| Abstract Title: | From GC-MS to LC-MS/MS: Further Advances in Adrenal Cancer Diagnosis |
| Abstract Type: | Seminar |
| Session Choice: | Advances in Clinical Analysis |
| Presenter Name: | Dr Angela Taylor |
| Co-authors: | Dr Michael Biehl Dr Alice Sitch Dr Irina Bancos Prof Wiebke Arlt |
| Company/Organisation: | University of Birmingham |
| Country: | United Kingdom |
Abstract Information :
Discriminating adrenocortical adenoma (ACA) from adrenocortical carcinoma (ACC) represents a continuous challenge in patients with adrenal masses.
We previously established urine steroid metabolomics, (the combination of urinary steroid profiling by GC-MS and machine learning-based data analysis), to distinguish adrenal cancer from benign adrenal tumours, achieving higher sensitivity and specificity than currently available imaging techniques. (JCEM2011; 96 (12): 3775-84). However, GC-MS is labour-intensive, expensive and low-throughput. Here we developed a urinary steroid profiling method using liquid chromatography tandem mass spectrometry (LC-MS/MS) and report method optimisation (using Multi-platform Unbiased-optimisation of Spectrometry via Closed Loop Experimentation software MUSCLE), validation, and cross validation to GC-MS.
We analysed 24-hr urinary steroid excretion by GC-MS and LC-MS/MS in:
- 481-anonymised comparison urines from a range of disorders associated with steroid excess and deficiency
- 129 healthy controls
- 99-ACA
- 40-ACC
To determine diagnostic accuracy, (the ability to distinguish ACC from ACA), ROC curves were generated. GC-MS and LC-MS/MS demonstrated similar diagnostic accuracy, (area under the ROC curve average 0.969 (SD=0.044) and 0.954 (0.067) respectfully).
Development of this novel LC-MS/MS method represents a significant advance diagnosis of ACC.
