research-article

The Purpose for the HER2 Positive Breast Cancer Treatment Research

Authors:

Le ZhangAuthors Info & Claims

ICBBB '17: Proceedings of the 7th International Conference on Bioscience, Biochemistry and Bioinformatics

Pages 34 - 37

https://doi.org/10.1145/3051166.3051172

Published: 21 January 2017 Publication History

Abstract

The epidermal growth factor receptor (HER2) positive breast cancer is malignant and hard to cure. The clinical application of targeted cancer treatment can significantly improve the treatment efficiency for the HER2 positive breast cancer patients. This propose briefly introduces the HER2 positive breast cancer, gene chip pretreatment and breast cancer targeted therapy at the start. And then, it proposes a process to identify several tumor drivers to predict the survival and drug sensitivity of patients as well as carry out cancer targeted therapy by mining gene chip data.

References

[1]

P. Auvinen, R. Tammi, J. Parkkinen, M. Tammi, U. Ågren, R. Johansson, et al., "Hyaluronan in Peritumoral Stroma and Malignant Cells Associates with Breast Cancer Spreading and Predicts Survival," American Journal of Pathology, vol. 156, pp. 529--536, 2000.

[2]

C. K. Kuhl, S. Schrading, C. C. Leutner, N. Morakkabati-Spitz, E. Wardelmann, R. Fimmers, et al., "Mammography, breast ultrasound, and magnetic resonance imaging for surveillance of women at high familial risk for breast cancer," Journal of Clinical Oncology Official Journal of the American Society of Clinical Oncology, vol. 23, pp. 8469--76, 2005.

[3]

R. R. Silva, R. Bascioni, S. Rossini, L. Zuccatosta, R. Mattioli, A. Pilone, et al., "HSP90 Inhibition is Effective in Breast Cancer: A Phase 2 Trial of Tanespimycin (17AAG) plus Trastuzumab in Patients with HER2-Positive Metastatic Breast Cancer Progressing on Trastuzumab," Clinical Cancer Research An Official Journal of the American Association for Cancer Research, vol. 17, pp. 5132--9, 2011.

[4]

A. Darlix, P. J. Lamy, E. Lopez-Crapez, A. L. Braccini, N. Firmin, G. Romieu, et al., "Serum HER2 extra-cellular domain, S100ß and CA 15-3 levels are independent prognostic factors in metastatic breast cancer patients," Bmc Cancer, vol. 16, pp. 1--14, 2016.

[5]

Z. Latif, A. D. Watters, I. Dunn, K. Grigor, M. A. Underwood, and J. M. S. Bartlett, "HER2/neu gene amplification and protein overexpression in G3 pT2 transitional cell carcinoma of the bladder: a role for anti-HER2 therapy?," European Journal of Cancer, vol. 40, pp. 56--63, 2004

[6]

M. G. Von, B. M. Müller, S. Loibl, J. Budczies, C. Hanusch, S. Darb-Esfahani, et al., "Cytoplasmic poly(adenosine diphosphate-ribose) polymerase expression is predictive and prognostic in patients with breast cancer treated with neoadjuvant chemotherapy," Journal of Clinical Oncology Official Journal of the American Society of Clinical Oncology, vol. 29, pp. 2150--2157, 2011.

[7]

C. M. Schlotter, U. Vogt, H. Allgayer, and B. Brandt, "Molecular targeted therapies for breast cancer treatment," Breast Cancer Research Bcr, vol. 10, p.: 211., 2008.

[8]

C. K. Ng, L. G. Martelotto, A. Gauthier, H. C. Wen, S. Piscuoglio, R. S. Lim, et al., "Intra-tumor genetic heterogeneity and alternative driver genetic alterations in breast cancers with heterogeneous HER2 gene amplification," Genome Biology, vol. 16, pp. 1--21, 2015.

[9]

Y. J. Song, S. H. Shin, J. S. Cho, M. H. Park, J. H. Yoon, and Y. J. Jegal, "The role of lymphovascular invasion as a prognostic factor in patients with lymph node-positive operable invasive breast cancer," Oncogene, vol. 14, pp. 198--203, 2011.

[10]

R. N. Van Gelder, M. E. Von Zastrow, J. D. Barchas, and J. H. Eberwine, "Multi-gene expression profile," ed: US, 2006.

[11]

S. Mook, H. Bonnefoi, G. Pruneri, D. Larsimont, J. Jaskiewicz, M. D. Sabadell, et al., "Daily clinical practice of fresh tumour tissue freezing and gene expression profiling; logistics pilot study preceding the MINDACT trial," European Journal of Cancer, vol. 45, pp. 1201--8, 2009.

[12]

M. Kong, J. Wang, and B. Li, "Application of phage display technology in targeted therapy of breast cancer," The Chinese-German Journal of Clinical Oncology, vol. 12, pp. 246--248, 2013.

[13]

E. A. S. M. PhD and C. P. Rodriguez, "Genomic medicine and targeted therapy for solid tumors," Journal of Surgical Oncology, vol. 111, pp. 38--42, 2015.

[14]

K. Strasser-Weippl, N. Horick, I. E. Smith, J. O'Shaughnessy, B. Ejlertsen, F. Boyle, et al., "Long-term hazard of recurrence in HER2+ breast cancer patients untreated with anti-HER2 therapy," Breast Cancer Research Bcr, vol. 17, pp. 1--7, 2015.

[15]

P. H. Guzzi and M. Cannataro, "Micro-Analyzer: Automatic preprocessing of Affymetrix microarray data," Computer Methods & Programs in Biomedicine, vol. 111, pp. 402--9, 2013.

Digital Library

[16]

W. G. Alvord, J. A. Roayaei, O. A. Quiñones, and K. T. Schneider, "A microarray analysis for differential gene expression in the soybean genome using Bioconductor and R," Briefings in Bioinformatics, vol. 8, pp. 415--31, 2007.

[17]

C. Taslim, K. Huang, T. Huang, and S. Lin, "Analyzing ChIP-seq data: preprocessing, normalization, differential identification, and binding pattern characterization," Methods in Molecular Biology, vol. 802, pp. 275--91, 2012.

[18]

S. Lee, S. Stewart, I. Nagtegaal, J. Luo, Y. Wu, G. Colditz, et al., "Differentially Expressed Genes Regulating the Progression of Ductal Carcinoma In Situ to Invasive Breast Cancer," Cancer Research, vol. 72, pp. 4574--86, 2011.

[19]

M. J. V. D. Vijver, Y. D. He, L. J. V. T. Veer, H. Dai, A. A. M. Hart, D. W. Voskuil, et al., "A Gene-Expression Signature as a Predictor of Survival in Breast Cancer," New England Journal of Medicine, vol. 347, pp. 1999--2009, 2002.

[20]

I. U. E. Nesterov and A. S. Nemirovskii, "Interior Point Polynomial Algorithms in Convex Programming, SAM," Studies in Applied Mathematics Philadelphia Siam, vol. 13, 2015.

[21]

K. Pauole, K. Madole, J. Garhammer, M. Lacourse, and R. Rozenek, "Reliability and Validity of the T-Test as a Measure of Agility, Leg Power, and Leg Speed in College-Aged Men and Women," Journal of Strength & Conditioning Research, vol. 14, pp. 443--450, 2000.

[22]

M. Fan, P. S. Yan, C. Hartman-Frey, L. Chen, H. Paik, S. L. Oyer, et al., "Diverse gene expression and DNA methylation profiles correlate with differential adaptation of breast cancer cells to the antiestrogens tamoxifen and fulvestrant," Cancer Research, vol. 66, pp. 11954--66, 2006.

[23]

B. S. A. Macskassy, A. Banerjee, B. D. Davison, and H. Hirsh, "Groups in Data: An Introduction to Cluster Analysis," in Kdd, 2014.

[24]

K. S. Pollard and M. J. V. D. Laan, "Cluster Analysis of Genomic Data with Applications in R," Bepress, pp. 309--311, 2005.

Cited By

Chen CTsai MShih Y(2018)Early Prediction of Ventilator Weaning with Decision Tree AnalysisProceedings of the 2018 8th International Conference on Bioscience, Biochemistry and Bioinformatics10.1145/3180382.3180398(99-103)Online publication date: 18-Jan-2018
https://dl.acm.org/doi/10.1145/3180382.3180398
Yuvarajan VSathiyabhama BUdhaya Kumar S(undefined)A Comparison of Machine Learning Techniques for Survival Prediction in Breast Cancer Gene Expression DataSSRN Electronic Journal10.2139/ssrn.3126112
https://doi.org/10.2139/ssrn.3126112

The Purpose for the HER2 Positive Breast Cancer Treatment Research
1. Applied computing
  1. Life and medical sciences

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ICBBB '17: Proceedings of the 7th International Conference on Bioscience, Biochemistry and Bioinformatics

January 2017

72 pages

ISBN:9781450348324

DOI:10.1145/3051166

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 21 January 2017

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ICBBB '17

ICBBB '17: 7th International Conference on Bioscience, Biochemistry and Bioinformatics

January 21 - 23, 2017

Bangkok, Thailand

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Cited By

Chen CTsai MShih Y(2018)Early Prediction of Ventilator Weaning with Decision Tree AnalysisProceedings of the 2018 8th International Conference on Bioscience, Biochemistry and Bioinformatics10.1145/3180382.3180398(99-103)Online publication date: 18-Jan-2018
https://dl.acm.org/doi/10.1145/3180382.3180398
Yuvarajan VSathiyabhama BUdhaya Kumar S(undefined)A Comparison of Machine Learning Techniques for Survival Prediction in Breast Cancer Gene Expression DataSSRN Electronic Journal10.2139/ssrn.3126112
https://doi.org/10.2139/ssrn.3126112

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