Current Activities In Pharmacoproteomics

Widespread interest and commercial development of proteomic technologies have led to a number of discoveries, notably in the field of protein markers and more particularly in the form of protein isoforms and broadly based expression patterns. This information is largely unavailable other than at the level of transcription, because most proteomic technologies have been focused on the manifestations of posttranslational processing. As such, pharmacoproteomics complements genomic microarray profiling by providing data on gene products that are inaccessible to the latter technology.

SELDI technology has been used to identify patterns of protein expression by analyzing the relative intensity of molecular weight peaks over a large range of native protein sizes. An extensive study by the Food and Drug Administration (FDA) to identify serum markers of ovarian cancer was successful in defining a molecular weight expression pattern entirely consistent with the occurrence of the disease (46). This collaboration went on to define a haptoglobin isoform that is elevated in ovarian cancer, giving 90% specificity and 71% sensitivity, a level of accuracy exceeding any other serum-based assay for the disease. In another collaboration, SELDI was used to identify the CD8+ antiviral factor (CAF) associated with CD8+ T-lymphocytes of HIV-positive individuals who belong to the subpopulation of long-term nonprogressors, that is, do not go on to develop AIDS. Tandem MS of the peaks identified by SELDI revealed that the differential proteins thus identified belonged to the alpha defensin family. These findings enabled further validation studies supporting the role of these proteins in the suppression of viral replication (47). This study neatly demonstrated how the focused application of proteomics yielded rapid results on an HIV intervention opportunity, which was first identified 16 years beforehand.

The identification of reliable sets of progression markers remains a Holy Grail in medical oncology. SELDI technology has also been applied to this area in order to comprehensively analyze changes associated with the malignant state. Invasive pancreatic adenocarcinoma is a rapidly fatal disease and early identification is vital to increase the chances of successful intervention. Rosty et al. (48) reported the identification of a differentially expressed protein, HIP/PAP-I, secreted at high levels into the pancreatic fluid in 67% patients with pancreatic adenocarcinoma. Another group used cell lines derived from head and neck squamous cell carcinomas to identify proteins differentially expressed in metastases versus the primary tumor (49). A considerable body of published and unpublished data generated by SEREX analyses has been compiled into a database of serological cancer antigens (50).

In contrast to the "whole proteome" analyses favored by the technology platforms, some groups have applied proteomic annotation techniques to established areas of research. In a study that focused on a central signal transduction pathway, Lewis et al. (51) combined functional proteomics with selective deregulation of mitogen-activated protein kinases (MKK1 and MKK2), identifying 25 targets of the MKK/extracellular signal-regulated kinase (ERK) cascade, 20 of which appeared to be novel effectors of this pathway. These diverse targets suggested novel roles for this signaling cascade in cellular processes of nuclear transport, nucleotide excision repair, nucleosome assembly, membrane trafficking, and cytoskeletal regulation. Applying proteomics to a clinical study, Chen et al. (52) conducted a comparative analysis of 93 lung adenocarcinomas with 10 normal lung samples, with the objective of examining the isoform status of Oncoprotein 18, a key regulator of microtubule dynamics that influences cell growth and differentiation. They observed an upregulation of the protein in lung carcinomas with an increased proportion of phosphorylated isoforms, which they verified through conventional mRNA quantification, Western blotting, and immunohistochemistry.

Others have adopted an almost hypothesis-free approach to target discovery. Accepting the limitations of primary tissue analysis and the 2D gel system, some have taken the view that MS throughput is now so great that simply enriching subcellular fractions extracted from cultured cell lines can yield a significant proportion of potential targets in the annotated output. Crude membrane fractions of the colon carcinoma cell line, LIM1215, yielded 284 different protein annotations, of which more than a third were known membrane proteins (27). A later study defined 615 proteins of the human heart mitochondrial proteome, using 1D gel fractionation and high throughput MS (26). Using refinements of the membrane preparation protocol to increase plasma membrane representation and pools of estrogen receptor-positive or -negative breast cancer cell lines, Adam et al. (25) identified 500 proteins, of which 31% were known to be associated with the plasma membrane. This group utilized a peptide selection strategy aimed at avoiding reannotation of proteins commonly observed in proteomic analyses, with the result that a high proportion of novel and uncharacterized proteins were identified. These proteins were then analyzed in the context of primary cancer samples using conventional techniques of mRNA quantification, fluorescent tagging, and IHC analysis, which can be applied to very small amounts of sample and which are much more accessible in the large numbers required for clinical validation (Figure 1). This group was able to identify three novel plasma membrane proteins with clinical relevance to breast cancer; subsequent studies demonstrated protein functions consistent with neoplastic growth in two of these previously uncharacterized proteins (53). Above all, this study demonstrated the way in which large-scale screening of proteins with disease association from subcellular fractions enriched for drug targets can accelerate the target discovery process. Novel sequences are given relevance if it can be demonstrated that they are both natural translation products and plasma membrane-associated—this pro-teomic filter considerably narrows the subset of uncharacterized proteins, making the subsequent task of validation more efficient. The accrual of large amounts of proteomic data of this kind, with disease/tissue and fractionation information, will also enable informatic filtering akin to "electronic northerns" so that large numbers of commonly

Figure 1 Cancer marker discovery by proteomics. Membrane protein fractions from selected cancer cell lines are separated on standard 1D polyacrylamide gels. Trypsinolysed peptides from thin slices of the gel are subjected to ion fragmentation MS/MS, and the resulting spectra compared with publicly available protein sequence databases, based on translations of cDNA and predicted gene sequences. Detection of uncharacterized proteins can be verified by conventional techniques, for example, mRNA quantification, IHC—shown here, a novel protein expressed predominantly in the cytoplasm of differentiated carcinoma but exclusively in the membrane of de-differentiated cells. Abbreviations: IHC, immunohistochemistry; MS/MS, tandem mass spectrometry; NCBI, National Center for Biotechnology Information; PCR, polymerase chain reaction.

Figure 1 Cancer marker discovery by proteomics. Membrane protein fractions from selected cancer cell lines are separated on standard 1D polyacrylamide gels. Trypsinolysed peptides from thin slices of the gel are subjected to ion fragmentation MS/MS, and the resulting spectra compared with publicly available protein sequence databases, based on translations of cDNA and predicted gene sequences. Detection of uncharacterized proteins can be verified by conventional techniques, for example, mRNA quantification, IHC—shown here, a novel protein expressed predominantly in the cytoplasm of differentiated carcinoma but exclusively in the membrane of de-differentiated cells. Abbreviations: IHC, immunohistochemistry; MS/MS, tandem mass spectrometry; NCBI, National Center for Biotechnology Information; PCR, polymerase chain reaction.

expressed or nondruggable proteins can be excluded from outputs, further streamlining the process.

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