General keynote: proteomic patterns in sera serve as biomarkers of ovarian cancer.
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Biomarkers, Tumor↗
Biomedical subjects
Ali M Ardekani
Publications and source records attributed to Ali M Ardekani.
Molecular diagnostics: an FDA perspective.
The potential medical applications of microarrays and in vitro diagnostic devices for global assessments of DNA sequence variations, relative RNA abundance and measurements of proteins have generated much excitement, and some skepticism, within the biomedical community. It has been suggested that within the next decade these microarrays and diagnostic devices will be routinely used in the selection, assessment and quality control of the best drugs for pharmaceutical development, at the bedside for diagnostics and for clinical monitoring of both desired and adverse outcomes of therapeutic interventions. Realizing such potential will be a challenge to the entire scientific community as often breakthroughs which show great promise at the bench fail to meet the requirements of clinicians and regulatory scientists, and to make the transition into common clinical and regulatory practice. The development of a co-operative framework between regulators, product sponsors and technology experts will be essential for realizing the revolutionary promise these platforms could have on the evolution of drug development, regulatory science, the practice of medicine and public health.
Biomarkers↗
Use of proteomic patterns in serum to identify ovarian cancer.
BACKGROUND: New technologies for the detection of early-stage ovarian cancer are urgently needed. Pathological changes within an organ might be reflected in proteomic patterns in serum. We developed a bioinformatics tool and used it to identify proteomic patterns in serum that distinguish neoplastic from non-neoplastic disease within the ovary. METHODS: Proteomic spectra were generated by mass spectroscopy (surface-enhanced laser desorption and ionisation). A preliminary "training" set of spectra derived from analysis of serum from 50 unaffected women and 50 patients with ovarian cancer were analysed by an iterative searching algorithm that identified a proteomic pattern that completely discriminated cancer from non-cancer. The discovered pattern was then used to classify an independent set of 116 masked serum samples: 50 from women with ovarian cancer, and 66 from unaffected women or those with non-malignant disorders. FINDINGS: The algorithm identified a cluster pattern that, in the training set, completely segregated cancer from non-cancer. The discriminatory pattern correctly identified all 50 ovarian cancer cases in the masked set, including all 18 stage I cases. Of the 66 cases of non-malignant disease, 63 were recognised as not cancer. This result yielded a sensitivity of 100% (95% CI 93--100), specificity of 95% (87--99), and positive predictive value of 94% (84--99). INTERPRETATION: These findings justify a prospective population-based assessment of proteomic pattern technology as a screening tool for all stages of ovarian cancer in high-risk and general populations.
CA-125 Antigen↗
Genomics and proteomics: application of novel technology to early detection and prevention of cancer.
Advances in molecular biology over the past decade have helped to enhance our understanding of the complex interplay between genetic, transcriptional and translational alterations in human cancers. These molecular changes are the basis for an evolving field of high-throughput cancer discovery techniques using microscopic amounts of patient-based materials. Laser capture microdissection allows pure populations of cells to be isolated from both the tumor and stroma in order to identify subtle differences in RNA and protein expression. Comparative analysis of these alterations between normal, pre-invasive, and invasive tissue using powerful bioinformatics programs has allowed us to identify novel tumor markers, profile complex protein pathways, and develop new molecular-based therapies. Continued refinement of such high-throughput microtechnologies will enable us to rapidly query patient specimens to identify novel methods for early detection, treatment, and follow-up of a wide array of human cancers.
Genomics↗
Proteomic technologies to study diseases of the lymphatic vascular system.
Now that the human genome has been mapped, a new challenge has emerged: deciphering the various products of individual genes. Consequently, new proteomic technologies are being developed to monitor and identify protein function and interactions responsible for the total activities of the cell. The application of these new proteomic technologies to study cellular activities, will lead to a faster sample throughput and increased sensitivity for the detection of individual proteins, thus providing major opportunities for the discovery of new biomarkers for the early detection of protein alterations associated with the progression of the disease state.
Animals↗
Clinical potential of proteomics in the diagnosis of ovarian cancer.
The need for specific and sensitive markers of ovarian cancer is critical. Finding a sensitive and specific test for its detection has an important public health impact. Currently, there are no effective screening options available for patients with ovarian cancer. CA-125, the most widely used biomarker for ovarian cancer, does not have a high positive predictive value and it is only effective when used in combination with other diagnostic tests. However, pathologic changes taking place within the ovary may be reflected in biomarker patterns in the serum. Combination of mass spectra generated by new proteomic technologies, such as surface-enhanced laser desorption ionization time-of-flight (SELDI-TOF) and artificial-intelligence-based informatic algorithms, have been used to discover a small set of key protein values and discriminate normal from ovarian cancer patients. Serum proteomic pattern analysis might be applied ultimately in medical screening clinics, as a supplement to the diagnostic work-up and evaluation.
Biomarkers, Tumor↗