Completed Cancer Cells, Biochemistry & Physiology

BioProton: Biologically relevant dose for Proton Therapy Planning

In plain English

AI plain-English summary

Proton therapy beams currently deliver the same radiation dose across an entire tumour, even though oxygen-starved cancer cells are up to three times more resistant to radiation damage than well-oxygenated ones. This matters because tumours grow unevenly—cells far from blood vessels become hypoxic, and these low-oxygen regions survive standard proton doses more easily, allowing the cancer to regrow. Current treatment planning ignores this biological variation, delivering a uniform dose that over-treats some areas and under-treats others. The BioProton project will combine computer modelling with imaging to map oxygen levels inside a tumour, then calculate a non-uniform proton dose that delivers more radiation to hypoxic, resistant zones and less to well-oxygenated ones. If successful, this approach could increase the probability of tumour control while reducing damage to surrounding healthy tissue—meaning fewer side effects for patients and better survival rates. This is applied medical physics with a direct clinical target. The research does not aim to discover new biology; it aims to make existing proton therapy smarter by incorporating what is already known about oxygen and radiation resistance into treatment planning software.

View original technical description
Oxygen plays an important role in life on earth. The air that we breathe provides cells with the oxygen required for energy production. This need for oxygen increases for cells that rapidly multiply such as those associated with cancer; however, the supply is limited. As a tumour increases in size not all parts will be located near to vessels carrying oxygen rich blood. This results in a reduction in the oxygen levels in cells located furthest away from the blood vessel. It has been shown that these cells with low levels of oxygen (termed hypoxic) are more resistant to damage from radiation than those that are well oxygenated. This is also known to be the case for irradiation with protons. In proton therapy, a beam of protons is fired at the tumour in order to destroy the DNA in the cancerous cells, thus killing the tumour. The amount of energy and number of protons required to achieve this is determined by the tumour volume. Currently in proton therapy the tumour is irradiated such that the whole tumour volume receives the same dose (energy deposited per unit mass). If, however, parts of the irradiated tumour are more resistant to the radiation than others this technique of delivering a uniform dose across the tumour volume is not optimal. The research planned in this project aims to address this through the use of computer modelling and imaging to produce a method of increasing the dose to those low-oxygen radiation-resistant parts of the tumour whilst delivering an appropriately lower dose to the well oxygenated regions. This advancement will improve proton beam therapy and benefit any patient undergoing this form of cancer treatment. The benefits will include increased chance of survival and fewer side effects associated with the treatment

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Researchers

Adam Henry Aitkenhead (Co-Investigator)Catharine West (Co-Investigator)Gillian Whitfield (Co-Investigator)James O'Connor (Co-Investigator)Karen Kirkby (Principal Investigator)Michael Merchant (Co-Investigator)Michael Taylor (Principal Investigator)Neil Burnet (Co-Investigator)Norman Kirkby (Co-Investigator)Ranald Iain MacKay (Co-Investigator)Sergei Fedotov (Co-Investigator)

Related Research

Grants with similar aims, by meaning.

Experimental and computational analysis of the cellular responses to proton beam therapy
New insights into the cellular response to complex DNA damage induced by proton beam therapy
Developing a technique to measure levels of tumour hypoxia during proton beam therapy through gamma-ray spectroscopy
Ion Beam Radiotherapies: Comparison of Protons, Antiprotons and Heavier Ions
Novel image reconstruction techniques with application to proton radiotherapy for optimisation of cancer treatment

Original classification

Research Grant

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