Funding outcome

According to the Canadian Cancer Society, 1550 Canadian women were estimated to be diagnosed with cervical cancer {1.5% of all female cancers) in 2017. In spite of modern sophisticated treatments, survival of patients with cervical cancer is limited by local and metastatic recurrences. The standard treatment for locally advanced cervical cancer is using a combination of chemotherapy {using anti-cancer drugs) and radiotherapy {using radiation to destroy cancer cells). Currently, with evidence based on previously treated patients, the recommendation by international experts is to increase the quantity {'dose') of radiation prescribed for treatment in comparison to past prescriptions. The severity of radiation damage is dependent on several factors such as the dose and rate at which the dose is delivered, and is known to greatly vary amongst cancer centres and the technique used for treatment. With increased prescription doses, the consequence of treatment variation on the biological response of tumor and the normal tissues surrounding the tumor can differ substantially. Quantifying the effect of these dose differences using prediction models developed with the knowledge of the biological response of tumor and normal cells could improve outcomes in cervical cancer patients. Our research project has three key objectives: {i) to quantify of the biological parameters that most influence prescription doses based on tissue response {both tumor and normal) during radiotherapy treatment for cervical cancer; {ii) to perform experimental validation on the response of tumor and normal tissue to the identified parameters, and {iii) to evaluate efficacy of the biological parameters in predicting outcomes in patients treated with radiotherapy for cervical cancer. We propose through this study that by exploiting the potential of {i) and by experimental validation, will provide an opportunity to significantly improve outcomes of radiotherapy treatments for locally advanced cervical cancer and develop methods to provide personalized adaptive patient treatments for better outcomes. By evaluating the biological impact of different treatment parameters, we expect to determine the factors that contribute most to patient outcomes. The proposed biological modeling and experimental validation study will: {i) improve understanding of the treatment parameters that have the greatest influence on patient outcomes, {ii) enable more effective strategies for customizing patient-specific treatments, and {iii) likely reduce treatment related morbidity and mortality among cervical cancer survivors.