Posts Tagged ‘evpb0mg’

Lalonde E*, Ishkanian AS*, ….P’ng C, Collins CC, Squire JA, Jurisica I, Cooper C, Eeles R, Pintilie M, Dal Pra A, Davicioni E, Lam WL, Milosevic M, Neal DE, van der Kwast T, Boutros PC, Bristow RG (2014) “Tumour genomic a nd microenvironmental heterogeneity as integrated predictors for prostate cancer recurrence: a retrospective study” La ncet Oncology 15(13):1521-1532 (PMID: 25456371)

May 17, 2016

Genomic & microenvironmental heterogeneity as integrated predictors for prostate #cancer recurrence
http://www.ncbi.nlm.nih.gov/pubmed/25456371 CNVs & hypoxia

* Lalonde E*, Ishkanian AS*, ….P’ng C, Collins CC, Squire JA, Jurisica I, Cooper C, Eeles R, Pintilie M, Dal Pra A, Davicioni E, Lam WL, Milosevic M, Neal DE, van der Kwast T, Boutros PC, Bristow RG (2014) “Tumour genomic and microenvironmental heterogeneity as integrated predictors for prostate cancer recurrence: a retrospective study” Lancet Oncology 15(13):1521-1532 (PMID: 25456371)

The novelty of the paper is that it is the first study integrating DNA-based signatures and microenviroment-based signature for cancer prognosis. The authors found four prognostic indices, i.e. cancer genomic subtype (generated from clusters of CNV profiles), genomic instability (represented by the percentage of genome alteration), DNA signature (276 genes identified from random forests), and tumor hypoxia (the microenvironment signature), to be effective in predicting patient survival in different groups. Standard clinical univariate and multivariate analyses were performed.

Boutros PC…., van der Kwast T, Bristow RG* (2015) “Spatial genomic heterogeneity within localized, mult i-focal prostate cancer” Nature Genetics 47(7):736-745 (PMID: 26005866)

January 25, 2016

Spatial genomic heterogeneity w/in…prostate #cancer
http://www.nature.com/ng/journal/v47/n7/full/ng.3315.html WGS analysis of many sites suggests divergent tumor evolution

Boutros…, van der Kwast, Bristow (2015) “Spatial genomic
heterogeneity within localized, multi-focal prostate cancer” Nature Genetics 47(7):736-745 (PMID: 26005866)

This work represents the first systematic relation of intraprostatic genomic heterogeneity to predicted clinical outcomes at the level of whole-genome sequencing (WGS). Five patients, with index tumors of Gleason score 7, were subjected to a WGS protocol with spatial sampling of 23 distinct tumor regions to assess intraprostatic heterogeneity. In their analysis, Boutros et al, discovered recurrent amplification of MYCL, which is associated with TP53 loss. This finding is one of the first clear functional distinctions between MYC family members in prostate cancer and suggests that MYCL amplification may be preferentially localized in the index lesion. Overall, the authors believe their results are useful in the development of prognostic biomarkers that are necessary to achieve personalized prostate cancer medicine. It is important to note that such diagnostic biopsy protocols can miss regions of more aggressive cancers resulting in the patient being under-staged.

Ewing AD*, Houlahan KE…..Stuart JM, Boutros PC (2015) “Combining accurate tumour genome simulation with crow d-sourcing to benchmark somatic single nucleotide variant detection” Nature Methods 12(7):623-630 (PMID: 25984700)

December 28, 2015

Tumor genome simulation w/ #crowdsourcing to benchmark…SNV detection http://www.nature.com/nmeth/journal/v12/n7/full/nmeth.3407.html Addresses lack of gold standards & privacy

Ewing, Houlahan…..Stuart, Boutros (2015) “Combining accurate
tumour genome simulation with crowd-sourcing to benchmark somatic
single nucleotide variant detection” Nature Methods 12(7):623-630
(PMID: 25984700)

A crowdsourced benchmark of somatic mutation detection algorithms was
introduced for the ICGC-TCGA DREAM challenge. This has the advantage
of dealing with the lack of gold standard data and the issue of
sharing private genomic data. All groups worked on three different
simulated tumor-normal pairs generated with BAMSurgeon, by directly
adding synthetic mutations to existing reads. An ensemble of
pipelines outperforms the best individual pipeline in all cases,
assessed on the basis of recall, precision and F-score.
Parameterization and genomic localization both have an effect on
pipeline performance, while characteristics of prediction errors
differed for most pipelines.