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Integrative Statistical Inferences for Drug Sensitivity Biomarkers in Cancer

Ehsan Ullah, Saila Shama, Noora Al Muftah, Ian Richard Thompson, Reda Rawi, Raghvendra Mall, Halima Bensmail
doi: https://doi.org/10.1101/194670
Ehsan Ullah
1Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
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Saila Shama
1Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
2Faculty of Applied Science and Engineering, Engineering Science, University of Toronto, 40 Saint George Street, Toronto, Canada M4C 3C2
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Noora Al Muftah
1Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
3Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA
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Ian Richard Thompson
4Qatar Biomedical Research Institute, Hamad Bin Khalifa University, Doha, Qatar
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Reda Rawi
1Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
5Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health,Bethesda, MD 20814, USA
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Raghvendra Mall
1Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
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Halima Bensmail
1Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
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Abstract

Personal medicine has been associated with different patient responses to different anti-cancer therapies. Recently, scientists are looking not only for new biomarkers associated with a disease such as cancer but also identifying biomarkers that predict patients who are most likely to respond to a particular cancer treatment. Orderly endeavors to relate cancer mutational information with biological conditions may encourage the interpretation of somatic mutation indexes into significant biomarkers for patient stratification.

We have screened and incorporated a board of cancer cell lines from Genomics of Drug Sensitivity in Cancer (GDSC) database to recognize genomic highlights related with drug sensitivity. We used mutation, DNA copy number variation, and gene expression information from Catalogue of Somatic Mutations in Cancer (COSMIC) and The Cancer Genome ATLAS (TCGA) for cell lines with their reactions to associate focused and cytotoxic treatments with approved drugs and drugs under clinical and preclinical examination.

We discovered mutated cancer genes were related with cell reaction to, mostly accessible, cancer medications and some mutated genes were related with sensitivity to an expansive scope of therapeutic agents. By connecting drug activity to the useful many-sided quality of cancer genomes, efficient pharmacogenomic profiling in tumor cell lines gives an intense biomarker revelation stage to guide balanced malignancy remedial systems.

Our study highlights that gene ANK2 amplification, and gene CELSER1 amplification and deletion are highly associated with anti-leukemic drug candidate LFM-A13. It also highlights that gene NUP214 and ROS1 copy number and gene NSD1 amplification are as a group highly associated with the parkinson drug Nilotinib. Finally, our study confirms that gene BRAF mutation is interacting with the BRAF-selective inhibitors drugs PLX4720 and SB590885. On the other hand, our study provides two open source analysis packages: bastah for the multitask-association analysis, and UNGeneAnno for automatic annotation of the variants.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted September 27, 2017.
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Integrative Statistical Inferences for Drug Sensitivity Biomarkers in Cancer
Ehsan Ullah, Saila Shama, Noora Al Muftah, Ian Richard Thompson, Reda Rawi, Raghvendra Mall, Halima Bensmail
bioRxiv 194670; doi: https://doi.org/10.1101/194670
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Integrative Statistical Inferences for Drug Sensitivity Biomarkers in Cancer
Ehsan Ullah, Saila Shama, Noora Al Muftah, Ian Richard Thompson, Reda Rawi, Raghvendra Mall, Halima Bensmail
bioRxiv 194670; doi: https://doi.org/10.1101/194670

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