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Translational biomarker discovery in clinical metabolomics: an introductory tutorial

By:
Contributors: David Broadhurst, PhD

Metabolomics. 2013 Apr;9(2):280-299. Epub 2012 Dec 4.

Xia J1, Broadhurst DI, Wilson M, Wishart DS.

 

Abstract

Metabolomics is increasingly being applied towards the identification of biomarkers for disease diagnosis, prognosis and risk prediction. Unfortunately among the many published metabolomic studies focusing on biomarker discovery, there is very little consistency and relatively little rigor in how researchers select, assess or report their candidate biomarkers. In particular, few studies report any measure of sensitivity, specificity, or provide receiver operator characteristic (ROC) curves with associated confidence intervals. Even fewer studies explicitly describe or release the biomarker model used to generate their ROC curves. This is surprising given that for biomarker studies in most other biomedical fields, ROC curve analysis is generally considered the standard method for performance assessment. Because the ultimate goal of biomarker discovery is the translation of those biomarkers to clinical practice, it is clear that the metabolomics community needs to start “speaking the same language” in terms of biomarker analysis and reporting-especially if it wants to see metabolite markers being routinely used in the clinic. In this tutorial, we will first introduce the concept of ROC curves and describe their use in single biomarker analysis for clinical chemistry. This includes the construction of ROC curves, understanding the meaning of area under ROC curves (AUC) and partial AUC, as well as the calculation of confidence intervals. The second part of the tutorial focuses on biomarker analyses within the context of metabolomics. This section describes different statistical and machine learning strategies that can be used to create multimetabolite biomarker models and explains how these models can be assessed using ROC curves. In the third part of the tutorial we discuss common issues and potential pitfalls associated with different analysis methods and provide readers with a list of nine recommendations for biomarker analysis and reporting. To help readers test, visualize and explore the concepts presented in this tutorial, we also introduce a web-based tool called ROCCET (ROC Curve Explorer & Tester, http://www.roccet.ca). ROCCET was originally developed as a teaching aid but it can also serve as a training and testing resource to assist metabolomics researchers build biomarker models and conduct a range of common ROC curve analyses for biomarker studies.

PubMed

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Why did the chicken cross the road…?

Konstantin Stoletov and Lian Willetts co-first-authored an article published recently in Nature Communications titled “Quantitative in vivo whole genome motility screen reveals novel therapeutic targets to block cancer metastasis“. These two researchers, along with fellow Lewis lab members and collaborators from the University of Calgary and Vanderbilt University set out to determine what genes and signaling networks are involved in the rate-limiting steps of solid tumour cell motility, in vivo. But the team was hampered by the lack of an effective, quantitative, in vivo imaging platform. They wanted to visualize the movement of tumour cells, or lack of, in real-time AND use this intravital imaging platform to screen a large bank of tumour cells harboring single gene mutations for cells that show a loss of motility.

https://www.nature.com/articles/s41467-018-04743-2

When tumour cells metastasize they get into (intravasate) the hosts’ bloodstream and use the vascular system like roadways to travel throughout the body. This lets the tumour cells colonize new microenvironments where they will proliferate and form new tumours. So metastatis is really dependent on tumour cell motility. Although there are many different types of solid tumours known, previous research suggests that if the tumour cells can mobilize and metastasize then the expressed motility-related genes share homology across tumour types. This is great news because it would mean that therapeutic targets aimed at stopping motility could also stop metastasis for many tumour types!

Dr. Konstantin Stoletov

 

Dr. Lian Willetts
Dr. Lian Willetts

The Lewis lab researchers and their collaborators developed an in vivo, fluorescent, time-lapse screening platform that uses shell-less avian embryos for tumour growth and formation. The avian embryo is an excellent tumour model because the tumour cells will grow on the chorioallantoic membrane in a single cell layer, making in vivo cell motility imaging actually doable.

Using this platform the team screened over 30 000 human genes for the ones needed for cell motility and ultimately found 17 genes that looked to be effective metastasis-blocking gene targets. Stoletov, along with other Lewis lab members, are continuing this research by studying these 17 attractive candidates further to determine which one (s) would make therapeutic metastasis-blocking targets.

This article has generated a lot of interest in the scientific community and in the general public! Check out the links below to mentions and articles in the media.

https://www.biocentury.com/bc-innovations/translation-brief/2018-07-18/how-chicken-embryo-screen-identified-entos%E2%80%99-

https://www.ualberta.ca/medicine/news/2018/june/putting-the-brakes-on-metastatic-cancer

Stay tuned for a podcast that will be posted soon from “Parsing Science” where the hosts interview Dr. John Lewis about this work!

https://www.parsingscience.org/coming_soon/
UPDATE Oct 12, 2018: The podcast with John Lewis on Parsing Science called “Halting Cancers’ Spread“, is now available!

- Perrin Beatty