The landscape of precision cancer medicine clinical trials in the United States

Roper, Nitin; Stensland, Kristian; Hendricks, Ryan; Galsky, Matthew D.

doi:10.1016/j.ctrv.2015.02.009

Cited by 46 publications

(23 citation statements)

References 23 publications

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“…This analysis of the relative number of clinical trials for drug class or molecular criteria enables a view into the strengths and weaknesses in targeting actionable variants in clinical trials. Previous analysis of the landscape of cancer clinical trials in the USA, in the absence of a structured curated database, has required significant effort using data extracted manually via keyword search of clinicaltrials.gov [9]. The organization of data elements curated into the JAX-CKB database facilitates rapid analysis of data from clinical trials through customized queries of the database.…”

Section: Resultsmentioning

confidence: 99%

The clinical trial landscape in oncology and connectivity of somatic mutational profiles to targeted therapies

et al. 2016

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BackgroundPrecision medicine in oncology relies on rapid associations between patient-specific variations and targeted therapeutic efficacy. Due to the advancement of genomic analysis, a vast literature characterizing cancer-associated molecular aberrations and relative therapeutic relevance has been published. However, data are not uniformly reported or readily available, and accessing relevant information in a clinically acceptable time-frame is a daunting proposition, hampering connections between patients and appropriate therapeutic options. One important therapeutic avenue for oncology patients is through clinical trials. Accordingly, a global view into the availability of targeted clinical trials would provide insight into strengths and weaknesses and potentially enable research focus. However, data regarding the landscape of clinical trials in oncology is not readily available, and as a result, a comprehensive understanding of clinical trial availability is difficult.ResultsTo support clinical decision-making, we have developed a data loader and mapper that connects sequence information from oncology patients to data stored in an in-house database, the JAX Clinical Knowledgebase (JAX-CKB), which can be queried readily to access comprehensive data for clinical reporting via customized reporting queries. JAX-CKB functions as a repository to house expertly curated clinically relevant data surrounding our 358-gene panel, the JAX Cancer Treatment Profile (JAX CTP), and supports annotation of functional significance of molecular variants. Through queries of data housed in JAX-CKB, we have analyzed the landscape of clinical trials relevant to our 358-gene targeted sequencing panel to evaluate strengths and weaknesses in current molecular targeting in oncology. Through this analysis, we have identified patient indications, molecular aberrations, and targeted therapy classes that have strong or weak representation in clinical trials.ConclusionsHere, we describe the development and disseminate system methods for associating patient genomic sequence data with clinically relevant information, facilitating interpretation and providing a mechanism for informing therapeutic decision-making. Additionally, through customized queries, we have the capability to rapidly analyze the landscape of targeted therapies in clinical trials, enabling a unique view into current therapeutic availability in oncology.

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Section: Resultsmentioning

confidence: 99%

The clinical trial landscape in oncology and connectivity of somatic mutational profiles to targeted therapies

et al. 2016

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“…17 Thus far, only the following 5 genes have been found to be commonly mutated in UM: GNAQ , GNA11 , BAP1 , SF3B1 , and EIF1AX . In this study, we found mutation frequencies of 89% (72 of 81) for GNAQ and GNA11 , 45% (29 of 64) for BAP1 , 23% (19 of 81) for SF3B1 , and 17% (14 of 81) for EIF1AX , which is similar to previous reports.…”

Section: Discussionmentioning

confidence: 99%

Driver Mutations in Uveal Melanoma

et al. 2016

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“…Previous studies have consistently shown that among drugs failing in later stages of development, there is a lack of biomarker stratification in comparison with those drugs that succeed . Although targeted agents and biomarker selection may result in more efficient drug development, it limits the population in which the drug may be effective, and can make the design and recruitment for clinical trials costlier . Further exploration of these competing costs and benefits would be of interest in future studies.…”

Section: Discussionmentioning

confidence: 99%

Drug development for breast, colorectal, and non–small cell lung cancers from 1979 to 2014

Nixon

Khan

Imam

et al. 2017

Cancer

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BACKGROUND Understanding the drug development pathway is critical for streamlining the development of effective cancer treatments. The objective of the current study was to delineate the drug development timeline and attrition rate of different drug classes for common cancer disease sites. METHODS Drugs entering clinical trials for breast, colorectal, and non–small cell lung cancer were identified using a pharmaceutical business intelligence database. Data regarding drug characteristics, clinical trials, and approval dates were obtained from the database, clinical trial registries, PubMed, and regulatory Web sites. RESULTS A total of 411 drugs met the inclusion criteria for breast cancer, 246 drugs met the inclusion criteria for colorectal cancer, and 315 drugs met the inclusion criteria for non–small cell lung cancer. Attrition rates were 83.9% for breast cancer, 87.0% for colorectal cancer, and 92.0% for non–small cell lung cancer drugs. In the case of non–small cell lung cancer, there was a trend toward higher attrition rates for targeted monoclonal antibodies compared with other agents. No tumor site‐specific differences were noted with regard to cytotoxic chemotherapy, immunomodulatory, or small molecule kinase inhibitor drugs. Drugs classified as “others” in breast cancer had lower attrition rates, primarily due to the higher success of hormonal medications. Mean drug development times were 8.9 years for breast cancer, 6.7 years for colorectal cancer, and 6.6 years for non–small cell lung cancer. CONCLUSIONS Overall oncologic drug attrition rates remain high, and drugs are more likely to fail in later‐stage clinical trials. The refinement of early‐phase trial design may permit the selection of drugs that are more likely to succeed in the phase 3 setting. Cancer 2017;123:4672‐4679. © 2017 American Cancer Society.

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The landscape of precision cancer medicine clinical trials in the United States

Cited by 46 publications

References 23 publications

The clinical trial landscape in oncology and connectivity of somatic mutational profiles to targeted therapies

The clinical trial landscape in oncology and connectivity of somatic mutational profiles to targeted therapies

Driver Mutations in Uveal Melanoma

Drug development for breast, colorectal, and non–small cell lung cancers from 1979 to 2014

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