Proteomics has advantages over genomics for determining medical or disease status
The need for diagnostic tests that detect cancer at its earliest stages has never been greater. Advanced surgical techniques and targeted therapies offer the possibility that cancer may one day become a manageable disease. But this will only be possible if the cancer is detected early in its course, while it is still treatable.
Blood-based biomarkers hold the best promise for developing rapid, accurate, minimally invasive, and fairly priced cancer diagnostics with high sensitivity and specificity. In the proper format, such tests might serve a number of important objectives: detecting early-stage cancers, confirming cancer diagnosis, tumor staging, or suggesting a course of therapy. They could even be used to foster the development of new cancer therapy regimens, either new drugs or novel applications of existing drugs and combinations.
PROTEINS AS BIOMARKERS
Among possible biomarkers, proteins relate the most information about patients and their diseases at any point in time. The U.S. Food and Drug Administration defines a protein biomarker as "a protein or protein fragment that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or pharmacological responses to a therapeutic intervention."
Proteomics has shown great potential for diagnosing early-stage cancer. This promising science seeks to identify proteins, often occurring in very low abundance, and link them to physiologic or disease states. A separate effort is also underway with respect to genes in the more mature field of genomics. Uncovering protein biomarkers through proteomic analysis, however, is not an easy task.
Researchers encounter a number of obstacles when applying proteomics to disease diagnostics. The most daunting is the wide concentration dynamic range (on the order of 1010) for naturally occurring human proteins. Thus, the protein product of a gene critical to a disease pathway may occur in very low abundance relative to the degree to which its gene is activated.
The other major problem is that tools for gene analysis are much more advanced than their analogous proteomic analysis methods. But, whereas several whole-genome chips have been marketed for humans and other species, no equivalent product exists for proteins. This is partly due to the lack of a binding mechanism possessing the robustness and easy accessibility of nucleotide base-pair interactions; it is also because proteins are much more abundant than genes. One gene can give rise, through various splicing and recombination mechanisms, to multiple proteins. These factors do not make proteomics work impossible, but they do make it more difficult.
With the completion of the Human Genome Project and ensuing research, identification of protein products of genes has increased exponentially. One consequence of a much larger, deeper protein database is that the application of proteomics to medical diagnostics has progressed from being at the research stage just a few years ago to being on the verge of commercialization today.
Proteomics has certain advantages over genomics for determining medical or disease status. DNA analysis reveals the risk or potential for developing a disease. DNA'S immediate downstream product, RNA, indicates what might possibly be wrong. Emergence of a disease phenotype, as it relates to DNA or RNA, depends not only on a particular gene or genetic mutation but also on having that gene in its active form. In addition, it depends on the impact of factors such as diet, environment, exposure to toxins, or the action of unknown genes or gene regulators.
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In contrast, proteins reflect the patient'S actual health or disease status at the moment the test is taken. One could say that proteins, the products of genes, are the disease or its direct biochemical manifestation. As such, proteins open a diagnostic window. Under the right conditions, this window provides a glimpse into the earliest stages of an illness, often before symptoms become apparent.
PROTEINS AS CANCER DIAGNOSTICS
Breast cancer diagnosis illustrates the potential benefit of a proteomics-diagnostic approach. A typical patient diagnosed with breast cancer by mammography and biopsy has likely had the disease for as many as 10 years. Moreover, as a cancer screening tool, mammography alone has a sensitivity of only 70% overall, even using current digital technology. A recent study found that mammography possessed a sensitivity of just 41% when a 15-month follow-up period was used to define false negatives. Magnetic resonance imaging (MRI) detects breast cancer earlier than mammography and is much more sensitive. The American Cancer Society'S most recent screening guidelines for women at high risk for breast cancer suggest that regular mammograms be supplemented with an annual breast MRI exam beginning at age 30.
Despite its added benefits, MRI is expensive as diagnostic tests go, costing four to five times as much as mammography. In addition, the number of instruments currently installed is far too low to support widespread screening.
While imaging will continue to have a place in localizing and treating tumors, a screening test that is comparable in cost to mammography is also needed to complement that screening method, perhaps as a gateway to MRI in high-risk women with negative mammo-grams. Such a test would help to overcome the high incidence of false negatives obtained with mammography alone.
One could say that proteins, the products of genes, are the disease or its direct biochemical manifestation. As such, proteins open a diagnostic window.
Power3 Medical Products, Inc. (Houston) is a clinical diagnostics company commercializing powerful proteomics methods for diagnosing a range of serious human illnesses. Among its development-stage tests are diagnostics for cancer, neurologic diseases, and cancer drug resistance. In developing its breast cancer diagnostic, Power3 used 2-D gel electrophoresis to quantify differentially expressed protein biomarkers related to that disease from a group of 39 breast cancer patients and 38 controls with benign breast disease. The result was a proteomic profile of the serum and a statistical model that differentiates breast cancer patients from control subjects.
From thousands of expressed proteins, researchers identified a panel of 22 protein biomarkers whose expression differs significantly between the test groups. These proteins were selected by image analysis and identified using high performance liquid chromatography and mass spectrometry.
No single biomarker among these 22 possessed any statistically significant predictive or diagnostic capability. When taken together, however, the 22 proteins achieved 90% sensitivity and 90% specificity in discriminating cancer from benign disease. Perhaps as significantly, the researchers were able to differentiate early- from late-stage cancer, because these states possess unique biomarker profiles on their own.
Recent progress in mass spectrometry, particularly surface-enhanced laser desorption and ionization time of flight (SELDI-TOF), has led to insights into the proteomic basis of ovarian cancer and Alzheimer'S disease. SELDI methods have, consequently, renewed interest in proteomics as a diagnostic tool.
In this area, Power3 employed a reliable proteomic technique as its primary analysis tool. This tool, 2D gel electrophoresis and image analysis of the protein spots, was used on patient samples to study the development and progression of breast cancer. Power3'S BC-SeraPro is a blood serum test designed to diagnose breast cancer. The test is based on proteomic technology, in which a blood serum sample drawn from a patient is subjected to protein separation by two-dimensional gel electrophoresis, resulting in separation of approximately 1,500 protein spots. The gel is stained with a highly sensitive fluorescent dye and is scanned with a fluorescent laser scanner to generate a digital gel image. The gel image is subjected to analysis and extraction of quantitative information on each spot, and protein spots that are quantitatively different between cancer and non-cancer samples are selected as biomarkers.
A statistical model generated by screening hundreds of breast cancer patients, benign breast-lesion patients, and healthy normal individuals is used to analyze the quantitative information on these biomarkers. The statistical model is fed with quantitative information on 22 pre-selected protein bio-markers from the gel images. The biomarkers are selected for their ability to discriminate breast cancer patients from non-cancerous subjects. The statistical model evaluates the quantitative information on those 22 protein biomarkers in the patient serum sample and automatically assigns a probability score. The probability score indicates to the physician whether or not the patient has cancer. The score reflects how closely the patient sample fits into the training model. These statistical model results can indicate if the patient should be recommended for further follow up by the clinician.
Power3 has employed similar proteomics techniques to identify protein biomarkers that distinguish important neurodegenerative diseases, particularly Alzheimer'S disease, Parkinson'S disease, amyotrophic lateral sclerosis (ALS), and Alzheimer'S-like or mixed dementias. The latter make up about 35% of all dementia cases in the United States.
As with breast cancer, the goal of detection and diagnosis of serious neurologic illnesses is to initiate treatment early in the course of an illness, with the idea of improving quality of life and perhaps delaying progression of the disease. Because the cost of treating patients with these diseases exceeds $100 billion per year in the United States, there is tremendous need for diagnostic tools that get patients into the right treatment program early, as well as for monitoring the course of therapy for safety and efficacy.
Currently, diagnosis of neurodegenerative diseases is based on symptoms and physician judgment. The risk of misdiagnosis, while always present, is particularly serious when patients might be prescribed the wrong medication or no medication at all. Accurate diagnosis has the benefit of suggesting the most appropriate treatment and, during therapy, helping physicians to assess the course of the disease or the patient'S response to medication.
Parkinson'S disease is the second most common neurode-generative disease in adults, affecting 1.5 million people in the United States. Clinical tests for early diagnosis or for identifying drug targets do not exist. Physicians have difficulty differentiating early-stage Parkinson'S from disorders with similar symptoms. Often, confirmation of diagnosis does not occur until the disease is well advanced. When this happens, patients miss the opportunity for early medical intervention that may improve their lives and delay progression of the disease.
Researchers have not yet found a biomarker that is capable of identifying neurodegenerative diseases on its own, but Power3 has found a novel use for selected protein in a panel that differentiates patients with Parkinson'S, ALS, and Alzheimer'S disease from normal subjects. The company has identified a panel of 59 protein biomarkers, which together achieve very high sensitivity and specificity for Alzheimer'S disease. This panel of biomarkers was also found to have comparable sensitivities and specificities for both Parkinson'S disease and ALS.
The company also has a program that identifies proteomic markers for drug resistance in chemotherapy. Although in its early stages, this program is expected to help identify the optimal chemotherapeutic regimen for specific tumors based on the cancer cells' likelihood of developing early drug resistance.
PROTEIN DIAGNOSTICS IN PERSPECTIVE
Taken together, demand for diagnosis of breast cancer, neurodegenerative diseases, and drug resistance add up to a sizable portion of the global $19 billion annual clinical diagnostics market. More than $6 billion is spent annually on screening and diagnosis of neurodegenerative diseases such as Alzheimer'S, Parkinson'S, and ALS. Diagnosing these illnesses is particularly difficult given the emergence of diseases with similar clinical symptoms.
Researchers have not yet found a biomarker that is capable of identifying neurodegenerative diseases on its own.
Similarly, the breast cancer diagnostic market is valued at more than $7 billion annually. Drug resistance is a serious problem that occurs with all cancers treated by chemotherapy and biological anti-tumor agents. The value of a rapid, reliable, cost-effective test for drug resistance can be measured in terms of the cost-both economic and human-of failed chemotherapy and the benefits of getting the right drug to the right patient.
More than ever, the optimal application of surgical and medical treatments depends on accurate diagnosis. Whether these tests are based on genomics, proteomics, or metabolomics, an accurate reflection of the patient'S current disease state is of utmost importance. Proteomics provides the most valuable information on current disease state for many important illnesses. �