2024-02-13 07:00:07
As the effect of radiotherapy varies greatly from one cancer to another, identifying genomic signatures capable of determining the expected response to radiation might better guide medical staff in personalizing treatments.
Professor Venkata Manem, affiliated with the Faculty of Medicine of Laval University and the Research Center of the CHU de Québec – Université Laval, has just taken a promising step forward for preclinical research in the field of precision radiation oncology.
Currently, medical staff use a one-size-fits-all radiotherapy model, with a determined dose and frequency of radiation, without taking into account the genomic characteristics of the tumor.
“Some cancers will be more sensitive or more resistant to different types and regimens of radiation. By identifying patients who can have lower doses, we might reduce the toxicity of the treatment. We might also adjust the dose for tumors that are more resistant or pair it with another therapy”, explains researcher Manem, previously professor under grant at the University of Quebec at Trois-Rivières.
For now, prediction markers for radiotherapy can be applied generally to all cancers, but the team is aiming for specific markers depending on the tissue in which the tumor grows. “With the availability of tissue-specific data, we might potentially obtain signatures for different types of cancers such as breast, prostate and lung cancers,” enthuses Professor Venkata Manem.
“All tumors are different, even if they are classified in the same group, at the same stage and with the same anatomical characteristics. They differ in many aspects, such as the mutations present, the microenvironment and the immune component. All these factors can affect the response to radiotherapy,” adds Alona Kolnohuz, first author of the study.
Using cell line data combined with bioinformatics and machine learning-based approaches, the research team targeted a molecular indicator of sensitivity that might be subject to preclinical testing before being translated into clinical.
“The majority of studies in the field use the number of cells that survive a given radiation dose, 2 Gy for example, which is equivalent to looking at a single point to draw conclusions. Our approach instead uses the area under the curve “Our results demonstrate that this approach should be considered as an indicator of response to radiation in preclinical studies, because it considers a broader range of biological processes,” explains Professor Manem.
The next part of his research involves validating these molecular signatures with patient data and developing a clinical test using methods based on machine learning. The team also wishes to identify radiosensitizing compounds likely to increase the therapeutic effectiveness of radiation.
“With the emergence of omics and artificial intelligence-based technologies, the time has come for precision medicine to take a big step forward and move away from the conventional one-size-fits-all framework, emphasizes Venkata Manem. We believe that radiation sensitivity markers have enormous potential to aid decision-making, personalize treatment and improve outcomes.”
The study was published in the scientific journal BMC Cancer. The signatories are Alona Kolnohuz, Leyla Ebrahimpour, Sevinj Yolchuyeva and Venkata Manem.
Doctoral student in molecular medicine Alona Kolnohuz, first author of the study, and Venkata Manem, professor of the Faculty of Medicine and researcher at the Research Center of the CHU de Québec – Université Laval
1707808345
#predict #sensitivity #radiotherapy
