Some cancer mutations slow tumor growth

Some cancer mutations slow tumor growth

There are 1 comment on the Science, Industry and Business story from Feb 5, 2013, titled Some cancer mutations slow tumor growth. In it, Science, Industry and Business reports that:

A scanning electron micrograph of a squamous cell carcinoma, a type of skin cancer.

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Since: Dec 05

Reading, PA

#1 Feb 6, 2013
Sometimes the genetic signal may not be the driver mutation. Other signaling pathways, like passenger mutations, could be operative. It turns out that most mutations in cancers are passengers. It had been thought by molecular scientists that driver mutations are the ones that cause cancer cells to grow, whereas passengers are co-travellers that make no contribution to cancer development.

Efforts to restore or replace the function of these deleterious mutations, or turn them against cancer cells, have yet to show promising results. These technologies have limited success due to their inherent limitations in lack of clarity in distinguishing driver mutations in pathways from those of passengers.

Cells speak to each other and the messages they send are interpreted via intracellular and extracellular pathways. They are not individual entities but networks. A harmonic oscillation develops between tumor, stroma, vasculature and cytokines. In this mix, the cancer cell is but one piece of the puzzle.

Some of the tumor promotion signals in the form of small interfering RNAs may arise not from the cancer cells but instead from the surrounding stroma. How then will genomic analyses of cancer cells play out in the real world of human tumor biology and clinical response prediction?

In regards to cell-lines, researchers are calling for new models that can assay primary cancer cells directly from patients to sketch a more realistic molecular portrait of primary tumors. In other words, using "live" fresh cells instead of cell-lines.

A majority of anticancer drugs fail in phase II efficacy stage of clinical development due to a lack of technologies to identify and appropriately stratify patients according to their tumor pathway dependence. Researchers need to use a better translational tool for further clinical development of novel anticancer drugs.

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