Barnards: Q: Combination of genes? A: Biology too complex to address to target multiple genes in a functional model. #AACR14
1:41pm April 5th 2014 via Hootsuite
Bernards: Genome analysis is fine (alterations of pathways), but need functional genetic analysis with cross-talk between pathways #AACR14
1:39pm April 5th 2014 via Hootsuite
Bernards: They have four ongoing combination experiments based upon synthetic lethality screens #AACR14
1:37pm April 5th 2014 via Hootsuite
Bernards: Looked at negatively selected haripins, 580 kinases, 3 significant ones that had different hairpins target same gene #AACR14
1:35pm April 5th 2014 via Hootsuite
Bernards: What is resp. for BRAF inhibitors in colon cancer? BRAF V600E mutants; which kinase synergizes with PLX? Used shRNA kinome #AACR14
1:33pm April 5th 2014 via Hootsuite
Bernards: FACS-assisted shRNA genetic screen for EGFR expression + vemurafenib resistance; SOX10 knowckdown publ. last week #AACR14
1:29pm April 5th 2014 via Hootsuite
First up: Rene Bernards Netherlands Cancer Inst "Functional genetic screens and their use in cancer research" #AACR14
1:28pm April 5th 2014 via Hootsuite
Next Session: "Functional Cancer Genomics" #AACR14
1:27pm April 5th 2014 via Hootsuite
Khurana: Gerstein's lab tool called FunSeq here: http://t.co/KyJjzXy4OY #AACR14
1:09pm April 5th 2014 via Hootsuite
Khurana: Somatic mut's do not follow same patterns of polymorphism selection; ID'd candidate drivers #AACR14
Khurana: Referring to her work published in Khurana Science 2013 PubMed: http://t.co/KukgQ4JyJQ non-coding regions w/ fn impact #AACR14
1:06pm April 5th 2014 via Hootsuite
Khurana: Looking at germline regions of importance, and cancer mut's then in these regions are driver mutation events #AACR14
1:05pm April 5th 2014 via Hootsuite
Khurana: (Oops, she's looking at SNPs and negative selection, not ncRNAs) #AACR14
1:04pm April 5th 2014 via Hootsuite
Khurana: Dev. method for looking at driver ncRNA: conserved ncRNA regions, enriched for rare var's, depleted common var's #AACR14
1:03pm April 5th 2014 via Hootsuite
Khurana: miRNA and lncRNA mechanisms; HOTAIR and PTEN expression reduction #AACR14
1:02pm April 5th 2014 via Hootsuite
Khurana: (For details on this ENCODE work see Gerstein el al Nature 2012 PubMed: http://t.co/nhVZbo7KdP ) #AACR14
1:00pm April 5th 2014 via Hootsuite
Khurana: Looking at TF binding regions, assign to target genes, correlate to exp. data. 119 TF's, 9K target genes, 28K edges #AACR14
12:59pm April 5th 2014 via Hootsuite
.@teamoncology It is known that the gut absorbs many types of molecules, first time I heard that microRNAs are included in that
12:57pm April 5th 2014 via Hootsuite in reply to
.@teamoncology Looking at RNA-Seq from plasma, ID'd microRNA from corn (from a Western diet !); rice miRNA from an Asian one. Fascinating.
12:56pm April 5th 2014 via Hootsuite in reply to
Khurana: Figure from Ecker Nature 2012 PubMed http://t.co/K3elDXC3va showing chromatin organization, functional elements #AACR14
12:54pm April 5th 2014 via Hootsuite
Khurana: Nice illus. of data: TCGA 2.5PBytes, TCGA 910 TBases in CGHub; TGB 220GB; ADSP 46GB #AACR14
12:52pm April 5th 2014 via Hootsuite
Khurana: ncRNA from WGS; from TCGA, TGP, ICGC, ENCODE consortia. From cancer genomes, to population scale seq, to non-coding #AACR14
12:51pm April 5th 2014 via Hootsuite
Next up: Ekta Khurana Yale Univ "Information in non-coding DNA" #AACR14
12:49pm April 5th 2014 via Hootsuite
Guo: Illustrated exponential growth of publications of WES / RNA-Seq, possibilities for data mining #AACR14
Guo: 'Food RNA' - food microRNA in human samples. Diff. rice / corn in diet Wang PLOS One 2012 PubMed http://t.co/rzvdurCABD #AACR14
12:48pm April 5th 2014 via Hootsuite
Guo: Mining RNA-Seq data, can look at SNV & Indels; even microsat instability. Can look at RNA editing, allele-spec expression #AACR14
12:43pm April 5th 2014 via Hootsuite
Guo: Also looking at mtRNA from WES, and also viral sequence (HBV, HPV) #AACR14
12:41pm April 5th 2014 via Hootsuite
Guo: Took Tibetan WES of 50 individuals, and published non-exome high-quality SNVs. #AACR14
12:38pm April 5th 2014 via Hootsuite
Guo: Since capture isn't perfect, you get a lot of add'l data (off-target); ¬50% off-target; intron / intergenic functions #AACR14
12:34pm April 5th 2014 via Hootsuite
Guo: Known knowns, known unknowns and unknown unknowns: we know SNV, CNVs, structural var's. Others: non-targeted SNV, mtDNA, add'l #AACR14
12:32pm April 5th 2014 via Hootsuite
Next up: Yan Guo Vanderbilt Univ "Lost treasures in sequence data" #AACR14
12:30pm April 5th 2014 via Hootsuite
Reis-Filho's comment: Mentions E. Mardis could help on the crowdsourcing effort #AACR14
12:29pm April 5th 2014 via Hootsuite
Reis-Filho: (HER2 comment): Could get a nice repository of knowledge. #AACR14
12:28pm April 5th 2014 via Hootsuite
Reis-Filho: (HER2 comment): Rapidly exceeds any cancer biologist's expertise; suggest using Wikipedia (seriously) on genes #AACR14
12:27pm April 5th 2014 via Hootsuite
Reis-Filho:Q (comment from person who did HER2 work): review from structural biologist yields best predictions (not scalable) #AACR14
Reis-Filho: Concl: drivers vs passengers still an inexact science; need data from multiple levels #AACR14
12:26pm April 5th 2014 via Hootsuite
Reis-Filho: In functional validation though, the story may not be so clear-cut #AACR14
12:23pm April 5th 2014 via Hootsuite
Reis-Filho: Functional studies 9/14 were drivers functionally. I767M in HER2 called driver by several callers, but not functional. #AACR14
12:22pm April 5th 2014 via Hootsuite
Reis-Filho: No predictor called all the 14 mutations in HER2 #AACR14
12:21pm April 5th 2014 via Hootsuite
Reis-Filho: Looking at BRAF and PIK3CA specific mutation constructs, these approaches work. But - combining predictive + functional? #AACR14
12:20pm April 5th 2014 via Hootsuite
.@drdonsdizon You are most welcome - enjoying these talks immensely (just taking notes and sharing them!)
12:19pm April 5th 2014 via Hootsuite in reply to
Reis-Filho: MutationAssessor with TCGA was only 74% acc, 89% sens. Better; but 'no perfect predictor available' #AACR14
12:17pm April 5th 2014 via Hootsuite
Reis-Filho: Results of the 'Pepsi Challenge': CHASM had 89% accuracy. But trained from COSMIC. W/ TCGA: 50% acc.! 0% sens. #AACR14
Reis-Filho: Gnad et al 2013 PubMed http://t.co/WH32ALbNxB looked at mutation predictors. Most popular is CHASM, leveraging COSMIC #AACR14
12:15pm April 5th 2014 via Hootsuite
Reis-Filho: These mutation predictors look at conservation, struct changes, prot annotations, training sets plus bioinf. #AACR14
12:14pm April 5th 2014 via Hootsuite
Reis-Filho: Looking at many mutation predictors (PolyPhen, SIFT et al) - they come out every week #AACR14
Reis-Filho: MuSiC reference for predicting driver mutations Dees et al 2012 PubMed: http://t.co/AMV56zJYhP #AACR14
12:13pm April 5th 2014 via Hootsuite
Reis-Filho: Predicting drivers: three papers as examples MuSiC, Two others (PubMed): #AACR14 http://t.co/JP52QvJhnM http://t.co/wdM1iqB4WC
12:12pm April 5th 2014 via Hootsuite
Reis-Filho: Passengers - no selective advantage, looking at Titan (TTN) - 16% of Br Ca have TTN mutations. TTN exp. in muscle #AACR14
12:09pm April 5th 2014 via Hootsuite
Reis-Filho: Tumor suppressor drivers are truncating or frameshift mutations #AACR14
12:08pm April 5th 2014 via Hootsuite
