Oncotarget

Research Papers:

Predicting clinical outcomes using cancer progression associated signatures

Jared Mamrot _, Nathan E. Hall and Robyn A. Lindley

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Abstract

Jared Mamrot1,2, Nathan E. Hall1 and Robyn A. Lindley1,3

1 GMDx Group Ltd, Melbourne, Victoria, Australia

2 Department of Obstetrics and Gynaecology, Monash University, Clayton, VIC, Australia

3 Department of Clinical Pathology, The Victorian Comprehensive Cancer Centre, Faculty of Medicine, Dentistry & Health Sciences, University of Melbourne, VIC, Australia

Correspondence to:

Jared Mamrot,email: jared.mamrot@gmdxgen.com

Keywords: innate immunity; biomarker; mutagenesis; oncogenesis; cancer progression

Received: November 05, 2020     Accepted: March 22, 2021     Published: April 13, 2021

Copyright: © 2021 Mamrot et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

ABSTRACT

Somatic mutation signatures are an informative facet of cancer aetiology, however they are rarely useful for predicting patient outcome. The aim of this study is to evaluate the utility of a panel of 142 mutation-signature–associated metrics (P142) for predicting cancer progression in patients from a ‘TCGA PanCancer Atlas’ cohort. The P142 metrics are comprised of AID/APOBEC and ADAR deaminase associated SNVs analyzed for codon context, strand bias, and transitions/transversions. TCGA tumor-normal mutation data was obtained for 10,437 patients, representing 31 of the most prevalent forms of cancer. Stratified random sampling was used to split patients into training, tuning and validation cohorts for each cancer type. Cancer specific machine learning (XGBoost) models were built using the output from the P142 panel to predict patient Progression Free Survival (PFS) status as either “High PFS” or “Low PFS”. Predictive performance of each model was evaluated using the validation cohort. Models accurately predicted PFS status for several cancer types, including adrenocortical carcinoma, glioma, mesothelioma, and sarcoma. In conclusion, the P142 panel of metrics successfully predicted cancer progression status in patients with some, but not all cancer types analyzed. These results pave the way for future studies on cancer progression associated signatures.


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