Cell signalling pathways involved in cancer drug resistance have been identified for the first time by a team at Duke University. Researchers mapped signalling pathways involved in drug resistance in cells from three types of cancer: melanoma (a skin cancer), myelofibrosis (a blood cancer), and breast cancer. Their findings, published in two papers in Science Signaling, indicated that common processes were behind the cancer cells becoming resistant to drug treatments.
[caption id="attachment_133" align="alignleft" width="300"] Malignant melanoma stained with eosin and hematoxylin. Image source: KGH. CC3.0.[/caption]
Drug resistance, where cancer cells overcome the effects of treatment and grow in spite of it, is an issue in many types of cancer. The difficulty in finding a single “cure for cancer”, a phrase many scientists decry, is due to the varieties of cancer that exist. Different cell types and locations of tumours play a huge in role in what kind of treatments can be effected, but drug resistance is a common denominator.
Lead author on the studies, Dr Kris Wood, said “The most logical way to solve the problem is to understand why tumor cells become resistant to drugs, and develop strategies to thwart these processes.”
They developed a screening technology that broadly surveyed the signalling pathways that may trigger drug resistance. This technology corroborated earlier studies and also identified previously undescribed pathways, which were further identified in tumour cells from drug-resistant cancer patients.
Wood revealed that the Notch-1 pathway drove resistance in breast cancer and melanoma, while two pathways downstream of the RAS signalling molecule were involved in myelofibrosis. These pathways, when activated, promote resistance by suppressing cell death. “Interestingly, the mechanisms are quite similar among all three of the cancer types," Wood said.
While there is no magic bullet for treating cancer, the prospect of a common denominator in drug resistance signalling pathways presents a new target for cancer therapies and categorising patients to best suit their treatments.