Molecular Mutations in Human Neoplastic Disease

Application of Nucleic Acid Technology in Assessment of Familial Risk, Diagnosis, and Prognosis of Human Cancers
  • William B. Coleman
  • Gregory J. Tsongalis
Part of the Pathology and Laboratory Medicine book series (PLM)


Cancer represents a significant health problem worldwide. The successful curative treatment of almost every form of this disease depends on early diagnosis, and in the case of solid tumors, surgical resection with or without adjuvant therapy. Intensive research efforts during the last two decades have increased our understanding of carcinogenesis and have identified a genetic basis for the multistep nature of cancer development (1–3). In several human tumor systems, specific genetic alterations have been shown to correlate with well defined histopathologic stages of tumor development and progression (4,5). Although the significance of molecular mutations to the etiological mechanisms of tumor development has been debated, a causal role for such genetic lesions is now commonly accepted for a number of human tumors. Thus, genetic lesions represent an integral part of the processes of neoplastic transformation and tumor progression, and as such represent potentially valuable markers for cancer detection and staging (6–9). Through the application of specific and sensitive nucleic acid methodologies, the clinical laboratory of the future will be able to effectively screen populations at high risk for the development of cancer, potentially impacting the early detection and diagnosis of human cancers. In addition, development of new molecular diagnostic assays will expand the ability of clinicians to accurately stage tumor development, monitor progression of metastatic disease, and evaluate therapeutic out-come, facilitating the application of effective intervention strategies in the treatment of human tumors.


Ovarian Cancer Germline Mutation Lynch Syndrome Malignant Peripheral Nerve Sheath Tumor Hereditary Nonpolyposis Colorectal Cancer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer Science+Business Media New York 1997

Authors and Affiliations

  • William B. Coleman
  • Gregory J. Tsongalis

There are no affiliations available

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