Abstract
Cancer is a large family of genetic diseases that involve abnormal cell growth. Genetic mutations can vary from one patient to another. Therefore, personalized precision is required to increase the reliability of prognostic predictions and the benefit of therapeutic decisions. The most important issues concerning gene analysis are strong noise, high dimensionality and minor differences between observations. Therefore, parallel coordinates have been also used in order to better analyze the data manifold and select the more meaningful genes. Later, it has been chosen to implement a supervised feature selection algorithm in order to work on a subset of features only avoiding the high dimensional problem. Other traditional methods of dimensionality reduction and projection are here used on subset features in order to better analyze the data manifold and select the more meaningful gene. Previous studies show that mutations of genes NRAS, KRAS and BRAF lead to a dramatic decrease in therapeutic effectiveness. The following analysis tries to explore in an unconventional way gene expressions over tissues which are wild type regarding to these genes.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
de Bono, J.S., Ashworth, A.: Translating cancer research into targeted therapeutics. Nature 467, 543–549 (2010)
Hidalgo, M., et al.: Patient-derived Xenograft models: an emerging platform for translational cancer research. Cancer Discov. 4, 998–1013 (2014)
Tentler, J.J., et al.: Patient-derived tumour xenografts as models for oncology drug development. Nat. Rev. Clin. Oncol. 9, 338–350 (2012)
Byrne, A.T., et al.: Interrogating open issues in cancer precision medicine with patient derived xenografts. Nat. Rev. Cancer (2017). https://doi.org/10.1038/nrc.2016.140
Bertotti, A., et al.: A molecularly annotated platform of patient- derived xenografts (‘xenopatients’) identifies HER2 as an effective therapeutic target in cetuximab-resistant colorectal cancer. Cancer Discov. 1, 508–523 (2011)
Zanella, E.R., et al.: IGF2 is an actionable target that identifies a distinct subpopulation of colorectal cancer patients with marginal response to anti‐EGFR therapies. Sci. Transl. Med. 7(272), 272ra12
Bertotti, A., et al.: The genomic landscape of response to EGFR blockade in colorectal cancer. Nature 526, 263–267 (2015)
Sartore-Bianchi, A., et al.: Dual-targeted therapy with trastuzumab and lapatinib in treatment-refractory, KRAS codon 12/13 wild-type, HER2-positive metastatic colorectal cancer (HERACLES): a proof-of-concept, multicentre, open-label, phase 2 trial. Lancet Oncol. 17, 738–746 (2016)
Illumina: Array‐based gene expression analysis. Data Sheet Gene Expressions at http://res.illumina.com/documents/products/datasheets/datasheet_gene_exp_analysis.pdf (2011)
Wegman, E.J.: Hyperdimensional data analysis using parallel coordinates. J. Am. Stat. Assoc. 85(411), 664–675 (1990)
Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. 224–227 (1979)
Demartines, P., Hérault, J.: Curvilinear component analysis: a self-organizing neural network for nonlinear mapping of data sets. IEEE Trans. Neural Netw. 8(1), 148–154 (1997)
Tibshirani, R.: Regression shrinkage and selection via the lasso. J. Roy. Stat. Soc. (1996)
USA National Center for Biotechnology Information at https://www.ncbi.nlm.nih.gov/
Human Protein Atlas available from https://www.proteinatlas.org/
Christopher, M.B.: Pattern Recognition and Machine Learning. Springer (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer International Publishing AG, part of Springer Nature
About this chapter
Cite this chapter
Barbiero, P., Bertotti, A., Ciravegna, G., Cirrincione, G., Pasero, E., Piccolo, E. (2019). Supervised Gene Identification in Colorectal Cancer. In: Esposito, A., Faundez-Zanuy, M., Morabito, F., Pasero, E. (eds) Quantifying and Processing Biomedical and Behavioral Signals. WIRN 2017 2017. Smart Innovation, Systems and Technologies, vol 103. Springer, Cham. https://doi.org/10.1007/978-3-319-95095-2_23
Download citation
DOI: https://doi.org/10.1007/978-3-319-95095-2_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-95094-5
Online ISBN: 978-3-319-95095-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)