The master observational trial–a novel method to unify precision oncology data collection.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e19313-e19313
Author(s):  
Dane J. Dickson ◽  
Jennifer Maria Johnson ◽  
Raymond C. Bergan ◽  
Rebecca Owens ◽  
Vivek Subbiah ◽  
...  
Keyword(s):  
Real World ◽  
Unmet Needs ◽  
Data Availability ◽  
Precision Oncology ◽  
Real World Data ◽  
Observational Trial ◽  
World Data ◽  
Patient Consent ◽  
National Implementation ◽  
Real World Datasets

e19313 Background: The Master Observational Trial (MOT) was recently created as a new master protocol that hybridizes the power of master interventional trials with the richness of real-world data (Cell, 2020). The MOT can be described as a series of prospective observational studies that are tied together through a common protocol, infrastructure, and organization. The MOT has broad application in many disease states but is particularly powerful in oncology. We herein expand our prior work to describe key details regarding how the MOT concept can fill multiple unmet needs in oncology. Methods: Through published information, white papers, and expert opinions we identified key unmet needs of oncology stakeholders. We reviewed the publicly available information of structure, organization, and data availability of the five largest genomic-outcome real-world data efforts. Common concerns included variability and reliability of biomarkers, the scientific rigor in real-world data, data silos, patient consent, and duplicated or disparate activities. We then determined how a specific application of the MOT in oncology could answer stakeholder concerns, integrate with current efforts, and also how to provide a model that would be equally valuable to academic and community clinics. Results: We identified significant scientific challenges with many of the current oncology real-world datasets in answering key concerns of stakeholders. We developed the Master Registry of Oncology Outcomes Associated with Testing and Treatment (ROOT) as the first national implementation of an oncology-centric MOT. We modeled how ROOT could fill scientific gaps in current data efforts and integrate with interventional and real-world efforts and help answer key concerns of stakeholders. We also identified solutions that would allow community and academic groups to participate in the same effort. Conclusions: An oncology-centric MOT has the potential to improve the quality of RWD in oncology and advance precision oncology in ways that are not fully addressed by current retrospective efforts. Reference Dickson DJ, Johnson J, Owens R, Bergan R, Subbiah V, Kurzrock R. (2020). The Master Observational Trial: A New Class of Master Protocol to Advance Precision Medicine. Cell 180, 9-14. Clinical trial information: NCT04028479 .

scholarly journals Real-world data: how they can help to improve quality of care

2021 ◽  
Vol 8 ◽  
pp. 134-139
Author(s):  
Giovanni Corrao ◽  
Giovanni Alquati ◽  
Giovanni Apolone ◽  
Andrea Ardizzoni ◽  
Giuliano Buzzetti ◽  
...  
Keyword(s):  
Public Health ◽  
Quality Of Care ◽  
Real World ◽  
Ethical Issues ◽  
Data Availability ◽  
Quality Data ◽  
Real World Data ◽  
World Data ◽  
Critical Issues

The current COVID pandemic crisis made it even clearer that the solutions to several questions that public health must face require the access to good quality data. Several issues of the value and potential of health data and the current critical issues that hinder access are discussed in this paper. In particular, the paper (i) focuses on “real-world data” definition; (ii) proposes a review of the real-world data availability in our country; (iii) discusses its potential, with particular focus on the possibility of improving knowledge on the quality of care provided by the health system; (iv) emphasizes that the availability of data alone is not sufficient to increase our knowledge, underlining the need that innovative analysis methods (e.g., artificial intelligence techniques) must be framed in the paradigm of clinical research; and (v) addresses some ethical issues related to their use. The proposal is to realize an alliance between organizations interested in promoting research aimed at collecting scientifically solid evidence to support the clinical governance of public health.

scholarly journals Application of Real-World Data to External Control Groups in Oncology Clinical Trial Drug Development

2022 ◽  
Vol 11 ◽  
Author(s):  
Timothy A. Yap ◽  
Ira Jacobs ◽  
Elodie Baumfeld Andre ◽  
Lauren J. Lee ◽  
Darrin Beaupre ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Drug Development ◽  
Real World ◽  
External Control ◽  
Control Group ◽  
Precision Oncology ◽  
Efficacy And Safety ◽  
Control Groups ◽  
Real World Data ◽  
World Data

Randomized controlled trials (RCTs) that assess overall survival are considered the “gold standard” when evaluating the efficacy and safety of a new oncology intervention. However, single-arm trials that use surrogate endpoints (e.g., objective response rate or duration of response) to evaluate clinical benefit have become the basis for accelerated or breakthrough regulatory approval of precision oncology drugs for cases where the target and research populations are relatively small. Interpretation of efficacy in single-arm trials can be challenging because such studies lack a standard-of-care comparator arm. Although an external control group can be based on data from other clinical trials, using an external control group based on data collected outside of a trial may not only offer an alternative to both RCTs and uncontrolled single-arm trials, but it may also help improve decision-making by study sponsors or regulatory authorities. Hence, leveraging real-world data (RWD) to construct external control arms in clinical trials that investigate the efficacy and safety of drug interventions in oncology has become a topic of interest. Herein, we review the benefits and challenges associated with the use of RWD to construct external control groups, and the relevance of RWD to early oncology drug development.

Use of a real-world data approach to rapidly generate outcomes data following a case study of a novel treatment combination in pancreatic adenocarcinoma.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e16735-e16735
Author(s):  
Lola Rahib ◽  
Karen Chen ◽  
Allyson J. Ocean ◽  
Changqing Xie ◽  
Austin Duffy ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Partial Response ◽  
Median Time ◽  
Real World ◽  
Clinical Benefit ◽  
Progressive Disease ◽  
Metastatic Pancreatic Cancer ◽  
Precision Oncology ◽  
Real World Data ◽  
World Data

e16735 Background: We use a real-world data approach to report on safety and benefits on metastatic pancreatic cancer pts who were treated with a MEK inhibitor plus hydroxychloroquine (HCQ) after exhausting all other treatment options. MEK inhibition acts on the KRAS pathway, which in turn increases autophagy as a resistance mechanism, furthermore, HCQ inhibits autophagy causing a cytotoxic effect. This combination was shown to diminish tumor volume in xenograft mouse models and a partial response in one heavily pre-treated patients was reported. Methods: XCELSIOR is an IRB approved, patient-centric, real-world data and outcomes registry for developing operational and analytic methods in precision oncology. Searching the XCELSIOR database, we identified 14 pts for whom this regimen had been considered. As part of their participation in XCELSIOR, these patients shared access to their full medical records, which were collected, processed, and abstracted into a 21 CFR 11 compliant database for analysis. We additionally collected de-identified data on 12 pts treated with this combination from five academic centers. Three more patients are expected to start treatment soon. Results: Between March 2018 and January 2020, 15 patients treated with the trametinib/HCQ combination and 3 patients treated with cobimetinib/HCQ were identified in XCELSIOR and five academic institutions. The median age at diagnosis was 64 (range 43-74) and 56% were male. For patients treated with trametinib/HCQ, the median time on treatment was 67 days (range 5-172 days), 11 patients were treated for more than 30 days (median time 97 days). The median PFS for this group was 2.9 months and the median OS was 7.4 months. The clinical benefit rate was 60% for the 10 evaluable patients treated with trametinib/HCQ, 1 patient had a partial response (previously published), 5 had stable disease (for at least 8 weeks) and 4 had progressive disease (physician reported). 2/3 patients treated with cobimetinib/HCQ were on treatment for more than 30 days and all three had progressive disease within 7 weeks. The most common side effects were Grade 1 fatigue and Grade 1/2 rash for both combinations. An additional 3 patients will start treatment soon and will be included in the analysis. Conclusions: Combinatorial MEK and autophagy inhibition was well tolerated in heavily treated metastatic pancreatic cancer patients. Trametinib/HCQ demonstrates some clinical benefit for this group. We demonstrate the feasibility of utilizing real-world data in precision oncology. Clinical trial information: NCT03793088 .

scholarly journals A framework for validating AI in precision medicine: considerations from the European ITFoC consortium

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Rosy Tsopra ◽  
Xose Fernandez ◽  
Claudio Luchinat ◽  
Lilia Alberghina ◽  
Hans Lehrach ◽  
...  
Keyword(s):  
Treatment Response ◽  
Real World ◽  
Clinical Decision Making ◽  
Precision Oncology ◽  
Clinical Validation ◽  
Learning Approaches ◽  
Real World Data ◽  
Privacy And Security ◽  
The Future ◽  
Real World Datasets

Abstract Background Artificial intelligence (AI) has the potential to transform our healthcare systems significantly. New AI technologies based on machine learning approaches should play a key role in clinical decision-making in the future. However, their implementation in health care settings remains limited, mostly due to a lack of robust validation procedures. There is a need to develop reliable assessment frameworks for the clinical validation of AI. We present here an approach for assessing AI for predicting treatment response in triple-negative breast cancer (TNBC), using real-world data and molecular -omics data from clinical data warehouses and biobanks. Methods The European “ITFoC (Information Technology for the Future Of Cancer)” consortium designed a framework for the clinical validation of AI technologies for predicting treatment response in oncology. Results This framework is based on seven key steps specifying: (1) the intended use of AI, (2) the target population, (3) the timing of AI evaluation, (4) the datasets used for evaluation, (5) the procedures used for ensuring data safety (including data quality, privacy and security), (6) the metrics used for measuring performance, and (7) the procedures used to ensure that the AI is explainable. This framework forms the basis of a validation platform that we are building for the “ITFoC Challenge”. This community-wide competition will make it possible to assess and compare AI algorithms for predicting the response to TNBC treatments with external real-world datasets. Conclusions The predictive performance and safety of AI technologies must be assessed in a robust, unbiased and transparent manner before their implementation in healthcare settings. We believe that the consideration of the ITFoC consortium will contribute to the safe transfer and implementation of AI in clinical settings, in the context of precision oncology and personalized care.

PMU71 Real World Data Availability in Latin America: Assessment and Implications for Research and Healthcare Decision Making

2021 ◽  
Vol 24 ◽  
pp. S157
Author(s):  
H. Monsanto ◽  
C. Parellada ◽  
J. Orengo ◽  
J.S. Velasco ◽  
Bavel J van ◽  
...  
Keyword(s):  
Decision Making ◽  
Latin America ◽  
Real World ◽  
Data Availability ◽  
Healthcare Decision ◽  
Real World Data ◽  
World Data ◽  
Healthcare Decision Making

Real-world adherence in patients with metastatic colorectal cancer (mCRC) treated with trifluridine/tipiracil (FTD/TPI) or regorafenib (REG).

2018 ◽  
Vol 36 (30_suppl) ◽  
pp. 11-11
Author(s):  
Anuj K. Patel ◽  
Victoria Barghout ◽  
Mihran Ara Yenikomshian ◽  
Guillaume Germain ◽  
Philippe Jacques ◽  
...  
Keyword(s):  
Real World ◽  
Lower Risk ◽  
Proportional Hazards ◽  
Medication Possession Ratio ◽  
Baseline Period ◽  
Cox Proportional Hazards ◽  
Data Availability ◽  
Real World Data ◽  
World Data ◽  
Nationally Representative

11 Background: Adherence to oral chemotherapies is a critical but difficult to measure factor in the care of patients with advanced cancer. FTD/TPI and REG have both demonstrated prolonged survival in patients with refractory mCRC, but with notably different side effect profiles. This study utilizes real-world data to assess adherence and discontinuation of patients treated with FTD/TPI or REG and explore the effect of sequencing on adherence. Methods: Adults diagnosed with mCRC were identified using the nationally representative IQVIA Real-World Data Adjudicated Claims – US database (10/2014–07/2017). The first dispensing date of FTD/TPI or REG (if after FTD/TPI approval [10/2015]) was defined as the index date and the 3 months before as the baseline period. The observation period spanned from the index to the earliest date of a switch to another mCRC agent, end of continuous enrollment, or end of data availability. Medication possession ratio (MPR), proportion of days covered (PDC) at 3 months, and discontinuation (i.e., allowable gap≥45 days) were compared. Logistic (odds ratio [OR]) and Cox proportional hazards (hazard ratio [HR]) regressions, adjusting for baseline characteristics, were used to compare adherence and discontinuation, respectively. A subgroup analysis was conducted among switchers (FTD/TPI to REG vs REG to FTD/TPI). Results: A total of 469 FTD/TPI and 311 REG users were identified. FTD/TPI users had higher compliance with an MPR ≥ 80% (OR = 2.47; p < 0.001) and PDC ≥ 80% (OR = 2.77; p < 0.001). FTD/TPI users had lower risk of discontinuation (HR = 0.76; p = 0.006). Among switchers (96 FTD/TPI to REG; 83 REG to FTD/TPI), those switching from FTD/TPI to REG were more likely to have an MPR ≥ 80% (OR = 2.91; p < 0.001) and PDC ≥ 80% (OR = 4.60; p < 0.001) compared to REG to FTD/TPI switchers. Additionally, FTD/TPI to REG switchers had a lower risk of first treatment discontinuation (HR = 0.66; p = 0.009). Conclusions: In this study, FTD/TPI users had significantly higher compliance, lower discontinuation rate, and switchers treated first with FTD/TPI had better compliance, demonstrating that claims data can provide insight into oral chemotherapy adherence patterns in mCRC.

scholarly journals PO2RDF: Representation of Real-world Data for Precision Oncology Using Resource Description Framework

2021 ◽  
Author(s):  
Yiqing ZHAO ◽  
Anastasios Dimou ◽  
Feichen Shen ◽  
Nansu Zong ◽  
Jaime I. Davila ◽  
...  
Keyword(s):  
Real World ◽  
Targeted Therapies ◽  
Resource Description Framework ◽  
Clinical Decision Making ◽  
Egfr Mutations ◽  
Precision Oncology ◽  
Real World Data ◽  
World Data ◽  
Description Framework ◽  
Resource Description

Abstract Background: Next-generation sequencing provides comprehensive information about individuals’ genetic makeup and is commonplace in precision oncology practice. Due to the heterogeneity of individual patient’s disease conditions and treatment journeys, not all targeted therapies were initiated despite actionable mutations. To better understand and support the clinical decision-making process in precision oncology, there is a need to examine real-world associations of patients’ genetic information and treatment choice.Methods: To fill the gap of insufficient use of real-world data (RWD) in electronic health records (EHRs), we generated a single Resource Description Framework (RDF) resource, called PO2RDF (precision oncology to RDF) by integrating information regarding gene, variant, disease, and drug from genetic reports and EHRs. Results: There are total 2,309,014 triples contained in the PO2RDF. Among them 32,815 triples are related to Gene, 34,695 triples are related to Variant, 8,787 triples are related to Disease, 26,154 triples are related to Drug. We performed one use case analysis to demonstrate the usability of the PO2RDF: we examined real-world associations between EGFR mutations and targeted therapies to confirm existing knowledge and detect off-label use. Conclusions: In conclusion, our work proposed to use RDF to organize and distribute clinical RWD that is otherwise inaccessible externally. Our work serves as a pilot study that will lead to new clinical applications and could ultimately stimulate progress in the field of precision oncology.

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