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  1. 1. Detec%on  of  Soma%c  Muta%ons  by  Targeted  NGS     Andy  Bredemeyer   Washington  University  in  St.  Louis   Pathology  Consult  Service   Genomics  and  Pathology  Services  Washington  University’s  Cancer  Muta%on  Profiling  Test   •  1000X  avg  coverage   •  Detec%on  of  SNVs,  indels,   •  High  sensi%vity  of  coding   •  25  genes  reported  clinically   structural  varia%on   region  variants  at  10%  AF  Considera%ons  for  clinical  valida%on  of  assay  and  informa%cs:  1.  DNA  input  quan%ty  and  quality     •  Small  tumor  biopsies  leading  to  low  library  complexity     •  FFPE  specimens  and  variability  of  fresh  %ssue  quality  2.  Low  frequency  variant/low  allele  burden  detec%on   •  Soma%c  variants  present  in  a  frac%on  of  the  genomes  sampled  (10-­‐25%  or  lower)   ‒  Presence  of  non-­‐tumor  derived  DNA   ‒  Clonal  complexity   ‒  Future  considera%on:  minimal  residual  disease  detec%on  3.  Indel,  copy  number  varia%on,  and  transloca%on  detec%on   •  NGS  references  should  incorporate  the  spectrum  of  varia%ons    and  variant  complexity  
  2. 2. One slide introduction : John West, CEO, Personalis Inc.•  New VC-backed startup in Menlo Park, CA (•  Clinical-quality genome sequencing & medically-relevant interpretation•  Founders from Stanford (majority MD’s) & Solexa / Illumina•  Collaborations : Stanford, Harvard, ISB•  Extensive publication record in genetic analysis accuracy issues (esp. NGS)•  Work guided by extensive manually-curated databases linking genetic variation with disease & drug metabolism•  2010 : Quake-genome clinical interpretation•  2009-2011 : Family quartet genome analysis•  Current work : –  Larger families including CEPH1463 / NA12878; Multiplatform –  Lab & informatics for accuracy (vs absolutely lowest cost)•  Using “gold-standard” genomes to characterize error mechanisms•  Design lab & informatic methods to address them•  Interested in joining NIST consortium
  3. 3. Jim  Mullikin,  Director  NIH  Intramural  Sequencing  Center  (NISC)  •  Computa%onal  genomics  researcher  from  the  days  of  the  ramp-­‐up  of   the  human  genome  project  in  1997.      •  Involved  in  The  SNP  Consor%um  project,  and  later  the  HapMap  project.      •  I  worked  on  the  early  structural  varia%on  analysis  with  Evan  Eichler  and   selected  NA12878  as  one  of  the  eight  samples  for  that  project.      •  The  1000  genomes  project  selected  this  individual  as  well   –  She  is  of  European  ancestry   –  Together  with  her  parents  cons%tuted  one  of  the  trios  for  the  1000G  pilot   project  •  NISC  is  keenly  interested  in  working  with  new  sequencing  technologies.  •  The  Genome-­‐in-­‐a-­‐Bocle  goal  of  providing  a  standard  human  reference   sample  for  this  field  will  be  invaluable.  
  4. 4. Church  et  al.,  2011  PLoS  Biology  
  5. 5. UGT2B17   MHC   MAPT   GRCh37  (hg19)   7  alternate  haplotypes   at  the  MHC   Alternate  loci  released  as:   FASTA   AGP   Alignment  to  chromosome  
  6. 6. MHC  (chr6)  Chr  6  representa%on  (PGF)  Alt_Ref_Locus_2  (COX)  
  7. 7. Which  Picture  Do  You  Believe?   A   B   23241623 23246895 23246895 50 30 40 30 E E 20 C C CoverageCoverage A A G G T T − − 20 M M 10 10 0 0 23.24165 23.24175 23.24185 23.24195 23.24170 23.24180 23.24190 23.2470 23.2471 23.2472 23.2469 23.2470 23.2471 23.2472 23.2473 23.2474 Position (Mb) Position (Mb)
  8. 8. CDC/NCBI Clinical NGS RM Projectq  Two human cell lines (NA19240, NA12878)q  Existing AND new sequence data (NGS & Sanger) from 36 clinical gene panels, WES, WGS (volunteer labs)q  Assess data quality (coverage, quality scores, etc); accept data which meets predefined criteriaq  NCBI will analyze and host data files online for public access. Will display: q  All data sets with metrics (coverage, platform, software, etc) q  Consensus sequence track- regions of “high, medium or low” sequence confidence indicatedq  Develop guidance for using online data files as tools for test validation and NGS trouble-shootingq  Publish manuscript of this process and the findingsq  This data is available to NIST Office of Surveillance, Epidemiology, and Laboratory Services Laboratory Science, Policy and Practice Program Office
  9. 9. CDC/NCBI Clinical NGS RM Project Participants: §  Sivakumar Gowrisankar - Partners§  Subramanian Ajay - Illumina §  Srinka Ghosh- Complete Genomics§  Tina Hambuch- Illumina §  Jay Kaufman- Complete Genomics§  Elaine Lyon- ARUP §  Richard Leach- Complete Genomics§  Rong Mao - ARUP §  Shashi Kulkarni- Wash. U§  Karl Voelkerding- ARUP §  Elaine Mardis- Wash U.§  Nazneen Aziz- CAP §  Savita Shrivastava – Wash U.§  Ephram Chin- Baylor §  Marc Salit- NIST§  Victor Wei Zhang - Baylor §  Justin Zook- NIST§  Cristina Da Silva - Emory §  Richa Agarwala - NCBI§  Madhuri Hegde- Emory §  Deanna Church - NCBI§  John Compton- GeneDx §  Donna Maglott – NCBI§  Soma Das- U. Chicago §  Jim Ostell - NCBI§  Dan Farkas- Sequenome §  Chris O’Sullivan – NCBI§  Matt Ferber- Mayo §  Wendy Rubinstein - NCBI§  Ed Highsmith- Mayo §  Steve Sherry- NCBI§  §  Manohar Furtado- Life Technologies Chunlin Xiao – NCBI§  Ute Geigenmuller – Harvard §  Lorraine Toji- Coriell§  Birgit Funke- Partners §  Lisa Kalman- CDC
  10. 10. The Disclaimer:The findings and conclusions in thisreport are those of the author and do notnecessarily represent the official positionof the Centers for Disease Control andPrevention/the Agency for ToxicSubstances and Disease Registry. Lisa Kalman, PhD
  11. 11. ACMG  Efforts  regarding  WGS/WES    1.  Technical  guidelines  for  NextGenera%on  sequencing  (from   Lab  QA  Commicee).   1.  Addresses  Targeted  mul%-­‐gene  panes,  WES,  and  WGS   2.  Content,  method,  data  analysis,  variant  filtering,  sequencing  of  regions  with  homology,  companion   technologies  and  result  confirma%on)   3.  Ini%al  Valida%on  (test,  plalorm)   4.  Data  analysis  op%miza%on   5.  Metrics  and  performance  parameters  (analy%c  sensi%vity/specificity,  false  posi%ve/nega%ve,   clinical  sensi%vity,  assay  robustness/precision,  limits  of  detec%on)   6.  Ongoing  valida%on  of  modifica%ons  of  test,  plalorm,  analysis  pipeline   7.  Reference  materials  for  QC  and  PT.  Includes  warning  about  using  cell  lines.  Includes  possibility  of   using  simulated  electronic  sequence  (for  non-­‐wet  lab  component).  2.  Development  of  model  consent  for  WGS/WES  3.  List  of  “secondary  findings”  that  should  be  reported  4.  Collabora%on  amongst  ACMG/CAP/AMP/ASHG  to  develop  recommended   terminology  for  variant  classifica%on  in  rela%on  to  disease  risk/causa%on  5.  New  CPT  codes  –  will  gene%c  tests  be  paid  on  the  Clinical  Lab  or  Physician   Fee  Schedules,  or  both  
  12. 12. Horizon  Discovery  –  reference  standards  for  Next  Genera6on  Sequencing   1.  HorizonDx  combines  three  core  capabili6es   •  Highly  accurate  gene  engineering  technology  à  generates  isogenic  human  cell  lines   •  FFPE  %ssue  modelling  capability  à  FFPE  blocks  containing  defined  cell  ra%os   •  World-­‐class  molecular  characteriza%on  à  droplet  digital  PCR,  STR  and  SNP6   2.  HorizonDx  developing  a  mul6-­‐plex  reference  standard  for  NGS   •  Combining  >10  clinically  relevant  oncogene  muta%ons  into  a  single  gDNA/FFPE  standard   •  Staggered  allele  burdens  from  ranging  from  1-­‐25%   •  First  commercially  available  NGS  standard   3.  The  case  for  including  MCF10a  as  a  reference  genome   •  Normal  cell  line,  well  characterized  and  highly  u%lized  for  cancer  research   •  Would  pave  the  way  for  the  crea%on  of  a  disease  reference  genome  ,  or  analyte  specific  reference   material  which  offers  high  prac%cal  u%lity   •  Horizon  has  >100  knock-­‐in/knock-­‐out  cell  lines  in  MCF10a  background  to  leverage  into  the  consor%um   To  find  out  more:   visit:  or  contact  Joshua  Kapp  at     12  
  13. 13. RNA-Sequencing Standards Groups1.  FDA: Cluster   Samples   Sequencing Quality Control (SeQC)-eeded   Internal  Controls  N SOPs  Essen6al  Beau6fully  (Sadly)  by  Prep   SOLiD, Illumina Helicos, 454, Across  Test  Sites  2.  ABRF:Reference   Study: 454, IonTorrent (PGMHas     Make  Enough   NGS Aligner  Claims  are     Every  PlaUorm   & Proton) Illumina,iren’s  Call   Biosciences Material   a  S Pacific Pros/Cons   Biological   Noise   Chemical/ Op%cal   Informa%c  
  14. 14. Base  Modifica%ons  of   DNA:   5-­‐methylcytosine   known  biological   5-­‐hydroxymethylcytosine     8-­‐oxoguanine   glucosylated  5-­‐hydroxymethylcytosine   importance   4-­‐methylcytosine   6-­‐methyladenine   8-­‐oxoadenine   5-­‐formylcytosine   5-­‐carboxycytosine   β-­‐D-­‐Glucosyl-­‐hydroxymethyluracil  (J  base)   Phosphorothioa%on  (backbone)   1-­‐methyladenine   3-­‐methylcytosine   Inosine   5-­‐hydroxycytosine   O6-­‐methylguanine   O4-­‐methylthymine   5-­‐hydroxyuracil   5-­‐hydroxymethyluracil   The  four  ribonucleo%des  (backbone)     RNA:   6-­‐methyladenosine  Forterre  and  Grosjean,  2009   1-­‐methyladenosine