1 Laboratory of Pathology, National Cancer Institute and 2 NCI/FDA Clinical Proteomics Program, Bethesda, MD, USA
* Correspondence to: Dr E. M. Posadas, Building 10, Room 12N226, 10 Center Drive MSC 1906, Bethesda, MD 20892-1906, USA. Tel: +1-301-451-4982; Fax: +1-301-402-0172; Email: posadase{at}mail.nih.gov
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Abstract |
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Key words: cancer, early detection, laser capture microdissection, proteomics, surface-enhanced laser desorption ionization, tissue lysate arrays
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Introduction |
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The field of proteomics encompasses tools, technologies and approaches targeted at studying variations within the proteome of a given biosystem. In clinical applications, the proteome encompasses the protein content in a patient. Cancer is commonly described as a genetic disease. A gene alone, however, is only potential information that must be put into a functional form. The DNA is transcribed into RNA then translated into protein, the manifestation of the genetic information. There is information in the proteome that cannot be predicted simply from its related nucleic acid sequence. A number of alterations or modifications can occur at transcriptional, translational and posttranslational levels to affect function. These include gene amplification, alternative RNA splicing, co-translational modification, posttranslational modifications, and differential stability and secretion of proteins [1]. Laboratory testing used in the clinical setting is targeted at expressed proteins and not genomic information. Therapeutics are now being developed that work at the level of the proteome, such as targeted small molecules and recombinant humanized monoclonal antibodies (rhuMAbs). Examples of these agents include imatinib mesylate (Gleevec), gefitinib (Iressa), sorafenib (BAY 43-9006), bevacizumab (rhuMAb against vascular endothelial growth factor), trastuzumab (Herceptin; rhuMAb against Her2) and cetuximab (Erbitux; rhuMAb against epidermal growth factor receptor).
The field of proteomics studies proteins in an effort to catalog them and to understand their role in biology and pathology so they may be applied to early diagnosis and to optimizing treatments. The applications of proteomic technologies may benefit the oncologic community in several areas related to biomarker discovery and treatment: serum screening and tissue sample analysis for the early detection of malignancy and molecular signal mapping to implement rational pharmacoproteomic therapeutic interventions.
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Proteomic technologies |
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Laser capture microdissection
Laser capture microdissection (LCM) is a technique that allows for procurement of pure cell populations from heterogeneous tissue sections under direct microscopic visualization in a clean fashion [6]. LCM can be used to isolate tumor and stroma from a single core of tissue, providing the opportunity for independent analysis of the tumor and its local microenvironment. It employs a pulsed infrared laser to activate a thermoplastic film placed over the cells of interest, causing the film to become fused to the cells. Laser shots measure 7.550 µm in diameter, and are repeated until all cells of interest are collected onto a film-coated plastic cap. The cap is then lifted away from the tissue and the plastic-fused cells are removed en bloc from the tissue specimen. These can be placed into lysis buffer to liberate DNA, RNA or protein [7
, 8
].
Selection of cells by this method allows clean separation of malignant, in situ and various normal cell subpopulations within a single biopsy specimen. Application of this technology improves the ability of other tools to detect DNA, RNA or protein signals that may be of relatively low abundance by diminishing confounding or diluting protein signals [9]. Most importantly, collection of cells with LCM preserves the molecular composition and architecture of the cells so that direct comparisons of transcriptional and translational messages can be made between tissue microcompartments of the same sample, providing a snapshot of a tumor's in vivo biological and physiological properties.
Tissue lysate arrays
Protein microarrays can be applied to serum [10], and lyzed tissue samples [11
, 12
] with acceptable reliability. Protein aliquots are printed onto nitrocellulose-coated glass slides using a pin arrayer. Each spot on the array can contain a homogeneous or heterogeneous set of proteins [13
15
]. Protein microarray formats fall into two major classes, forward phase arrays (FPAs) and reverse phase arrays [RPAs; also called tissue lysate arrays (TLAs)], depending on whether the analyte is captured from the solution phase or bound to the solid phase (Figure 1).
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TLAs are RPAs wherein the lysate or protein of interest is arrayed without selection via a capture molecule. This array can then be queried with an optimized antibody or ligand probe, or an unknown biological component. The TLA format immobilizes an individual test sample in each array spot, such that an array can be comprised of a variety of different patient samples or cellular lysates. Each array is incubated with one detection protein or antibody, and a single end point is measured across the arrayed cohort and can be directly compared across multiple samples. Replicates can be reproducibly printed at a given sitting, increasing quality control over a series of queried arrays. TLAs are now being applied to basic, translational and clinical research, including clinical trials. Clinical trials have been designed by our group in which we obtain 18-gauge core needle biopsies of sentinel tumor masses before and during molecular targeted drug therapy [18]. These samples are flash-frozen until needed. Frozen sections are cut and subjected to LCM for isolation of cell populations. Captured cells are lyzed, and arrays are then produced (Figure 2). The printed arrays have a shelf-life of 1224 months. TLAs are being used to study the changes in the total protein content and activation state of identified molecular targets associated with signaling cascades affecting survival, apoptosis and angiogenesis pathways that are putatively downstream or transactivated by the pharmacologically targeted molecules.
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Clinical applications of proteomics |
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Ovarian cancer
Ovarian cancer continues to be the leading cause of death from gynecological malignancies in the USA and Europe. Oncologists attribute this to the inability to identify ovarian cancer at a stage when surgical therapy may be curative. Current diagnostic measures are limited in efficacy and too often establish diagnosis when the disease is advanced. This has driven a search for newer more sensitive and specific methods of detection of early ovarian cancers.
Numerous groups are pursuing serological approaches to ovarian cancer diagnosis. The most common approach to date is enzyme-linked immunosorbent assay-based quantitative techniques measuring one or several proteins at a time, and then using various forms of one or several proteins at the same time [23]. Investigators have been able to identify characteristics of SELDI-TOF and MALDI-TOF mass spectral patterns in sera of patients that may yield a sensitive and specific signature for ovarian cancer [22
]. Our group used spectral patterns generated from unfractionated serum samples from patients with cancer and a series of controls. A pattern-matching heuristic algorithm was trained with spectra from known patients to recognize key features, a protein signature, allowing it to discriminate between unaffected women and patients with cancer. The signature was tested on a set of blinded patients and was able to discriminate all cancers correctly, including all stage I cases. The study yielded an overall sensitivity of 100%, with a specificity of 95%. The specificity needed to yield a 10% positive predictive value for screening ovarian cancer in the general population is 99.6% [24
, 25
]. This technology continues to undergo development at multiple centers.
Progress has been made since that initial report. Several groups are using similar tools for ovarian cancer and other cancers. Our group has further refined the technique to use a quadropole tandem liquid chromatographyMSMS system. This system has higher resolution than that used previously, and is highly reproducible. The sensitivity and specificity results of models now being generated are consistently 100% and 100% [26]. Importantly, this new approach allows isolation of the individual features for peptide sequence analysis and identification. Over 1000 proteins and peptides that may be selective or specific to ovarian cancer have now been sequenced from serum samples from women with early- and advanced-stage ovarian cancer. This allows both the biomarker component of the field and the etiologic study of ovarian cancer to advance simultaneously.
Prostate cancer
Prostate cancer is now the most commonly diagnosed cancer in American men and the second leading cause of cancer-related deaths in men. Current screening procedures include clinical examination and serum prostate-specific antigen (PSA) measurement. Absolute PSA levels are difficult to interpret, as non-malignant prostatic diseases can also increase PSA levels [27]. Furthermore, there are emerging data from the Prostate Cancer Prevention Trial and other sources showing that a significant number of men with PSA scores <4 ng/ml may have undetected prostate cancer [28
]. This underscores the need to find better screening measures than PSA alone that are deployable in a high-throughput and affordable fashion, and that identify men with high-grade aggressive disease for whom intervention has a clinical impact on survival.
The use of LCM for analysis of prostatic tissue has been described by several groups of investigators [8, 12
, 29
31
] and has been proposed as a method to discover new biomarkers for diagnosis. Zheng et al. [32
] have employed a combination of LCM and SELDI to analyze prostatic tissue from patients with both non-malignant benign prostatic hyperplasia (BPH) and invasive prostate cancers. They were able to identify a protein present in 94% of prostate cancers (n=17) examined that was not present in normal epithelium (n=17) or BPH samples (n=17). The function and identity of this protein is still being determined; however, the situation reflects the power of these technologies to find such discriminatory information even with limited user input. Other groups have tried mass spectral techniques on serum samples [20
, 31
, 33
35
]. Our group [20
] examined blinded serum samples from 266 men, which were analyzed using SELDI with a trained bioinformatics algorithm. The test was able to correctly identify 36/38 men (95%) with prostate cancer (PSA >4 ng/ml with biopsy-proven disease), while 177/228 (78%) of men without known disease (PSA <1 ng/ml) were identified.
Lehrer et al. [34] examined serum from three sets of men: patients with prostate cancer (n=11) or BPH (n=12), or unaffected controls (n=12), and were able to identify three protein peaks, using SELDI-TOF technology, present in cancer samples that were absent in both BPH and normal tissue. Li et al. [35
] examined archival serum samples of 345 men from a single institution: 246 underwent radical retropubic prostatectomy and 99 had no evidence of prostate cancer on biopsy. Three peaks were identified with the ability to discriminate patients with cancer from unaffected individuals. The markers were compared individually and then as a composite. Their composite biomarker test yielded sensitivity and specificity of 67% and 65%, respectively, at the point of optimal efficiency. PSA testing alone in the same sample set yielded sensitivity and specificity of 38%. Thus, despite the relative lack of sensitivity and specificity, this approach was superior to standard PSA testing. In addition, in a set of seven patients, a signature obtained preoperatively for organ-confined disease changed in all cases to a non-cancerous spectrum. This signature was not altered at 6 weeks, showing the ability of this approach to robustly detect changes.
Pancreatic cancer
Pancreatic adenocarcinoma has one of the lowest survival rates for any solid cancer [36]. The poor prognosis is attributed to the fact that most patients do not develop overt symptoms until the disease has disseminated or caused local organ dysfunction [36
, 37
]. CA 19-9 is currently the accepted serum marker for pancreatic cancer, but is Food and Drug Administration-approved only for monitoring treatment response. Current methods of diagnosis including CA 19-9 are ineffective for identifying small, surgically resectable cancers. Preliminary studies of serum protein profiling differentiated between patients with surgically resectable pancreatic cancer and patients with non-malignant pancreatic disease and healthy controls [36
]. The two most discriminating protein peaks in one study had a sensitivity of 78% and specificity of 97%, and were even more accurate when used in conjunction with CA 19-9 [36
]. Proteins differentially expressed in the pancreatic fluid have also been studied as potential biomarkers for pancreatic adenocarcinoma.
Hepatocarcinomaintestinepancreas/pancreatitis-associated protein I (HIP/PAP-I), a protein released from pancreatic acini during acute pancreatitis and overexpressed in hepatocellular carcinoma, was identified by SELDI. It is significantly elevated in patients with pancreatic adenocarcinoma over controls [37], and may serve as a new biomarker for this disease. Current efforts at serum-based approaches using SELDI also have yielded promising results. In a study set of 116 patients, 61 with cancer and 55 controls, pancreatic cancer was detected with 95% sensitivity and 97% specificity.
Breast cancer
Breast cancer is the second leading cause of cancer death in American women. Despite advances in understanding the biology of this disease, early diagnosis and intervention is the most important factor affecting survival. Mammography is a significant advancement, but is inadequate in detection of non-calcified, premalignant and non-invasive disease. Nipple-aspirate fluid initially appeared a promising source for the detection of potential biomarkers, as it a direct sampling of breast epithelial cells. However, no clinically useful results are available [38]. Serum proteomic pattern diagnostics are now being applied to this disease. Li et al. [39
] have identified three protein peaks using SELDI-TOF mass spectrometry that discriminate between stage 0I cancer patients and non-cancer controls. Other recently identified breast cancer biomarkers using SELDI include Hsp27, 14-3-3 sigma, and mammaglobin/lipophilin B complex [40
, 41
]. Application of SELDI-based technologies to serum screening in breast cancer screening is promising. Preliminary results show pattern recognizing algorithms were able to identify cancer patients with 90% sensitivity and 71% specificity in a series of 317 patient samples: 142 patients without cancer and 165 with cancer.
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Conclusions |
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Received for publication April 30, 2004. Revision received July 19, 2004. Accepted for publication July 23, 2004.
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References |
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