Wednesday, April 2, 2008

Combining Genomic and Clinical Data for Cancer Therapy

This just came out today. So check it out.

An article entitled " Gene Expression Signatures, Clinicopathological Features, and Individualized Therapy in Breast Cancer" in the Journal of the American Medical Association, retrospectively studied traditional diagnostic standards of breast cancer outcomes — such as patient age, tumor size, and so on — and information about gene expression by modern genomic technology in a thousand breast cancer tumors. The conclusion is appealing: Gene expression patterns can, indeed, define subgroups of women with different prognoses and treatment responses.

“The combination of these two methods, one of which uses the clinical description of a patient’s breast cancer and the other which looks at gene expression at the molecular level in a patient’s tumor, may allow us to [match drugs with patients] with unprecedented accuracy,” senior author Anil Potti, an investigator at Duke University, said in a statement.

Traditionally, breast cancer evaluation is based on factors the so called TNM classification system, such as the patient’s age, tumor size, the level of lymph node involvement, and the degree of metastasis. These clinicopathological features could be employed to make predictions about clinical outcomes and help doctor’s to determine whether adjuvant cancer therapies such as chemotherapy or radiation therapy are warranted or necessary for different patients. However the estimation simply based on these factors are not always meaningful, e.g. it tends to overestimate cancer recurrence in younger patients.

To determine whether genomic data can provide additional information, the researchers studied women with early-stage breast cancer who had been followed for on average 11 years after initial assessment.
Indeed, the researchers did find that "molecular traits of patients in the poor prognostic clusters were highly specific and distinct from those of the good prognostic carriers”.

As the authors pointed out, identifying these subgroups may not only refine predictions about patient outcomes, it also provides information about patients’ underlying biology and the tumor microenvironment. That’s because gene expression patterns reveal different genetic pathways that are activated or silenced in different tumors during the long tumor formation progress.
For instance, low expression of cancer risk genes, chromosomal instability, and so on predict good outcome. However, high expression of genes associated with oncogenic pathway activation and wound healing etc. tend to be associated with poor outcome. Some genetic signatures also might indicate different responses to chemotherapy.

As wrote in an accompanying editorial in the same issue of JAMA, by Northwestern University researchers Chiang-Ching Huang and Markus Bredel, “This is one of the largest studies in human cancer showing the ability of gene expression profiles to improve risk stratification beyond established risk assessment algorithms that take into account clinicopathological variables”. This study “demonstrates the potential value of using microarray-based gene signatures to refine outcome predictions.”

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