Abstract CT218: SYNERGY-AI: Artificial intelligence based precision oncology clinical trial matching and registry

2019 ◽  
Author(s):  
Selin Kurnaz ◽  
Arturo Loaiza-Bonilla
Keyword(s):  
Artificial Intelligence ◽  
Clinical Trial ◽  
Precision Oncology ◽  
Oncology Clinical Trial

SYNERGY-AI: Artificial intelligence based precision oncology clinical trial matching and registry.

2019 ◽  
Vol 37 (4_suppl) ◽  
pp. TPS717-TPS717
Author(s):  
Selin Kurnaz ◽  
Arturo Loaiza-Bonilla ◽  
Jason Lawrence Freedman ◽  
Belisario Augusto Arango ◽  
Kristin Johnston ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Clinical Trial ◽  
Hematological Malignancies ◽  
Machine Learning Algorithms ◽  
Precision Oncology ◽  
Level Of Evidence ◽  
Dynamic Matching ◽  
Oncology Clinical Trial ◽  
Tumor Boards ◽  
The Impact

TPS717 Background: Precision oncology encompasses the implementation of high level of evidence disease-specific and biomarker-driven diagnostic and treatment recommendations for optimized cancer care. Artificial Intelligence (AI), telemedicine and value-based care may optimize clinical trial enrollment (CTE) and overall cost-benefit. This ongoing, international registry for cancer pts evaluates the feasibility and clinical utility of an AI-based precision oncology clinical trial matching tool, powered by a virtual tumor boards (VTB) program, and its clinical impact on pts with advanced cancer to facilitate CTE, as well as the financial impact, and potential outcomes of the intervention. Methods: The SYNERGY-AI Registry is an international prospective, observational cohort study of eligible adult and pediatric pts with advanced solid and hematological malignancies, for whom the decision to consider CTE has already been made by their primary providers (PP). Using a proprietary application programming interface (API) linked to existing electronic health records (EHR) platforms, individual clinical data is extracted, analyzed and matched to a parametric database of existing institutional and non-institutional CTs. Machine learning algorithms allow for dynamic matching based on CT allocation and availability for optimized matching. Patients voluntarily enroll into registry, which is non-interventional with no protocol-mandated tests/procedures—all treatment decisions are made at the discretion of PP in consultation with their pts, based on the AI CT matching report, and VTB support. CTE will be assessed on variables including biomarkers, barriers to enrollment. Study duration anticipated as ~36 mo (~24-mo enrollment followed by 12 mo of data collection, to occur every 3 mo). The primary analysis will be performed 12 mo after last pt enrolled. The impact time to initiation of CTE on PFS and OS will be estimated by Kaplan-Meier and Cox multivariable survival analysis. Enrollment is ongoing, with a target of ≥ 1500 patients. Key inclusion criteria: Pts with solid and hematological malignancies; cancer-related biomarkers. Key exclusion criteria: ECOG PS > 2; abnormal organ function; hospice enrollment Clinical trial information: NCT03452774.

scholarly journals SYNERGY-AI: Artificial intelligence-based precision oncology clinical trial matching and registry.

2019 ◽  
Vol 5 (suppl) ◽  
pp. 22-22
Author(s):  
Selin Kurnaz ◽  
Arturo Loaiza-Bonilla
Keyword(s):  
Artificial Intelligence ◽  
Clinical Trial ◽  
Hematological Malignancies ◽  
Machine Learning Algorithms ◽  
Precision Oncology ◽  
Level Of Evidence ◽  
Dynamic Matching ◽  
Oncology Clinical Trial ◽  
Tumor Boards ◽  
The Impact

22 Background: Precision oncology encompasses the implementation of high level of evidence disease-specific and biomarker-driven diagnostic and treatment recommendations for optimized cancer care. Artificial Intelligence (AI), telemedicine and value-based care may optimize clinical trial enrollment (CTE) and overall cost-benefit. This ongoing, international registry for cancer pts evaluates the feasibility and clinical utility of an AI-based precision oncology clinical trial matching tool, powered by a virtual tumor boards (VTB) program, and its clinical impact on pts with advanced cancer to facilitate CTE, as well as the financial impact, and potential outcomes of the intervention. Methods: The SYNERGY-AI Registry is an international prospective, observational cohort study of eligible adult and pediatric pts with advanced solid and hematological malignancies, for whom the decision to consider CTE has already been made by their primary providers (PP). Using a proprietary application programming interface (API) linked to existing electronic health records (EHR) platforms, individual clinical data is extracted, analyzed and matched to a parametric database of existing institutional and non-institutional CTs. Machine learning algorithms allow for dynamic matching based on CT allocation and availability for optimized matching. Patients voluntarily enroll into registry, which is non-interventional with no protocol-mandated tests/procedures—all treatment decisions are made at the discretion of PP in consultation with their pts, based on the AI CT matching report, and VTB support. CTE will be assessed on variables including biomarkers, barriers to enrollment. Study duration anticipated as ~36 mo. The impact time to initiation of CTE on PFS and OS will be estimated by Kaplan-Meier and Cox multivariable survival analysis. Enrollment is ongoing, with a target of ≥ 1500 patients. Key inclusion criteria: Pts with solid and hematological malignancies; Pts cancer-related biomarkers. Key exclusion: ECOG PS > 2; abnormal organ function; hospice. Results: To be presented. Conclusions: AI-based, patient-driven CTE is feasible, highly effective and paradigm-changing. Clinical trial information: NCT03452774.

IBM's Watson Analytics for Health Care

2016 ◽  
pp. 117-134 ◽  
Author(s):  
Mayank Aggarwal ◽  
Mani Madhukar
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Clinical Trial ◽  
Information Technology ◽  
Platform As A Service ◽  
Medical Issues ◽  
Oncology Clinical Trial ◽  
Apache Lucene ◽  
Ibm Watson ◽  
Health Applications

With the advent of Internet and Computers, Information Technology (IT) has become a major tool to aid medical issues. IBM Watson is one such initiative by IBM, which provides integration with any application to build Internet of Things (IoT), based health applications and also assists by its existing services. The strength of Watson is its data analytics and Artificial Intelligence. The four variants of Watsons are Watson Discovery Advisor, Oncology, Clinical Trial Matching and Curam. It is based on Open Source Apache UIMA, Apache Lucene. Its integration with IBM Bluemix Cloud, Platform as a Service (PaaS) makes it easily available to users.

scholarly journals OCTANE: Oncology Clinical Trial Annotation Engine

2019 ◽  
pp. 1-11 ◽  
Author(s):  
Jia Zeng ◽  
Md Abu Shufean ◽  
Yekaterina Khotskaya ◽  
Dong Yang ◽  
Michael Kahle ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
False Positive Rate ◽  
False Negative ◽  
False Negative Rate ◽  
Cancer Center ◽  
Precision Oncology ◽  
Expert Review ◽  
Oncology Clinical Trial ◽  
Oncology Clinical Trials

PURPOSE Many targeted therapies are currently available only via clinical trials. Therefore, routine precision oncology using biomarker-based assignment to drug depends on matching patients to clinical trials. A comprehensive and up-to-date trial database is necessary for optimal patient-trial matching. METHODS We describe processes for establishing and maintaining a clinical trial database, focusing on genomically informed trials. Furthermore, we present OCTANE (Oncology Clinical Trial Annotation Engine), an informatics framework supporting these processes in a scalable fashion. To illustrate how the framework can be applied at an institution, we describe how we implemented an instance of OCTANE at a large cancer center. OCTANE consists of three modules. The data aggregation module automates retrieval, aggregation, and update of trial information. The annotation module establishes the database schema, implements data integration necessary for automation, and provides an annotation interface. The update module monitors trial change logs, identifies critical change events, and alerts the annotators when manual intervention may be needed. RESULTS Using OCTANE, we annotated 5,439 oncology clinical trials (4,438 genomically informed trials) that collectively were associated with 1,453 drugs, 779 genes, and 252 cancer types. To date, we have used the database to screen 4,220 patients for trial eligibility. We compared the update module with expert review, and the module achieved 98.5% accuracy, 0% false-negative rate, and 2.3% false-positive rate. CONCLUSION OCTANE is a general informatics framework that can be helpful for establishing and maintaining a comprehensive database necessary for automating patient-trial matching, which facilitates the successful delivery of personalized cancer care on a routine basis. Several OCTANE components are publically available and may be useful to other precision oncology programs.

scholarly journals A computational method for prioritizing targeted therapies in precision oncology: performance analysis in the SHIVA01 trial

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Istvan Petak ◽  
Maud Kamal ◽  
Anna Dirner ◽  
Ivan Bieche ◽  
Robert Doczi ◽  
...  
Keyword(s):  
Clinical Benefit ◽  
Progressive Disease ◽  
Genetic Alterations ◽  
Outcome Data ◽  
Computational Method ◽  
Molecular Profile ◽  
Precision Oncology ◽  
Driver Genes ◽  
Oncology Clinical Trial ◽  
Molecularly Targeted Agents

AbstractPrecision oncology is currently based on pairing molecularly targeted agents (MTA) to predefined single driver genes or biomarkers. Each tumor harbors a combination of a large number of potential genetic alterations of multiple driver genes in a complex system that limits the potential of this approach. We have developed an artificial intelligence (AI)-assisted computational method, the digital drug-assignment (DDA) system, to prioritize potential MTAs for each cancer patient based on the complex individual molecular profile of their tumor. We analyzed the clinical benefit of the DDA system on the molecular and clinical outcome data of patients treated in the SHIVA01 precision oncology clinical trial with MTAs matched to individual genetic alterations or biomarkers of their tumor. We found that the DDA score assigned to MTAs was significantly higher in patients experiencing disease control than in patients with progressive disease (1523 versus 580, P = 0.037). The median PFS was also significantly longer in patients receiving MTAs with high (1000+ <) than with low (<0) DDA scores (3.95 versus 1.95 months, P = 0.044). Our results indicate that AI-based systems, like DDA, are promising new tools for oncologists to improve the clinical benefit of precision oncology.

Functional precision medicine in colorectal cancer based on patient-derived tumoroids and in-vitro sensitivity drug testing.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e15567-e15567
Author(s):  
Lars Henrik Jensen ◽  
Anders Kristian Moeller Jakobsen ◽  
Birgitte Mayland Havelund ◽  
Cecilie Abildgaard ◽  
Chris Vagn-Hansen ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Clinical Trial ◽  
Primary Endpoint ◽  
Drug Testing ◽  
Progression Free Survival ◽  
Patient Specific ◽  
Precision Oncology ◽  
Sensitivity Testing ◽  
In Vitro Sensitivity

e15567 Background: Precision oncology based on in-vitro, functional assays has potential advantages compared to the much more common molecular approach, but the clinical benefit is unknown. We here report the results from the largest prospective interventional clinical trial testing the clinical outcome in colorectal cancer patients treated with drugs showing cytotoxic effect in matched patient-derived tumoroids. Methods: This single-center, phase II trial included patients with metastatic colorectal cancer previously exposed to all standard therapies. Specimens from one to three 18-16 G core needle biopsies were manually dissected, enzymatically treated, cultivated, and incubated to form 3D spherical microtumors, i.e. tumoroids. In the assay for in-vitro sensitivity testing, the tumoroids were challenged with single drugs and combinations thereof to determine patient-specific responses. Using tumoroid screening technology (IndiTreat, 2cureX, Copenhagen, Denmark), results were generated by comparing the sensitivity of the individual patient’s tumoroids with a reference panel from other patients. The testing included standard cytostatics and drugs with proven effect in previous early-phase clinical trials, a total of 15 drugs. The primary endpoint was the fraction of patients with progression-free survival (PFS) at two months. Based on placebo arms in randomized last-line trials, a minimal relevant difference of 20% (20% to 40%) was stated. Using Simon's two-stage design, a sample size of 45 patients was calculated with at least 14 PFS at two months (significance 5%, power 90%). Results: Ninety patients were enrolled from 9/2017 to 9/2020. Biopsies from 82 patients were obtained and sent for tumoroid formation of which 44 (54%, 95% CI 42-65) were successful and at least one treatment was suggested. Thirty-four patients initiated treatment according to the response obtained in the drug assays within a median of 51 days from inclusion (IQR 39-63). The primary endpoint, PFS at two months, was met in 17 of 34 patients (50%, 95%CI 32-68). There were no radiological responses. Median PFS was 81 days (95% CI 51-112) and median OS was 189 days (95% CI 103-277). Conclusions: Precision oncology using a functional approach with patient-derived tumoroids and in-vitro drug sensitivity testing seems feasible. The approach is limited by the fraction of patients with successful tumoroid development. The primary endpoint was met, as half of the patients were without progression at two months. Further clinical studies are justified. Clinical trial information: NCT03251612.

Practical Aspects of Implementing and Applying Health Care Cloud Computing Services and Informatics to Cancer Clinical Trial Data

2021 ◽  
pp. 826-832
Author(s):  
Jay G. Ronquillo ◽  
William T. Lester
Keyword(s):  
Health Care ◽  
Clinical Trial ◽  
Cancer Research ◽  
Cloud Computing ◽  
Clinical Trials ◽  
Laboratory Tests ◽  
Cancer Clinical Trial ◽  
Cancer Clinical Trials ◽  
Precision Oncology ◽  
Oncology Research

PURPOSE Cloud computing has led to dramatic growth in the volume, variety, and velocity of cancer data. However, cloud platforms and services present new challenges for cancer research, particularly in understanding the practical tradeoffs between cloud performance, cost, and complexity. The goal of this study was to describe the practical challenges when using a cloud-based service to improve the cancer clinical trial matching process. METHODS We collected information for all interventional cancer clinical trials from ClinicalTrials.gov and used the Google Cloud Healthcare Natural Language Application Programming Interface (API) to analyze clinical trial Title and Eligibility Criteria text. An informatics pipeline leveraging interoperability standards summarized the distribution of cancer clinical trials, genes, laboratory tests, and medications extracted from cloud-based entity analysis. RESULTS There were a total of 38,851 cancer-related clinical trials found in this study, with the distribution of cancer categories extracted from Title text significantly different than in ClinicalTrials.gov ( P < .001). Cloud-based entity analysis of clinical trial criteria identified a total of 949 genes, 1,782 laboratory tests, 2,086 medications, and 4,902 National Cancer Institute Thesaurus terms, with estimated detection accuracies ranging from 12.8% to 89.9%. A total of 77,702 API calls processed an estimated 167,179 text records, which took a total of 1,979 processing-minutes (33.0 processing-hours), or approximately 1.5 seconds per API call. CONCLUSION Current general-purpose cloud health care tools—like the Google service in this study—should not be used for automated clinical trial matching unless they can perform effective extraction and classification of the clinical, genetic, and medication concepts central to precision oncology research. A strong understanding of the practical aspects of cloud computing will help researchers effectively navigate the vast data ecosystems in cancer research.

Sign in / Sign up

Export Citation Format

Share Document