Efforts have been made to leverage technology to accurately identify tumor characteristics and predict how each cancer patient may respond to medications. This involves collecting data from various sources such as genomic data, histological information, functional drug profiling, and drug metabolism using techniques like polymerase chain reaction, sanger sequencing, next-generation sequencing, fluorescence in situ hybridization, immunohistochemistry staining, patient-derived tumor xenograft models, patient-derived organoid models, and therapeutic drug monitoring. The utilization of diverse detection technologies in clinical practice has made "individualized treatment" possible, but the desired level of accuracy has not been fully attained yet. Here, we briefly summarize the conventional and state-of-the-art technologies contributing to individualized medication in clinical settings, aiming to explore therapy options enhancing clinical outcomes.
Keywords: Personalized medicine; genetic detection; immunohistochemistry; patient-derived organoids; patient-derived xenografts; therapeutic drug monitoring.
This paper reviews different technologies that can help personalize cancer treatment for patients, including DNA sequencing and special lab models. It discusses both modern and traditional methods that identify how tumors respond to therapies and how drugs are processed in the body. By using these technologies in medical practice, doctors can find the best treatment options tailored to each individual cancer patient.