linkstream2 microblog

Model-Based Approach to Inferring…#Cancer Mutation Signatures http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1005657 Assuming independence betw 3 NTs, 11 v 95 parameters

QT:{{”
The first contribution of this paper is to suggest a more parsimonious approach to modelling mutation signatures, with the benefit of producing both more stable estimates and more easily interpretable signatures. In brief, we substantially reduce the number of parameters per signature by breaking each mutation pattern into “features”, and assuming independence across mutation features. For example, consider the case where a mutation pattern is defined by the substitution and its two flanking bases. We break this into three features
(substitution, 3′ base, 5′ base), and characterize each mutation signature by a probability distribution for each feature (which, by our independence assumption, are multiplied together to define a distribution on mutation patterns). Since the number of possible values for each feature is 6, 4, and 4 respectively this requires 5 + 3 + 3 = 11 parameters instead of 96 − 1 = 95 parameters. Furthermore, extending this model to account for ±n neighboring bases requires only 5 + 6nparameters instead of 6 × 42n − 1. For example, considering ±2 positions requires 17 parameters instead of 1,535. Finally,
incorporating transcription strand as an additional feature adds just one parameter, instead of doubling the number of parameters. “}}

Neutral tumor #evolution across #cancer types
http://www.nature.com/ng/journal/v48/n3/full/ng.3489.html Initial burst of driver events followed by random mutations

Similarity #network fusion for aggregating data types
http://www.nature.com/nmeth/journal/v11/n3/full/nmeth.2810.html Combines mRNA, miRNA & gene fusions to classify cancer subtypes http://compbio.cs.toronto.edu/SNF/SNF

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.

Dynamics of genomic clones in breast #cancer PDX at #singlecell resolution http://www.nature.com/nature/journal/v518/n7539/full/nature13952.html Extensive trees of samples & some WGS

Peter Eirew,
Adi Steif,
Jaswinder Khattra,
Gavin Ha,
…
Jazmine Brimhall,
Arusha Oloumi,
Tomo Osako
et al.

Nature 518, 422–426 (19 February 2015) doi:10.1038/nature13952

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.

Humans 2.0 http://www.newyorker.com/magazine/2015/11/16/the-gene-hackers @eric_lander: “What I love: #CRISPR [can] KO every gene &
identify…the [cancer] cell’s Achilles’ heels”

QT:{{”
“What I love most about the CRISPR process is that you can take any cancer-cell line, knock out every gene, and identify every one of the cell’s Achilles’ heels,” Eric Lander, the fifty-eight-year-old director of the Broad, told me recently. Lander, who was among the leaders of the Human Genome Project, said that he had never
encountered a more promising research tool. “You can also use CRISPR to systematically study the ways that a cancer cell can escape from a treatment,” he said. “That should make it possible to build a comprehensive road map for cancer.”

Lander went on to say that each vulnerability of a tumor might be attacked by a single drug. But cancer cells elude drugs in many ways, and, to succeed, a therapy may need to block them all. That strategy has proved effective for infectious diseases like AIDS. “Remember the pessimism about H.I.V.,” he said, referring to the early years of the AIDS epidemic, when a diagnosis was essentially a death sentence. Eventually, virologists developed a series of drugs that interfere with the virus’s ability to replicate. The therapy became truly successful, however, only when those drugs, working together, could block the virus completely.
“}}

#Chromatin…shapes the mutational landscape of cancer
http://www.nature.com/nature/journal/v518/n7539/full/nature14221.html Low DNase correlates w/ high SNVs in melanoma. True generally?

[Protein] Glypican-1 [uniquely] identifies [circulating] cancer #exosomes & detects…cancer
http://www.nature.com/nature/journal/v523/n7559/full/nature14581.html Maybe also for @exRNA

QT:{{”
Exosomes are lipid-bilayer-enclosed extracellular vesicles that contain proteins and nucleic acids. They are secreted by all cells and circulate in the blood. Specific detection and isolation of
…we identify a cell surface
proteoglycan, glypican-1 (GPC1), specifically enriched on
cancer-cell-derived exosomes. GPC1+ circulating exosomes (crExos) were monitored …”}}

http://www.nature.com/nature/journal/v523/n7559/full/nature14581.html

A subway map of #cancer #pathways
http://www.nature.com/nrc/poster/subpathways The cell cycle appears to be midtown. #DataViz via metaphor.