Abstract 561: Does molecular staging trump clinical staging: a pan-cancer, big-data analysis of TCGA data
Authors: Nicholas Shannon, Melissa Ching Ching Teo, Narayanan Gopalakrishna Iyer
Journal: Cancer Research (2017)

Abstract True integration of clinical staging and molecular prognostic biomarkers has been hampered by a lack of comprehensive data sets with adequate power. In the last two years, The Cancer Genome Atlas (TCGA) has generated and made available a repository of rich, high volume -omics datasets including molecular profiling together with matched clinical data. Methods: Clinical and transcriptomics data were downloaded for TCGA datasets (38 projects) representing a total of 14,043 patients. Matched expression and clinical data was available for 34 cohorts, covering 10554 patients (median n=270, range 15-1091) and survival data was available for 24 cohorts (7854 patients, median 294 per cohort), with a median follow-up of 10.7 months (range 1.9 to 61 months). Prediction models were constructed based on the latest AJCC staging guide, a novel five gene molecular prognostic signature specifically derived for each cancer type, and a combined model utilising both. Accuracy of survival prediction was calculated as the area under the receiver operator characteristic curve (AUC), based on 2-year overall survival prediction. Results: Pan-cancer analyses demonstrate accuracy of clinical staging from 0.55-0.91 (median 0.72), while molecular prognostication varies from 0.7-0.99 (median 0.84). The combined model is consistently more accurate, 0.71-0.99 (median 0.89, p&amp;lt;0.01), where the benefit of adding molecular data improved accuracy (median +0.13, +0.03-0.36). The absolute benefit of adding molecular data to clinical staging correlated more closely with strength of clinical prediction (R2=0.35) than strength of molecular prediction (R2=0.08). When clinical staging was more less accurate (clinical prediction &amp;lt; 0.7), there was a greater contribution of molecular data, compared to cancers with more accurate clinical staging (mean +0.15 vs +0.07, respectively; p=0.015). Interestingly, of the six tumours with poor clinical prediction (AUC &amp;lt; 0.7), three were squamous cell carcinomas (SCC) arising from the head and neck, cervix or lung respectively. The fact that there were no datasets representing SCC in the good clinical prediction group (AUC &amp;gt; 0.7) suggests an important need for more accurate prognostic models in this subtype. Conclusion: The potential role for molecular staging as an adjunct to clinical staging remains undefined. In this study, we demonstrate for the first time the importance of molecular prognostication across a range of cancer sub-types, but especially where conventional staging performs poorly. The latter sub-group is further enriched for squamous cell cancers, where molecular information offers dramatic improvement in accuracy (+0.15 to AUC), and hence, should be prioritized for assessment of molecular signatures. The lack of integrating molecular data could also reflect limitations in our understanding of tumour biology, which in turn needs to be translated into potential actionable targets and therapeutic benefit. Citation Format: Nicholas Shannon, Melissa Ching Ching Teo, Narayanan Gopalakrishna Iyer. Does molecular staging trump clinical staging: a pan-cancer, big-data analysis of TCGA data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 561. doi:10.1158/1538-7445.AM2017-561