Advertisement

A Local-Network Guided Linear Discriminant Analysis for Classifying Lung Cancer Subtypes using Individual Genome-Wide Methylation Profiles

  • Yanming LiEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1069)

Abstract

Accurate and efficient prediction of lung cancer subtypes is clinically important for early diagnosis and prevention. Predictions can be made using individual genomic profiles and other patient-level covariates, such as smoking status. With the ultrahigh-dimensional genomic profiles, the most predictive biomarkers need to be first selected. Most of the current machine learning techniques only select biomarkers that are strongly correlated with the outcome disease. However, many biomarkers, even though have marginally weak correlations with the outcome disease, may execute a strong predictive effect on the disease status. In this paper, we employee an ultrahigh-dimensional classification method, which incorporates the weak signals into predictions, to predict lung cancer subtypes using individual genome-wide DNA methylation profiles. The results show that the prediction accuracy is significantly improved when the predictive weak signals are included. Our approach also detects the predictive local gene networks along with the weak signal detection. The local gene networks detected may shed lights on the cancer developing and progression mechanisms.

Keywords

Cancer subtype prediction Linear discriminant analysis Local gene network Ultrahigh-dimensionality 

References

  1. 1.
    Bell, D.W., et al.: Inherited susceptibility to lung cancer may be associated with the T790M drug resistance mutation in EGFR. Nat. Genet. 37, 1315–1316 (2005)CrossRefGoogle Scholar
  2. 2.
    Fan, J., Fan, Y.: High-dimensional classification using features annealed independence rules. Ann. Statist. 36, 2605–2637 (2008)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Fan, J., Lv, J.: Sure independence screening for ultrahigh dimensional feature space (with discussion). J. R. Statist. Soc. B. 70, 849–911 (2008)CrossRefGoogle Scholar
  4. 4.
    Ferrer, I., Zugazagoitia, J., Herbertz, S., John, W., Paz-Ares, L., Schmid-Bindert, G.: KRAS-mutant non-small cell lung cancer: from biology to therapy. Cancer Biol Ther. 124, 53–64 (2018)Google Scholar
  5. 5.
    Johansson, A., Flanagan, J.M.: Epigenome-wide association studies for breast cancer risk and risk factors. Trends Cancer Res. 12, 19–28 (2017)Google Scholar
  6. 6.
    Kerr, E.M., Martins, C.P.: Metabolic rewiring in mutant KRAS lung cancer. FEBS J. 285(1), 28–41 (2018).  https://doi.org/10.1111/febs.14125CrossRefGoogle Scholar
  7. 7.
    Kim, J., et al.: XPO1-dependent nuclear export is a druggable vulnerability in KRAS-mutant lung cancer. Nature 538(7623), 114–117 (2016)CrossRefGoogle Scholar
  8. 8.
    Liao, D.: Emerging roles of the EBF family of transcription factors in tumor suppression. Mol. Cancer Res. 7(12), 1893–1901 (2009).  https://doi.org/10.1158/1541-7786.MCR-09-0229CrossRefGoogle Scholar
  9. 9.
    Li, Y., Hong, H.G., Li, Y.: Multiclass linear discriminant analysis with ultrahigh-dimensional features. Biometrics (2019).  https://doi.org/10.1111/biom.13065CrossRefGoogle Scholar
  10. 10.
    Li, Y., Hong, H.G., Ahmed, S.E., Li, Y.: Weak signals in high-dimensional regression: detection, estimation and prediction. Appl. Stoch. Models Bus. Ind. 35, 283–298 (2019).  https://doi.org/10.1002/asmb.2340MathSciNetCrossRefGoogle Scholar
  11. 11.
    Lou, S., et al.: Whole-genome bisulfite sequencing of multiple individuals reveals complementary roles of promoter and gene body methylation in transcriptional regulation. Genome Biol. 15(7), 408 (2014)CrossRefGoogle Scholar
  12. 12.
    McKay, J.D., et al.: Large-scale association analysis identifies new lung cancer susceptibility LOCI and heterogeneity in genetic susceptibility across histological subtypes. Nature Genet. 49, 1126–1132 (2017)CrossRefGoogle Scholar
  13. 13.
    Mechanic, L.E., Bowman, E.D., Welsh, J.A., Khan, M.A., Hagiwara, N., Enewold, L., Shields, P.G., Burdette, L., Chanock, S., Harris, C.C.: Common genetic variation in TP53 is associated with lung cancer risk and prognosis in African Americans and somatic mutations in lung tumors. Cancer Epidemiol. Biomark. Prev. 16(2), 214–222 (2007)CrossRefGoogle Scholar
  14. 14.
    Mogi, A., Kuwano, H.: TP53 mutations in nonsmall cell lung cancer. J Biomed. Biotechnol. 2011, 583929 (2011).  https://doi.org/10.1155/2011/583929CrossRefGoogle Scholar
  15. 15.
    Parsons, D.W., et al.: An integrated genomic analysis of human glioblastoma multiforme. Science 321(5897), 1807–1812 (2008)CrossRefGoogle Scholar
  16. 16.
    Pfeifer, G.P., Rauch, T.A.: DNA methylation patterns in lung carcinomas. Semin. Cancer Biol. 19(3), 181–187 (2009).  https://doi.org/10.1016/j.semcancer.2009.02.008CrossRefGoogle Scholar
  17. 17.
    Rollin, J., Blechet, C., Regina, S., Tenenhaus, A., Guyetant, S., Gidrol, X.: The intracellular localization of ID2 expression has a predictive value in non small cell lung cancer. PLoS ONE 4(1), e4158 (2009).  https://doi.org/10.1371/journal.pone.0004158CrossRefGoogle Scholar
  18. 18.
    Román, M., Baraibar, I., López, I., Nadal, E., Rolfo, C., Vicent, S., Gil-Bazo, I.: KRAS oncogene in non-small cell lung cancer: clinical perspectives on the treatment of an old target. Mol. Cancer 17(1), 33 (2012).  https://doi.org/10.1186/s12943-018-0789-xCrossRefGoogle Scholar
  19. 19.
    Pieter, A., van den Heuvel, J., Jing, J., Wooster, R.F., Bachman, K.E.: Analysis of glutamine dependency in non-small cell lung cancer GLS1 splice variant GAC is essential for cancer cell growth. Cancer Biol. Ther. 13(12), 1185–1194 (2012)CrossRefGoogle Scholar
  20. 20.
    Schinstine, M., Filie, A.C., Torres-Cabala, C., Abati, A., Linehan, W.M., Merino, M.: Fine-needle aspiration of a Xp11.2 translocation/TFE3 fusion renal cell carcinoma metastatic to the lung: report of a case and review of the literature. Diagn. Cytopathol. 34(11), 751–756 (2006)CrossRefGoogle Scholar
  21. 21.
    Shah, S.P., et al.: Frequent mutations of genes encoding ubiquitin-mediated proteolysis pathway components in clear cell renal cell carcinoma. Nature genet. 44(1), 17–19 (2012)CrossRefGoogle Scholar
  22. 22.
    Sjöblom, T., et al.: The consensus coding sequences of human breast and colorectal cancers. Science 314(5797), 268–274 (2006)CrossRefGoogle Scholar
  23. 23.
    Söderman, S.P., et al.: Analysis of single nucleotide polymorphisms in the region of CLDN2-MORC4 in relation to inflammatory bowel disease. World J. Gastroenterol. 19(30), 4935–4943 (2013)CrossRefGoogle Scholar
  24. 24.
    Timofeeva, M.N., et al.: Influence of common genetic variation on lung cancer risk: meta-analysis of 14,900 cases and 29,485 controls. Hum. Mol. Genet. 21, 4980–4995 (2012)CrossRefGoogle Scholar
  25. 25.
    To, M.D., Wong, C.E., Karnezis, A.N., et al.: KRAS regulatory elements and exon 4A determine mutation specificity in lung cancer. Nature Genet. 40, 1240–1244 (2008)CrossRefGoogle Scholar
  26. 26.
    Ulanet, D.B., et al.: Mesenchymal phenotype predisposes lung cancer cells to impaired proliferation and redox stress in response to glutaminase inhibition. PLoS ONE 9(12), e115144 (2014).  https://doi.org/10.1371/journal.pone.0115144CrossRefGoogle Scholar
  27. 27.
    Varela, I., et al.: Exome sequencing identifies frequent mutation of the SWI/SNF complex gene PBRM1 in renal carcinoma. Nature 469(7331), 539–542 (2011)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.University of MichiganAnn ArborUSA

Personalised recommendations