He:Q: c-Myc controls 35% of genes; why this miRNA? A: miR19 have mult. targets; but AKT up-regulated. miR-92: repress c-Myc #AACR14
6:42pm April 5th 2014 via Hootsuite
He: Evol history of miR-92 much broader, miR-17/18/20/19 only vertebrates. Unique functionality #AACR14 PubMed: http://t.co/PZOgaHm6mD
6:38pm April 5th 2014 via Hootsuite
He:Also shRNA Fbw7 knockdown shows effect. Cross-talk between oncogene c-Myc & tumor suppressor PI3K/AKT #AACR14
6:37pm April 5th 2014 via Hootsuite
He: miR-92 overexpression affects c-Myc dosage, discovered Fbw7 that miR-92 targets, show dependence #AACR14
6:35pm April 5th 2014 via Hootsuite
He: ID 19a-b component is critical for oncogenesis, along with c-myc. 19 / 92 have opposite effects in same cistron #AACR14
6:30pm April 5th 2014 via Hootsuite
He: Disfunctional 92 - similar accel. tumorigenesis, suggest not cooperative, but internal antagonistic components #AACR14
6:29pm April 5th 2014 via Hootsuite
He: mIR-17-92, mouse b-cell model; del. 92 component, stronger effect #AACR14
6:28pm April 5th 2014 via Hootsuite
He: Lessons from Lac operon - by analogy, working cooperatively perhaps? In mir-17-92 gives rise to six mature miRNAs; 3 families #AACR14
6:26pm April 5th 2014 via Hootsuite
He: Unique function: 50% miRNA precursors give rise to one product. 50% - polycistronic structure. Multiple targets, specificity #AACR14
6:25pm April 5th 2014 via Hootsuite
He: 2008 onward miRNA therapeutics in primates tested, from discovery in 1993. Today 2578 human miRNAs #AACR14
6:24pm April 5th 2014 via Hootsuite
Next up: Lin He, Univ CA Berkeley "Outside coding genome: MiRNAs make a big difference" #AACR14
6:22pm April 5th 2014 via Hootsuite
Educ Session: "Outside the Coding Genome: Non-Coding RNAs in the Oncogene and Tumor Suppressor Network" #AACR14
Kim: Nature Rev Genet article comparing engineered nucleases PubMed: http://t.co/BHdjA5jqaz showing success rates, target length #AACR14
6:00pm April 5th 2014 via Hootsuite
Kim: Two companies, ToolGen and Haplogen help with knockout cell panel in haploid cell lines #AACR14
5:59pm April 5th 2014 via Hootsuite
Kim: Also used ssODN - protein, guide RNA, and add'l oligonucleotide introducing rest. enzyme site #AACR14
5:58pm April 5th 2014 via Hootsuite
Kim: Cas9 xf protein showed to degrade in 12h. Via plasmid, lasts 3d. RNPs can reduce off-target effects, tested with 2bp mismatch #AACR14
5:56pm April 5th 2014 via Hootsuite
Kim: Listed five (of many) CRISPR papers. Looking at guide RNA's and off-target effect dependencies PubMed: http://t.co/2oc9OTb7Ve #AACR14
5:55pm April 5th 2014 via Hootsuite
Kim: Moving onto CRISPR - Cas9 used for knockouts; Cas9 uses guide RNA's for specificity. Electroporate Cas9 protein, cloning-free #AACR14
5:52pm April 5th 2014 via Hootsuite
Kim: TALEN library resource, helped >200 researchers last year. http://t.co/hUltDKuCh7 #AACR14
5:49pm April 5th 2014 via Hootsuite
Kim: Moving on to TALENs, reviewed his work published 2013 Nature Biotech PubMed http://t.co/r97q5qW4Fj 99% successful #AACR14
5:48pm April 5th 2014 via Hootsuite
Kim: Then their group developed a series of ZfN's for targeted knock-outs. But: in cell lines, ass'd with cytotoxicity #AACR14
5:46pm April 5th 2014 via Hootsuite
Kim: Carroll et al paper in 2009 using ZfN for genome engineering in Drosophila PubMed: http://t.co/Vptt3JEIKs #AACR14
5:44pm April 5th 2014 via Hootsuite
Kim: Focus is now on CRISPR. Review of Fok I nucleases + first zinc-finger nuclease at Hopkins 96 PubMed http://t.co/woTsSDOf2h #AACR14
5:42pm April 5th 2014 via Hootsuite
Kim: Genome editing - concurrent dsDNA breaks, can induce translocations, 'method of the year' in 2012 Nature Methods #AACR14
5:40pm April 5th 2014 via Hootsuite
Next up: Jin-Soo Kim Nat'l Univ Seoul "Targeted gene disruption in mammalian cell lines using programmable nucleases" #AACR14
5:38pm April 5th 2014 via Hootsuite
Carette: Q:Stable karyotype after screening? A:Do see diploid cells that overgrow the haploid ones; doesn't happen very often. #AACR14
Carette: Adv: complete knockout, high coverage (>100/gene), no obv. off-target effect, Disadv: limited to haploid cell lines; pooled #AAC
5:34pm April 5th 2014 via Hootsuite
Carette: Suggest dom. negative AXIN2 function #AACR14
5:33pm April 5th 2014 via Hootsuite
Carette: + Wnt regulators, many known +'ve regulators ID'd; novel: AXIN, part of destruction complex. not 5' insertion as usual #AACR14
5:32pm April 5th 2014 via Hootsuite
Carette: Looked at Wnt / GFP reporter; selected for +/- regulators; found 2 negative regulators #AACR14
5:30pm April 5th 2014 via Hootsuite
Carette: Able to look at drug mode of action w haploid genetic screen in 5 cmpds, and ID'd genes '11 PubMed http://t.co/9R2bEnGlOI #AACR14
5:26pm April 5th 2014 via Hootsuite
Carette: Mapping of insertions, looked at significance of distance, found 2 favored locations (CASP8 and FADD) #AACR14
5:22pm April 5th 2014 via Hootsuite
Carette: Mutagenized 100M cells, select using TRAIL (apoptotic mech), amplify insertion sites, NGS to determine location #AACR14
5:20pm April 5th 2014 via Hootsuite
Carette: 1M mutagenized cells, 900K insertion sites in appox 50% in genes. Every gene hit ave 30-fold #AACR14
Carette: Put in a retroviral gene trap, being able to do forward genetics with complete knockouts (showed Western data) #AACR14
5:19pm April 5th 2014 via Hootsuite
Carette: Did insertional mutagenesis in haploid human cells. Carette Science 09 PubMed http://t.co/ODnMS7Qcy5 #AACR14
5:18pm April 5th 2014 via Hootsuite
Carette: Yeast is haploid, but wanted a yeast-like genetic approach for LS mutagenesis in human cells #AACR14
5:17pm April 5th 2014 via Hootsuite
Carette: X-ray irradiation of Drosophila - pioneered in '27 Muller - but mammalian cells are diploid, no crossings #AACR14
5:16pm April 5th 2014 via Hootsuite
Carette: Large-scale disruption approach, not shRNA or CRISPR based #AACR14
5:15pm April 5th 2014 via Hootsuite
From: "Loss of Function Genetics in Mammalian Cells" session: Jan Carette, Stanford "Haploid genetic screens in human cells" #AACR14
RT @splon: #AACR14 @rahman_nazneen feels most cancer genetic guidelines were designed to limit testing when it was a limited resource.
5:01pm April 5th 2014 via Hootsuite
Rahman: Starting with ovarian ca, as there is >10% BRCA risk without family history of cancer #AACR14
.@DrStelling But they are looking at a predisposition (germline) model, that's not so time-sensitive.
4:59pm April 5th 2014 via Hootsuite in reply to
Rahman: 96 samples / 14d; can do 576/week on a HiSeq 2.5K. #AACR14
4:56pm April 5th 2014 via Hootsuite
Rahman: Constructed extensive classifications of all possible (42K) BRCA potential variants and their effect from multiple datasets #AACR14
4:55pm April 5th 2014 via Hootsuite
Rahman: Confounding is population-specific SNPs - thus the need for other kinds of variant data #AACR14
4:51pm April 5th 2014 via Hootsuite
Rahman: CIGMA data inputs are of many types - from in silico prediction, to control data, functional assays, case variant data #AACR14
Rahman: CIGMA: divided into mgmt categories; informed, evidence-based; HTP; dynamic and iteratively improved #AACR14
4:50pm April 5th 2014 via Hootsuite
Rahman: Collectively rare variants are common - and all are suspect, cannot be ruled out. Analysis tool called CIGMA #AACR14
4:49pm April 5th 2014 via Hootsuite
Rahman: From 1000 samples: 117 var's/sample; 3-4 rare; >80% rare (<1%); appx 10% has a rare BRCA var. #AACR14
4:48pm April 5th 2014 via Hootsuite
