Detec%on  of  Soma%c  Muta%ons  by  Targeted  NGS    

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  

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  •  25  genes  reported  clinically  

•  Detec%on  of  SNVs,  indels,  structural  varia%on  

•  High  sensi%vity  of  coding  region  variants  at  10%  AF  

One slide introduction : John West, CEO, Personalis Inc.

•  New VC-backed startup in Menlo Park, CA (www.personalis.com) •  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

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.  

Church  et  al.,  2011  PLoS  Biology  

http://genomereference.org  

GRCh37  (hg19)  

http://genomereference.org  

7  alternate  haplotypes  at  the  MHC  

Alternate  loci  released  as:  FASTA  AGP  

Alignment  to  chromosome  

UGT2B17   MHC   MAPT  

MHC  (chr6)  Chr  6  representa%on  (PGF)  

Alt_Ref_Locus_2  (COX)  

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Which  Picture  Do  You  Believe?  

A   B  

Office of Surveillance, Epidemiology, and Laboratory Services Laboratory Science, Policy and Practice Program Office

CDC/NCBI Clinical NGS RM Project q  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 criteria

q  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 indicated q  Develop guidance for using online data files as tools for test validation

and NGS trouble-shooting q  Publish manuscript of this process and the findings

q  This data is available to NIST

CDC/NCBI Clinical NGS RM Project §  Subramanian Ajay - Illumina §  Tina Hambuch- Illumina §  Elaine Lyon- ARUP §  Rong Mao - ARUP §  Karl Voelkerding- ARUP §  Nazneen Aziz- CAP §  Ephram Chin- Baylor §  Victor Wei Zhang - Baylor §  Cristina Da Silva - Emory §  Madhuri Hegde- Emory §  John Compton- GeneDx §  Soma Das- U. Chicago §  Dan Farkas- Sequenome §  Matt Ferber- Mayo §  Ed Highsmith- Mayo §  Manohar Furtado- Life Technologies §  Ute Geigenmuller – Harvard §  Birgit Funke- Partners

Participants: §  Sivakumar Gowrisankar - Partners §  Srinka Ghosh- Complete Genomics §  Jay Kaufman- Complete Genomics §  Richard Leach- Complete Genomics §  Shashi Kulkarni- Wash. U §  Elaine Mardis- Wash U. §  Savita Shrivastava – Wash U. §  Marc Salit- NIST §  Justin Zook- NIST §  Richa Agarwala - NCBI §  Deanna Church - NCBI §  Donna Maglott – NCBI §  Jim Ostell - NCBI §  Chris O’Sullivan – NCBI §  Wendy Rubinstein - NCBI §  Steve Sherry- NCBI §  Chunlin Xiao – NCBI §  Lorraine Toji- Coriell §  Lisa Kalman- CDC

The findings and conclusions in this report are those of the author and do not necessarily represent the official position of the Centers for Disease Control and Prevention/the Agency for Toxic Substances and Disease Registry.

The Disclaimer:

Lisa Kalman, PhD LKalman@cdc.gov

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  

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:  www.horizondx.com  or  contact  Joshua  Kapp  at  j.kapp@horizondiscovery.com    

RNA-Sequencing Standards Groups

1.  FDA: Sequencing Quality Control (SeQC)- Helicos, 454, SOLiD, Illumina

2.  ABRF: NGS Study: 454, IonTorrent (PGM & Proton) Illumina, Pacific Biosciences

Noise  

Biological  

Informa%c  Chemical/Op%cal  

Samples  Cluster  Beau6fully  (Sadly)  by  Prep   SOPs  Essen6al   Internal  Controls  Needed  

Across  Test  Sites  

Make  Enough  Reference  Material  

Aligner  Claims  are    a  Siren’s  Call  

Every  PlaUorm  Has    Pros/Cons  

Base  Modifica%ons  of  known  biological  

importance  

DNA:  5-­‐methylcytosine  5-­‐hydroxymethylcytosine    8-­‐oxoguanine  glucosylated  5-­‐hydroxymethylcytosine  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  1-­‐methyladenosine  Forterre  and  Grosjean,  2009