In an effort to improve the effectiveness of radiotherapy, researchers have been working to find gene signatures that can predict the response to radiation. This could enable clinicians to personalize treatment plans for individual patients and improve outcomes. Professor Venkata Manem, associated with the Faculty of Medicine at Université Laval and the Center de recherche CHU de Québec—Université Laval, has made significant progress in the field of precision radiation oncology through a recent study published in the journal BMC Cancer.
Currently, radiotherapy is administered using a standardized approach, with a fixed dose and frequency of treatment, regardless of the genomic characteristics of the tumor being treated.
However, certain cancers may exhibit varying sensitivities or resistance to different types of radiation regimens. By identifying patients who can benefit from lower doses, treatment toxicity can be reduced. On the other hand, doses can be adjusted for more resistant tumors or combined with other therapies to improve outcomes, explained Manem, a former assistant professor at the Université du Québec à Trois-Rivières.
While the developed radiosensitivity markers can currently be applied broadly to all types of cancer, the research team hopes to eventually build tissue-specific biomarkers. “With the availability of tissue-specific data, we could eventually have signatures for different types of cancers such as breast, prostate, and lung cancers,” said Professor Venkata Manem.
It is important to recognize that all tumors are unique, even within the same classification, stage, and anatomical features. They differ in terms of mutations, microenvironment, and immune components, all of which can impact the response to radiation, added Alona Kolnohuz, the first author of the study.
Utilizing cell line data in conjunction with bioinformatics and machine learning-based approaches, the research team has developed a molecular predictor of radiation response that can be tested in pre-clinical settings before being implemented in clinical studies.
Most studies in this field focus on measuring the number of cells that survive at a given radiation dose, such as 2Gy. However, Manem’s approach uses the area under the radiation dose-response curve (AUC) instead. “Based on our findings, we concluded that AUC should be considered as a pre-clinical radioresponse indicator in future studies as it captures a wider range of biological processes,” explained Professor Manem.
The next phase of the research involves validating the molecular signature in patient data and developing a clinical assay using interpretable machine learning methods. The team is also working on identifying radiosensitizing compounds that can increase the therapeutic efficacy of radiation.
According to Venkata Manem, with the emergence of genomic and AI-driven technologies, the time is ripe for precision medicine to move away from the traditional one-size-fits-all approach. “We envision that the developed gene signature of radiation sensitivity has the enormous potential to aid decision-making, personalize treatments, and improve outcomes,” Manem stated.
In conclusion, the identification of genomic markers for predicting radiation sensitivity in cancer treatment has the potential to revolutionize radiotherapy by enabling personalized treatment plans tailored to individual patients. This could lead to improved outcomes and reduced treatment toxicity.
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1. Source: Coherent Market Insights, Public sources, Desk research
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