NN: Why single cells? Resolving diverse genomes in populations, rare cells, and substructure. Bulk tissue view of basal carcinoma #AACR15
1:04pm April 18th 2015 via Hootsuite
Nicholas Navin (MD Andersen) ‘Cancer genomics: one cell at a time’ NN #AACR15
1:03pm April 18th 2015 via Hootsuite
RT @fluidigm: The #AACR workshop on #SingleCell sequencing is about to start in room 115! http://t.co/KIp3njlkQi
NS: For mouse: what LOF mutations promote mutagenesis? Known tumor suppressors, new candidates. Cell 2015 http://t.co/VyiEK85NPh #AACR15
12:11pm April 18th 2015 via Twitter Web Client
NS: Improved the vector titer for this method Nature Meth '14 http://t.co/eux7xvtAjP for both human and mouse libraries #AACR15
12:08pm April 18th 2015 via Twitter Web Client
NS: GeCKO screen targets are mutated in exome seq of vemurafenib-resistant tumors #AACR15
12:07pm April 18th 2015 via Twitter Web Client
NS:Showed large difference in consistency between CRISPR and RNAi screens 2013 RNAi ref http://t.co/4XW3SZZLa9 #AACR15
12:05pm April 18th 2015 via Twitter Web Client
NS: Pos selection Wagel '11 ref: http://t.co/10PxTKplIu Vemurafenib treatment Science 2014 ref http://t.co/zB61yt8gjr #AACR15
12:03pm April 18th 2015 via Twitter Web Client
NS: 65K sgRNA library (3-4/gene, 18K genes), all cloned into Lentiviral vector, can select. Genome-scale CRISPR KnockOut (GeCKO) #AACR15
11:58am April 18th 2015 via Twitter Web Client
NS: Strategy: find constitutively-expressed genes. Used BodyMap genes from @illumina (16 tissues). Design http://t.co/IKBfmtJjT1 #AACR15
11:57am April 18th 2015 via Twitter Web Client
NS: Lentivirus: can control MOI, get stable integration, select via puromycin. Shows FACS of HEC cells w/GFP, cp RNAi to CRISPR #AACR15
11:54am April 18th 2015 via Twitter Web Client
NS: Parameters: one sgRNA per cell; must barcode for ID; needs to be complete; specificity needed. Uses lentiviral delivery #AACR15
11:52am April 18th 2015 via Twitter Web Client
NS:Can put on many sgRNAs on a chip, as a library. Non-homologous end-joining to get gene knockouts. #AACR15
11:51am April 18th 2015 via Twitter Web Client
NS: Illustrates ZfN's (96), TALENs (09), but all require new proteins. CRISPR-Cas9 (11): guide RNA gives specificity, #AACR15
11:50am April 18th 2015 via Twitter Web Client
NS: Start w/phenotype, what multiple genetic elements and interactions causing phenotype? #AACR15
11:48am April 18th 2015 via Twitter Web Client
NS: Which var's create drug resistance? How to test rapidly in parallel? Focus traditionally: one gene, many phenotypes #AACR15
11:47am April 18th 2015 via Twitter Web Client
NS: Number of TCGA samples in 2014 reached 10K. But only reading; what about writing? Take var's and put back into other cells #AACR15
11:46am April 18th 2015 via Twitter Web Client
Neville Sanjana (Broad Inst.) “Genome-scale CRISPR/Cas9 screening: technology and applications” NS #AACR15
11:44am April 18th 2015 via Twitter Web Client
DA:Q:Can CRISPR be used for mouse and SL? A:Oppy is there, can be exploited. #AACR15
11:43am April 18th 2015 via Twitter Web Client
DA: PAX5 disruption (del, mutation, translocations); in mouse, thymectomy, ENU treatment. Led to new ALL genes in human #AACR15
11:42am April 18th 2015 via Twitter Web Client
DA: In mouse, LRP1B homozyg. deleted. Led to discovery in human. #AACR15
11:39am April 18th 2015 via Twitter Web Client
DA:Also - can assess the fidelity of mouse cancer models. Varela 2010 http://t.co/6c0hoA2fJ6 Mouse tumors are 'quiet' #AACR15
11:38am April 18th 2015 via Twitter Web Client
DA: Why large-scale sequencing in mouse? Cancers have higher driver:passenger ratio. Kras-G12D; Trp53: can ID new drivers #AACR15
11:36am April 18th 2015 via Twitter Web Client
DA: Linda Chin (MDA) NEDD9 amplified in human cancers (8% of melanomas). NEDD9 identified in mouse. #AACR15
11:35am April 18th 2015 via Twitter Web Client
DA: Now onto mouse w/BrafV600E melanomas, combined with Tn screens. Discovered ERAS, interacts w/PI3K. #AACR15
11:32am April 18th 2015 via Twitter Web Client
DA: Candidate Cancer Gene Database http://t.co/m6f0Idkvbm #AACR15
11:29am April 18th 2015 via Twitter Web Client
DA: Data from ICGC, TCGA data (7,653 cancer genomes): pattern of tumor suppressor, 1-5% in LOF of Cux1 #AACR15
11:27am April 18th 2015 via Twitter Web Client
DA: Found Cux1 gene - 19/44 tumors had Cux1 insertion. Reduced Cux1, activates PI3K #AACR15
11:26am April 18th 2015 via Twitter Web Client
DA: In T-cell ALL: use MX1-Cre to turn on transposase. Can find insertion in well-char ALL genes (Notch1 etc) but novel too. #AACR15
11:25am April 18th 2015 via Twitter Web Client
DA: Tn example: T2Onc, with loss of function, or gain of function. Can ID promoters, suppressors, oncogenes dep. on where they land #AACR15
11:23am April 18th 2015 via Twitter Web Client
DA: Tn-mediated mutagenesis - simulates mutational acquisition. In mice - sleeping beauty (from fish), piggy bath (cabbage moth) #AACR15
11:22am April 18th 2015 via Twitter Web Client
DA: Making the case for mouse model: interventions not possible (nor ethical) in humans. CTL-4, WNT signaling. #AACR15
11:21am April 18th 2015 via Twitter Web Client
David Adams (Wellcome Trust) "Large-scale genetic screens in mice: pathways, drivers and drug resistance" DA #AACR15
11:20am April 18th 2015 via Twitter Web Client
ER:Raises SL rescue - how resistance responds to treatment, the evolving pathways. #AACR15
11:18am April 18th 2015 via Twitter Web Client
ER:Q:What about CRISPR knockouts? A:Expect their SL's on better experimental data 'will be better'. #AACR15
11:15am April 18th 2015 via Twitter Web Client
ER: 23 drugs across 593 lines, and 32 drugs cross 231 lines: sig accurate predictions obtained for 83 / 70 drugs (unsuper/super) #AACR15
11:11am April 18th 2015 via Twitter Web Client
ER: Using machine learning techniques, have been able to improve SL model to 80%. #AACR15
11:09am April 18th 2015 via Twitter Web Client
ER: Able to get 75% of cancer cell lines (w/phenotype) match the gene essentiality #AACR15
11:06am April 18th 2015 via Twitter Web Client
ER: Figure though does show hubs of types of interactions. SL network enriched in suppressors, oncogenes, proliferation etc #AACR15
11:01am April 18th 2015 via Twitter Web Client
ER: "This is a meaningless slide but I have to show it" - hairball of SL-interactions between 2,077 genes, 2,816 interactions #AACR15
11:00am April 18th 2015 via Twitter Web Client
ER: Take predictions, match drugs that target SL genes for repurposing. Example: VHL SL-partners ID 6 drugs; each sensitive #AACR15
10:59am April 18th 2015 via Twitter Web Client
ER: 23K genes, 529M x 9 datasets of gene pairs, 4,671M tests. From six publ. screens, est accuracy of 80%. ROC curve shown #AACR15
10:57am April 18th 2015 via Twitter Web Client
ER: Can use shRNA-based functional examination - inactive A, B knockdown and showed to be essential. Also pairwise gene co-exp. #AACR15
10:54am April 18th 2015 via Twitter Web Client
ER: Developed DAISY to choose synthetic lethals from cancer genomes Cancer Cell ref http://t.co/VA92iTnrXW #AACR15
10:52am April 18th 2015 via Twitter Web Client
ER: 2013 Nature Med ref http://t.co/i1WYtiNea5 perspective paper #AACR15
10:50am April 18th 2015 via Twitter Web Client
ER: SL if single inhibition of either gene is not lethal, but inhibition of both is. Harwell 1997 http://t.co/W1HDVwVVrs #AACR15
10:49am April 18th 2015 via Twitter Web Client
Eyton Ruppin (Univ MD) “Predicting cancer-specific vulnerability via data-driven detection of synthetic lethality" ER #AACR15
10:47am April 18th 2015 via Twitter Web Client
DP: Drug cocktails: onc drivers, non-onc dependencies, network nodes, novel & common vulnerabilities, boost immune system #AACR15
10:43am April 18th 2015 via Twitter Web Client
DP: Can get synthetic lethality by inhibiting DDR and increasing hypoxia. May apply to other tumor types. #AACR15
10:40am April 18th 2015 via Twitter Web Client
DP: Cp shBRAF vs. shCHEK1, in vivo response different. Observes loss of DDR kinases in vivo after expansion. #AACR15
10:37am April 18th 2015 via Twitter Web Client
