Clinical Trials in Precision Oncology

Abstract BACKGROUND Availability of genomic information used in the management of cancer treatment has outpaced both regulatory and reimbursement efforts. Many types of clinical trials are underway to validate the utility of emerging genome-based biomarkers for diagnostic, prognostic, and predictive applications. Clinical trials are a key source of evidence required for US Food and Drug Administration approval of therapies and companion diagnostics and for establishing the acceptance criteria for reimbursement. CONTENT Determining the eligibility of patients for molecular-based clinical trials and the interpretation of data emerging from clinical trials is significantly hampered by 2 primary factors: the lack of specific reporting standards for biomarkers in clinical trials and the lack of adherence to official gene and variant naming standards. Clinical trial registries need specifics on the mutation required for enrollment as opposed to allowing a generic mutation entry such as, “EGFR mutation.” The use of clinical trials data in bioinformatics analysis and reporting is also gated by the lack of robust, state of the art programmatic access support. An initiative is needed to develop community standards for clinical trial descriptions and outcome reporting that are modeled after similar efforts in the genomics research community. SUMMARY Systematic implementation of reporting standards is needed to insure consistency and specificity of biomarker data, which will in turn enable better comparison and assessment of clinical trial outcomes across multiple studies. Reporting standards will facilitate improved identification of relevant clinical trials, aggregation and comparison of information across independent trials, and programmatic access to clinical trials databases.

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Reporting of harms in oncological clinical study reports submitted to the European Medicines Agency compared to trial registries and publications—a methodological review

BMC Medicine ◽

10.1186/s12916-021-01955-0 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Asger S. Paludan-Müller ◽

Perrine Créquit ◽

Isabelle Boutron

Keyword(s):

Clinical Trial ◽

Clinical Trials ◽

Clinical Study ◽

Primary Source ◽

European Medicines Agency ◽

Clinical Trial Registries ◽

Number Of Patients ◽

Journal Publications ◽

Clinical Study Reports ◽

Completeness Of Reporting

Abstract Background An accurate and comprehensive assessment of harms is a fundamental part of an accurate weighing of benefits and harms of an intervention when making treatment decisions; however, harms are known to be underreported in journal publications. Therefore, we sought to compare the completeness of reporting of harm data, discrepancies in harm data reported, and the delay to access results of oncological clinical trials between three sources: clinical study reports (CSRs), clinical trial registries and journal publications. Methods We used the EMA clinical data website to identify all trials submitted to the EMA between 2015 and 2018. We retrieved all CSRs and included all phase II, II/III or III randomised controlled trials (RCTs) assessing targeted therapy and immunotherapy for cancer. We then identified related records in clinical trial registries and journals. We extracted harms data for eight pre-specified variables and determined the completeness of reporting of harm data in each of the three sources. Results We identified 42 RCTs evaluating 13 different drugs. Results were available on the EMA website in CSRs for 37 (88%) RCTs, ClinicalTrials.gov for 36 (86%), the European Clinical Trials Register (EUCTR) for 20 (48%) and in journal publications for 32 (76%). Harms reporting was more complete in CSRs than other sources. We identified marked discrepancies in harms data between sources, e.g. the number of patients discontinuing due to adverse events differed in CSRs and clinical trial registers for 88% of trials with data in both sources. For CSRs and publications, the corresponding number was 90%. The median (interquartile range) delay between the primary trial completion date and access to results was 4.34 (3.09–7.22) years for CSRs, 2.94 (1.16–4.52) years for ClinicalTrials.gov, 5.39 (4.18–7.33) years for EUCTR and 2.15 (0.64–5.04) years for publications. Conclusions Harms of recently approved oncological drugs were reported more frequently and in more detail in CSRs than in trial registries and journal publications. Systematic reviews seeking to address harms of oncological treatments should ideally use CSRs as the primary source of data; however, due to problems with access, this is currently not feasible.

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

JCO Clinical Cancer Informatics ◽

10.1200/cci.21.00018 ◽

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.

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Results availability and timeliness of registered COVID-19 clinical trials: interim cross-sectional results from the DIRECCT study

BMJ Open ◽

10.1136/bmjopen-2021-053096 ◽

2021 ◽

Vol 11 (11) ◽

pp. e053096

Author(s):

Maia Salholz-Hillel ◽

Peter Grabitz ◽

Molly Pugh-Jones ◽

Daniel Strech ◽

Nicholas J DeVito

Keyword(s):

Clinical Trial ◽

Clinical Trials ◽

Journal Article ◽

Cross Sectional Study ◽

Sensitivity Analyses ◽

Cross Sectional ◽

Registration Data ◽

Clinical Trial Registries ◽

Treatment And Prevention

ObjectiveTo examine how and when the results of COVID-19 clinical trials are disseminated.DesignCross-sectional study.SettingThe COVID-19 clinical trial landscape.Participants285 registered interventional clinical trials for the treatment and prevention of COVID-19 completed by 30 June 2020.Main outcome measuresOverall reporting and reporting by dissemination route (ie, by journal article, preprint or results on a registry); time to reporting by dissemination route.ResultsFollowing automated and manual searches of the COVID-19 literature, we located 41 trials (14%) with results spread across 47 individual results publications published by 15 August 2020. The most common dissemination route was preprints (n=25) followed by journal articles (n=18), and results on a registry (n=2). Of these, four trials were available as both a preprint and journal publication. The cumulative incidence of any reporting surpassed 20% at 119 days from completion. Sensitivity analyses using alternate dates and definitions of results did not appreciably change the reporting percentage. Expanding minimum follow-up time to 3 months increased the overall reporting percentage to 19%.ConclusionCOVID-19 trials completed during the first 6 months of the pandemic did not consistently yield rapid results in the literature or on clinical trial registries. Our findings suggest that the COVID-19 response may be seeing quicker results disclosure compared with non-emergency conditions. Issues with the reliability and timeliness of trial registration data may impact our estimates. Ensuring registry data are accurate should be a priority for the research community during a pandemic. Data collection is underway for the next phase of the DIssemination of REgistered COVID-19 Clinical Trials study expanding both our trial population and follow-up time.

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No-cost next generation sequencing of advanced cancer patients within the Strata Precision Oncology Network supports clinical trial enrollment.

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.15_suppl.3073 ◽

2019 ◽

Vol 37 (15_suppl) ◽

pp. 3073-3073

Author(s):

Marc Ryan Matrana ◽

Scott A. Tomlins ◽

Kat Kwiatkowski ◽

Khalis Mitchell ◽

Jennifer Marie Suga ◽

...

Keyword(s):

Clinical Trial ◽

Clinical Trials ◽

Next Generation Sequencing ◽

Clinical Samples ◽

Precision Oncology ◽

Cancer Type ◽

Next Generation ◽

Match Trial ◽

The Impact ◽

Generation Sequencing

3073 Background: Widespread integration of systematized next generation sequencing (NGS)-based precision oncology is hindered by numerous barriers. Hence, we developed the Strata trial (NCT03061305), a screening protocol to determine the impact of scaled precision oncology. Methods: We implemented no-cost NGS on formalin fixed paraffin embedded (FFPE) clinical samples for all patients with advanced tumors, a common portfolio of partnered therapeutic clinical trials, and robust infrastructure development across the Strata Precision Oncology Network. Results: Across the network of 17 centers, specimens from 8673/9222 (94%) patients were successfully tested in the Strata CLIA/CAP/NCI-MATCH accredited laboratory using comprehensive amplicon-based DNA and RNA NGS. Patients were tested with one of three StrataNGS test versions; the most recent panel assesses all classes of actionable alterations (mutations, copy number alterations, gene fusions, microsatellite instability, tumor mutation burden and PD-L1 expression). Median surface area of received FFPE tumor samples was 25mm2 (interquartile range 9-95mm2), and the median turnaround time from sample receipt to report was 6 business days. 2577 (27.9%) patients had highly actionable alterations, defined as alterations associated with within-cancer type FDA approved or NCCN guideline recommended therapies (1072 patients), NCI-MATCH trial arms (1467 patients), Strata-partnered therapeutic trials (327 patients), or specific alteration-matched FDA approved therapies in patients with cancers of unknown primary (71 patients). Of the 1467 patients matched to an NCI-MATCH trial arm, 15 enrolled. Of the 327 patients matched to one of nine Strata-partnered clinical trials, 77 (24%) were screen failures, while 250 (76%) have either enrolled or are being actively followed for enrollment upon progression. Conclusions: Through streamlined consent methods, electronic medical record queries, and high throughput laboratory testing at no cost to patients, we demonstrate that scaled precision oncology is feasible across a diverse network of healthcare systems when paired with access to relevant clinical trials. Clinical trial information: NCT03061305.

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Individualized Molecular Profiling for Allocation to Clinical Trials Singapore Study—An Asian Tertiary Cancer Center Experience

JCO Precision Oncology ◽

10.1200/po.20.00261 ◽

2021 ◽

pp. 859-875

Author(s):

Amanda O. L. Seet ◽

Aaron C. Tan ◽

Tira J. Tan ◽

Matthew C. H. Ng ◽

David W. M. Tai ◽

...

Keyword(s):

Clinical Trial ◽

Clinical Trials ◽

Tumor Tissue ◽

Molecular Profiling ◽

Tumor Board ◽

Cancer Center ◽

Precision Oncology ◽

Molecular Tumor Board ◽

Tumor Types ◽

Tertiary Cancer Center

PURPOSE Precision oncology has transformed the management of advanced cancers through implementation of advanced molecular profiling technologies to identify increasingly defined subsets of patients and match them to appropriate therapy. We report outcomes of a prospective molecular profiling study in a high-volume Asian tertiary cancer center. PATIENTS AND METHODS Patients with advanced cancer were enrolled onto a prospective protocol for genomic profiling, the Individualized Molecular Profiling for Allocation to Clinical Trials Singapore study, at the National Cancer Center Singapore. Primary objective was to identify molecular biomarkers in patient's tumors for allocation to clinical trials. The study commenced in February 2012 and is ongoing, with the results of all patients who underwent multiplex next-generation sequencing (NGS) testing until December 2018 presented here. The results were discussed at a molecular tumor board where recommendations for allocation to biomarker-directed trials or targeted therapies were made. RESULTS One thousand fifteen patients were enrolled with a median age of 58 years (range 20-83 years). Most common tumor types were lung adenocarcinoma (26%), colorectal cancer (15%), and breast cancer (12%). A total of 1,064 NGS assays were performed, on fresh tumor tissue for 369 (35%) and archival tumor tissue for 687 (65%) assays. TP53 (39%) alterations were most common, followed by EGFR (21%), KRAS (14%), and PIK3CA (10%). Of 405 NGS assays with potentially actionable alterations, 111 (27%) were allocated to a clinical trial after molecular tumor board and 20 (4.9%) were enrolled on a molecularly matched clinical trial. Gene fusions were detected in 23 of 311 (7%) patients tested, including rare fusions in new tumor types and known fusions in rare tumors. CONCLUSION Individualized Molecular Profiling for Allocation to Clinical Trials Singapore demonstrates the feasibility of a prospective broad molecular profiling program in an Asian tertiary cancer center, with the ability to develop and adapt to a dynamic landscape of precision oncology.

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Integrated deep learned transcriptomic and structure-based predictor of clinical trials outcomes

10.1101/095653 ◽

2016 ◽

Cited By ~ 10

Author(s):

Artem V. Artemov ◽

Evgeny Putin ◽

Quentin Vanhaelen ◽

Alexander Aliper ◽

Ivan V. Ozerov ◽

...

Keyword(s):

Clinical Trial ◽

Clinical Trials ◽

Systems Biology ◽

Phase I ◽

Biological Data ◽

Drug Induced ◽

Multi Stage ◽

Pathway Activation ◽

Trial Outcomes ◽

Analytical Tools

AbstractDespite many recent advances in systems biology and a marked increase in the availability of high-throughput biological data, the productivity of research and development in the pharmaceutical industry is on the decline. This is primarily due to clinical trial failure rates reaching up to 95% in oncology and other disease areas. We have developed a comprehensive analytical and computational pipeline utilizing deep learning techniques and novel systems biology analytical tools to predict the outcomes of phase I/II clinical trials. The pipeline predicts the side effects of a drug using deep neural networks and estimates drug-induced pathway activation. It then uses the predicted side effect probabilities and pathway activation scores as an input to train a classifier which predicts clinical trial outcomes. This classifier was trained on 577 transcriptomic datasets and has achieved a cross-validated accuracy of 0.83. When compared to a direct gene-based classifier, our multi-stage approach dramatically improves the accuracy of the predictions. The classifier was applied to a set of compounds currently present in the pipelines of several major pharmaceutical companies to highlight potential risks in their portfolios and estimate the fraction of clinical trials that were likely to fail in phase I and II.

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Therapeutic improvements expected in the near future for schizophrenia and schizoaffective disorder: an appraisal of phase III clinical trials of schizophrenia-targeted therapies as found in US and EU clinical trial registries

Expert Opinion on Pharmacotherapy ◽

10.1517/14656566.2016.1149164 ◽

2016 ◽

Vol 17 (7) ◽

pp. 921-936 ◽

Cited By ~ 16

Author(s):

Ricardo P. Garay ◽

Leslie Citrome ◽

Ludovic Samalin ◽

Chen-Chung Liu ◽

Morten S. Thomsen ◽

...

Keyword(s):

Clinical Trial ◽

Clinical Trials ◽

Schizoaffective Disorder ◽

Targeted Therapies ◽

Phase Iii ◽

Phase Iii Clinical Trials ◽

Clinical Trial Registries ◽

Near Future

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Scarce quality assurance documentation in major clinical trial registries for approved medicines used in post-marketing clinical trials

Trials ◽

10.1186/s13063-019-3277-8 ◽

2019 ◽

Vol 20 (1) ◽

Cited By ~ 1

Author(s):

Yorokpa Joachim Doua ◽

Hanneke Dominicus ◽

Julius Mugwagwa ◽

Suzelle Magalie Gombe ◽

Jude Nwokike

Keyword(s):

Clinical Trial ◽

Clinical Trials ◽

Quality Assurance ◽

Clinical Trial Registries ◽

Post Marketing ◽

Major Clinical Trial

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BIOMARKER STRATEGIES FOR GEROSCIENCE-GUIDED CLINICAL TRIALS

Innovation in Aging ◽

10.1093/geroni/igz038.2731 ◽

2019 ◽

Vol 3 (Supplement_1) ◽

pp. S745-S746

Author(s):

Jamie N Justice ◽

George A Kuchel ◽

Nir Barzilai ◽

Stephen Kritchevsky

Keyword(s):

Clinical Trial ◽

Clinical Trials ◽

Biological Aging ◽

Expert Committee ◽

Biomarkers Of Aging ◽

Age Related ◽

Trial Outcomes ◽

Biology Of Aging ◽

Potential Use ◽

Selection Of

Abstract Significant progress in the biology of aging and animal models supports the geroscience hypothesis: by targeting biological aging the onset of age-related diseases can be delayed. Geroscience investigators will test this hypothesis in a multicenter clinical trial, to determine if interventions on biological aging processes can prevent accumulation of multiple age-related diseases and aging phenotypes in older adults. Prodigious activity is underway to develop markers of biological aging, but currently there is no aging biomarker consensus to support geroscience-guided clinical trial outcomes. We convened an expert committee to establish a framework for selection of blood-based biomarkers, emphasizing: feasibility/reliability; aging relevance; ability to predict clinical trial outcomes; and responsiveness to intervention. We applied this framework and identified a short-list of blood-based biomarkers with potential use in multicenter trials on aging. We review progress on efforts to test these candidate biomarkers of aging and development of biomarkers strategy for geroscience-guided clinical trials.

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