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Radiogenomics: Lung Cancer-Related Genes Mutation Status Prediction

  • Catarina DiasEmail author
  • Gil Pinheiro
  • António Cunha
  • Hélder P. Oliveira
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11868)

Abstract

Advances in genomics have driven to the recognition that tumours are populated by different minor subclones of malignant cells that control the way the tumour progresses. However, the spatial and temporal genomic heterogeneity of tumours has been a hurdle in clinical oncology. This is mainly because the standard methodology for genomic analysis is the biopsy, that besides being an invasive technique, it does not capture the entire tumour spatial state in a single exam. Radiographic medical imaging opens new opportunities for genomic analysis by providing full state visualisation of a tumour at a macroscopic level, in a non-invasive way. Having in mind that mutational testing of EGFR and KRAS is a routine in lung cancer treatment, it was studied whether clinical and imaging data are valuable for predicting EGFR and KRAS mutations in a cohort of NSCLC patients. A reliable predictive model was found for EGFR (AUC = 0.96) using both a Multi-layer Perceptron model and a Random Forest model but not for KRAS (AUC = 0.56). A feature importance analysis using Random Forest reported that the presence of emphysema and lung parenchymal features have the highest correlation with EGFR mutation status. This study opens new opportunities for radiogenomics on predicting molecular properties in a more readily available and non-invasive way.

Keywords

Radiogenomics Mutation status Predictive models 

Notes

Acknowledgments

This work is financed by the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme and by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia within project POCI-01-0145-FEDER-030263.

References

  1. 1.
    Bakr, S., et al.: A radiogenomic dataset of non-small cell lung cancer. Sci. Data 5, 180202 (2018)CrossRefGoogle Scholar
  2. 2.
    Bakr, S., et al.: Data for NSCLC Radiogenomics Collection (2017).  https://doi.org/10.7937/K9/TCIA.2017.7hs46erv. https://wiki.cancerimagingarchive.net/x/W4G1AQ, type: dataset
  3. 3.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)CrossRefGoogle Scholar
  4. 4.
    Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)CrossRefGoogle Scholar
  5. 5.
    Chen, Z., Fillmore, C.M., Hammerman, P.S., Kim, C.F., Wong, K.K.: Non-small-cell lung cancers: a heterogeneous set of diseases. Nat. Rev. Cancer 14(8), 535 (2014)CrossRefGoogle Scholar
  6. 6.
    Clark, K., et al.: The cancer imaging archive (TCIA): maintaining and operating a public information repository. J. Digit. Imaging 26(6), 1045–1057 (2013).  https://doi.org/10.1007/s10278-013-9622-7CrossRefGoogle Scholar
  7. 7.
    Digumarthy, S.R., Padole, A.M., Gullo, R.L., Sequist, L.V., Kalra, M.K.: Can CT radiomic analysis in NSCLC predict histology and EGFR mutation status? Medicine 98(1) (2019)CrossRefGoogle Scholar
  8. 8.
    Dogan, S., et al.: Molecular epidemiology of EGFR and KRAS mutations in 3,026 lung adenocarcinomas: higher susceptibility of women to smoking-related KRAS-mutant cancers. Clin. Cancer Res. 18(22), 6169–6177 (2012)CrossRefGoogle Scholar
  9. 9.
    Ellison, G., Zhu, G., Moulis, A., Dearden, S., Speake, G., McCormack, R.: EGFR mutation testing in lung cancer: a review of available methods and their use for analysis of tumour tissue and cytology samples. J. Clin. Pathol. 66(2), 79–89 (2013)CrossRefGoogle Scholar
  10. 10.
    Ferlay, J., et al.: Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int. J. Cancer 136(5), E359–E386 (2015)CrossRefGoogle Scholar
  11. 11.
    Gevaert, O., et al.: Predictive radiogenomics modeling of EGFR mutation status in lung cancer. Sci. Rep. 7, 41674 (2017)CrossRefGoogle Scholar
  12. 12.
    Gevaert, O., et al.: Non–small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data—methods and preliminary results. Radiology 264(2), 387–396 (2012).  https://doi.org/10.1148/radiol.12111607, pMID: 22723499CrossRefGoogle Scholar
  13. 13.
    Hames, M.L., Chen, H., Iams, W., Aston, J., Lovly, C.M., Horn, L.: Correlation between KRAS mutation status and response to chemotherapy in patients with advanced non-small cell lung cancer. Lung Cancer 92, 29–34 (2016)CrossRefGoogle Scholar
  14. 14.
    Hansell, D.M., Bankier, A.A., MacMahon, H., McLoud, T.C., Muller, N.L., Remy, J.: Fleischner society: glossary of terms for thoracic imaging. Radiology 246(3), 697–722 (2008)CrossRefGoogle Scholar
  15. 15.
    Janssen-Heijnen, M.L., Coebergh, J.W.W.: Trends in incidence and prognosis of the histological subtypes of lung cancer in North America, Australia, New Zealand and Europe. Lung Cancer 31(2–3), 123–137 (2001)CrossRefGoogle Scholar
  16. 16.
    Liu, Y., et al.: Radiomic features are associated with EGFR mutation status in lung adenocarcinomas. Clin. Lung Cancer 17(5), 441–448 (2016)CrossRefGoogle Scholar
  17. 17.
    Mei, D., Luo, Y., Wang, Y., Gong, J.: CT texture analysis of lung adenocarcinoma: can radiomic features be surrogate biomarkers for EGFR mutation statuses. Cancer Imaging 18(1), 52 (2018)CrossRefGoogle Scholar
  18. 18.
    Mok, T.S., et al.: Gefitinib or carboplatin-paclitaxel in pulmonary adenocarcinoma. N. Engl. J. Med. 361(10), 947–957 (2009)CrossRefGoogle Scholar
  19. 19.
    O’dowd, E.L., et al.: What characteristics of primary care and patients are associated with early death in patients with lung cancer in the UK? Thorax 70(2), 161–168 (2015)CrossRefGoogle Scholar
  20. 20.
    Papadopoulou, E., et al.: Determination of EGFR and KRAS mutational status in greek non-small-cell lung cancer patients. Oncol. Lett. 10(4), 2176–2184 (2015)CrossRefGoogle Scholar
  21. 21.
    Riely, G.J., Marks, J., Pao, W.: KRAS mutations in non–small cell lung cancer. Proc. Am. Thorac. Soc. 6(2), 201–205 (2009)CrossRefGoogle Scholar
  22. 22.
    Rosenblatt, F.: The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 65(6) (1958)CrossRefGoogle Scholar
  23. 23.
    Scrivener, M., de Jong, E.E., van Timmeren, J.E., Pieters, T., Ghaye, B., Geets, X.: Radiomics applied to lung cancer: a review. Transl. Cancer Res. 5(4), 398–409 (2016)CrossRefGoogle Scholar
  24. 24.
    Siegelin, M.D., Borczuk, A.C.: Epidermal growth factor receptor mutations in lung adenocarcinoma. Lab. Invest. 94(2), 129 (2014)CrossRefGoogle Scholar
  25. 25.
    Varghese, A.M., et al.: Lungs dont forget: comparison of the KRAS and EGFR mutation profile and survival of collegiate smokers and never smokers with advanced lung cancers. J. Thorac. Oncol. 8(1), 123–125 (2013)CrossRefGoogle Scholar
  26. 26.
    Zhou, H., et al.: Poor response to platinum-based chemotherapy is associated with KRAS mutation and concomitant low expression of BRAC1 and TYMS in NSCLC. J. Int. Med. Res. 44(1), 89–98 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Catarina Dias
    • 1
    • 2
    Email author
  • Gil Pinheiro
    • 1
  • António Cunha
    • 1
    • 3
  • Hélder P. Oliveira
    • 1
    • 4
  1. 1.Instituto de Engenharia de Sistemas e Computadores, Tecnologia e CiênciaPortoPortugal
  2. 2.Faculdade de EngenhariaUniversidade do PortoPortoPortugal
  3. 3.Universidade de Trás-os-Montes e Alto DouroVila RealPortugal
  4. 4.Faculdade de CiênciasUniversidade do PortoPortoPortugal

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