Whole transcriptome signature for prognostic prediction (WTSPP): application of whole transcriptome signature for prognostic prediction in cancer

Abstract

Developing prognostic biomarkers for specific cancer types that accurately predict patient survival is increasingly important in clinical research and practice. Despite the enormous potential of prognostic signatures, proposed models have found limited implementations in routine clinical practice. Herein, we propose a generic, RNA sequencing platform independent, statistical framework named whole transcriptome signature for prognostic prediction to generate prognostic gene signatures. Using ovarian cancer and lung adenocarcinoma as examples, we provide evidence that our prognostic signatures overperform previous reported signatures, capture prognostic features not explained by clinical variables, and expose biologically relevant prognostic pathways, including those involved in the immune system and cell cycle. Our approach demonstrates a robust method for developing prognostic gene expression signatures. In conclusion, our statistical framework can be generally applied to all cancer types for prognostic prediction and might be extended to other human diseases. The proposed method is implemented as an R package (PanCancerSig) and is freely available on GitHub (https://github.com/Cheng-Lab-GitHub/PanCancer_Signature).

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Fig. 1: The OV signature is predictive of patient survival in six independent ovarian cancer gene expression datasets.
Fig. 2: The OV signature provides additional prognostic value over clinical variables.
Fig. 3: The OV signature can be used to stratify individual clinical variables.
Fig. 4: The OV signature outperforms 14 published ovarian cancer-specific gene signatures.
Fig. 5: The LUAD signature is predictive of patient survival in independent lung adenocarcinoma gene expression datasets.

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Acknowledgements

This work is supported by the Cancer Prevention Research Institute of Texas (CPRIT) (RR180061 to CC) and the National Cancer Institute of the National Institutes of Health (1R21CA227996 to CC). CC is a CPRIT Scholar in Cancer Research.

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ES, YZ, and KZ performed the analysis. ES and YZ produced the figures. YW generated the R package. ES, YZ, and CC conceived the research and designed the method and experiments. YZ and CC curated the data. ES, YZ, FSV, KZ, HY, and CC interpreted the results. ES drafted the manuscript. ES, YZ, YW, FSV, KZ, HY, and CC read and approved the final manuscript. CC directed the project.

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Correspondence to Chao Cheng.

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Schaafsma, E., Zhao, Y., Wang, Y. et al. Whole transcriptome signature for prognostic prediction (WTSPP): application of whole transcriptome signature for prognostic prediction in cancer. Lab Invest 100, 1356–1366 (2020). https://doi.org/10.1038/s41374-020-0413-8

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