Ashleigh Cheyne (United Kingdom)

Imperial College London Life Sciences/Medicine
I am a final year PhD student at Imperial College London working with the Larrouy-Maumus and Levin groups across the Departments of Life Sciences and Medicine. My research interests lie in using multi-omics tools and analyses to understand Mycobacterium tuberculosis (Mtb) infections. I have analysed various datasets, including genomic, transcriptomic, and metabolomic data, to investigate Mtb infection looking at both host and bacterial sides of infection. Recently, I have worked on transcriptomic biomarkers for paediatric Tuberculosis with Prof Michael Levin, Dr Myrsini Kaforou, and Dr Sandra Newton as part of a larger multi-country analysis to validate and discover paediatric biomarker signatures.

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 Conference Calendar

PERFORMANCE OF HOST BLOOD TRANSCRIPTOMIC SIGNATURES FOR DIAGNOSIS OF PAEDIATRIC TUBERCULOSIS A SOUTH AFRICAN CASE-CONTROL STUDY. (ID 1564)

Session Name
2510 - PARALLEL SYMPOSIUM 12 (JOINT SESSION WITH MSF): PEDIATRIC INFECTIOUS DISEASES CHALLENGES IN HUMANITARIAN SETTINGS (ID 102)
Lecture Time
10:59 - 11:06
Room
Hall 04
Presenter

Abstract

Background

The lack of an accurate diagnostic tests for paediatric tuberculosis (TB) is a major contributing factor to the burden of TB in children. Host blood transcriptomic signatures have shown potential for being used as diagnostic tests. However, most of these signatures were discovered in adult datasets, but their performance has not been assessed in paediatric studies. Here, we perform a comparison of published transcriptomic signatures to assess their potential in distinguishing TB from other diseases (OD) in children.

Methods

117 children with TB and OD were recruited in South Africa between 2009 and 2013 and whole blood RNA-sequencing was performed. After reviewing the literature, we identified 26 transcriptomic signatures that fulfilled our selection criteria on derivation, which were then assessed both using the models described in their original publication and were also refitted to assess their full potential in classifying the patients in our dataset.

Results

Out of the 26 signatures tested using the previously described models, none achieved the optimum WHO Target Product Profile guidelines for sensitivity (>85%) and specificity (>92%) of a novel non-sputum based diagnostic test. However, when we constructed optimised models, 3/26 signatures met the optimal criteria for distinguishing active TB from OD. We observed a relationship between signature size and performance.

Conclusions

Our results highlight that robust and generalisable models for diagnostic transcriptomic signatures are needed to exploit the full potential of gene expression signals measured in blood, accelerating their development into clinically usable diagnostic tests.

Clinical Trial Registration

Not applicable

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