Mutational oncogenic signatures on structurally resolved protein interacting interfaces

2015 ◽  
Author(s):  
Luz Garcia-Alonso ◽  
Joaquin Dopazo
Keyword(s):  
Cancer Patients ◽  
Protein Interactions ◽  
Three Dimensional ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Protein Protein Interactions ◽  
Cancer Occurrence ◽  
Domain Interactions ◽  
Cancer Mutations ◽  
Cancer Genome Atlas

The importance of the context of interactions in the proteins mutated in cancer is long known. However, our knowledge on how mutations affecting to protein-protein interactions (PPIs) are related to cancer occurrence and progression is still poor. Here, we extracted the missense somatic mutations from 5920 cancer patients of 33 different cancer types, taken from the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), and mapped them onto a structurally resolved interactome, which integrates three-dimensional atomic-level models of domain-domain interactions with experimentally determined PPIs, involving a total of 7580 unique interacting domains that participate in 13160 interactions connecting 4996 proteins. We observed that somatic nonsynonymous mutations tend to concentrate in ordered regions of the affected proteins and, within these, they have a clear preference for the interacting interfaces. Also, we have identified more than 250 interacting interfaces candidate to drive cancer. Examples demonstrate how mutations in the interacting interfaces are strongly associated with patient survival time, while similar mutations in other areas of the same proteins lack this association. Our results suggest that the perturbation caused by cancer mutations in protein interactions is an important factor in explaining the heterogeneity between cancer patients.

scholarly journals Small-molecule binding sites to explore protein–protein interactions in the cancer proteome

2016 ◽  
Vol 12 (10) ◽  
pp. 3067-3087 ◽  
Author(s):  
David Xu ◽  
Shadia I. Jalal ◽  
George W. Sledge ◽  
Samy O. Meroueh
Keyword(s):  
Small Molecule ◽  
Protein Interactions ◽  
Binding Sites ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Mrna Levels ◽  
Protein Protein Interactions ◽  
Poor Survival ◽  
Cancer Genome Atlas ◽  
Genome Atlas

The Cancer Genome Atlas (TCGA) offers an unprecedented opportunity to identify small-molecule binding sites on proteins with overexpressed mRNA levels that correlate with poor survival.

scholarly journals Japanese version of The Cancer Genome Atlas, JCGA, established using fresh frozen tumors obtained from 5143 cancer patients

2020 ◽  
Vol 111 (2) ◽  
pp. 687-699 ◽  
Author(s):  
Takeshi Nagashima ◽  
Ken Yamaguchi ◽  
Kenichi Urakami ◽  
Yuji Shimoda ◽  
Sumiko Ohnami ◽  
...  
Keyword(s):  
Cancer Patients ◽  
Japanese Version ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Fresh Frozen ◽  
Cancer Genome Atlas ◽  
Genome Atlas

scholarly journals Network-aware mutation clustering of cancer

2018 ◽  
Author(s):  
Swetansu Pattnaik ◽  
Catherine Vacher ◽  
Hong Ching Lee ◽  
Warren Kaplan ◽  
David M. Thomas ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Cancer Biology ◽  
Difficult Problem ◽  
The Cancer Genome Atlas ◽  
Precision Oncology ◽  
Protein Protein Interactions ◽  
Dna Mutation ◽  
Mutation Profile ◽  
Cancer Genome Atlas ◽  
Protein Modules

AbstractThe grouping of cancers across tissue boundaries is central to precision oncology, but remains a difficult problem. Here we present EPICC (Experimental Protein Interaction Clustering of Cancer), a novel technique to cluster cancer patients based on DNA mutation profile, that leverages knowledge of protein-protein interactions to reduce noise and amplify biological signal. We applied EPICC to data from The Cancer Genome Atlas (TCGA), and both recapitulated known cancer clusterings, and identified new cross-tissue cancer groups that may indicate novel cancer molecular subtypes. Investigation of EPICC clusters revealed new protein modules which were recurrently mutated across cancers, and indicate new avenues for research into cancer biology. EPICC leveraged the Vodafone DreamLab citizen science platform, and we provide our results as a resource for researchers to investigate the role of protein modules in cancer.

scholarly journals Prognostic values of GMPS, PR, CD40, and p21 in ovarian cancer

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6301 ◽  
Author(s):  
Ping Wang ◽  
Zengli Zhang ◽  
Yujie Ma ◽  
Jun Lu ◽  
Hu Zhao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Cancer Patients ◽  
Differential Expression ◽  
Cancer Progression ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Progression Model ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Early detection and prediction of prognosis and treatment responses are all the keys in improving survival of ovarian cancer patients. This study profiled an ovarian cancer progression model to identify prognostic biomarkers for ovarian cancer patients. Mouse ovarian surface epithelial cells (MOSECs) can undergo spontaneous malignant transformation in vitro cell culture. These were used as a model of ovarian cancer progression for alterations in gene expression and signaling detected using the Illumina HiSeq2000 Next-Generation Sequencing platform and bioinformatical analyses. The differential expression of four selected genes was identified using the gene expression profiling interaction analysis (http://gepia.cancer-pku.cn/) and then associated with survival in ovarian cancer patients using the Cancer Genome Atlas dataset and the online Kaplan–Meier Plotter (http://www.kmplot.com) data. The data showed 263 aberrantly expressed genes, including 182 up-regulated and 81 down-regulated genes between the early and late stages of tumor progression in MOSECs. The bioinformatic data revealed four genes (i.e., guanosine 5′-monophosphate synthase (GMPS), progesterone receptor (PR), CD40, and p21 (cyclin-dependent kinase inhibitor 1A)) to play an important role in ovarian cancer progression. Furthermore, the Cancer Genome Atlas dataset validated the differential expression of these four genes, which were associated with prognosis in ovarian cancer patients. In conclusion, this study profiled differentially expressed genes using the ovarian cancer progression model and identified four (i.e., GMPS, PR, CD40, and p21) as prognostic markers for ovarian cancer patients. Future studies of prospective patients could further verify the clinical usefulness of this four-gene signature.

A structural network associated with the kallikrein-kinin and renin-angiotensin systems

2010 ◽  
Vol 391 (4) ◽  
Author(s):  
Veronika Stoka ◽  
Vito Turk
Keyword(s):  
Protein Interactions ◽  
Three Dimensional ◽  
Type I ◽  
Protein Protein Interactions ◽  
Renin Angiotensin ◽  
Protein Protein Interaction ◽  
Protein Interfaces ◽  
Structural Network ◽  
Domain Interactions ◽  
Fibronectin Type

Abstract The kallikrein-kinin and renin-angiotensin (KKS-RAS) systems represent two highly regulated proteolytic systems that are involved in several physiological and pathological processes. Although their protein-protein interactions can be studied using experimental approaches, it is difficult to differentiate between direct physical interactions and functional associations, which do not involve direct atomic contacts between macromolecules. This information can be obtained from an atomic-resolution characterization of the protein interfaces. As a result of this, various three-dimensional-based protein-protein interaction databases have become available. To gain insight into the multilayered interaction of the KKS-RAS systems, we present a protein network that is built up on three-dimensional domain-domain interactions. The essential domains that link these systems are as follows: Cystatin, Peptidase_C1, Thyroglobulin_1, Insulin, CIMR (Cation-independent mannose-6-phosphate receptor repeat), fn2 (Fibronectin type II domain), fn1 (Fibronectin type I domain), EGF, Trypsin, and Serpin. We found that the CIMR domain is located at the core of the network, thus connecting both systems. From the latter, all domain interactors up to level 4 were retrieved, thus displaying a more comprehensive representation of the KKS-RAS structural network.

scholarly journals Survival analysis of recurrent events on prostate cancer: facts from cancer genome

2018 ◽  
Vol 51 (2) ◽  
pp. 145-164
Author(s):  
Farhin Rahman ◽  
Munni Begum
Keyword(s):  
Prostate Cancer ◽  
Risk Factors ◽  
Cancer Patients ◽  
Genetic Risk ◽  
Recurrent Events ◽  
Genetic Risk Factors ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Time To Event ◽  
Cancer Genome Atlas

Many diseases and clinical outcomes may recur to the same patient. These events are termed as recurrent events. Several statistical models have been proposed in the literature to analyze recurrent events. In this study, we identify the clinical and the genetic risk factors for recurring tumors among prostate cancer patients from The Cancer Genome Atlas (TCGA). Five statistical approaches for modeling recurrent time-to-event are implemented to identify and to determine the effects of the clinical and the genetic risk factors of tumor recurrence. In particular, we consider Andersen-Gill (A-G), Wei-Lin-Weissfeld (WLW), Prentice-Williams-Peterson Total Time (PWP-TT), Prentice-Williams-Peterson Gap Time (PWPGT) and Frailty models. We present and discuss the risk factors influencing the recurrence of tumors and their impacts in prostate cancer patients obtained from five commonly used models in this paper.

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