Ovarian cancer: can proteomics give new insights for therapy and diagnosis?

Proteomic profiles of peritoneal fluid-derived small extracellular vesicles correlate with patient outcome in ovarian cancer

Cancer-derived small extracellular vesicles (sEVs) are capable of modifying the tumor microenvironment and promoting tumor progression. Ovarian cancer (OvCa) is a lethal malignancy that preferentially spreads through the abdominal cavity. Thus, the secretion of such vesicles into the peritoneal fluid could be a determinant factor in the dissemination and behavior of this disease. We designed a prospective observational study to assess the impact of peritoneal fluid-derived sEVs (PFD-sEVs) in OvCa clinical outcome. For this purpose, 2 patient cohorts were enrolled: patients with OvCa who underwent a diagnostic or cytoreductive surgery and nononcological patients, who underwent abdominal surgery for benign gynecological conditions and acted as the control group. Systematic extraction of PFD-sEVs from surgical samples enabled us to observe significant quantitative and qualitative differences associated with cancer diagnosis, disease stage, and platinum chemosensitivity. Proteomic profiling of PFD-sEVs led to the identification of molecular pathways and proteins of interest and to the biological validation of S100A4 and STX5. In addition, unsupervised analysis of PFD-sEV proteomic profiles in high-grade serous ovarian carcinomas (HGSOCs) revealed 2 clusters with different outcomes in terms of overall survival. In conclusion, comprehensive characterization of PFD-sEV content provided a prognostic value with potential implications in HGSOC clinical management.

Getting to know ovarian cancer: Focusing on the effect of LncRNAs in this cancer and the effective signaling pathways

This article undertakes a comprehensive investigation of ovarian cancer, examining the complex nature of this challenging disease. The main focus is on understanding the role of long non-coding RNAs (lncRNAs) in the context of ovarian cancer (OC), and their regulatory functions in disease progression. Through extensive research, the article identifies specific lncRNAs that play significant roles in the intricate molecular processes of OC. Furthermore, the study examines the signaling pathways involved in the development of OC, providing a detailed comprehension of the underlying molecular mechanisms. By connecting lncRNA dynamics with signaling pathways, this exploration not only advances our understanding of ovarian cancer but also reveals potential targets for therapeutic interventions. The findings open up opportunities for targeted treatments, highlighting the importance of personalized approaches in addressing this complex disease and driving progress in ovarian cancer research and treatment strategies.

Deciphering the potential of proteomic-based biomarkers in women's reproductive diseases: empowering precision medicine in gynecology

CONTEXT

Gynecological disorders represent a complex set of malignancies that result from a diverse array of molecular changes affecting the lives of over a million women worldwide. Ovarian, Endometrial, and Cervical cancers, Endometriosis, PCOS are the most prevalent ones that pose a grave threat to women's health. Proteomics has emerged as an invaluable tool for developing novel biomarkers, screening methods, and targeted therapeutic agents for gynecological disorders. Some of these biomarkers have been approved by the FDA, but regrettably, they have a constrained diagnostic accuracy in early-stage diagnosis as all of these biomarkers lack sensitivity and specificity. Lately, high-throughput proteomics technologies have made significant strides, allowing for identification of potential biomarkers with improved sensitivity and specificity. However, limited successes have been shown with translation of these discoveries into clinical practice.

OBJECTIVE

This review aims to provide a comprehensive overview of the current and potential protein biomarkers for gynecological cancers, endometriosis and PCOS, discusses recent advances and challenges, and highlights future directions for the field.

CONCLUSION

We propose that proteomics holds great promise as a powerful tool to revolutionize the fight against female reproductive diseases and can ultimately improve personalized patient outcomes in women's biomedicine.

Applications of Proteomics in Ovarian Cancer: Dawn of a New Era

The ability to identify ovarian cancer (OC) at its earliest stages remains a challenge. The patients present an advanced stage at diagnosis. This heterogeneous disease has distinguishable etiology and molecular biology. Next-generation sequencing changed clinical diagnostic testing, allowing assessment of multiple genes, simultaneously, in a faster and cheaper manner than sequential single gene analysis. Technologies of proteomics, such as mass spectrometry (MS) and protein array analysis, have advanced the dissection of the underlying molecular signaling events and the proteomic characterization of OC. Proteomics analysis of OC, as well as their adaptive responses to therapy, can uncover new therapeutic choices, which can reduce the emergence of drug resistance and potentially improve patient outcomes. There is an urgent need to better understand how the genomic and epigenomic heterogeneity intrinsic to OC is reflected at the protein level, and how this information could potentially lead to prolonged survival.

The proteomic landscape of ovarian cancer cells in response to melatonin

Ovarian cancer (OC) is the most lethal gynecological malignancy with a highly negative prognosis. Melatonin is an indoleamine secreted by the pineal gland during darkness and has shown antitumor activity in both in vitro and in vivo experiments. Herein, we investigated the influence of melatonin on the proteome of human ovarian carcinoma cells (SKOV-3 cell line) using the Ultimate 3000 LC Liquid NanoChromatography equipment coupled to a Q-Exactive mass spectrometry. After 48 h of treatment, melatonin induced a significant cytotoxicity especially with the highest melatonin concentration. The proteomic profile revealed 639 proteins in the control group, and 98, 110, and 128 proteins were altered by melatonin at the doses of 0.8, 1.6, and 2.4 mM, respectively. Proteins associated with the immune system and tricarboxylic acid cycle were increased in the three melatonin-exposed groups of cells. Specifically, the dose of 2.4 mM led to a reduction in molecules associated with protein synthesis, especially those of the ribosomal protein family. We also identified 28 potential genes shared between normal ovarian tissue and OC in all experimental groups, and melatonin was predicted to alter genes encoding ribosomal proteins. Notably, the set of proteins changed by melatonin was linked to a better prognosis for OC patients. We conclude that melatonin significantly alters the proteome of SKOV-3 cells by changing proteins involved with the immune response and mitochondrial metabolism. The concentration of 2.4 mM of melatonin promoted the largest number of protein changes. The evidence suggests that melatonin may be an effective therapeutic strategy against OC.

LncRNAs in Ovarian Cancer Progression, Metastasis, and Main Pathways: ceRNA and Alternative Mechanisms

Ovarian cancer (OvCa) develops asymptomatically until it reaches the advanced stages with metastasis, chemoresistance, and poor prognosis. Our review focuses on the analysis of regulatory long non-coding RNAs (lncRNAs) competing with protein-coding mRNAs for binding to miRNAs according to the model of competitive endogenous RNA (ceRNA) in OvCa. Analysis of publications showed that most lncRNAs acting as ceRNAs participate in OvCa progression: migration, invasion, epithelial-mesenchymal transition (EMT), and metastasis. More than 30 lncRNAs turned out to be predictors of survival and/or response to therapy in patients with OvCa. For a number of oncogenic (CCAT1, HOTAIR, NEAT1, and TUG1 among others) and some suppressive lncRNAs, several lncRNA/miRNA/mRNA axes were identified, which revealed various functions for each of them. Our review also considers examples of alternative mechanisms of actions for lncRNAs besides being ceRNAs, including binding directly to mRNA or protein, and some of them (DANCR, GAS5, MALAT1, and UCA1 among others) act by both mechanisms depending on the target protein. A systematic analysis based on the data from literature and Panther or KEGG (Kyoto Encyclopedia of Genes and Genomes) databases showed that a significant part of lncRNAs affects the key pathways involved in OvCa metastasis, EMT, and chemoresistance.

Challenges and Opportunities in Clinical Applications of Blood-Based Proteomics in Cancer

Simple Summary

The traditional approach in identifying cancer related protein biomarkers has focused on evaluation of a single peptide/protein in tissue or circulation. At best, this approach has had limited success for clinical applications, since multiple pathological tumor pathways may be involved during initiation or progression of cancer which diminishes the significance of a single candidate protein/peptide. Emerging sensitive proteomic based technologies like liquid chromatography mass spectrometry (LC-MS)-based quantitative proteomics can provide a platform for evaluating serial serum or plasma samples to interrogate secreted products of tumor–host interactions, thereby revealing a more “complete” repertoire of biological variables encompassing heterogeneous tumor biology. However, several challenges need to be met for successful application of serum/plasma based proteomics. These include uniform pre-analyte processing of specimens, sensitive and specific proteomic analytical platforms and adequate attention to study design during discovery phase followed by validation of discovery-level signatures for prognostic, predictive, and diagnostic cancer biomarker applications.

Abstract

Blood is a readily accessible biofluid containing a plethora of important proteins, nucleic acids, and metabolites that can be used as clinical diagnostic tools in diseases, including cancer. Like the on-going efforts for cancer biomarker discovery using the liquid biopsy detection of circulating cell-free and cell-based tumor nucleic acids, the circulatory proteome has been underexplored for clinical cancer biomarker applications. A comprehensive proteome analysis of human serum/plasma with high-quality data and compelling interpretation can potentially provide opportunities for understanding disease mechanisms, although several challenges will have to be met. Serum/plasma proteome biomarkers are present in very low abundance, and there is high complexity involved due to the heterogeneity of cancers, for which there is a compelling need to develop sensitive and specific proteomic technologies and analytical platforms. To date, liquid chromatography mass spectrometry (LC-MS)-based quantitative proteomics has been a dominant analytical workflow to discover new potential cancer biomarkers in serum/plasma. This review will summarize the opportunities of serum proteomics for clinical applications; the challenges in the discovery of novel biomarkers in serum/plasma; and current proteomic strategies in cancer research for the application of serum/plasma proteomics for clinical prognostic, predictive, and diagnostic applications, as well as for monitoring minimal residual disease after treatments. We will highlight some of the recent advances in MS-based proteomics technologies with appropriate sample collection, processing uniformity, study design, and data analysis, focusing on how these integrated workflows can identify novel potential cancer biomarkers for clinical applications.

Mass Spectrometric (MS) Analysis of Proteins and Peptides

The human genome is sequenced and comprised of ~30,000 genes, making humans just a little bit more complicated than worms or flies. However, complexity of humans is given by proteins that these genes code for because one gene can produce many proteins mostly through alternative splicing and tissue-dependent expression of particular proteins. In addition, post-translational modifications (PTMs) in proteins greatly increase the number of gene products or protein isoforms. Furthermore, stable and transient interactions between proteins, protein isoforms/proteoforms and PTM-ed proteins (protein-protein interactions, PPI) add yet another level of complexity in humans and other organisms. In the past, all of these proteins were analyzed one at the time. Currently, they are analyzed by a less tedious method: mass spectrometry (MS) for two reasons: 1) because of the complexity of proteins, protein PTMs and PPIs and 2) because MS is the only method that can keep up with such a complex array of features. Here, we discuss the applications of mass spectrometry in protein analysis.

Detection of Epithelial Ovarian Cancer using C8Magnetic Bead Separation and MALDI-TOF Plasma Proteome Profiling in Egyptian Females

Background:

Ovarian cancer is the seventh most common cancer in females with the highest mortality rate of all gynecological cancers due to its late discovery and ambiguous symptoms. Thus, there is a need for new promising strategies to diagnose ovarian cancer. We aimed at finding a characteristic plasma proteome pattern that could be used for the detection of epithelial ovarian cancer, in comparison with benign ovarian masses and healthy controls. We also aimed at differentiating between profiling of plasma proteins in early and advanced stages of ovarian cancer and between serous and non-serous histopathological types.

Methods:

The combination of MagSi-proteomics C8 beads, Ultraflextreme MALDI-TOF and ClinPro Tools software was used to compare the plasma protein spectra from 50 patients with epithelial ovarian cancer, 20 patients with benign ovarian masses and 50 age matched healthy females.

Results:

A plasma proteome profile of 21 peaks differentiated patients with epithelial ovarian cancer from healthy controls with a sensitivity of 73 % and a specificity of 82.8% upon external validation, while a 5-peak profile differentiated patients with epithelial ovarian cancer from patients with benign ovarian masses with a sensitivity of 81% and a specificity of 73.7%. A 20 peak profile was generated to discriminate between early and late stages of the disease with 88.3% recognition capability and 70% cross validation.

Conclusion:

MALDI-TOF proteomic profiling represents a promising potential tool for diagnosing epithelial ovarian cancer, discriminating between early and advanced stages and between serous and non- serous types.

Poly (ADP-ribose) polymerase inhibitors combined with other small-molecular compounds for the treatment of ovarian cancer

Ovarian cancer is a heterogeneous disease with complex molecular and genetic hallmarks. Benefitting from profound understanding of molecular mechanisms in ovarian cancer pathogenesis, novel targeted drugs have been actively explored in preclinical studies and clinical trials. Considered as one of the most potent and effective targeted therapies for the treatment of ovarian cancer, poly (ADP-ribose) polymerase (PARP) inhibitors (PARPis) take advantages of synthetic lethality mechanisms to prevent DNA damage repair in cancer cells and cause their death, especially in cancers with BRCA mutations. Mounting evidence has indicated that the combination of PARPis with cytotoxic drugs or other targeted drugs has shown favorable synergistic effects. Excitingly, the antitumor activity of combination therapy of PARPis has been actively tested in multiple clinical trials and in-vitro or in-vivo experiments. In this review, we will briefly discuss the molecular mechanisms of PARPis combined with other therapeutic small-molecular compounds for the treatment of ovarian cancer.

Pre- and Post-analytical Factors in Biomarker Discovery

The translation of promising biomarkers, which were identified in biomarker discovery experiments, to clinical assays is one of the key challenges in present-day proteomics research. Many so-called "biomarker candidates" fail to progress beyond the discovery phase, and much emphasis is placed on pre- and post-analytical variability in an attempt to provide explanations for this bottleneck in the biomarker development pipeline. With respect to such variability, there is a large number of pre- and post-analytical factors which may impact the outcomes of proteomics experiments and thus necessitate tight control. This chapter highlights some of these factors and provides guidance for addressing them on the basis of examples from previously published proteomics studies.

Proteome Imaging: From Classic to Modern Mass Spectrometry-Based Molecular Histology

In order to overcome the limitations of classic imaging in Histology during the actually era of multiomics, the multi-color "molecular microscope" by its emerging "molecular pictures" offers quantitative and spatial information about thousands of molecular profiles without labeling of potential targets. Healthy and diseased human tissues, as well as those of diverse invertebrate and vertebrate animal models, including genetically engineered species and cultured cells, can be easily analyzed by histology-directed MALDI imaging mass spectrometry. The aims of this review are to discuss a range of proteomic information emerging from MALDI mass spectrometry imaging comparative to classic histology, histochemistry and immunohistochemistry, with applications in biology and medicine, concerning the detection and distribution of structural proteins and biological active molecules, such as antimicrobial peptides and proteins, allergens, neurotransmitters and hormones, enzymes, growth factors, toxins and others. The molecular imaging is very well suited for discovery and validation of candidate protein biomarkers in neuroproteomics, oncoproteomics, aging and age-related diseases, parasitoproteomics, forensic, and ecotoxicology. Additionally, in situ proteome imaging may help to elucidate the physiological and pathological mechanisms involved in developmental biology, reproductive research, amyloidogenesis, tumorigenesis, wound healing, neural network regeneration, matrix mineralization, apoptosis and oxidative stress, pain tolerance, cell cycle and transformation under oncogenic stress, tumor heterogeneity, behavior and aggressiveness, drugs bioaccumulation and biotransformation, organism's reaction against environmental penetrating xenobiotics, immune signaling, assessment of integrity and functionality of tissue barriers, behavioral biology, and molecular origins of diseases. MALDI MSI is certainly a valuable tool for personalized medicine and "Eco-Evo-Devo" integrative biology in the current context of global environmental challenges.

Mass spectrometry-based proteomics techniques and their application in ovarian cancer research

Ovarian cancer has emerged as one of the leading cause of gynecological malignancies. So far, the measurement of CA125 and HE4 concentrations in blood and transvaginal ultrasound examination are essential ovarian cancer diagnostic methods. However, their sensitivity and specificity are still not sufficient to detect disease at the early stage. Moreover, applied treatment may appear to be ineffective due to drug-resistance. Because of a high mortality rate of ovarian cancer, there is a pressing need to develop innovative strategies leading to a full understanding of complicated molecular pathways related to cancerogenesis. Recent studies have shown the great potential of clinical proteomics in the characterization of many diseases, including ovarian cancer. Therefore, in this review, we summarized achievements of proteomics in ovarian cancer management. Since the development of mass spectrometry has caused a breakthrough in systems biology, we decided to focus on studies based on this technique. According to PubMed engine, in the years 2008-2010 the number of studies concerning OC proteomics was increasing, and since 2010 it has reached a plateau. Proteomics as a rapidly evolving branch of science may be essential in novel biomarkers discovery, therapy decisions, progression predication, monitoring of drug response or resistance. Despite the fact that proteomics has many to offer, we also discussed some limitations occur in ovarian cancer studies. Main difficulties concern both complexity and heterogeneity of ovarian cancer and drawbacks of the mass spectrometry strategies. This review summarizes challenges, capabilities, and promises of the mass spectrometry-based proteomics techniques in ovarian cancer management.

Comparative secretome of ovarian serous carcinoma: Gelsolin in the spotlight

Ovarian cancer is one of the most common types of reproductive cancer, and has the highest mortality rate amongst gynecological cancer subtypes. The majority of ovarian cancers are diagnosed at an advanced stage, resulting in a five-year survival rate of ~30%. Early diagnosis of ovarian cancer has improved the five-year survival rate to ≥90%, thus the current imperative requirement is to identify biomarkers that would allow the early detection, diagnosis and monitoring of the progression of the disease, or of novel targets for therapy. In the present study, secreted proteins from purified ovarian control, benign and cancer cells were investigated by mass spectrometry, in order to identify novel specific markers that are easy to quantify in patients sera. A total of nine proteins revealed significant differential secretion from control and benign cells, in comparison with ovarian cancer cells. The mRNA expression levels of three of these proteins (Dickkopf protein 3, heat shock protein 10 kDa and gelsolin) were subsequently evaluated by reverse transcription-quantitative polymerase chain reaction. Combined with the protein level in serum, the present study identified that gelsolin may be a useful marker of ovarian cancer.

The diagnostic value of serum HE4 and CA-125 and ROMA index in ovarian cancer

Ovarian cancer is a common malignancy of the female reproductive system. Tumor markers serve as tools in the diagnosis of the disease. The aim of the present study was to determine the diagnostic value of sera levels of carbohydrate antigen-125 (CA-125), human epididymis protein 4 (HE4) as well as the area under the curve of the receiver operating characteristic (ROC) and the risk of ovarian malignancy algorithm (ROMA) index in ovarian cancer. The sera were measured using an electrochemiluminescence immunoassay on 158 individuals (64 patients with ovarian cancer, 64 with ovarian benign tumor and 30 healthy individuals) between September 2013 and May 2015. The results showed that levels of HE4 and CA-125 in the sera of the ovarian benign tumor group as well as their ROMA index were significantly higher (P<0.05) than those of the ovarian benign tumor and control groups, regardless of pre- or postmenopausal status. However, the level of CA-125 was significantly higher (P<0.05) in the ovarian benign tumor group compared with the healthy group, while the level of HE4 was similar in the two groups. The sensitivity of the ROMA index was higher (P<0.01) with detection of HE4 and CA-125. In the ovarian cancer group, the areas under ROC curves of ROMA, HE4 and CA-125 were 0.994, 0.990 and 0.941, respectively. The specificity and positive predictive value of HE4 in the premenopausal ovarian cancer group reached 98.36 and 95%, respectively. In conclusion, the results showed that the serum level of HE4 and the ROMA index are important indicators in the diagnosis of ovarian cancer. However, in addition to HE4 and CA-125 detection, the ROMA index is extremely valuable in improving the diagnostic efficiency of ovarian cancer.

Classification of Samples with Order-Restricted Discriminant Rules

In recent years, mass spectrometry techniques have helped proteomics to become a powerful tool for the early diagnosis of cancer, as they help to discover protein profiles specific to each pathological state. One of the questions where proteomics is giving useful practical results is that of classifying patients into one of the possible severity levels of an illness, based on some features measured on the patient. This classification is usually made using one of the many discrimination procedures available in statistical literature. We present in this chapter recently developed restricted discriminant rules that use additional information in terms of orderings on the means, and we illustrate how to apply them to mass spectrometry data using R package dawai. Specifically, we use proteomic prostate cancer data, and we describe all steps needed, including data preprocessing and feature extraction, to build a discriminant rule that classifies samples in one of several disease stages, thus helping diagnosis. The restricted discriminant rules are compared with some standard classifiers that do not take into account the additional information, showing better performance in terms of error rates.

Cancer proteomics: developments in technology, clinical use and commercialization

In the last two decades, advances in genomic, transcriptomic and proteomic methods have enabled us to identify and classify cancers by their molecular profiles. Many anticipate that a molecular taxonomy of cancer will not only lead to more effective subtyping of cancers but also earlier diagnoses, more informative prognoses and more targeted treatments. This article reviews recent technological developments in the field of proteomics, recent discoveries in proteomic cancer biomarker research and trends in clinical use. Readers are also informed of examples of successful commercialization, and the future of proteomics in cancer diagnostics.

Proteomics in cancer biomarkers discovery: challenges and applications

With the introduction of recent high-throughput technologies to various fields of science and medicine, it is becoming clear that obtaining large amounts of data is no longer a problem in modern research laboratories. However, coherent study designs, optimal conditions for obtaining high-quality data, and compelling interpretation, in accordance with the evidence-based systems biology, are critical factors in ensuring the emergence of good science out of these recent technologies. This review focuses on the proteomics field and its new perspectives on cancer research. Cornerstone publications that have tremendously helped scientists and clinicians to better understand cancer pathogenesis; to discover novel diagnostic and/or prognostic biomarkers; and to suggest novel therapeutic targets will be presented. The author of this review aims at presenting some of the relevant literature data that helped as a step forward in bridging the gap between bench work results and bedside potentials. Undeniably, this review cannot include all the work that is being produced by expert research groups all over the world.

Differential levels of L-homocysteic acid and lysophosphatidylcholine (16:0) in sera of patients with ovarian cancer

Ovarian cancer (OVC) is one of the most difficult types of cancer to detect in the early stages of its development. There have been numerous attempts to identify a biomarker for OVC; however, an accurate diagnostic marker has yet to be identified. The present study profiled OVC candidate metabolites from the serum to identify potential diagnostic markers for OVC. Data regarding low-mass ions (LMIs) in the serum were obtained using matrix-assisted laser desorption/ionization (MALDI)-time-of-flight analysis. MALDI-mass spectrometry (MS) analysis of each serum sample was repeated six times in order to reduce the likelihood of experimental errors. The intensity of the LMI mass peaks were normalized using total peak area sums. The normalized intensity of LMI was used in principal component analysis-discriminant analysis to differentiate between 142 patients with OVC and 100 healthy control participants. Liquid chromatography-MS/MS was used to identify the selected LMIs. Extracted ion chromatogram analysis was used to measure the relative quantity of candidate metabolites from the LMI mass peak areas. The concentration of common metabolites in the serum was determined using ELISA. The top 20 LMI mass peaks with a weigh factor over 0.05 were selected to distinguish between the patients with OVC and the controls. Among the LMIs, two with 184.05 and 496.30 m/z were identified as L-homocysteic acid (HCA) and lysophosphatidylcholine (LPC) (16:0), respectively. The relative quantity of LPC (16:0) was found to be decreased in the OVC serum (P=0.05), while the quantity of HCA was observed to be significantly higher in the OVC serum (P<0.001). HCA was not detected in 59 cases out of the 63 control participants; however, the majority of the cases of OVC (16/25) exhibited significantly higher quantities of HCA. When the cutoff was 10 nmol/ml, the sensitivity and specificity of HCA were 64.0 and 96.9%, respectively. The level of LPC (16:0) was significantly correlated with tumor grade (P=0.045). HCA and LPC (16:0) showed correlation with stage and tumor histology, but the limited sample size resulted in a lack of statistical significance. The findings of the present study suggest that HCA may have potential to be a biomarker for OVC. The stratified screening including LPC (16:0) did not significantly increase the power for OVC screening; however, the present study showed that profiling LMIs in serum may be useful for identifying candidate metabolites for OVC screening.

Proteomic strategies for the discovery of novel diagnostic and therapeutic targets for infectious diseases

Viruses have developed numerous and elegant strategies to manipulate the host cell machinery to establish a productive infectious cycle. The interaction of viral proteins with host proteins plays an important role in infection and pathogenesis, often bypassing traditional host defenses such as the interferon response and apoptosis. Host–viral protein interactions can be studied using a variety of proteomic approaches ranging from genetic and biochemical to large‐scale high‐throughput technologies. Protein interactions between host and viral proteins are greatly influenced by host signal transduction pathways. In this review, we will focus on comparing proteomic information obtained through differing technologies and how their integration can be used to determine the functional aspect of the host response to infection. We will briefly review and evaluate techniques employed to elucidate viral–host interactions with a primary focus on Protein Microarrays (PMA) and Mass Spectrometry (MS) as potential tools in the discovery of novel therapeutic targets. As many potential molecular markers and targets are proteins, proteomic profiling is expected to yield both clearer and more direct answers to functional and pharmacologic questions.