Integrated Proteotranscriptomics of Breast Cancer Reveals Globally Increased Protein-mRNA Concordance Associated with Subtypes and Survival

BACKGROUND: Transcriptome analysis of breast cancer discovered distinct disease subtypes of clinical significance. However, it remains a challenge to define disease biology solely based on gene expression because tumor biology is often the result of protein function. Here, we measured global proteome and transcriptome expression in human breast tumors and adjacent non-cancerous tissue and performed an integrated proteotranscriptomic analysis. METHODS: We applied a quantitative liquid chromatography/mass spectrometry-based proteome analysis using an untargeted approach and analyzed protein extracts from 65 breast tumors and 53 adjacent non-cancerous tissues. Additional gene expression data from Affymetrix Gene Chip Human Gene ST Arrays were available for 59 tumors and 38 non-cancerous tissues in our study. We then applied an integrated analysis of the proteomic and transcriptomic data to examine relationships between them, disease characteristics, and patient survival. Findings were validated in a second dataset using proteome and transcriptome data from The Cancer Genome Atlas and the Clinical Proteomic Tumor Analysis Consortium. RESULTS: We found that the proteome describes differences between cancerous and non-cancerous tissues that are not revealed by the transcriptome. The proteome, but not the transcriptome, revealed an activation of infection-related signal pathways in basal-like and triple-negative tumors. We also observed that proteins rather than mRNAs are increased in tumors and show that this observation could be related to shortening of the 3\u27 untranslated region of mRNAs in tumors. The integrated analysis of the two technologies further revealed a global increase in protein-mRNA concordance in tumors. Highly correlated protein-gene pairs were enriched in protein processing and disease metabolic pathways. The increased concordance between transcript and protein levels was additionally associated with aggressive disease, including basal-like/triple-negative tumors, and decreased patient survival. We also uncovered a strong positive association between protein-mRNA concordance and proliferation of tumors. Finally, we observed that protein expression profiles co-segregate with a Myc activation signature and separate breast tumors into two subgroups with different survival outcomes. CONCLUSIONS: Our study provides new insights into the relationship between protein and mRNA expression in breast cancer and shows that an integrated analysis of the proteome and transcriptome has the potential of uncovering novel disease characteristics

Liquid Tissue: Proteomic Profiling of Formalin-Fixed Tissues

Identification and quantitation of candidate biomarker proteins in large numbers of individual tissues is required to validate specific proteins, or panels of proteins, for clinical use as diagnostic, prognostic, toxicological, or therapeutic markers. Mass spectrometry (MS) provides an exciting analytical methodology for this purpose. Liquid Tissue MS protein preparation allows researchers to utilize the vast, already existing, collections offormalin-fixed paraffin-embedded (FFPE) tissues for the procurement of peptides and the analysis across a variety of MS platforms

The Use of Urine Proteomic and Metabonomic Patterns for the Diagnosis of Interstitial Cystitis and Bacterial Cystitis

The advent of systems biology approaches that have stemmed from the sequencing of the human genome has led to the search for new methods to diagnose diseases. While much effort has been focused on the identification of disease-specific biomarkers, recent efforts are underway toward the use of proteomic and metabonomic patterns to indicate disease. We have developed and contrasted the use of both proteomic and metabonomic patterns in urine for the detection of interstitial cystitis (IC). The methodology relies on advanced bioinformatics to scrutinize information contained within mass spectrometry (MS) and high-resolution proton nuclear magnetic resonance (1H-NMR) spectral patterns to distinguish IC-affected from non-affected individuals as well as those suffering from bacterial cystitis (BC). We have applied a novel pattern recognition tool that employs an unsupervised system (self-organizing-type cluster mapping) as a fitness test for a supervised system (a genetic algorithm). With this approach, a training set comprised of mass spectra and 1H-NMR spectra from urine derived from either unaffected individuals or patients with IC is employed so that the most fit combination of relative, normalized intensity features defined at precise m/z or chemical shift values plotted in n-space can reliably distinguish the cohorts used in training. Using this bioinformatic approach, we were able to discriminate spectral patterns associated with IC-affected, BC-affected, and unaffected patients with a success rate of approximately 84%

The Use of Urine Proteomic and Metabonomic Patterns for the Diagnosis of Interstitial Cystitis and Bacterial Cystitis

The advent of systems biology approaches that have stemmed from the sequencing of the human genome has led to the search for new methods to diagnose diseases. While much effort has been focused on the identification of disease-specific biomarkers, recent efforts are underway toward the use of proteomic and metabonomic patterns to indicate disease. We have developed and contrasted the use of both proteomic and metabonomic patterns in urine for the detection of interstitial cystitis (IC). The methodology relies on advanced bioinformatics to scrutinize information contained within mass spectrometry (MS) and high-resolution proton nuclear magnetic resonance ((1)H-NMR) spectral patterns to distinguish IC-affected from non-affected individuals as well as those suffering from bacterial cystitis (BC). We have applied a novel pattern recognition tool that employs an unsupervised system (self-organizing-type cluster mapping) as a fitness test for a supervised system (a genetic algorithm). With this approach, a training set comprised of mass spectra and (1)H-NMR spectra from urine derived from either unaffected individuals or patients with IC is employed so that the most fit combination of relative, normalized intensity features defined at precise m/z or chemical shift values plotted in n-space can reliably distinguish the cohorts used in training. Using this bioinformatic approach, we were able to discriminate spectral patterns associated with IC-affected, BC-affected, and unaffected patients with a success rate of approximately 84%

Proteomic Analysis of Traumatic Brain Injury: The Search for Biomarkers

Although there are a number of causes of traumatic brain injury (TBI), the armed conflict in Iraq and Afghanistan has brought this disorder to the attention of the global community. A biomarker that would enable army medics to rapidly diagnose the severity of TBI on the battle-field would be a huge asset. Unfortunately, the study of TBI has not historically attracted the proteomic research community\u27s interest as other disorders have, such as cancer. On the positive side, however, many of the analytical and technological challenges that were overcome in the development of biofluid proteomic methods are now being applied to the study of TBI. In this review, we discuss and highlight select examples of discovery-driven proteomic studies focused on finding effective biomarkers for TBI

Proteomic Patterns for Early Cancer Detection

The advent of proteomics has brought with it the hope of discovering novel biomarkers that can be used to diagnose diseases, predict susceptibility, and monitor progression. Much of this effort has focused on the mass spectral identification of the thousands of proteins that populate complex biosystems such as serum and tissues. A revolutionary approach in proteomic pattern analysis has emerged as an effective method for the early diagnosis of diseases such as ovarian, breast, and prostate cancer. This technology is capable of analyzing hundreds of clinical samples per day and has the potential to be a novel, highly sensitive diagnostic tool for the early detection of diseases, or as a predictor of response to therapy

Proteomic Patterns: Their Potential for Disease Diagnosis

Alterations in proteins abundance, structure, or function, act as useful indicators of pathological abnormalities prior to development of clinical symptoms and as such are often useful diagnostic and prognostic biomarkers. The underlying mechanism of diseases such as cancer are, however, quite complicated in that often multiple dysregulated proteins are involved. It is for this reason that recent hypotheses suggest that detection of panels of biomarkers may provide higher sensitivities and specificities for disease diagnosis than is afforded with single markers. Recently, a novel approach based on the analysis of protein patterns has emerged that may provide a more effective means to diagnose diseases, such as ovarian and prostate cancer. The method is based on the use of surface-enhanced laser desorption/ionization (SELDI) time-of-flight mass spectrometry (TOF-MS) to detect differentially captured proteins from clinical samples, such as serum and plasma. This analysis results in the detection of proteomic patterns that have been shown in recent investigations to distinguish diseased and unaffected subjects to varying degrees. This review will discuss the basics of SELDI protein chip technology and highlight its recent applications in disease biomarker discovery with emphasis on cancer diagnosis

Peer Reviewed: SELDI-TOF MS for Diagnostic Proteomics

By combining chromatographic retention with MS, SELDI-TOF MS can generate protein profiles from as little as 1 μL of serum or as few as 25–50 cells

Profiling of Secreted Proteins from Human Ovarian Cancer Cell Lines by Surface-Enhanced Laser Desorption Ionization Time-of-Flight Mass Spectrometry

Surface‐enhanced laser desorption ionization (SELDI) time‐of‐flight mass spectrometry (TOF MS) was used to profile six human ovarian cancer cell lines. Non‐confluent cell cultures were exchanged into serum free medium and allowed to grow for either 24 or 48 hours, at which time the medium was collected and analyzed using a Ciphergen SELDI‐TOF MS and a QSTAR Pulsar QqTOF mass spectrometer fitted with a SELDI source. The spectra showed the presence of several low molecular weight species, as well as differences and similarities in the proteins detected in the media of the six ovarian tumor cell lines. The same species were detected at 24 and 48 hours and reproducible changes in their relative abundances were observed

The Approach and Design of a Surrogate Protease Substrate for Disease Biomarker Discovery

Biomarker discovery investigations utilizing sera samples typically show differences in peptides originating from high abundant proteins. These peptides, produced from the activity of one or more protease(s), by themselves may not reflect a disease-associated biomarker(s), but in fact, show the downstream effect of disease specific protease(s). The ability to select “true” biomarkers of disease is mired by the inter- and intrapatient variability typically observed in a proteomics approach to serum biomarker discovery. As an example, serum from an individual was collected with three types of serum separator tubes (SST) and stored at 4°C for prolonged periods of time. The postcollection integrity of the serum was examined using microcapillary nanoflow reversed-phase liquid chromatography (nano-RPLC) tandem mass spectrometry (MS/MS) of the trypsin-digested, low molecular weight protein fraction. An increase in unique peptides per protein was observed for the highest abundant proteins with prolonged storage times. Of note was the presence of truncated variations of high abundant parent peptides, which presumably arise from the action of endogenous exo- and endo-peptidases in human serum. Based on these observations, a surrogate peptide was synthesized with a central affinity label as a means to screen enzymatic activity in serum. The surrogate peptide was used to screen cell-free media of two human breast cancer cell lines, SKBr3 and MDA-MB-231, for differential enzymatic activity as an initial disease model. In light of these results, the use of a “spiked” protease substrate may provide an additional means to differentiate between sera from diseased versus healthy individuals