Applications of Cancer Biomarkers

The main clinical applications of cancer biomarkers are:

  • • Classification of tumors
  • • Prognosis of disease progression
  • • Prediction of response to therapy
  • • Monitoring of response to therapy

Use of Biomarkers for Cancer Classification

Cancer Classification Using Microarrays

Classification of a cancer based on gene expression profile is important for personalizing cancer therapy. In the process of expression profiling, robotically printed DNA microarrays are used to measure the expression of tens of thousands of genes at a time; this creates a molecular profile of the RNA in a tumor sample. A variety of analytic techniques are used to classify cancers on the basis of their gene- expression profiles. There are two general approaches. In an unsupervised approach, pattern-recognition algorithms are used to identify subgroups of tumors that have related gene-expression profiles. In a supervised approach, statistical methods are used to relate gene-expression data and clinical data. Determination of tumor marker genes from gene expression data requires bioinformatic tools because expression levels of many genes are not measurably affected by carcinogenic changes in the cells. These molecular biomarkers give valuable additional information for tumor diagnosis/ prognosis and will be important for the development of personalized therapy of cancer.

Gene expression microarray technology is helpful in all phases of the discovery, development and subsequent use of new cancer therapeutics, e.g., the identification of potential targets for molecular therapeutics. It can be used to identify molecular biomarkers for proof of concept studies, pharmacodynamic endpoints and prognostic markers for predicting outcome and patient selection. Expression profiling can be used alongside gene knockout or knockdown methods such as RNA interference.

Proteomic Classification of Cancer

The use of rapid, high throughput MS-based fingerprints of peptides and proteins may prove to be valuable for new molecular classification of human tumors and disease stages. Coupled with LCM, high-density protein arrays and antibody arrays will have a substantial impact on proteomic profiling of human cancers.

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