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.

scholarly journals In-depth phenotyping for clinical stratification of Gaucher disease

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Simona D’Amore ◽  
Kathleen Page ◽  
Aimée Donald ◽  
Khadijeh Taiyari ◽  
Brian Tom ◽  
...  
Keyword(s):  
Real World ◽  
Gaucher Disease ◽  
Orthopaedic Surgery ◽  
Clinical Decision Making ◽  
Burden Of Illness ◽  
Fragility Fractures ◽  
Neurological Manifestations ◽  
Real World Data ◽  
World Data ◽  
Advanced Therapies

Abstract Background The Gaucher Investigative Therapy Evaluation is a national clinical cohort of 250 patients aged 5–87 years with Gaucher disease in the United Kingdom—an ultra-rare genetic disorder. To inform clinical decision-making and improve pathophysiological understanding, we characterized the course of Gaucher disease and explored the influence of costly innovative medication and other interventions. Retrospective and prospective clinical, laboratory and radiological information including molecular analysis of the GBA1 gene and comprising > 2500 variables were collected systematically into a relational database with banking of collated biological samples in a central bioresource. Data for deep phenotyping and life-quality evaluation, including skeletal, visceral, haematological and neurological manifestations were recorded for a median of 17.3 years; the skeletal and neurological manifestations are the main focus of this study. Results At baseline, 223 of the 250 patients were classified as type 1 Gaucher disease. Skeletal manifestations occurred in most patients in the cohort (131 of 201 specifically reported bone pain). Symptomatic osteonecrosis and fragility fractures occurred respectively in 76 and 37 of all 250 patients and the first osseous events occurred significantly earlier in those with neuronopathic disease. Intensive phenotyping in a subgroup of 40 patients originally considered to have only systemic features, revealed neurological involvement in 18: two had Parkinson disease and 16 had clinical signs compatible with neuronopathic Gaucher disease—indicating a greater than expected prevalence of neurological features. Analysis of longitudinal real-world data enabled Gaucher disease to be stratified with respect to advanced therapies and splenectomy. Splenectomy was associated with an increased hazard of fragility fractures, in addition to osteonecrosis and orthopaedic surgery; there were marked gender differences in fracture risk over time since splenectomy. Skeletal disease was a heavy burden of illness, especially where access to specific therapy was delayed and in patients requiring orthopaedic surgery. Conclusion Gaucher disease has been explored using real-world data obtained in an era of therapeutic transformation. Introduction of advanced therapies and repeated longitudinal measures enabled this heterogeneous condition to be stratified into obvious clinical endotypes. The study reveals diverse and changing phenotypic manifestations with systemic, skeletal and neurological disease as inter-related sources of disability.

scholarly journals Ondansetron use is associated with lower COVID-19 mortality in a Real-World Data network-based analysis

2021 ◽  
Author(s):  
Gregory M Miller ◽  
Austin J Ellis ◽  
Rangaprasad Sarangarajan ◽  
Amay Parikh ◽  
Leonardo O Rodrigues ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Long Term Effects ◽  
Real World Data ◽  
World Data ◽  
Antiemetic Agent ◽  
Future Loss ◽  
Selection Operator

Objective: The COVID-19 pandemic generated a massive amount of clinical data, which potentially holds yet undiscovered answers related to COVID-19 morbidity, mortality, long term effects, and therapeutic solutions. The objective of this study was to generate insights on COVID-19 mortality-associated factors and identify potential new therapeutic options for COVID-19 patients by employing artificial intelligence analytics on real-world data. Materials and Methods: A Bayesian statistics-based artificial intelligence data analytics tool (bAIcis®) within Interrogative Biology® platform was used for network learning, inference causality and hypothesis generation to analyze 16,277 PCR positive patients from a database of 279,281 inpatients and outpatients tested for SARS-CoV-2 infection by antigen, antibody, or PCR methods during the first pandemic year in Central Florida. This approach generated causal networks that enabled unbiased identification of significant predictors of mortality for specific COVID-19 patient populations. These findings were validated by logistic regression, regression by least absolute shrinkage and selection operator, and bootstrapping. Results: We found that in the SARS-CoV-2 PCR positive patient cohort, early use of the antiemetic agent ondansetron was associated with increased survival in mechanically ventilated patients. Conclusions: The results demonstrate how real world COVID-19 focused data analysis using artificial intelligence can generate valid insights that could possibly support clinical decision-making and minimize the future loss of lives and resources.

Analyzing treatment patterns and time to the next treatment in chronic lymphocytic leukemia real-world data using automated temporal phenotyping.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e19512-e19512
Author(s):  
Kyeryoung Lee ◽  
Zongzhi Liu ◽  
Meng Ma ◽  
Yun Mai ◽  
Christopher Gilman ◽  
...  
Keyword(s):  
Chronic Lymphocytic Leukemia ◽  
Real World ◽  
Clinical Decision Making ◽  
Single Agent ◽  
Treatment Patterns ◽  
Lymphocytic Leukemia ◽  
Time Interval ◽  
Real World Data ◽  
World Data ◽  
Therapeutic Class

e19512 Background: Targeted therapy is an important treatment for chronic lymphocytic leukemia (CLL). However, optimal strategies for deploying small molecule inhibitors or antibody therapies in the real world are not well understood, largely due to a lack of outcomes data. We implemented a novel temporal phenotyping algorithm pipeline to derive lines of therapy (LOT) and disease progression in CLL patients. Here, the CLL treatment pattern and time to the next treatment (TTNT) were analyzed in real-world data (RWD) using patient electronic health records. Methods: We identified a CLL cohort with LOT from the Mount Sinai Data Warehouse (2003-2020). Each LOT consisted of either a single agent or combinations defined by NCCN CLL guidelines. We developed a natural language processing (NLP)-based temporal phenotyping approach to automatically identify the number of lines and therapeutic regimens. The sequence of treatment and time interval for each patient were derived from the systematic treatment data. Time to event analysis and multivariate (i.e., age, gender, race, other treatment patterns) Cox proportional hazard (CoxPH) models were used to analyze the patterns and predictors of TTNT. Results: Four hundred eleven CLL patients received 1 to 7 LOTs. Ibrutinib was the predominant 1st LOT (40.8% of patients) followed by anti-CD20-based antibody therapies and chemotherapy in 30.6 and 19.2% of patients, respectively, followed by Acalabrutinib, Venetoclax, and Idelalisib in 3.4, 2.7, and 0.7% of patients, respectively (Table 1). The 2nd to 5th LOT showed the same or similar trends. We next analyzed the TTNT in the 1st line of each therapeutic class. Acalabrutinib resulted in a longer median TTNT than Ibrutinib. Both Acalabrutinib and Ibrutinib showed longer TTNT compared to Venetoclax (median TTNTs were 742 and 598 vs. 373 days: HR = 0.23, p=0.015 and HR = 0.48, p=0.03, respectively). In addition, patients with age equal to or older than 65 showed longer TNNT (HR=0.16, p=0.016). Conclusions: Our result shows the potential of RWD usage in clinical decision making as real-world evidence reported here is consistent with results derived from clinical trial data. Linking this study to genetic data and other covariates affecting treatment outcomes may provide additional insights into the optimal sequences of the targeted therapies in CLL. Table 1: Therapeutic class and patient numbers (%) in each line.[Table: see text]

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.

Answering real-world questions using real-world data: Understanding dynamic treatment decisions and outcomes in metastatic colorectal cancer (mCRC).

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e18061-e18061
Author(s):  
Hui-Li Wong ◽  
Koen Degeling ◽  
Azim Jalali ◽  
Jeremy David Shapiro ◽  
Suzanne Kosmider ◽  
...  
Keyword(s):  
Simulation Model ◽  
Data Visualization ◽  
Real World ◽  
Clinical Decision Making ◽  
Discrete Event ◽  
Routine Practice ◽  
Real World Data ◽  
Specific Patient ◽  
World Data ◽  
Wide Range

e18061 Background: The wide range of possible combinations and sequences available for mCRC treatment presents a major challenge to clinicians, who need to determine the optimal approach for an individual patient or patient subset. In the absence of clinical trial evidence, real world data are an increasingly valuable resource that can be utilized not only to understand treatment patterns and outcomes in routine practice, but also to define an optimal treatment strategy for individual patients across multiple lines of therapy. Methods: Real world data from an Australian mCRC registry were used to develop an interactive data visualization tool that displays treatment variation, customizable to different levels of detail and specific patient subsets, based on patient and disease characteristics. Next, a discrete event simulation model was developed to predict progression-free (PFS) and overall survival (OS) for first line palliative treatment with doublet chemotherapy alone or with bevacizumab, based on data of 867 patients that were treated accordingly. Results: Of 2694 Australian patients enrolled, 2057 (76%) started 1st line treatment with chemotherapy and/or a biologic agent, 1087 (40%) and 428 (16%) received 2nd and 3rd line therapy, respectively. Combined, these 3 lines of treatment accounted for 733 unique sequences. After recoding treatment to the most intensive chemotherapy and the first exposed biologic, 472 unique sequences remained. In exploratory analyses, the simulation model estimated that median 1st line PFS (95% CI) of 219 (25%) patients could be improved from 175 (156, 199) to 269 days (247, 293) by targeting a different treatment. Conclusions: This was an initial exploration of the potential for data visualization and simulation modeling to inform understanding of practice in mCRC and to guide clinical decision making. Such tools allow clinicians and health system providers to define variation in practice patterns and to identify opportunities to improve care and outcomes. Ultimately, the aim is to improve the delivery of personalized cancer care, where other applications such as conditional survival and cost-effectiveness analyses may be useful.

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 .

Verification of statistical oncological endpoints on encrypted data: Confirming the feasibility of real-world data sharing without the need to reveal protected patient information.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e18725-e18725
Author(s):  
Ravit Geva ◽  
Barliz Waissengrin ◽  
Dan Mirelman ◽  
Felix Bokstein ◽  
Deborah T. Blumenthal ◽  
...  
Keyword(s):  
Data Sharing ◽  
Real World ◽  
Clinical Decision Making ◽  
Homomorphic Encryption ◽  
Data Sets ◽  
Real World Data ◽  
Data Set ◽  
Raw Data ◽  
Encrypted Data ◽  
World Data

e18725 Background: Healthcare data sharing is important for the creation of diverse and large data sets, supporting clinical decision making, and accelerating efficient research to improve patient outcomes. This is especially vital in the case of real world data analysis. However, stakeholders are reluctant to share their data without ensuring patients’ privacy, proper protection of their data sets and the ways they are being used. Homomorphic encryption is a cryptographic capability that can address these issues by enabling computation on encrypted data without ever decrypting it, so the analytics results are obtained without revealing the raw data. The aim of this study is to prove the accuracy of analytics results and the practical efficiency of the technology. Methods: A real-world data set of colorectal cancer patients’ survival data, following two different treatment interventions, including 623 patients and 24 variables, amounting to 14,952 items of data, was encrypted using leveled homomorphic encryption implemented in the PALISADE software library. Statistical analysis of key oncological endpoints was blindly performed on both the raw data and the homomorphically-encrypted data using descriptive statistics and survival analysis with Kaplan-Meier curves. Results were then compared with an accuracy goal of two decimals. Results: The difference between the raw data and the homomorphically encrypted data results, regarding all variables analyzed was within the pre-determined accuracy range goal, as well as the practical efficiency of the encrypted computation measured by run time, are presented in table. Conclusions: This study demonstrates that data encrypted with Homomorphic Encryption can be statistical analyzed with a precision of at least two decimal places, allowing safe clinical conclusions drawing while preserving patients’ privacy and protecting data owners’ data assets. Homomorphic encryption allows performing efficient computation on encrypted data non-interactively and without requiring decryption during computation time. Utilizing the technology will empower large-scale cross-institution and cross- stakeholder collaboration, allowing safe international collaborations. Clinical trial information: 0048-19-TLV. [Table: see text]

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.

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