Moore Certainly an N=1, but 1y cancer-free. #AGBT16
8:47pm February 11th 2016 via Hootsuite
Moore: Examples - high JUN expression. Hypertension medication (approved) and 'the tumor completely disappeared' "like a miracle' #AGBT16
Moore: Lists driver genes(expected list). Getting diagnosis, inform potential therapy, enable clinical trial enrollement #AGBT16
8:44pm February 11th 2016 via Hootsuite
.@East_OnWest Your welcome - like @becky_kusko, I'm just sharing my own notes with everyone.
8:42pm February 11th 2016 via Hootsuite in reply to East_OnWest
RT @East_OnWest: @DaleYuzuki Thanks for all the great #AGBT16 coverage today, you're a #tweetmaster
8:42pm February 11th 2016 via Hootsuite
Moore: Using integrated pathway analysis with RNA-Seq data. Several reports generated #AGBT16
8:41pm February 11th 2016 via Hootsuite
Moore: Many tools - SV, CNV, Somatic SNV. KB has many annotations, 8042 refs etc 'the most valuable part of all of this' #AGBT16
8:39pm February 11th 2016 via Hootsuite
Moore: Generate 'as many types of data as we can' CNV, LOH, gene exp, infectious agents, mutation sig, SNV, indels, fusions and SV #AGBT16
8:38pm February 11th 2016 via Hootsuite
Moore: About a 8w turnaround time; molecular tumor board; both DNA and RNA. Now they want WGS, before just a panel #AGBT16
8:37pm February 11th 2016 via Hootsuite
Moore: Personalized oncogenomics: 437 cases enrolled, incl 24 pediatric. Long array (50) different cancers represented #AGBT16
8:35pm February 11th 2016 via Hootsuite
Moore:First patient in 2010, 'took a month of sequencing' '10 ref https://t.co/PqaRSP2taS but felt this N=1 study did help #AGBT16
8:34pm February 11th 2016 via Hootsuite
Moore: Mentions this work in current issue of CSHL Molecular Case Studies as part of the #AGBT16 event bag https://t.co/3WHLrvh9wl
8:32pm February 11th 2016 via Hootsuite
Richard Moore (BC Cancer Agency) Whole genome and transcriptome sequencing for personalized cancer therapy: first 300 cases #AGBT16
8:30pm February 11th 2016 via Hootsuite
Keating: In transplantation, microbiome important, as well as proteomics, working on those fronts as well. #AGBT16
8:27pm February 11th 2016 via Hootsuite
Keating: Also using Roche 5MB capture for MHC. They have outcome data for 15y. #AGBT16
Keating: 35K samples now part of iGeneTRAiN int'l consortium for GWAS; are working with @10xgenomics to phase MHC around HLA-A. #AGBT16
8:26pm February 11th 2016 via Hootsuite
Keating:Developing a miRNA guided minimization of rejection and immuno-supp therapy. #AGBT16
8:25pm February 11th 2016 via Hootsuite
@Becky_Kusko I met.@bioinfomagician at last year's #AGBT16 Jonathan will try to catch you perhaps tonight!
8:22pm February 11th 2016 via Hootsuite in reply to bioinfomagician
RT @Becky_Kusko: Wow just finally met @DaleYuzuki in person! Not sure how that took so long to happen. #AGBT16
8:20pm February 11th 2016 via Hootsuite
Keating: Kidney rejection via needle biopsy, but has complications and with cause. Assess 10 mRNAs in CTOT -04 study #AGBT16
8:16pm February 11th 2016 via Hootsuite
Keating: 5y survival - Kidney best, Lung worst. Hasn't really changed in 5y. Chronic renal failure in non-renal transplants too #AGBT16
8:15pm February 11th 2016 via Hootsuite
Keating: Immunosuppression drug tox is substantial. Kidney is $200K and Liver is $250K and heart is $860K. #AGBT16
8:14pm February 11th 2016 via Hootsuite
Keating: Post-transplant in 1y: 10% in kidney, 35% in heart. 6 biopsies in first year taken before irreversible damage occurs #AGBT16
8:12pm February 11th 2016 via Hootsuite
Brendan Keating (U Penn) Detection and validation of signatures of liver transplantation rejection diagnoses #AGBT16
8:11pm February 11th 2016 via Hootsuite
Lincoln: Second JAMA Onc. ref regarding this validation work https://t.co/yP0QbtVisj #AGBT16
8:10pm February 11th 2016 via Hootsuite
Lincoln: Strict, lower criteria that needs other methods to validate. Lower bound of conf. - and take a set at 0.11% FP SNV rate #AGBT16
8:08pm February 11th 2016 via Hootsuite
Lincoln: Second method to confirm for reporting: most var's are easy and accurate. Think about multiple thresholds are key #AGBT16
8:07pm February 11th 2016 via Hootsuite
Lincoln: It's hard to get these samples; Lisa Kalman from CDC is organizing a new GeT-RM website here: https://t.co/lMRDCQbkMZ #AGBT16
8:05pm February 11th 2016 via Hootsuite
Lincoln: Statistics: a 0.1% analytic FN reate but pt sample has a 10% FN rate. 'Huff's book: how to lie w/statistics #AGBT16
8:04pm February 11th 2016 via Hootsuite
Lincoln: Sizable fraction in that 23% - no exonic variants in 5 of 29 genes. Almost all were 'simple SNVs'; 77% is biased to easy #AGBT16
8:03pm February 11th 2016 via Hootsuite
Lincoln: WG reference samples, NA12878, similar @giab data methods. But: GIAB has 23% non-high-confidence calls #AGBT16
8:02pm February 11th 2016 via Hootsuite
Lincoln: Positive controls? Combed Coriell, found 42 samples for 9/29 cancer genes. 7 not available, or failed inbound QC #AGBT16
8:01pm February 11th 2016 via Hootsuite
RT @matthewherper: Miscarriages reported in 3 American women with Zika virus https://t.co/PUgRy4Zr6N via statnews
Lincoln: reproducibility of coverage, optimizing protocols. 'It's hard. Easier to throw informatics at it.' #AGBT16
8:00pm February 11th 2016 via Hootsuite
Lincoln: For CNV this '11 BMC paper -https://t.co/dEBRCyexSs But most important - biochemical and process steps clean. #AGBT16
Lincoln: 2 indel callers, 2 CNV callers, GATK, CoalGen for homopolymer vars. #AGBT16
7:58pm February 11th 2016 via Hootsuite
Lincoln: Lots of examples. PMS2 - a 99.9% identical pseudogene. Highly GC, or AT rich. Can't do it off-the-shelf. #AGBT16
Lincoln: Only 13.4% clinically actionable. Shows 28bp polyA right before MSH2. Other examples of a 'mess'. 24bp tandem repeat MET #AGBT16
7:56pm February 11th 2016 via Hootsuite
Lincoln: In Vitae's >1000 sample analytic validity '15 JMD ref https://t.co/hPltfIDZgS #AGBT16
7:55pm February 11th 2016 via Hootsuite
Stephen Lincoln (Invitae) Clinically important variants are often technically challenging for NGS #AGBT16
7:52pm February 11th 2016 via Hootsuite
Hoischen: Looking at a per-error rate by position along the FGFR2 S252W locus, shows sig signal about the clonal nature #AGBT16
7:51pm February 11th 2016 via Hootsuite
Hoischen: Shows pieces (16) from a 71 y.o. testis looking at particular FGFR2 S252W mutation by section #AGBT16
7:50pm February 11th 2016 via Hootsuite
Hoischen: Recent PNAS paper https://t.co/cJtdMa6QF2 about these selfish mutations in testes over time #AGBT16
7:49pm February 11th 2016 via Hootsuite
Hoischen: Selfish mutations: mutated testis tissue will show clonal outgrowth - up to 1m long (!) #AGBT16
7:47pm February 11th 2016 via Hootsuite
Hoischen: Striking: five known paternal age effect disordered; 1000x increase birth prevalance cp to underlying mut rate. #AGBT16
Hoischen: 3rd application: de novo 'selfish mutations during spermatogenesis'. 80% of de novo's occur there. Increase w/paternal age #AGBT16
7:46pm February 11th 2016 via Hootsuite
Hoischen: Was able to validate via an invasive procedure. For paternal data - able to get 10/234 unique molecules (~4%) #AGBT16
7:44pm February 11th 2016 via Hootsuite
Hoischen: These markers spanned the repeat expansion. Father affected, son affected, ID'd the allele. 5ng w/pregnant mom #AGBT16
7:42pm February 11th 2016 via Hootsuite
Hoischen: Looked instead for tagging SNPs and look for that; since it was a dominant conditions. smMIP for 50 markers #AGBT16
7:41pm February 11th 2016 via Hootsuite
Hoischen: First gene - DMPK het (myotonic dystrophy). Problem: repeat expansions, never present in 170bp cfDNA. #AGBT16
