Identifying primary site of lung-limited Cancer of unknown primary based on relative gene expression orderings

Guanghao Liu,Mingzhang Lin,Zhongjie Tang,Mengyao Li,Xiaofang Lin,Zheng Guo,Hongdong Li,Lishuang Qi,Guini Hong

doi:10.1186/s12885-019-5274-4

Abstract

BackgroundPrecise diagnosis of the tissue origin for metastatic cancer of unknown primary (CUP) is essential for deciding the treatment scheme to improve patients’ prognoses, since the treatment for the metastases is the same as their primary counterparts. The purpose of this study is to identify a robust gene signature that can predict the origin for CUPs.MethodsThe within-sample relative gene expression orderings (REOs) of gene pairs within individual samples, which are insensitive to experimental batch effects and data normalizations, were exploited for identifying the prediction signature.ResultsUsing gene expression profiles of the lung-limited metastatic colorectal cancer (LmCRC), we firstly showed that the within-sample REOs in lung metastases of colorectal cancer (CRC) samples were concordant with the REOs in primary CRC samples rather than with the REOs in primary lung cancer. Based on this phenomenon, we selected five gene pairs with consistent REOs in 498 primary CRC and reversely consistent REOs in 509 lung cancer samples, which were used as a signature for predicting primary sites of metastatic CRC based on the majority voting rule. Applying the signature to 654 primary CRC and 204 primary lung cancer samples collected from multiple datasets, the prediction accuracy reached 99.36%. This signature was also applied to 24 LmCRC samples collected from three datasets produced by different laboratories and the accuracy reached 100%, suggesting that the within-sample REOs in the primary site could reveal the original tissue of metastatic cancers.ConclusionsThe result demonstrated that the signature based on within-sample REOs of five gene pairs could exactly and robustly identify the primary sites of CUPs.

Highlights

Precise diagnosis of the tissue origin for metastatic cancer of unknown primary (CUP) is essential for deciding the treatment scheme to improve patients’ prognoses, since the treatment for the metastases is the same as their primary counterparts
It has been reported that the within-sample relative gene expression orderings (REOs) of gene pairs within individual samples are insensitive to experimental batch effects [10,11,12], invariant to monotonic data transformation [13, 14], and robust against partial RNA degradation [15] as well as sampling site uncertainty within a tumor tissue [16]
High REO concordance between lung metastases of colorectal cancer (CRC) and primary CRC To evaluate whether the REO patterns of lung metastases of CRC were similar to the primary CRC or lung cancer, a total of 498 primary CRC samples and 509 primary lung cancer samples were used

Summary

Introduction

Precise diagnosis of the tissue origin for metastatic cancer of unknown primary (CUP) is essential for deciding the treatment scheme to improve patients’ prognoses, since the treatment for the metastases is the same as their primary counterparts. It has been reported that the within-sample relative gene expression orderings (REOs) of gene pairs within individual samples are insensitive to experimental batch effects [10,11,12], invariant to monotonic data transformation [13, 14], and robust against partial RNA degradation [15] as well as sampling site uncertainty within a tumor tissue [16] Based on these unique advantages, some classifiers based on REO signatures, such as TSP [10] and K-TSP [11], were proposed to identify transcriptional signatures for discriminating cancer subtypes [17,18,19], which obviate the need of data normalization for the discovery and validation datasets and can be applied to the individual level [17, 20, 21]

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