e-journal

Evolution and Controllability of Cancer Networks: A Boolean Perspective

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Cancer forms a robust system capable of maintaining stable functioning (cell sustenance and proliferation) despite perturbations. Cancer progresses as stages over time typically with increasing aggressiveness and worsening prognosis. Characterizing these stages and identifying the genes driving transitions between them is critical to understand cancer progression and
to develop effective anti-cancer therapies. In this work, we propose a novel model for the ‘cancer system’ as a Boolean state space in which a Boolean network, built from protein-interaction and gene-expression data from different stages of cancer, transits between Boolean satisfiability states by “editing” interactions and “flipping” genes. Edits reflect rewiring of the PPI network while flipping of genes reflect activation or silencing of genes between stages. We formulate a minimization problem MIN FLIP to identify these genes driving the transitions. The application of our model (called BoolSpace) on three case studies—pancreatic and breast tumours in human and post spinal-cord injury (SCI) in rats—reveals valuable insights into the phenomenon of cancer progression: (i) interactions involved in core cell-cycle and DNA-damage repair pathways are significantly rewired in tumours, indicating significant impact to key genome-stabilizing mechanisms; (ii) several of the genes flipped are serine/threonine kinases which act as biological switches, reflecting cellular switching
mechanisms between stages; and (iii) different sets of genes are flipped during the initial and final stages indicating a pattern to tumour progression. Based on these results, we hypothesize that robustness of cancer partly stems from “passing of the baton” between genes at different stages—genes from different biological processes and/or cellular components are involved in different stages of tumour progression thereby allowing tumour cells to evade targeted therapy, and therefore an effective therapy should target a “cover set” of these genes. A C/C++ implementation of BoolSpace is freely available at: http://www.bioinformatics.org.au/tools-data.
Index Terms—Cancer networks, cancer evolution, cancer robustness, strategy for targeted therapy

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Informasi Detail

Judul Seri

IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS

No. Panggil

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Penerbit

New York : IEEE., 2014

Deskripsi Fisik

IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, VOL. 11, NO. 1, JANUARY/FEBRUARY 2014

Bahasa

English

ISBN/ISSN

1545-5963

Klasifikasi

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Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

VOL. 11, NO. 1, JANUARY/FEBRUARY 2014

Subjek

TEKNIK

Info Detail Spesifik

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Pernyataan Tanggungjawab

Yuli/Agus

Versi lain/terkait

Lampiran Berkas

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