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Updated: Jun 3, 2025

Defining Gene Functions in Tumorigenesis by Ex vivo Ablation of Floxed Alleles in Malignant Peripheral Nerve Sheath Tumor Cells
Published on: August 25, 2021
Technological advances in clinical individualized medication for cancer therapy: from genes to whole organism
Jiejing Kai1, Xueling Liu1, Meijia Wu1
1Department of Clinical Pharmacy, Zhejiang Provincial Key Laboratory for Drug Evaluation and Clinical Research, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Abstract:
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.
Insights
Leveraging technology for cancer treatment involves analyzing genomic and histological data. While personalized medicine advances, achieving precise tumor characteristic identification and drug response prediction remains a key goal for improved patient outcomes.
Area of Science:
- Oncology
- Genomics
- Pharmacology
Background:
- Technological advancements aim to precisely identify tumor characteristics and predict patient drug responses.
- Genomic data, histological information, and drug metabolism studies are crucial for personalized medicine.
Purpose of the Study:
- To summarize conventional and state-of-the-art technologies for individualized medication.
- To explore therapy options for enhancing clinical outcomes in cancer patients.
Main Methods:
- Utilizing diverse data sources: genomic, histological, functional drug profiling, and drug metabolism.
- Employing techniques such as polymerase chain reaction, next-generation sequencing, and patient-derived models.
- Incorporating therapeutic drug monitoring for treatment optimization.
Main Results:
- Diverse detection technologies enable "individualized treatment" in clinical practice.
- Current methods have advanced personalized medicine but have not yet reached the desired accuracy levels.
- Ongoing research focuses on refining technologies for better prediction of drug efficacy.
Conclusions:
- Accurate identification of tumor characteristics and drug response prediction are vital for effective cancer therapy.
- Continued development and integration of advanced technologies are necessary to fully realize the potential of personalized medicine.
- The exploration of novel therapeutic strategies aims to enhance clinical outcomes for cancer patients.
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