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Unconventional Treatment Strategy Controls -- Rather Than Eradicates -- Cancer

Last updated March 7, 2016

Approved by: Krish Tangella MD, MBA, FCAP

Klement et al., Science Translational Medicine (2016)

Standard chemotherapeutic approaches rely on maximum tolerated doses (MTD) of chemotherapy, invariably selecting for the drug-resistant population by killing off not only drug-sensitive cancer cells but also healthy blood vessels and stromal cells, including native immune responders. The eco-evolutionary model proposed by Enriquez-Navas et al. is aimed at controlling tumor growth by maintaining an equilibrium between sensitive and resistant populations by means of low-dose frequent chemotherapy. The work demonstrates, in two different mouse xenograft models of breast carcinoma, that this approach can be more effective than the standard MTD approach in preventing disease progression and providing sustained control. This material relates to a paper that appeared in the Feb. 24, 2016, issue of Science Translational Medicine, published by AAAS. The paper, by G.L. Klement at Tufts University School of Medicine in Boston, MA, and colleagues was titled, Eco-evolution of cancer resistance.'


Can we learn to live with--rather than kill--cancer? A new study suggests that frequent, low-dose chemotherapy that keeps tumor growth under control may be more effective than standard high-dose chemotherapy that seeks to eradicate cancer cells completely. The treatment strategy, which was tested in mice, flies in the face of conventional cancer therapy, which generally hits patients with the maximum drug dose possible to kill off the largest number of tumor cells.

Despite aggressive treatment, complete cancer eradication is rare and toxic side effects all too common. Recently, researchers have questioned the benefits of standard chemotherapy because while it destroys drug-sensitive tumor cells, it leaves behind drug-resistant cells. By eliminating the former population of tumor cells, the drug allows resistant cells to take over and drive tumor growth uncontrolled.

Taking into account the evolutionary forces that drive cancer resistance, Pedro Enriquez-Navas and colleagues designed an evolution-based treatment strategy that adjusts the drug dose based on how the tumor responds. Rather than trying to shrink the tumor completely, the so-called adaptive therapy seeks to stabilize the tumor by maintaining a small population of drug-sensitive tumor cells to suppress the growth of resistant cells.

The researchers tested the approach with the chemotherapy drug paclitaxel in mice with two different types of breast cancer. Standard chemotherapy shrunk the mouse breast tumors, but only to have them grow back as soon as treatment stopped. Another treatment regimen that skips doses whenever the tumor shrunk also inevitably resulted in tumor progression.

In contrast, adaptive therapy consisting of high initial drug doses followed by progressively lower doses as the tumor responded was more effective in controlling tumor growth than either standard therapy or dose skipping.

In fact, the treatment allowed between 60 and 80% of the mice to be weaned off the drug completely without relapsing for an extended period of time. A related Focus by Giannoula Klement discusses how the study's eco-evolutionary model of cancer may prompt researchers and clinicians to rethink current therapeutic strategies for cancer.


The above post is reprinted from materials provided by American Association for the Advancement of ScienceNote: Materials may be edited for content and length.

Disclaimer: DoveMed is not responsible for the adapted accuracy of news releases posted to DoveMed by contributing universities and institutions.

Primary Resource:

Enriquez-Navas, P. M., Kam, Y., Das, T., Hassan, S., Silva, A., Foroutan, P., ... & Gatenby, R. A. (2016). Exploiting evolutionary principles to prolong tumor control in preclinical models of breast cancer. Science Translational Medicine8(327), 327ra24-327ra24.

Reviewed and Approved by a member of the DoveMed Editorial Board
First uploaded: March 7, 2016
Last updated: March 7, 2016