In a research article featured in Cancer Discovery, researchers from the University Of California San Diego School Of Medicine employed an innovative machine learning algorithm to address a significant obstacle in the field of cancer research: the anticipation of chemotherapy resistance in cancer.
Malignant cells depend on molecular mechanisms for duplication
Every cell, encompassing malignant cells, depends on intricate molecular mechanisms for duplicating DNA during regular cell division. The majority of chemotherapy treatments operate by interfering with this DNA replication apparatus within swiftly dividing cancerous cells. Despite acknowledging that the genetic makeup of a tumor significantly impacts its distinct response to drugs, the extensive array of mutations present in tumors has rendered the anticipation of drug resistance a formidable task.
The innovative algorithm surmounts this obstacle by investigating the combined impact of numerous genetic mutations on a tumor’s response to drugs hindering DNA replication.
The study focused on evaluating a model’s performance in predicting responses to cisplatin, a widely used chemotherapy drug, using cervical cancer tumors. The model demonstrated successful identification of tumors at higher risk for treatment resistance and also revealed insights into the molecular mechanisms associated with this resistance.
AI crucial in enhancing understanding DNA replication process
Previously clinicians were aware of certain mutations linked to treatment resistance, but these isolated mutations lacked significant predictive value. According to Trey Ideker, Ph.D., a professor in the Department of Medicine at UC San Diego, a greater number of mutations than initially recognized can influence a tumor’s response to treatment.
Artificial intelligence plays a crucial role in enhancing our comprehension of the intricate nature of DNA replication, specifically in the context of tumor response to drugs. The complexity arises from the collaboration of hundreds of proteins in intricate arrangements during the replication process.
Mutations in any component of this system can significantly alter the tumor’s response to chemotherapy. AI serves as a valuable tool, allowing the simultaneous analysis of a multitude of mutations and aiding in overcoming the challenges associated with understanding drug responses in tumors.
