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Philadelphia, PA – New precision technology, based on protein structures and function, is making it possible for clinicians to detect cancer earlier than ever and provide individualized treatment. The use of proteomics, the technical name for this activity, for the early detection and treatment of cancer is discussed in data published in the Proceedings for the 94th Annual Meeting of the American Association for Cancer Research (AACR). Cancer researchers believe that proteomics is revolutionizing the way cancer and other major illnesses are detected. With the discovery of new proteins and mapping of the human genome, proteomics may be the vanguard of a new generation of tools used to detect cancer during its early phases and to tailor therapy for individual patients. “Incorporating proteomics into medical evaluation is helping to advance the cancer research field, from early diagnosis to the identification of high-risk patients,” according to Lance Liotta, MD, PhD, of the CCR NCI and Co-Director with Emanuel Petricoin PhD of the NIH/NCI-FDA Clinical Proteomics Program. Multiple High-Resolution Serum Proteomic Patterns for Ovarian Cancer Detection The infant process of serum proteomic pattern diagnostics has generally relied on low-resolution mass spectrometry to detect a protein pattern that would signal disease. In a study presented by researchers at the NCI/FDA Clinical Proteomics Program, researchers extended the analysis to a higher resolution method using multiple accurate protein patterns to detect cancer. Using a well-controlled serum study from 248 women at risk for ovarian cancer, researchers extended the original pattern analysis to a higher resolution based on multiple patterns. The results showed 59 different patterns that were more than 85 percent accurate in disease diagnosis, four of which were 100 percent accurate. Researchers report that multiple proteomic patterns do exist within human sera mass spectra, and that finding multiple patterns provides an extremely accurate method of detecting disease in the earliest stages. “In comparing the high- and low-resolution processes, we noticed that the high-resolution was not only more accurate in detecting patterns, but was more sensitive in the spectrometry, reliable, and predictable in results reporting, without adding any confusion of use,” said Timothy Veenstra, PhD, of the NCI, and lead investigator of the study. “Hopefully we will be able to use this technology to more accurately detect ovarian cancer at a much earlier stage of development, drastically increasing the chances of survival.” Visualization and Data-Mining of Serum Proteomic Data for Early Cancer Detection Various bioinformatics tools have successfully revealed discriminatory patterns in proteomic datasets. However, none had the ability to provide a global view of the entire dataset along with the ability to zoom in and out of regions of interest. In collaboration with researchers at Brookhaven National Laboratory who have used visualization techniques in analyzing massive datasets from physics experiments, visualization tools were applied to provide a global view of the entire proteomic dataset, along with the ability to adjust the focus into very specific regions of interest. As a result, the size of the proteomic datasets were greatly reduced, re-analyzed and contiguous regions of mass species were found showing discriminatory abilities. “Using these visualization tools, the identification of proteomic patterns that lead to disease diagnosis can be done with greater sensitivity and accuracy,” according to Donald Johann Jr., MD, of the NIH/NCI-FDA Clinical Proteomics Program, and lead investigator of the study. “This has reduced the risk of error, increased our productivity, and provides an efficient method to better learn from this new information archive.” Beyond identifying the presence of ovarian cancer and other diseases, these tools may allow researchers to determine how far the disease has progressed by studying the proteomic pattern of cancer as a function of stage. Clinical Proteomics: Technology for Monitoring Drug Treatment Effects on In Vivo Signal Pathways in Cancer Cell Samples Obtained by Biopsy Using proteomics to discern treatment effectiveness may assist in creating personalized therapy in the future, according to a study conducted by researchers at the NCI. “We suspect that by using this technique to analyze protein profiles, we will soon be able to tailor specific therapies to each individual patient’s needs,” said Virginia Espina, MS MT(ASCP), of the NCI, and lead investigator of the study. In the study tissue biopsy samples were collected at various points before and after treatment. Using laser capture microdissection, malignant, pre-malignant or normal cell populations were isolated. Using reverse-phase protein arrays, key phosphorylation events were identified, as determined by specific antibodies. Noting that a signal protein may be key in determining whether a cell follows a survival or apoptotic pathway, researchers could use the protein to discern the changes in the cellular pathways. The direction of the pathway determined the reaction to treatment. After treatment, significant suppression of the pro-survival pathway was observed, thus noting that this particular treatment was appropriate for the patient’s needs. Researchers concluded that discerning the success of the treatment at the level of the pathway may allow for identification of treatment non-responders. A further important application will be the individualized design of combination therapy that targets multiple points along an entire signal pathway. “Combination signal transduction therapy has the potential to achieve higher efficacy with lower toxicity” said Arpita Mehta Howard Hughes Scholar (abstract 4015). Proteomic Screening Using SELDI-TOF to Detect Biomarkers for Endometrial Cancer in Serum Proteomics may be the key to providing an accurate non-invasive screening test for better early detection of endometrial cancer, according to a study presented by researchers from the University of New Mexico Cancer Research & Treatment Center in Albuquerque. In the study, serum samples from early stage cancer patients were compared to control volunteers with no history of cancer. Using the SELDI-TOF mass spectrometer, spectra were analyzed, noting significant differences between the cancerous and non-cancerous sample sets. Researchers identified a specific protein profile that appears to distinguish early stage endometrial cancer samples from healthy samples. The data is under full investigation, but the use of the technology could potentially be a reliable screening method for the early detection of such diseases. “We believe this technology may assist in detecting endometrial cancer at the earliest stages, when the disease is easiest to treat,” said Charlotte Mobarak, PhD, lead investigator on this study.