Literature DB >> 21398405

Functional synergies yet distinct modulators affected by genetic alterations in common human cancers.

Marina Bessarabova1, Olga Pustovalova, Weiwei Shi, Tatiana Serebriyskaya, Alex Ishkin, Kornelia Polyak, Victor E Velculescu, Tatiana Nikolskaya, Yuri Nikolsky.   

Abstract

An important general concern in cancer research is how diverse genetic alterations and regulatory pathways can produce common signaling outcomes. In this study, we report the construction of cancer models that combine unique regulation and common signaling. We compared and functionally analyzed sets of genetic alterations, including somatic sequence mutations and copy number changes, in breast, colon, and pancreatic cancer and glioblastoma that had been determined previously by global exon sequencing and SNP (single nucleotide polymorphism) array analyses in multiple patients. The genes affected by the different types of alterations were mostly unique in each cancer type, affected different pathways, and were connected with different transcription factors, ligands, and receptors. In our model, we show that distinct amplifications, deletions, and sequence alterations in each cancer resulted in common signaling pathways and transcription regulation. In functional clustering, the impact of the type of alteration was more pronounced than the impact of the kind of cancer. Several pathways such as TGF-β/SMAD signaling and PI3K (phosphoinositide 3-kinase) signaling were defined as synergistic (affected by different alterations in all four cancer types). Despite large differences at the genetic level, all data sets interacted with a common group of 65 "universal cancer genes" (UCG) comprising a concise network focused on proliferation/apoptosis balance and angiogenesis. Using unique nodal regulators ("overconnected" genes), UCGs, and synergistic pathways, the cancer models that we built could combine common signaling with unique regulation. Our findings provide a novel integrated perspective on the complex signaling and regulatory networks that underlie common human cancers. ©2011 AACR

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Year:  2011        PMID: 21398405     DOI: 10.1158/0008-5472.CAN-10-3038

Source DB:  PubMed          Journal:  Cancer Res        ISSN: 0008-5472            Impact factor:   12.701


  4 in total

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Journal:  Integr Biol (Camb)       Date:  2012-07-18       Impact factor: 2.192

2.  Predicting relapse prior to transplantation in chronic myeloid leukemia by integrating expert knowledge and expression data.

Authors:  K Y Yeung; T A Gooley; A Zhang; A E Raftery; J P Radich; V G Oehler
Journal:  Bioinformatics       Date:  2012-01-31       Impact factor: 6.937

3.  JARID1B is a luminal lineage-driving oncogene in breast cancer.

Authors:  Shoji Yamamoto; Zhenhua Wu; Hege G Russnes; Shinji Takagi; Guillermo Peluffo; Charles Vaske; Xi Zhao; Hans Kristian Moen Vollan; Reo Maruyama; Muhammad B Ekram; Hanfei Sun; Jee Hyun Kim; Kristopher Carver; Mattia Zucca; Jianxing Feng; Vanessa Almendro; Marina Bessarabova; Oscar M Rueda; Yuri Nikolsky; Carlos Caldas; X Shirley Liu; Kornelia Polyak
Journal:  Cancer Cell       Date:  2014-06-16       Impact factor: 31.743

4.  Response and resistance to BET bromodomain inhibitors in triple-negative breast cancer.

Authors:  Shaokun Shu; Charles Y Lin; Housheng Hansen He; Robert M Witwicki; Doris P Tabassum; Justin M Roberts; Michalina Janiszewska; Sung Jin Huh; Yi Liang; Jeremy Ryan; Ernest Doherty; Hisham Mohammed; Hao Guo; Daniel G Stover; Muhammad B Ekram; Jonathan Brown; Clive D'Santos; Ian E Krop; Deborah Dillon; Michael McKeown; Christopher Ott; Jun Qi; Min Ni; Prakash K Rao; Melissa Duarte; Shwu-Yuan Wu; Cheng-Ming Chiang; Lars Anders; Richard A Young; Eric Winer; Antony Letai; William T Barry; Jason S Carroll; Henry Long; Myles Brown; X Shirley Liu; Clifford A Meyer; James E Bradner; Kornelia Polyak
Journal:  Nature       Date:  2016-01-06       Impact factor: 49.962

  4 in total

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