Molecular and genetic targets in early detection.
ABSTRACT Recent research has revealed the existence of specific mutations in cancer. These mutations are being investigated as targets to find subjects at high risk for cancer, to detect early cancer, to detect the early recurrence of established cancer, and to find micrometastasis. These mutations are reviewed for the major anatomic sites. Some of the clinical issues related to the application of these mutations and the limitations of using molecular targets are also considered. Current methods for determining the risk of cancer are reviewed. Risk assessment is essential for defining cohorts for chemoprevention and other interventions. The concept of using surrogate anatomic and functional sites for estimating risk is introduced. Finally, the increasing complexity of molecular genetic analysis and the biologic heterogeneity of cancer are discussed in relation to early detection.
- JNCI Journal of the National Cancer Institute 08/2001; · 14.34 Impact Factor
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ABSTRACT: It has become increasingly evident that the study of DNA is inadequate to explain many, if not most, aspects of the development and progression of neoplastic lesions from pre-invasive lesions to metastasis. Thus, the term "genetic" can no longer refer to just the study of the genome. Much of the action in genetic research now shifts to the methods by which the pre-mRNA from one gene is processed to yield multiple different proteins, different quantities of the same protein as well as other forms of regulating RNA. Thus, the age of post-transcriptional processing and epigenetic control of the transfer of information from the genome has arrived. The mechanisms of post-transcriptional processing and epigenetic control that must be characterized in greater detail including alternate splicing, regulation of mRNA degradation, RNA regulatory factors including those factors which extensively edit mRNAs, control of translation, and control of protein stability and degradation. This chapter reviews many of the processes that control information from the genome to proteins and how these factors lead from less than 40,000 genes to more than an order of magnitude increase more proteins which actually control the phenotypes of cells - normal or neoplastic. It is usually the products of genes (e.g., mRNA, microRNA and proteins) that are the molecular markers that will control translational research and ultimately, individualized (personal) medical approaches to disease. This chapter emphasizes how the process of neoplasia "hijacks" the normal processes of cellular operations, especially those processes that are important in the normal development of the organisms - including proliferation, cellular death, angiogenesis, cellular mobility and invasion, and immunoregulation to ensure neoplastic development, survival and progression. This chapter reviews the wide range of processes controlling the information that flows from the genome to proteins and emphasizes how molecular steps in pure processes can be used as biomarkers to study prevention, treatment and/or management of diseases.Cancer biomarkers: section A of Disease markers 01/2011; 9(1-6):41-64. · 0.97 Impact Factor
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ABSTRACT: In a medical diagnostic testing problem, multiple diagnostic tests are often available in distinguishing between diseased and nondiseased subjects. Different diagnostic tests are usually sensitive to different aspects of the disease. A desirable approach is to combine multiple diagnostic tests so as to obtain an optimal composite diagnostic test with higher sensitivity and specificity that detects the presence of the disease more accurately. To accomplish this, it has been observed via signal detection theory developed in the 1950s and 1960s, that the optimal combination of different diagnostic variables (i.e. the diagnostic test results) is determined by the likelihood ratio function for the diseased and nondiseased groups. The conventional approach is to fit parametric models for the diseased and nondiseased groups separately and then to use the fitted likelihood ratio function for the best combination of test results. However, this approach is not so robust if the underlying distribution functions are misspecified. Since the optimal combination depends only on the likelihood ratio function, it would be more appropriate to model this function directly. A two-sample semiparametric inference technique is applied to the model for the likelihood ratio function. We consider the best combination of multiple diagnostic tests, and study semiparametric likelihood estimation of the optimal receiver operating characteristic curve and the area under the curve. We present a bootstrap procedure along with some results on simulation and on analysis of two real data sets.Statistics in Medicine 12/2010; 29(28):2905-19. · 2.04 Impact Factor