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Abstract C154: Integrated genomics approach to identify driver alterations

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Abstract Several statistical tools have been developed to identify genes mutated at rates significantly higher than the background, indicative of positive selection, involving large sample cohort studies. However, studies involving relatively smaller sample sizes or single data-type genome wide analysis are inherently restricted due to their limited statistical power to identify low frequency genetic variation. We undertook integrated copy number, mutation and expression analyses of head and neck cancer cell lines assessing multiple levels of biological regulation in individual samples to enrich for driver events affected by two or more alterations. Besides identifying TP53, PTEN, HRAS and MET as major altered HNSCC hallmark genes, this analysis uncovered 34 novel candidate tumor suppressor and oncogenes altered by at least two types of alterations. Of these, we find a driver heterozygous truncating mutation in Nuclear receptor binding protein NRBP1, a pseudokinase, identical to that reported in other cancers and similar to activating mutant alleles in non-human model organisms. In addition to demonstrating the ability of integrated analyses to uncover biologically important genetic variation in relatively small datasets, we report for the first time, mutant NRBP1 is oncogenic when ectopically expressed in NIH 3T3 cells. We further show that knockdown of NRBP1 in oral carcinoma cell lines bearing such NRBP1 mutations inhibits transformation and survival. In over all, this approach could be of immense value for studies involving fewer or rare clinical specimen that are otherwise inherently restrictive due to the limited statistical power to detect alterations existing at lower frequency. Citation Format: Pratik Chandrani, Pawan Upadhyay, Prajish Iyer, Mayur Tanna, Madhur Shetty, Raghuram Venkata Gorantala, Ninad Oak, Ankita Singh, Rohan Chaubal, Manoj Ramteke, Sudeep Gupta, Amit Dutt. Integrated genomics approach to identify driver alterations. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2015 Nov 5-9; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl 2):Abstract nr C154.
Title: Abstract C154: Integrated genomics approach to identify driver alterations
Description:
Abstract Several statistical tools have been developed to identify genes mutated at rates significantly higher than the background, indicative of positive selection, involving large sample cohort studies.
However, studies involving relatively smaller sample sizes or single data-type genome wide analysis are inherently restricted due to their limited statistical power to identify low frequency genetic variation.
We undertook integrated copy number, mutation and expression analyses of head and neck cancer cell lines assessing multiple levels of biological regulation in individual samples to enrich for driver events affected by two or more alterations.
Besides identifying TP53, PTEN, HRAS and MET as major altered HNSCC hallmark genes, this analysis uncovered 34 novel candidate tumor suppressor and oncogenes altered by at least two types of alterations.
Of these, we find a driver heterozygous truncating mutation in Nuclear receptor binding protein NRBP1, a pseudokinase, identical to that reported in other cancers and similar to activating mutant alleles in non-human model organisms.
In addition to demonstrating the ability of integrated analyses to uncover biologically important genetic variation in relatively small datasets, we report for the first time, mutant NRBP1 is oncogenic when ectopically expressed in NIH 3T3 cells.
We further show that knockdown of NRBP1 in oral carcinoma cell lines bearing such NRBP1 mutations inhibits transformation and survival.
In over all, this approach could be of immense value for studies involving fewer or rare clinical specimen that are otherwise inherently restrictive due to the limited statistical power to detect alterations existing at lower frequency.
Citation Format: Pratik Chandrani, Pawan Upadhyay, Prajish Iyer, Mayur Tanna, Madhur Shetty, Raghuram Venkata Gorantala, Ninad Oak, Ankita Singh, Rohan Chaubal, Manoj Ramteke, Sudeep Gupta, Amit Dutt.
Integrated genomics approach to identify driver alterations.
[abstract].
In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2015 Nov 5-9; Boston, MA.
Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl 2):Abstract nr C154.

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