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Prediction of Lung Cancer Survival Based on Multiomic Data

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Book cover Intelligent Information and Database Systems (ACIIDS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13758))

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Abstract

Lung cancer is the leading cause of cancer death among both men and women, which mainly results from low effectiveness of the screening programs and late occurrence of symptoms, that are usually associated with advanced disease stages. Lung cancer shows high heterogeneity which was many times associated with its molecular background, providing the possibility to utilize machine learning approaches to aid both the diagnosis as well as the development of personalized treatments.

In this work we utilize multiple -omics datasets in order to assess their usefulness for predicting 2 year survival of lung adenocarcinoma using clinical data of 267 patients. By utilizing mRNA and microRNA expression levels, positions of somatic mutations, changes in the DNA copy number and DNA methylation levels we developed multiple single and multiple omics-based classifiers. We also tested various data aggregation and feature selection techniques, showing their influence on the classification accuracy manifested by the area under ROC curve (AUC).

The results of our study show not only that molecular data can be effectively used to predict 2 year survival in lung adenocarcinoma (AUC = 0.85), but also that information on gene expression changes, methylation and mutations provides much better predictors than copy number changes and data from microRNA studies. We were also able to show the classification performance obtained using different dimensionality reduction methods on the most problematic copy number variation dataset, concluding that gene and gene set aggregation provides the best classification results.

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References

  1. Gridelli, C., et al.: Non-small-cell lung cancer. Nat. Rev. Dis. Primers. 1, 15009 (2015)

    Article  Google Scholar 

  2. O’Brien, T.D., Jia, P., Aldrich, M.C., Zhao, Z.: Lung Cancer: One Disease or Many. Hum. Hered. 83, 65–70 (2018)

    Article  Google Scholar 

  3. Yang, Y., Wang, M., Liu, B.: Exploring and comparing of the gene expression and methylation differences between lung adenocarcinoma and squamous cell carcinoma. J. Cell. Physiol. 234, 4454–4459 (2019)

    Article  Google Scholar 

  4. Relli, V., Trerotola, M., Guerra, E., Alberti, S.: Distinct lung cancer subtypes associate to distinct drivers of tumor progression. Oncotarget 9, 35528–35540 (2018)

    Article  Google Scholar 

  5. Borczuk, A.C., Toonkel, R.L., Powell, C.A.: Genomics of lung cancer. Proc. Am. Thorac. Soc. 6, 152–158 (2009)

    Article  Google Scholar 

  6. Xiong, Y., Feng, Y., Qiao, T., Han, Y.: Identifying prognostic biomarkers of non-small cell lung cancer by transcriptome analysis. Cancer biomarkers : section A of Disease markers 27, 243–250 (2020)

    Article  Google Scholar 

  7. Cheung, C.H.Y., Juan, H.F.: Quantitative proteomics in lung cancer. J. Biomed. Sci. 24, 37 (2017)

    Article  Google Scholar 

  8. Qi, S.A., et al.: High-resolution metabolomic biomarkers for lung cancer diagnosis and prognosis. Sci. Rep. 11, 11805 (2021)

    Article  Google Scholar 

  9. Cancer Genome Atlas Research Network, T.: Comprehensive molecular profiling of lung adenocarcinoma. Nature 511, 543-550 (2014)

    Google Scholar 

  10. Cancer Genome Atlas Research Network: Comprehensive genomic characterization of squamous cell lung cancers. Nature 489, 519-525 (2012)

    Google Scholar 

  11. Simes, R.J.: Treatment selection for cancer patients: application of statistical decision theory to the treatment of advanced ovarian cancer. J. Chronic Dis. 38, 171–186 (1985)

    Article  Google Scholar 

  12. Astion, M.L., Wilding, P.: Application of neural networks to the interpretation of laboratory data in cancer diagnosis. Clin. Chem. 38, 34–38 (1992)

    Article  Google Scholar 

  13. Bryce, T.J., Dewhirst, M.W., Floyd, C.E., Jr., Hars, V., Brizel, D.M.: Artificial neural network model of survival in patients treated with irradiation with and without concurrent chemotherapy for advanced carcinoma of the head and neck. Int. J. Radiat. Oncol. Biol. Phys. 41, 339–345 (1998)

    Article  Google Scholar 

  14. Cruz, J.A., Wishart, D.S.: Applications of machine learning in cancer prediction and prognosis. Cancer informatics 2, 59–77 (2007)

    Google Scholar 

  15. Nguyen, T.M., et al.: Deep learning for human disease detection, subtype classification, and treatment response prediction using epigenomic data. Biomedicines 9 (2021)

    Google Scholar 

  16. Huang, Z., et al.: Deep learning-based cancer survival prognosis from RNA-seq data: approaches and evaluations. BMC Med. Genomics 13, 41 (2020)

    Article  Google Scholar 

  17. Wang, Y., Lin, X., Sun, D.: A narrative review of prognosis prediction models for non-small cell lung cancer: what kind of predictors should be selected and how to improve models? Annals of translational medicine 9, 1597 (2021)

    Article  Google Scholar 

  18. Schulz, S., et al.: Multimodal deep learning for prognosis prediction in renal cancer. Front. Oncol. 11, 788740 (2021)

    Article  Google Scholar 

  19. Zhu, W., Xie, L., Han, J., Guo, X.: The application of deep learning in cancer prognosis prediction. Cancers 12, (2020)

    Google Scholar 

  20. Ten Haaf, K., et al.: Risk prediction models for selection of lung cancer screening candidates: A retrospective validation study. PLoS Med. 14, e1002277 (2017)

    Article  Google Scholar 

  21. Ten Haaf, K., van der Aalst, C.M., de Koning, H.J., Kaaks, R., Tammemagi, M.C.: Personalising lung cancer screening: An overview of risk-stratification opportunities and challenges. Int J Cancer 149, 250–263 (2021)

    Article  Google Scholar 

  22. Yeo, Y., et al.: Individual 5-year lung cancer risk prediction model in korea using a nationwide representative database. Cancers 13 (2021)

    Google Scholar 

  23. Tufail, A.B., et al.: Deep learning in cancer diagnosis and prognosis prediction: a minireview on challenges, recent trends, and future directions. Comput. Math. Methods Med. 2021, 9025470 (2021)

    Article  Google Scholar 

  24. Gao, Y., Zhou, R., Lyu, Q.: Multiomics and machine learning in lung cancer prognosis. J. Thorac. Dis. 12, 4531–4535 (2020)

    Article  Google Scholar 

  25. Laios, A., et al.: Feature selection is critical for 2-year prognosis in advanced stage high grade serous ovarian cancer by using machine learning. Cancer control: journal of the Moffitt Cancer Center 28, 10732748211044678 (2021)

    Article  Google Scholar 

  26. Love, M.I., Huber, W., Anders, S.: Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014)

    Article  Google Scholar 

  27. Subramanian, A., et al.: Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. U.S.A. 102, 15545–15550 (2005)

    Article  Google Scholar 

  28. Francisco, C.-N.: Beta regression in R. Journal of Statistical Software 1–24 (2010)

    Google Scholar 

  29. Friedman, J., Hastie, T., Tibshirani, R.: Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1–22 (2010)

    Article  Google Scholar 

  30. Kursa, M., Rudnicki, W.: Feature selection with the boruta package. J. Stat. Softw. 36, 1–13 (2010)

    Article  Google Scholar 

  31. Kuhn, M.: Building predictive models in R using the caret package. J. Stat. Softw. 28, 1–26 (2008)

    Article  Google Scholar 

  32. Malik, V., Dutta, S., Kalakoti, Y., Sundar, D.: Multi-omics integration based predictive model for survival prediction of lung adenocarcinaoma. 2019 Grace Hopper Celebration India (GHCI) 1–5 (2019)

    Google Scholar 

  33. Jayasurya, K., et al.: Comparison of Bayesian network and support vector machine models for two-year survival prediction in lung cancer patients treated with radiotherapy. Med. Phys. 37, 1401–1407 (2010)

    Article  Google Scholar 

  34. Sun, T., et al.: Comparative evaluation of support vector machines for computer aided diagnosis of lung cancer in CT based on a multi-dimensional data set. Comput. Methods Programs Biomed. 111, 519–524 (2013)

    Article  Google Scholar 

  35. Hyun, S.H., Ahn, M.S., Koh, Y.W., Lee, S.J.: A machine-learning approach using PET-based radiomics to predict the histological subtypes of lung cancer. Clin. Nucl. Med. 44, 956–960 (2019)

    Article  Google Scholar 

  36. Wang, D.D., Zhou, W., Yan, H., Wong, M., Lee, V.: Personalized prediction of EGFR mutation-induced drug resistance in lung cancer. Sci. Rep. 3, 2855 (2013)

    Article  Google Scholar 

  37. Emaminejad, N., et al.: Fusion of quantitative image and genomic biomarkers to improve prognosis assessment of early stage lung cancer patients. I.E.E.E. Trans. Biomed. Eng. 63, 1034–1043 (2016)

    Google Scholar 

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Acknowledgements

This work was supported by Polish National Science Centre, grant number: UMO-2020/37/B/ST6/01959 and Silesian University of Technology statutory research funds. Calculations were performed on the Ziemowit computer cluster in the Laboratory of Bioinformatics and Computational Biology created in the EU Innovative Economy Programme POIG.02.01.00–00-166/08 and expanded in the POIG.02.03.01–00-040/13 project.

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Authors and Affiliations

  1. Department of Systems Biology and Engineering, Silesian University of Technology, Gliwice, Poland

    Roman Jaksik & Jarosław Śmieja

Authors
  1. Roman Jaksik
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  2. Jarosław Śmieja
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Corresponding author

Correspondence to Roman Jaksik .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Vietnam National University, Ho Chi Minh City, Ho Chi Minh City, Vietnam

    Tien Khoa Tran

  3. Al-Farabi Kazakh National University, Almaty, Kazakhstan

    Ualsher Tukayev

  4. National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  5. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

  6. University of Newcastle, Newcastle, NSW, Australia

    Edward Szczerbicki

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Jaksik, R., Śmieja, J. (2022). Prediction of Lung Cancer Survival Based on Multiomic Data. In: Nguyen, N.T., Tran, T.K., Tukayev, U., Hong, TP., Trawiński, B., Szczerbicki, E. (eds) Intelligent Information and Database Systems. ACIIDS 2022. Lecture Notes in Computer Science(), vol 13758. Springer, Cham. https://doi.org/10.1007/978-3-031-21967-2_10

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  • DOI: https://doi.org/10.1007/978-3-031-21967-2_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21966-5

  • Online ISBN: 978-3-031-21967-2

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