Translation research: from accurate diagnosis to appropriate treatment

Our plain-English summary of Translation research: from accurate diagnosis to appropriate treatment, by C.P. Webb, H.I. Pass (Journal of Translational Medicine, 2004).

The study

• Authors: C.P. Webb, H.I. Pass
• Journal: Journal of Translational Medicine
• Published: 2004;2(1):35
• Cited by: 23 articles, as counted by NIH iCite
• Read it: PubMed · Publisher · Free full text

Abstract

This review article focuses on the various aspects of translational research, where research on human subjects can ultimately enhance the diagnosis and treatment of future patients. While we will use specific examples relating to the asbestos related cancer mesothelioma, it should be stressed that the general approach outlined throughout this review is readily applicable to other diseases with an underlying molecular basis. Through the integration of molecular-based technologies, systematic tissue procurement and medical informatics, we now have the ability to identify clinically applicable "genotype"-"phenotype" associations across cohorts of patients that can rapidly be translated into useful diagnostic and treatment strategies. This review will touch on the various steps in the translational pipeline, and highlight some of the most essential elements as well as possible roadblocks that can impact success of the program. Critical issues with regard to Institutional Review Board (IRB) and Health Insurance Portability and Accountability Act (HIPAA) compliance, data standardization, sample procurement, quality control (QC), quality assurance (QA), data analysis, preclinical models and clinical trials are addressed. The various facets of the translational pipeline have been incorporated into a fully integrated computational system, appropriately named Dx2Tx. This system readily allows for the identification of new diagnostic tests, the discovery of biomarkers and drugable targets, and prediction of optimal treatments based upon the underlying molecular basis of the disease.

In plain English

This article is a review describing how the authors run translational research, from consent and privacy rules through sample and data collection, data analysis, mouse models and clinical trials, using a computer system they built called Dx2Tx. They illustrated it with gene expression data from mesothelioma tumors of patients in Phase II or Phase III mesothelioma protocols, including 21 tumor samples. In this example, two sample subgroups were linked to time to recurrence (p = 0.003), T-stage (p = 0.004), survival time (p = 0.0002) and platelet count (p = 0.005). The expression of 157 genes correlated with survival time (p less than 0.05). A test combining these genes with platelet count and T-stage was suggested to be about 90% accurate at determining 6-month survival after surgery. A drug-target filter pointed to EGFR inhibitors with eniluracil and topoisomerase II inhibitors as a possible combination still needing testing. The authors noted that correlations could arise by chance. The article gave no asbestos exposure information for these patients. The authors concluded that combining medical informatics with molecular technologies can make molecular-based medicine a reality.

We wrote this plain-English summary from the full article with the help of AI, and a second AI model checked each sentence against the article. It is not the authors' wording; the abstract and the article are their own account.

What the study did and found

• Design: Review of the authors' approach to translational research and their integrated computer system (Dx2Tx), illustrated with worked examples from their own mesothelioma gene expression data
• Who was studied: Patients with mesothelioma enrolled in Phase II or Phase III mesothelioma protocols; the gene expression example used 21 mesothelioma tumor samples. Place and dates of enrollment are not given.
• Exposure: The article reports no asbestos exposure information for the patients in the examples. Mesothelioma is described as an asbestos-related cancer. The authors state that proper controls for early-diagnosis work would be age-matched people exposed to similar doses of asbestos. They also describe a center that evaluates people exposed to asbestos at work or at home. No fiber types are named.
• Main results:
  • In mesothelioma tumor samples, two subgroups identified by clustering of gene expression were significantly associated with time to recurrence (p = 0.003), T-stage (p = 0.004), survival time (p = 0.0002) and platelet count (p = 0.005).
  • In the mesothelioma training example, the expression of 157 genes correlated with patient survival time (p less than 0.05). These genes were combined with platelet count and T-stage in the prediction algorithm.
  • For patients with mesothelioma, the combined clinical and molecular test was suggested to be approximately 90% accurate at determining 6-month survival after surgery, based on the area under the ROC curve.
  • Genes differentially expressed in recurrent compared with non-recurring mesotheliomas mapped to a cell-proliferation signaling pathway that appeared to be hyper-activated in aggressive tumors.
  • Filtering the mesothelioma data against a list of known drug targets identified EGFR inhibitors combined with eniluracil and topoisomerase II inhibitors as a possible combination treatment for mesothelioma.
• The authors' conclusion: The authors concluded that integrating medical informatics with molecular technologies, together with extensive standardized clinical data collected over time, can turn molecular-based medicine into a present reality.
• Limitations the authors state:
  • With large amounts of data from a relatively small sample population, many of the observed correlations could occur by random chance.
  • The data filters used to find candidate biomarkers are not tests of statistical significance.
  • The approach for predicting blood biomarkers had not yet been validated in the human disease.
  • The data came from patients treated under one particular treatment regimen, so prospective validation should follow the same regimen to avoid confounding.
  • The suggested drug combination is a hypothesis that requires testing in a preclinical model. The authors do not believe expression levels of a drug target alone are necessarily enough to predict whether a drug will work.

Topics

Medical terms in this study, explained in our Lexicon: Mesothelioma, Clinical Trial, Discovery, Biomarker.

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• A Proposed System Toward Standardizing Surgical-Based Treatments for Malignant Pleural Mesothelioma, From the Joint National Cancer Institute-International Association for the Study of Lung Cancer-Mesothelioma Applied Research Foundation Taskforce — J.S. Friedberg, M.J. Culligan, et al. (2019)
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