scholarly journals MatchMiner: An open source computational platform for real-time matching of cancer patients to precision medicine clinical trials using genomic and clinical criteria

2017 ◽  
Author(s):  
James Lindsay ◽  
Catherine Del Vecchio Fitz ◽  
Zachary Zwiesler ◽  
Priti Kumari ◽  
Bernd Van Der Veen ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Precision Medicine ◽  
Open Source ◽  
Cancer Patients ◽  
Patient Specific ◽  
Current Standard ◽  
Eligibility Criteria ◽  
Clinical Criteria ◽  
Cancer Medicine

AbstractBackgroundMolecular profiling of cancers is now routine at many cancer centers, and the number of precision cancer medicine clinical trials, which are informed by profiling, is steadily rising. Additionally, these trials are becoming increasingly complex, often having multiple arms and many genomic eligibility criteria. Currently, it is a challenging for physicians to match patients to relevant clinical trials using the patient’s genomic profile, which can lead to missed opportunities. Automated matching against uniformly structured and encoded genomic eligibility criteria is essential to keep pace with the complex landscape of precision medicine clinical trials.ResultsTo meet these needs, we built and deployed an automated clinical trial matching platform called MatchMiner at the Dana-Farber Cancer Institute (DFCI). The platform has been integrated with Profile, DFCI’s enterprise genomic profiling project, which contains tumor profile data for >20,000 patients, and has been made available to physicians across the Institute. As no current standard exists for encoding clinical trial eligibility criteria, a new language called Clinical Trial Markup Language (CTML) was developed, and over 178 genomically-driven clinical trials were encoded using this language. The platform is open source and freely available for adoption by other institutions.ConclusionMatchMiner is the first open platform developed to enable computational matching of patient-specific genomic profiles to precision cancer medicine clinical trials. Creating MatchMiner required developing clinical trial eligibility standards to support genome-driven matching and developing intuitive interfaces to support practical use-cases. Given the complexity of tumor profiling and the rapidly changing multi-site nature of genome-driven clinical trials, open source software is the most efficient, scalable, and economical option for matching cancer patients to clinical trials.

Algorithmic matching of genomic profiles to precision cancer medicine clinical trials at DFCI.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 6620-6620
Author(s):  
James Lindsay ◽  
Catherine Del Vecchio Fitz ◽  
Zachary Zwiesler ◽  
Priti Kumari ◽  
Khanh Tu Do ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Precision Medicine ◽  
Open Source ◽  
Patient Specific ◽  
Cancer Type ◽  
Eligibility Criteria ◽  
Enrollment Rates ◽  
And Gender ◽  
Computational Platform

6620 Background: Genomic profiling and access to precision medicine clinical trials are now standard at leading cancer institutes and many community practices. Interpreting patient-specific genomic information and tracking the complex criteria for precision medicine trials requires specialized computational tools, especially for multi-institutional basket studies such as NCI-MATCH and TAPUR. Methods: To address this challenge we have developed an open source computational platform for patient-specific clinical trial matching at Dana-Farber Cancer Institute (DFCI) called MatchMiner, which aides in both patient recruitment to precision medicine trials, as well as decision support for oncologists. Trial matches are computed based on genomic criteria, including mutations, CNAs, and SVs, as well as clinical and demographic information, including cancer type, age, and gender. A formal standard called clinical trial markup language (CTML) to encode complex clinical trial eligibility criteria has also been created. Results: MatchMiner is now available at DFCI. Currently 123 precision medicine clinical trials have been transformed into CTML and 13,000 patient records are available, with over 88% of current patients having at least 1 match (average 2.6). A total of 103 genes are specified as criteria for at least 1 trial. KRAS, TP53, PTEN, PIK3CA and BRAF are the genes driving the most number of matches. General usage statistics and trial enrollment rates are currently being monitored to determine the system effectiveness. As this is an open source initiative, the software is also now publically available at https://github.com/dfci/matchminer. Conclusions: We have developed an open source computational platform that enables patient-specific matching and recruitment to precision medicine clinical trials at DFCI. We are actively seeking collaborators and plan to make CTML a multi-institution standard for encoding complex clinical trial eligibility in a computable form.

Cancer clinical trials available in the community setting: A 5-year analysis from 1999–2004

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 6056-6056
Author(s):  
J. K. Keller ◽  
J. Bowman ◽  
J. A. Lee ◽  
M. A. Mathiason ◽  
K. A. Frisby ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Cancer Patients ◽  
Community Setting ◽  
Disease Stage ◽  
Cancer Center ◽  
Newly Diagnosed ◽  
Community Based ◽  
Eligibility Criteria ◽  
Major Barrier

6056 Background: Less than 5% of newly diagnosed cancer patients are accrued into clinical trials. In the community setting, the lack of appropriate clinical trials is a major barrier. Our prospective study in 2004 determined that 58% of newly diagnosed adult cancer patients at our community-based cancer center didn’t have a clinical trial available appropriate for their disease stage. Among those with clinical trials, 23% were subsequently found to be ineligible (Go RS, et al. Cancer 2006, in press). However, the availability of clinical trials may vary from year to year. Methods: A retrospective study was conducted to determine what clinical trials were available for newly diagnosed adult cancer patients at our institution from June 1999-July 2004. The study also investigated the proportions of newly diagnosed patients who had a clinical trial available appropriate for type and stage of disease and patients accrued. Results: Over the 5-year period, 207 (82, 87, 99, 102, 117, years 1–5, respectively) trials were available. Most (50.7%) trials were for the following cancers: breast (15.5%), lung (13.5%), head and neck (7.7%), colorectal (7.2%) and lymphoma (6.8%). ECOG (53%), RTOG (26%), and CTSU (9%) provided the majority of the trials. A total of 5,776 new adult cancer patients were seen during this period. Overall, 60% of the patients had a trial available appropriate for type and stage of their cancer, but only 103 (3%) were enrolled. There was a significant upward trend in the proportions of patients with available trials over the years (60.2%, 55.9%, 59.2%, 60.7%, 63.9%, years 1–5, respectively; Mantel-Haenszel P=.008). The proportion of patients with a trial available was highest for prostate (97.3%), lung (90.9%), and breast (73.9%), and lowest for melanoma (17.1%), renal (11.6%), and bladder (7.2%). The majority of patients accrued to trials had the following cancers: breast (32%), lung (17%), lymphoma (9%), colon (7%), and prostate (5%). Conclusions: Nearly half of the newly diagnosed adult patients at our center had no trials available appropriate for type and stage of their cancers. It is likely that if strict clinical trial eligibility criteria were applied, approximately 2/3 of our patients would not be eligible for a clinical trial. No significant financial relationships to disclose.

scholarly journals Bayesian adaptive design of early-phase clinical trials for precision medicine based on cancer biomarkers

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Shinjo Yada
Keyword(s):  
Neural Network ◽  
Clinical Trials ◽  
Precision Medicine ◽  
Early Phase ◽  
Patient Specific ◽  
Specific Gene ◽  
Cancer Type ◽  
Background Characteristics ◽  
Number Of Patients ◽  
Early Phase Clinical Trials

Abstract Cancer tissue samples obtained via biopsy or surgery were examined for specific gene mutations by genetic testing to inform treatment. Precision medicine, which considers not only the cancer type and location, but also the genetic information, environment, and lifestyle of each patient, can be applied for disease prevention and treatment in individual patients. The number of patient-specific characteristics, including biomarkers, has been increasing with time; these characteristics are highly correlated with outcomes. The number of patients at the beginning of early-phase clinical trials is often limited. Moreover, it is challenging to estimate parameters of models that include baseline characteristics as covariates such as biomarkers. To overcome these issues and promote personalized medicine, we propose a dose-finding method that considers patient background characteristics, including biomarkers, using a model for phase I/II oncology trials. We built a Bayesian neural network with input variables of dose, biomarkers, and interactions between dose and biomarkers and output variables of efficacy outcomes for each patient. We trained the neural network to select the optimal dose based on all background characteristics of a patient. Simulation analysis showed that the probability of selecting the desirable dose was higher using the proposed method than that using the naïve method.

scholarly journals Indwelling Device-Associated Biofilms in Critically Ill Cancer Patients—Study Protocol

Pathogens ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 306
Author(s):  
Olguta Lungu ◽  
Ioana Grigoras ◽  
Olivia Simona Dorneanu ◽  
Catalina Lunca ◽  
Teodora Vremera ◽  
...  
Keyword(s):  
Cancer Patients ◽  
Critically Ill ◽  
Pathogen Detection ◽  
Venous Catheter ◽  
Successful Outcome ◽  
Patient Specific ◽  
Clinical Value ◽  
Current Standard ◽  
Sepsis Management ◽  
Microorganism Identification

Health care-associated infections are a leading cause of inpatient complications. Rapid pathogen detection/identification is a major challenge in sepsis management that highly influences the successful outcome. The current standard of microorganism identification relies on bacterial growth in culture, which has several limitations. Gene sequencing research has developed culture-independent techniques for microorganism identification, with the aim to improve etiological diagnosis and, therefore, to change sepsis outcome. A prospective, observational, non-interventional, single-center study was designed that assesses biofilm-associated pathogens in a specific subpopulation of septic critically ill cancer patients. Indwelling device samples will be collected in septic patients at the moment of the removal of the arterial catheter, central venous catheter, endotracheal tube and urinary catheter. Concomitantly, clinical data regarding 4 sites (nasal, pharyngeal, rectal and skin) of pathogen colonization at the time of hospital/intensive care admission will be collected. The present study aims to offer new insights into biofilm-associated infections and to evaluate the infection caused by catheter-specific and patient-specific biofilm-associated pathogens in association with the extent of colonization. The analysis relies on the two following detection/identification techniques: standard microbiological method and next generation sequencing (NGS). Retrospectively, the study will estimate the clinical value of the NGS-based detection and its virtual potential in changing patient management and outcome, notably in the subjects with missing sepsis source or lack of response to anti-infective treatment.

A Framework for Systematic Assessment of Clinical Trial Population Representativeness Using Electronic Health Records Data

2021 ◽  
Vol 12 (04) ◽  
pp. 816-825
Author(s):  
Yingcheng Sun ◽  
Alex Butler ◽  
Ibrahim Diallo ◽  
Jae Hyun Kim ◽  
Casey Ta ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Electronic Health Records ◽  
The United States ◽  
Design Stage ◽  
Common Data Model ◽  
Free Text ◽  
Eligibility Criteria ◽  
Health Records ◽  
Electronic Health

Abstract Background Clinical trials are the gold standard for generating robust medical evidence, but clinical trial results often raise generalizability concerns, which can be attributed to the lack of population representativeness. The electronic health records (EHRs) data are useful for estimating the population representativeness of clinical trial study population. Objectives This research aims to estimate the population representativeness of clinical trials systematically using EHR data during the early design stage. Methods We present an end-to-end analytical framework for transforming free-text clinical trial eligibility criteria into executable database queries conformant with the Observational Medical Outcomes Partnership Common Data Model and for systematically quantifying the population representativeness for each clinical trial. Results We calculated the population representativeness of 782 novel coronavirus disease 2019 (COVID-19) trials and 3,827 type 2 diabetes mellitus (T2DM) trials in the United States respectively using this framework. With the use of overly restrictive eligibility criteria, 85.7% of the COVID-19 trials and 30.1% of T2DM trials had poor population representativeness. Conclusion This research demonstrates the potential of using the EHR data to assess the clinical trials population representativeness, providing data-driven metrics to inform the selection and optimization of eligibility criteria.

Improving hematology/oncology clinical research recruitment via universal prescreening: The VA Connecticut (VACT) Cancer Center experience.

2021 ◽  
Vol 39 (28_suppl) ◽  
pp. 79-79
Author(s):  
Jenny Jing Xiang ◽  
Alicia Roy ◽  
Christine Summers ◽  
Monica Delvy ◽  
Jessica Lee O'Donovan ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Clinical Trials ◽  
Clinical Research ◽  
Cancer Patients ◽  
Cancer Center ◽  
Cancer Type ◽  
Eligibility Criteria ◽  
Research Recruitment ◽  
Lung Cancer Patients ◽  
Study Enrollment

79 Background: Patient-trial matching is a critical step in clinical research recruitment that requires extensive review of clinical data and trial requirements. Prescreening, defined as identifying potentially eligible patients using select eligibility criteria, may streamline the process and increase study enrollment. We describe the real-world experience of implementing a standardized, universal clinical research prescreening protocol within a VA cancer center and its impact on research enrollment. Methods: An IRB approved prescreening protocol was implemented at the VACT Cancer Center in March 2017. All patients with a suspected or confirmed diagnosis of cancer are identified through tumor boards, oncology consults, and clinic lists. Research coordinators perform chart review and manually enter patient demographics, cancer type and stage, and treatment history into a REDCap (Research Electronic Data Capture) database. All clinical trials and their eligibility criteria are also entered into REDCap and updated regularly. REDCap generates real time lists of potential research studies for each patient based on his/her recorded data. The primary oncologist is alerted to a patient’s potential eligibility prior to upcoming clinic visits and thus can plan to discuss clinical research enrollment as appropriate. Results: From March 2017 to December 2020, a total of 2548 unique patients were prescreened into REDCAP. The mean age was 71.5 years, 97.5% were male, and 15.5% were African American. 32.57 % patients had genitourinary cancer, 17.15% had lung cancer, and 46.15% were undergoing malignancy workup. 1412 patients were potentially eligible after prescreening and 556 patients were ultimately enrolled in studies. The number of patients enrolled on therapeutic clinical trials increased after the implementation of the prescreening protocol (35 in 2017, 64 in 2018, 78 in 2019, and 55 in 2020 despite the COVID19 pandemic). Biorepository study enrollment increased from 8 in 2019 to 15 in 2020. The prescreening protocol also enabled 200 patients to be enrolled onto a lung nodule liquid biopsy study from 2017 to 2019. Our prescreening process captured 98.57% of lung cancer patients entered into the cancer registry during the same time period. Conclusions: Universal prescreening streamlined research recruitment operations and was associated with yearly increases in clinical research enrollment at a VA cancer center. Our protocol identified most new lung cancer patients, suggesting that, at least for this malignancy, potential study patients were not missed. The protocol was integral in our program becoming the top accruing VA site for NCI’s National Clinical Trial Network (NCTN) studies since 2019.

scholarly journals Are clinical trial eligibility criteria an accurate reflection of a real-world population of advanced non-small-cell lung cancer patients?

2018 ◽  
Vol 25 (4) ◽  
Author(s):  
K. Al-Baimani ◽  
H. Jonker ◽  
T. Zhang ◽  
G.D. Goss ◽  
S.A. Laurie ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Advanced Nsclc ◽  
Small Cell ◽  
Small Cell Lung ◽  
Eligibility Criteria ◽  
Ecog Ps

Background Advanced non-small-cell lung cancer (nsclc) represents a major health issue globally. Systemic treatment decisions are informed by clinical trials, which, over years, have improved the survival of patients with advanced nsclc. The applicability of clinical trial results to the broad lung cancer population is unclear because strict eligibility criteria in trials generally select for optimal patients.Methods We performed a retrospective chart review of all consecutive patients with advanced nsclc seen in outpatient consultation at our academic institution between September 2009 and September 2012, collecting data about patient demographics and cancer characteristics, treatment, and survival from hospital and pharmacy records. Two sets of arbitrary trial eligibility criteria were applied to the cohort. Scenario A stipulated Eastern Cooperative Oncology Group performance status (ecog ps) 0–1, no brain metastasis, creatinine less than 120 μmol/L, and no second malignancy. Less-strict scenario B stipulated ecog ps 0–2 and creatinine less than 120 μmol/L. We then used the two scenarios to analyze treatment and survival of patients by trial eligibility status.Results The 528 included patients had a median age of 67 years, with 55% being men and 58% having adenocarcinoma. Of those 528 patients, 291 received at least 1 line of palliative systemic therapy. Using the scenario A eligibility criteria, 73% were trial-ineligible. However, 46% of “ineligible” patients actually received therapy and experienced survival similar to that of the “eligible” treated patients (10.2 months vs. 11.6 months, p = 0.10). Using the scenario B criteria, only 35% were ineligible, but again, the survival of treated patients was similar in the ineligible and eligible groups (10.1 months vs. 10.9 months, p = 0.57).Conclusions Current trial eligibility criteria are often strict and limit the enrolment of patients in clinical trials. Our results suggest that, depending on the chosen drug, its toxicities and tolerability, eligibility criteria could be carefully reviewed and relaxed.

scholarly journals Safety and Effectiveness of Favipiravir for Novel Coronavirus (COVID-19): A Rapid Review of Available Evidence

2021 ◽  
Author(s):  
Mohammadreza Mobinizadeh ◽  
Morteza Arab-Zozani
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Therapeutic Potential ◽  
Data Extraction ◽  
Rapid Review ◽  
Eligibility Criteria ◽  
Clinical Trial Registration ◽  
Registration System ◽  
Novel Coronavirus ◽  
First Time

Context: Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) appeared for the first time in December 2019 in Wuhan, China. Due to the lack of unified and integrated evidence for Favipiravir, this study was conducted to rapidly review the existing evidence to help evidence-based decision-making on the therapeutic potential of this drug in the treatment of COVID-19 patients. Evidence Acquisition: This study is a rapid Health Technology Assessment (HTA). By searching pertinent databases, the research team collected relevant articles and tried to create a policy guide through a thematic approach. This rapid review was done in four steps: (1) Searching for evidence through databases; (2) screening the evidence considering eligibility criteria; (3) data extraction; and (4) analyzing the data through thematic analysis. Results: After applying the inclusion criteria, four studies were finally found, including three review studies and a clinical trial that was temporarily removed by its publisher from the journal’s website. After searching the sources mentioned in the articles, two ongoing clinical trials were found in China. Also, by searching the clinical trial website, www.clinicaltrials.gov, five clinical trials were found in the search. The result of the search in the clinical trial registration system in Iran showed a study that is in the process of patient recruitment. A limited number of other articles were found, mostly in the form of reflections from physicians or researchers and letters to editors who have predicted the drug’s performance on SARS-CoV-2, which needs further clinical study to be approved. Conclusions: With the available evidence, it is not possible to make a definite conclusion about the safety and efficacy of Favipiravir in the treatment of patients with COVID-19.

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