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Cancers 2019, 11(2), 256; https://doi.org/10.3390/cancers11020256

Reverse Engineering Cancer: Inferring Transcriptional Gene Signatures from Copy Number Aberrations with ICAro

1
Department of Paediatric Haematology, IRCCS Ospedale Pediatrico Bambino Gesù, 00146 Rome, Italy
2
SAFU Unit, IRCCS Regina Elena National Cancer Institute, 00144 Rome, Italy
*
Author to whom correspondence should be addressed.
These authors contributed equally.
Received: 28 December 2018 / Revised: 7 February 2019 / Accepted: 13 February 2019 / Published: 22 February 2019
(This article belongs to the Special Issue Application of Bioinformatics in Cancers)
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Abstract

The characterization of a gene product function is a process that involves multiple laboratory techniques in order to silence the gene itself and to understand the resulting cellular phenotype via several omics profiling. When it comes to tumor cells, usually the translation process from in vitro characterization results to human validation is a difficult journey. Here, we present a simple algorithm to extract mRNA signatures from cancer datasets, where a particular gene has been deleted at the genomic level, ICAro. The process is implemented as a two-step workflow. The first one employs several filters in order to select the two patient subsets: the inactivated one, where the target gene is deleted, and the control one, where large genomic rearrangements should be absent. The second step performs a signature extraction via a Differential Expression analysis and a complementary Random Forest approach to provide an additional gene ranking in terms of information loss. We benchmarked the system robustness on a panel of genes frequently deleted in cancers, where we validated the downregulation of target genes and found a correlation with signatures extracted with the L1000 tool, outperforming random sampling for two out of six L1000 classes. Furthermore, we present a use case correlation with a published transcriptomic experiment. In conclusion, deciphering the complex interactions of the tumor environment is a challenge that requires the integration of several experimental techniques in order to create reproducible results. We implemented a tool which could be of use when trying to find mRNA signatures related to a gene loss event to better understand its function or for a gene-loss associated biomarker research. View Full-Text
Keywords: transcriptional signatures; copy number variation; copy number aberration; TCGA mining; cancer CRISPR; firehose; gene signature extraction; gene loss biomarkers; gene inactivation biomarkers; biomarker discovery transcriptional signatures; copy number variation; copy number aberration; TCGA mining; cancer CRISPR; firehose; gene signature extraction; gene loss biomarkers; gene inactivation biomarkers; biomarker discovery
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Angeli, D.; Fanciulli, M.; Pallocca, M. Reverse Engineering Cancer: Inferring Transcriptional Gene Signatures from Copy Number Aberrations with ICAro. Cancers 2019, 11, 256.

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