictive power of somatic copy-number alterations, DNA methylation, and mRNA on protein expression, inferring the regulatory effects of miRNAs on protein expression, constructing a co-expression network of proteins and pathways, and identifying clinically relevant protein markers. This upgraded TCPA (v3.0) will provide the cancer research community with a more powerful tool for studying functional proteomics and making translational impacts.

The impact of DNA methylation on the cancer proteome.

Aberrant DNA methylation disrupts normal gene expression in cancer and broadly contributes to oncogenesis. We previously developed MethylMix, a model-based algorithmic approach to identify epigenetically regulated driver genes. MethylMix identifies genes where methylation likely executes a functional role by using transcriptomic data to select only methylation events that can be linked to changes in gene expression. However, given that proteins more closely link genotype to phenotype recent high-throughput proteomic data provides an opportunity to more accurately identify functionally relevant abnormal methylation events. Here we present a MethylMix analysis that refines nominations for epigenetic driver genes by leveraging quantitative high-throughput proteomic data to select only genes where DNA methylation is predictive of protein abundance. Applying our algorithm across three cancer cohorts we find that using protein abundance data narrows candidate nominations, where the effect of DNA methylation is often buffered at the protein level. Next, we find that MethylMix genes predictive of protein abundance are enriched for biological processes involved in cancer including functions involved in epithelial and mesenchymal transition. Moreover, our results are also enriched for tumor markers which are predictive of clinical features like tumor stage and we find clustering using MethylMix genes predictive of protein abundance captures cancer subtypes.

MiRFA: an automated pipeline for microRNA functional analysis with correlation support from TCGA and TCPA expression data in pancreatic cancer

BackgroundMicroRNAs (miRNAs) are small RNAs that regulate gene expression at a post-transcriptional level and are emerging as potentially important biomarkers for various disease states, including pancreatic cancer. In silico-based functional analysis of miRNAs usually consists of miRNA target prediction and functional enrichment analysis of miRNA targets. Since miRNA target prediction methods generate a large number of false positive target genes, further validation to narrow down interesting candidate miRNA targets is needed. One commonly used method correlates miRNA and mRNA expression to assess the regulatory effect of a particular miRNA.The aim of this study was to build a bioinformatics pipeline in R for miRNA functional analysis including correlation analyses between miRNA expression levels and its targets on mRNA and protein expression levels available from the cancer genome atlas (TCGA) and the cancer proteome atlas (TCPA). TCGA-derived expression data of specific mature miRNA isoforms from pancreatic cancer tissue was used.ResultsFifteen circulating miRNAs with significantly altered expression levels detected in pancreatic cancer patients were queried separately in the pipeline. The pipeline generated predicted miRNA target genes, enriched gene ontology (GO) terms and Kyoto encyclopedia of genes and genomes (KEGG) pathways. Predicted miRNA targets were evaluated by correlation analyses between each miRNA and its predicted targets. MiRNA functional analysis in combination with Kaplan-Meier survival analysis suggest that hsa-miR-885-5p could act as a tumor suppressor and should be validated as a potential prognostic biomarker in pancreatic cancer.ConclusionsOur miRNA functional analysis (miRFA) pipeline can serve as a valuable tool in biomarker discovery involving mature miRNAs associated with pancreatic cancer and could be developed to cover additional cancer types. Results for all mature miRNAs in TCGA pancreatic adenocarcinoma dataset can be studied and downloaded through a shiny web application at https://emmbor.shinyapps.io/mirfa/.

Abstract 4260: Novel insights into immunotherapy by deep mining of big cancer genomic data

Abstract Immunotherapy has revolutionized patient care and cancer research. To gain novel insights into immunotherapy, we performed the pan-cancer analyses of >11,000 tumor samples of 33 cancer types from The Cancer Genome Atlas and CPTAC as well as large amounts of in-house immunotherapy related data. Through the mining on these large-scale cancer genomics and proteomics data, I will discuss three novel ideas that would challenge the current paradigm for developing effective immunotherapy. (I) Checkpoint inhibition: antibody is NOT the only approach. I will discuss the role of enhancers in regulating immune checkpoint. (II) Neoantigen: Somatic mutations are NOT the only source. I will discuss the contribution of RNA editing to proteomics diversity and neoantigen in cancer. (III) Predictive markers: gene-based markers is NOT the only option. I will discuss the utility of a global energy index for predicting the response to anti-PD1 treatment. Collectively, these results suggest novel directions for future immunotherapy development. Citation Format: Han Liang. Novel insights into immunotherapy by deep mining of big cancer genomic data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4260.

Abstract 4933: RAPTOR protein overexpression is predictive of poor clinical outcomes in head and neck squamous cell carcinoma (HNSCC) patients

Abstract Head and neck squamous cell carcinoma (HNSCC) has a high mortality rate, and with rising incidences in Asia. Previously, we demonstrated that the PI3K pathway is the most frequently altered pathway in HNSCC (Lui et al, Cancer Discovery, 2013). However, cumulative PI3K pathway aberrations, nor PIK3CA aberrations, are not associated with clinical outcomes in HNSCC patients (P=n.s.). In this study, we investigate the prognostic significance of RAPTOR (Regulatory associated protein of mTOR), a key component of the PI3K signaling pathway, in HNSCC. To examine the clinical relevance of RAPTOR levels in human cancers, including HNSCC, we performed pan-cancer analyses of RAPTOR protein levels in the Cancer Proteome Atlas datasets (TCPA, USA). Strikingly, across 28 tumor types examined, we found that RAPTOR protein overexpression is most significantly associated with poor patient survival in HNSCC (P=0.00162***), followed by renal clear cell carcinoma (P=0.00198), and stomach cancer (P=0.0443; Log-Rank test; Table 1). We then extracted the original quantification data of RAPTOR protein array of HNC (TCPA, USA), and performed RAPTOR expression-survival correlation analyses on quantitative basis by Kaplan-Meier analysis with Log-Rank test. We found that HNSCC patients with RAPTOR protein overexpression in their primary tumors (mean+5SEM) have significantly poorer overall survival (median OS=27.56 months) than patients with RAPTOR protein underexpression (mean-5SEM; median OS=68.43 months). Moreover, this group of RAPTOR-high patients are found to have a higher risk of recurrences, with an Odd Ratio of 1.75 (P=0.05; Fisher’s Exact test). Notably, this poor OS of RAPTOR-high HNSCC is much shorter than that of TP53-mutated HNSCC in the same cohort (45.8 months; TCGA Provisional,N=510). Further, patients with extreme-high levels of RAPTOR protein expression (top 5% cases) have an even worst OS of only 11.56 months vs 48.16 months (the remaining 95% cases). Using our small cohort of Asian HNSCC tumors as a validation cohort, we show that RAPTOR protein overexpression is very common in Asian HNSCC (37.5%; 6/16 cases), suggestive of its likely contribution to HNSCC in Asia as well. In conclusion, RAPTOR-high status is likely to be a useful predictive of very poor HNSCC patient survival, possibly superior than the previously known survival-related biomarker,TP53 mutation. RAPTOR is one of the first components of the PI3K pathway shown to be associated with patient outcome in HNSCC. Acknowledgements:VWYL receives fundings from Health and Medical Research Fund(#15160691), Research Grant Council, Hong Kong(GRF#1711484,#17121616,# 14168571;T12-401/13-R), UICP(UIM/329;Innovation and Technology Fund); Hong Kong Cancer Fund. YCL is supported by Postdoctoral Hub(UIM/329) from Innovation and Technology Fund, Hong Kong government.J YKC supported by Dr Stanley Ho Medical Foundation. Citation Format: Hui Yan Poon, Yuchen Liu, Yu Xiong SU, Jason Y.k. Chan, Chin Wang Lau, Vivian W y Lui. RAPTOR protein overexpression is predictive of poor clinical outcomes in head and neck squamous cell carcinoma (HNSCC) patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4933.

Abstract 4393: Integrative proteomic analysis of prostate cancer reveals distinct regulation of RNA binding proteins during disease progression

Abstract To understand the etiology of the disease, and to find novel and more specific drug targets, the driver mutations and expressional changes in prostate cancer have been examined through extensive genomic and transcriptomic characterization. Although significant insight has been gained through these efforts, it is clear that not all molecular alterations influencing the tumor outcome can be captured through these approaches, and that a comprehensive understanding of the molecular events in cancer require thorough investigation of the proteome. To understand the functional consequences of genetic and transcriptional aberrations in prostate cancer, we aimed to reveal the proteomic changes during disease formation and progression. We performed high throughput mass spectrometry on clinical tissue samples of benign prostatic hyperplasia (BPH), untreated primary prostate cancer (PC) and castration resistant prostate cancer (CRPC). We performed an integrative analysis of the proteomic data with gene copy number, DNA methylation, and RNA expression data from the same samples. Furthermore, proteomic events correlating with the androgen receptor (AR) status of the tumors were analysed. We uncovered previously unrecognized molecular and pathway events and several novel AR-associated events in the prostate cancer proteomes to study further. We found significant changes in expression of RNA-binding proteins during disease formation and progression. Examining the relationship of RNA binding proteins at the RNA and protein expression level reveal that while many RNA binding proteins exhibit correlation between the expression levels, some seem regulated at the posttranslational level. Two RNA binding proteins, TDP-43 and FUS, which regulated at the protein, but not at RNA level during prostate cancer progression, show opposite behavior during disease progression and correlation with AR status of the tumors. In cultured prostate cancer cell models, we show that these proteins have specific, but divergent interactions with AR at the RNA and protein levels, and that they contribute differentially to AR activity-mediated responses. Thus, these proteins may significantly contribute to prostate cancer molecular evolution and may pinpoint possible targetable pathways in future prostate cancer therapy. Citation Format: Mauro Scaravilli, Annika Kohvakka, Pekka Ruusuvuori, Ebrahim Afyounian, Matti Nykter, Tapio Visakorpi, Leena Latonen. Integrative proteomic analysis of prostate cancer reveals distinct regulation of RNA binding proteins during disease progression [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4393.

In silico identification of epitopes present in human heat shock proteins (HSPs) overexpressed by tumour cells

Although many of heat shock proteins (HSPs) are crucial in homeostasis due to their role in maintaining cellular proteostasis by the integration of two pivotal processes—folding and degradation, several decades of cancer proteomics suggest that HSPs may improve cancer establishment and progression. Therefore, it is imperative to explore how these molecules impact patient outcomes and whether their interaction with the immune systems improves the protumour or antitumour environment. Here, using an immunoinformatics approach were investigated the best probable epitopes from ten HSPs (HSP90α, HSP90β, HSPA1A, HSPA1L, HSPA2, HSPA5, HSPA6, HSPB1, HSPB5 and HSP60/HSP10). To achieve this aim, antigenicity, immunogenicity (prediction of continuous and discontinuous B cell epitopes, binding peptides to HLA class I and HLA class II, and overlapping epitopes), analysis of conservancy and population coverage, and prediction of IgE epitopes were evaluated. According to the physicochemical properties used for their prediction (hydrophilicity, flexibility, accessibility and antigenicity propensity), ten continuous epitopes (one per HSPs) were considered as the best and also several regions of each molecule were identified as B discontinuous epitopes. Interestingly, peptides of HSP90β, HSPA2, HSPB1, and HSPB5 were predicted as both continuous and discontinuous B cell epitopes. For all the HSPs evaluated were identified potential overlapping epitopes (“NTFYSNKEI”, “TTYSCVGVF”, “TADRWRVSL”, “VKHFSPEEL” and “CEFQDAYVL”). Moreover, these peptides were negative for IgE epitopes and showed a large coverage in the human population (HLA-A*02, HLA-B*15, HLA-C*03, and HLA-C*12). Taken together, these data indicate that such epitopes may activate both the humoral and cell-mediated response, and thus serve as therapeutic targets for cancer. However, it must be assessed their efficacy and safety in vitro and in vivo before their translation in clinical trials.

Type-2 Fuzzy PCA Approach in Extracting Salient Features for Molecular Cancer Diagnostics and Prognostics.

Machine learning is becoming a powerful tool for cancer diagnosis and prognosis based on classification using high dimensional molecular data. However, extracting classification features from high-dimensional datasets remains a challenging problem. Principal component analysis (PCA) is a widely used method for dimensionality reduction. However, it is well-known that PCA and most PCA-based feature extraction methods are sensitive to noise, which may affect the accuracy of the subsequent classification. To address this problem, here we have proposed a robust fuzzy principal component analysis (PCA) with interval type-2 (IT-2) fuzzy membership functions for feature extraction. We have tested the performance of three widely used classifiers using the features extracted by proposed approaches and other feature extraction methods - PCA-based feature extraction methods (i.e. conventional PCA and fuzzy PCA), linear discriminant analysis (LDA), and support vector machine recursive feature elimination (SVM-RFE). The proposed feature extraction approaches showed better performance on cancer transcriptome and proteome datasets.

Simultaneous Integration of Multi-omics Data Improves the Identification of Cancer Driver Modules.

The identification of molecular pathways driving cancer progression is a fundamental challenge in cancer research. Most approaches to address it are limited in the number of data types they employ and perform data integration in a sequential manner. Here, we describe ModulOmics, a method to de novo identify cancer driver pathways, or modules, by integrating protein-protein interactions, mutual exclusivity of mutations and copy number alterations, transcriptional coregulation, and RNA coexpression into a single probabilistic model. To efficiently search and score the large space of candidate modules, ModulOmics employs a two-step optimization procedure that combines integer linear programming with stochastic search. Applied across several cancer types, ModulOmics identifies highly functionally connected modules enriched with cancer driver genes, outperforming state-of-the-art methods and demonstrating the power of using multiple omics data types simultaneously. On breast cancer subtypes, ModulOmics proposes unexplored connections supported by an independent patient cohort and independent proteomic and phosphoproteomic datasets.

SAT-349 Expression of Cyclin B1, Transferrin Receptor and Fibronectin Correlates with the Prognosis of Patients with Adrenal Cortical Carcinoma

Adrenal cortical carcinoma (ACC) is a rare cancer with poor prognosis. The main treatment of ACC is surgery, and the role of systemic chemotherapy is limited. However, up to 74% of patients are known to experience recurrence after surgery, even following complete resection (1). Therefore, it is important to know the biomarkers which can predict the prognosis beyond clinical stages. Previous studies have reported several prognostic factors of ACC including Ki67 and markers revealed by multi-omics approaches (2-4). However, the proteomic approach for predicting prognosis has not been investigated. Therefore, we aimed to investigate the prognostic biomarkers for ACCs using proteomic approach. We used the reverse phase protein array data from The Cancer Proteome Atlas. Differentially expressed proteins in metastatic and non-metastatic ACCs were identified using Benjamini-Hochberg method. We validated the identified proteins using the multivariate Cox’s proportional hazard regression analysis for metastasis including age and clinical staging. Additive prognostic values were analyzed using C-index and category free Net reclassification index (cfNRI). Among 46 ACC patients, cyclin B1, transferrin receptor (TFRC) and fibronectin were significantly overexpressed in patients with distant metastasis. In the Cox regression model, during median follow-up of 3.1 years, high expression status of cyclin B1 and TFRC were significantly associated with high mortality (hazard ratios [HR] (95% confidence interval [CI]), 6.13 (1.02-36.7), and 6.59 (1.14-38.2), respectively). However, high expression status of fibronectin was not associated with mortality (HR [95% CI], 3.92 [0.75-20.4]). When we combined protein expression signatures, high cyclin B1/high TFRC, high cyclin B1/high fibronectin and high TFRC/high fibronectin were highly associated with mortality: HR (95% CI) 13.72 (1.89-99.66), HR 9.22 (1.34-63.55) and HR 18.59 (2.54-135.88), respectively. Compared to age and staging model, addition of cyclin B1, TFRC, fibronectin, or combination of these proteins models all improved the risk prediction by C-index (0.780 to 0.818~0.864) and cfNRI (0.741~1.027, all P<0.05). Among ACC patients, overexpression of various combinations of cyclin B1, TFRC and fibronectin proteins were associated with poor prognosis. Also, these combinatory biomarkers showed additive prognostic values beyond age and clinical staging, and combination of cyclin B1 and TFRC showed poorest prognosis and highest predictive value among them. Thus, the combination of these three proteins can be useful prognostic markers for ACC. Reference: (1) Freire DS et al., Clin Endocrinol (Oxf). 2013 Oct;79(4):468-75. (2) Beuschlein F et al., J Clin Endocrinol Metab. 2015 Mar;100(3):841-9. (3) Assié G et al., Nat Genet. 2014 Jun;46(6):607-12. (4) Zheng S et al., Cancer Med. 2016 Aug;5(8):2117-25.

The Potential of Targeting P53 and HSP90 Overcoming Acquired MAPKi-Resistant Melanoma.

Melanoma is the deadliest form of skin cancer worldwide. The rising melanoma incidence and mortality, along with its high propensity for metastasis highlights the urgency to identify more effective therapeutic targets. Approximately, one half of advanced melanoma bears a mutation in the BRAF gene that makes BRAF as an important therapeutic target. Significant clinical benefit is associated with BRAF and MEK inhibitors (MAPKi) on targeting patients with BRAF V600 mutations. However, the frequent and rapid development of acquired resistance still is the major challenge facing the melanoma. Several mechanisms by which melanoma passes the inhibitory effects of MAPKi have been characterized and clinically translated, but additional alternations of genetic and epigenetic regulators outside of MAPK and/or AKT networks occurs in a quarter of patients with acquired MAPKi resistance. These studies implicate that targeting signaling networks external MAPK or AKT pathways is critical. In this review, we will focus on two approaches that are under evaluating for targeting melanoma: (1) against genome instability by p53 network restoration and (2) disrupt cancer proteome by chaperone inhibition.

Steps Towards Diagnosis by Cancer Proteomics

Steps Towards Diagnosis by Cancer Proteomics

Sliced inverse regression for integrative multi-omics data analysis.

Advancement in next-generation sequencing, transcriptomics, proteomics and other high-throughput technologies has enabled simultaneous measurement of multiple types of genomic data for cancer samples. These data together may reveal new biological insights as compared to analyzing one single genome type data. This study proposes a novel use of supervised dimension reduction method, called sliced inverse regression, to multi-omics data analysis to improve prediction over a single data type analysis. The study further proposes an integrative sliced inverse regression method (integrative SIR) for simultaneous analysis of multiple omics data types of cancer samples, including MiRNA, MRNA and proteomics, to achieve integrative dimension reduction and to further improve prediction performance. Numerical results show that integrative analysis of multi-omics data is beneficial as compared to single data source analysis, and more importantly, that supervised dimension reduction methods possess advantages in integrative data analysis in terms of classification and prediction as compared to unsupervised dimension reduction methods.

Computational Advances in the Label-free Quantification of Cancer Proteomics Data.

Due to its ability to provide quantitative and dynamic information on tumor genesis and development by directly profiling protein expression, the proteomics has become intensely popular for characterizing the functional proteins driving the transformation of malignancy, tracing the large-scale protein alterations induced by anticancer drug, and discovering the innovative targets and first-in-class drugs for oncologic disorders. To quantify cancer proteomics data, the label-free quantification (LFQ) is frequently employed. However, low precision, poor reproducibility and inaccuracy of the LFQ of proteomics data have been recognized as the key "technical challenge" in the discovery of anticancer targets and drugs. In this paper, the recent advances and development in the computational perspective of LFQ in cancer proteomics were therefore systematically reviewed and analyzed. PubMed and Web of Science database were searched for label-free quantification approaches, cancer proteomics and computational advances. First, a variety of popular acquisition techniques and state-of-the-art quantification tools are systematically discussed and critically assessed. Then, many processing approaches including transformation, normalization, filtering and imputation are subsequently discussed, and their impacts on improving LFQ performance of cancer proteomics are evaluated. Finally, the future direction for enhancing the computation-based quantification technique for cancer proteomics are also proposed. There is a dramatic increase in LFQ approaches in recent year, which significantly enhance the diversity of the possible quantification strategies for studying cancer proteomics.

Prostasomas: búsqueda de biomarcadores para la detección temprana del cáncer prostático

El cáncer de próstata es la segunda enfermedad más diagnosticada en hombres a nivel mundial, con una tasa de mortalidad creciente en los últimos años. Actualmente, se cuenta con dos pruebas de detección temprana: la medición de los niveles en sangre del antígeno prostático específico y el tacto rectal de la próstata. Sin embargo, estas pruebas no presentan óptima especificidad y sensibilidad para su detección. Aunque diferentes estudios han buscado nuevos biomarcadores mediante la implementación de tecnologías, como secuenciación de nueva generación, espectrometría de masas, entre otras, aún persisten las mismas desventajas, por lo que no les ha permitido a estos su uso en la práctica clínica; razón por la cual, el descubrimiento de nuevos biomarcadores para el diagnóstico de cáncer de próstata, constituye un desafío para la comunidad científica. Los prostasomas corresponden a vesículas extracelulares secretadas por el tejido prostático normal o tumoral que pueden ser detectadas en diferentes fluidos. Estructuralmente, los prostasomas difieren de otros exosomas, por su tamaño, composición de membrana y contenido específico de proteínas, lo que los convierten en una fuente potencial y novedosa de biomarcadores clínicos. En este contexto, esta revisión presenta un panorama general de los biomarcadores proteicos, aislados desde prostasomas presentes en diferentes fluidos biológicos, para el posible diagnóstico de cáncer de próstata. Para ello se realizó una búsqueda sistemática en PubMed para estudios en proteómica para cáncer de próstata, con criterios como: vesículas extracelulares, exosomas y prostasomas, asimismo, sangre, orina, líquido seminal, entre otras muestras biológicas.

SUMO-Binding Entities (SUBEs) as Tools for the Enrichment, Isolation, Identification, and Characterization of the SUMO Proteome in Liver Cancer

SUMO-Binding Entities (SUBEs) as Tools for the Enrichment, Isolation, Identification, and Characterization of the SUMO Proteome in Liver Cancer

Ranking Cancer Proteins by Integrating PPI Network and Protein Expression Profiles.

Proteomics, the large-scale analysis of proteins, is contributing greatly to understanding gene function in the postgenomic era. However, disease protein ranking using shotgun proteomics data has not been fully evaluated. In this study, we prioritized disease-related proteins by integrating the protein-protein interaction (PPI) network and protein differential expression profiles from colon and rectal cancer (CRC) or breast cancer (BC) proteomics. We applied Local Ranking (LR) and Global Ranking (GR) methods in network with three kinds of protein sets as a priori knowledge, which were known disease proteins (KDPs) that were collected from the Online Mendelian Inheritance in Man (OMIM) database, differentially expressed proteins (DEPs), and the collection of KDPs and their direct neighborhood with differential expression (eKDPs). The cross-validations showed that GR method outperformed LR method while using eKDPs as the initial training showed significantly higher accuracy compared to using the other two a priori sets. And then we validated the top ranked proteins using RNAi-based loss-of-function screens in the DepMap database. The results showed that 75% of top 20 proteins in CRC are necessary for tumor survival. In summary, the network-based Global Ranking with protein differential expression can efficiently prioritize cancer-related proteins and discover new candidate cancer genes or proteins.

Sample Treatment for Tissue Proteomics in Cancer, Toxicology, and Forensics.

Since the birth of proteomics science in the 1990, the number of applications and of sample preparation methods has grown exponentially, making a huge contribution to the knowledge in life science disciplines. Continuous improvements in the sample treatment strategies unlock and reveal the fine details of disease mechanisms, drug potency, and toxicity as well as enable new disciplines to be investigated such as forensic science.This chapter will cover the most recent developments in sample preparation strategies for tissue proteomics in three areas, namely, cancer, toxicology, and forensics, thus also demonstrating breath of application within the domain of health and well-being, pharmaceuticals, and secure societies.In particular, in the area of cancer (human tumor biomarkers), the most efficient and multi-informative proteomic strategies will be covered in relation to the subsequent application of matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) and liquid extraction surface analysis (LESA), due to their ability to provide molecular localization of tumor biomarkers albeit with different spatial resolution.With respect to toxicology, methodologies applied in toxicoproteomics will be illustrated with examples from its use in two important areas: the study of drug-induced liver injury (DILI) and studies of effects of chemical and environmental insults on skin, i.e., the effects of irritants, sensitizers, and ionizing radiation. Within this chapter, mainly tissue proteomics sample preparation methods for LC-MS/MS analysis will be discussed as (i) the use of LC-MS/MS is majorly represented in the research efforts of the bioanalytical community in this area and (ii) LC-MS/MS still is the gold standard for quantification studies.Finally, the use of proteomics will also be discussed in forensic science with respect to the information that can be recovered from blood and fingerprint evidence which are commonly encountered at the scene of the crime. The application of proteomic strategies for the analysis of blood and fingerprints is novel and proteomic preparation methods will be reported in relation to the subsequent use of mass spectrometry without any hyphenation. While generally yielding more information, hyphenated methods are often more laborious and time-consuming; since forensic investigations need quick turnaround, without compromising validity of the information, the prospect to develop methods for the application of quick forensic mass spectrometry techniques such as MALDI-MS (in imaging or profiling mode) is of great interest.