OncoCTMiner: streamlining precision oncology trial matching via molecular profile analysis
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Published on: April 11, 2016
Quan Xu1,2, Yueyue Liu1, Dawei Sun1,2
1Department of Bioinformatics, Beijing ChosenMed Clinical Laboratory Co. Ltd., Jinghai Industrial Park, 156 Jinghai 4th Road, Economic and Technological Development Area, Beijing 100176, China.
Abstract:
By establishing omics sequencing of patient tumors as a crucial element in cancer treatment, the extensive implementation of precision oncology necessitates effective and prompt execution of clinical studies for approving molecular-targeted therapies. However, the substantial volume of patient sequencing data, combined with strict clinical trial criteria, increasingly complicates the process of matching patients to precision oncology studies. To streamline enrollment in these studies, we developed OncoCTMiner, an automated pre-screening platform for molecular cancer clinical trials. Through manual tagging of eligibility criteria for 2227 oncology trials, we identified key bio-concepts such as cancer types, genes, alterations, drugs, biomarkers and therapies. Utilizing this manually annotated corpus along with open-source biomedical natural language processing tools, we trained multiple named entity recognition models specifically designed for precision oncology trials. These models analyzed 460 952 clinical trials, revealing 8.15 million precision medicine concepts, 9.32 million entity-criteria-trial triplets and a comprehensive precision oncology eligibility criteria database. Most significantly, we developed a patient-trial matching system based on cancer patients' clinical and genetic profiles, which can seamlessly integrate with the omics data analysis platform. This system expedites the pre-screening process for potentially suitable precision oncology trials, offering patients swifter access to promising treatment options. Database URL https://oncoctminer.chosenmedinfo.com.
Insights
Precision oncology requires efficient clinical trial matching. OncoCTMiner, an automated platform, analyzes trial criteria and patient profiles to expedite enrollment in molecular cancer clinical trials, improving patient access to targeted therapies.
Area of Science:
- Oncology
- Bioinformatics
- Clinical Trial Management
Background:
- Precision oncology relies on omics sequencing for cancer treatment.
- Matching patients to clinical trials is complex due to large data volumes and strict criteria.
- Streamlining patient enrollment in molecular targeted therapy trials is essential.
Purpose of the Study:
- To develop an automated platform, OncoCTMiner, for pre-screening patients in molecular cancer clinical trials.
- To create a comprehensive database of precision oncology eligibility criteria.
- To facilitate patient-trial matching based on clinical and genetic profiles.
Main Methods:
- Manual tagging of eligibility criteria for 2227 oncology trials to identify bio-concepts.
- Training named entity recognition (NER) models using annotated data and biomedical NLP tools.
- Analyzing 460,952 clinical trials to extract precision medicine concepts and criteria-trial relationships.
- Developing a patient-trial matching system integrating with omics data.
Main Results:
- Identification of key bio-concepts (cancer types, genes, alterations, drugs, biomarkers, therapies).
- Creation of a comprehensive precision oncology eligibility criteria database.
- Analysis revealed 8.15 million precision medicine concepts and 9.32 million entity-criteria-trial triplets.
- Development of an automated patient-trial matching system.
Conclusions:
- OncoCTMiner automates pre-screening for molecular cancer clinical trials.
- The platform expedites patient access to precision oncology treatments.
- This system enhances the efficiency of matching patients to suitable clinical trials.
