Cancer Genome Therapy

Numerous recent studies have demonstrated the use of genomic data, particularly gene expression signatures, as clinical prognostic factors in cancer and other complex diseases. These studies highlight the opportunity for strategies to achieve truly personalized cancer treatment. Particularly important has been the use of genome-scale gene expression analyses to identify discrete disease classes not previously recognized. Genomic techniques are transforming biology from an observational molecular science to a data-intensive quantitative genomic science. Most successful applications of genomic technology have been in the study of human cancer, in which gene expression patterns can be identified that provide phenotypic detail not previously obtainable by traditional methods of analysis: profiles and patterns that identify new subclasses of tumors, such as the distinction between acute myeloid leukemia and acute lymphoblastic leukemia. Indeed, much of the activity in employing genomic technologies to achieve the goal of personalized cancer therapy has been directed at the identification of targets for new drug development that uniquely attack a given tumor. Genomic techniques may also be useful in determining more targeted applications for existing cancer therapeutics, many of which are very effective for subsets of cancer patients.

  • Cancer-related micro RNA and m-RNA
  • Tumor Heterogenecity
  • Cancer Genomics and Proteomics impact factor
  • Molecular underpinnings of therapeutic targets

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