the present and future of next-generation sequencing in ... present and future of next-generation...

The Present and Future of Next-Generation

Sequencing in the Hematology Laboratory

Ahmet Zehir, PhD

Director of Clinical Bioinformatics

Molecular Diagnostics Service,

Department of Pathology

ISLH, 2017

Disclosures

I have nothing to disclose

Molecular Diagnostics

Mission: Identification of molecular alterations that diagnose hematological

malignancies, prognosticate for risk management, help predict outcomes for treatment

modalities, and allow for monitoring of disease across time


MacConaill and Garraway, Journal of Clinical Oncology, 2010








BRAF V600E -> Diagnostic marker for hairy cell leukemia

JAK2 V617F -> Diagnostic marker for clonal myeloproliferative neoplasm

Falini et al., Blood, 2016





Mutations in NPM1 favorable risk factor in AML

Mutations in TP53, RUNX1 poor prognostic markers in AML and MDS

Verhaak et al., Blood, 2005





BRAF V600E -> Vemurafenib

FLT3 internal tandem duplications (ITDs) -> TK inhibitors

Tiacci, et al., NEJM, 2015





Examples:

Tracing presence of driver mutations across multiple blood samples

Next Generation Sequencing

Example:

Illumina sequencing by synthesis

Papaemmanuil E, et al., NEJM, 2016

Next Generation Sequencing

Capture based PCR based

Illumina HiSeq 2000, NextSeq, MiniSeq IonTorrent Illumina MiSeq

Data Analysis

Hybridization vs Amplicon Capture

Capture Method

Variant Types

SNVsSmall insertions,

deletions

Large insertions,

deletions

SVs,

fusions

Amplicon Capture ✓ ✓

Hybridization Capture – Gene panels ✓ ✓ ✓ +/-

Hybridization Capture - Whole exome ✓ ✓ ✓ +/-

Hybridization Capture - Whole genome ✓ ✓ ✓ ✓

Amplicon based assaysRaindance thunderbolts

ASXL1 BCOR BCOR1 BRAF CALR CBL CBLB

CEBPA CSF3R DNMT3A ETV6 EZH2 FLT3 GATA1

GATA2 GNAS HRAS IDH1 IDH2 JAK1 JAK2

JAK3 KDM6A KIT KMT2A KRAS MEK1 MPL

MYD88 NOTCH1 NPM1 NRAS PHF6 PML PTEN

PTPN11 RAD21 RUNX1 SETBP1 SF3B1 SMC1A SMC3

SRSF2 STAG2 TET2 TP53 U2AF1 WT1 ZRSR2

At MSK, Over 4,000 patients have been tested with Raindance assay over the last 3 years

Monitoring post treatment

Donor SNP

Host SNPs

Raindance : Post transplant monitoring

Maria Arcila

MSK-IMPACT Heme

MSK-IMPACT Heme : gene listABL1 BCL11B CEBPA ETNK1 HDAC1 JAK1 MLH1 PCBP1 REL SMG1 U2AF2

ACTG1 BCL2 CHEK1 ETV6 HDAC4 JAK2 MOB3B PDCD1 RET SMO UBR5

AKT1 BCL6 CHEK2 EZH2 HDAC7 JAK3 MPEG1 PDGFRA RHOA SOCS1 VAV1

AKT2 BCOR CIC FAM46C HGF JARID2 MPL PDGFRB RICTOR SOX2 VAV2

AKT3 BCORL1 CIITA FANCA HIF1A JUN MRE11A PDPK1 RNF43 SP140 VHL

ALK BCR CRBN FANCC HIST1H1B KDM5A MSH2 PDS5B ROBO1 SPEN WHSC1

ALOX12B BIRC3 CREBBP FANCD2 HIST1H1C KDM5C MSH6 PHF6 ROS1 SPOP WT1

AMER1 BLM CRKL FAS HIST1H1D KDM6A MTOR PIGA RPTOR SRC XBP1

APC BRAF CRLF2 FAT1 HIST1H1E KDR MUTYH PIK3C2G RRAGC SRSF2 XPO1

AR BRCA1 CSF1R FBXO11 HIST1H2AC KEAP1 MYC PIK3C3 RTEL1 STAG1 ZRSR2

ARAF BRCA2 CSF3R FBXW7 HIST1H2AG KIT MYCL1 PIK3CA RUNX1 STAG2

ARHGEF28 BRD4 CTCF FGF19 HIST1H2AL KMT2A MYCN PIK3CG RUNX1T1 STAT3

ARID1A BRIP1 CTNNB1 FGF3 HIST1H2AM KMT2B MYD88 PIK3R1 SAMHD1 STAT5A

ARID1B BTG1 CUX1 FGF4 HIST1H2BC KMT2C NBN PIK3R2 SDHA STAT5B

ARID2 BTK CXCR4 FGFR1 HIST1H2BD KMT2D NCOR1 PIM1 SDHB STAT6

ARID3A CALR CYLD FGFR2 HIST1H2BG KRAS NCOR2 PLCG1 SDHC STK11

ARID3B CARD11 DAXX FGFR3 HIST1H2BJ KSR2 NCSTN PLCG2 SDHD SUFU

ARID3C CASP8 DDR2 FGFR4 HIST1H2BK LCK NF1 PMS2 SETBP1 SUZ12

ARID4A CBFB DDX3X FLCN HIST1H2BO LMO1 NF2 PNRC1 SETD1A SYK

ARID4B CBL DIS3 FLT1 HIST1H3B LTB NFE2 POT1 SETD1B TBL1XR1

ARID5A CCND1 DNMT3A FLT3 HIST1H3G MALT1 NFE2L2 PPP2R1A SETD2 TBX3

ARID5B CCND2 DOT1L FLT4 HLA-A MAP2K1 NKX2-1 PRDM1 SETD3 TERT

ASXL1 CCND3 DTX1 FOXL2 HNF1A MAP2K2 NOTCH1 PRKAR1A SETD4 TET1

ASXL2 CCNE1 DUSP22 FOXO1 HRAS MAP2K4 NOTCH2 PTCH1 SETD5 TET2

ATM CD274 EED FOXP1 ID3 MAP3K1 NOTCH3 PTEN SETD6 TET3

ATP6AP1 CD28 EGFR FURIN IDH1 MAP3K13 NOTCH4 PTPN1 SETD7 TGFBR2

ATP6V1B2 CD58 EGR1 FYN IDH2 MAP3K14 NPM1 PTPN11 SETD8 TNFAIP3

ATR CD79A EP300 GATA1 IGF1 MAPK1 NRAS PTPN2 SETDB1 TNFRSF14

ATRX CD79B EP400 GATA2 IGF1R MAPK3 NSD1 RAD21 SETDB2 TOP1

ATXN2 CDC73 EPHA3 GATA3 IGF2 MCL1 NT5C2 RAD50 SF3B1 TP53

AURKA CDH1 EPHA5 GNA11 IKBKE MDM2 NTRK1 RAD51 SGK1 TP63

AURKB CDK12 EPHA7 GNA12 IKZF1 MDM4 NTRK2 RAD51B SH2B3 TRAF2

AXIN1 CDK4 EPHB1 GNA13 IKZF3 MED12 NTRK3 RAD51C SMAD2 TRAF3

AXL CDK6 ERBB2 GNAQ IL7R MEF2B P2RY8 RAD51D SMAD4 TRAF5

B2M CDK8 ERBB3 GNAS INPP4B MEN1 PAK7 RAD52 SMARCA4 TSC1

BACH2 CDKN1B ERBB4 GNB1 IRF1 MET PALB2 RAD54L SMARCB1 TSC2

BAP1 CDKN2A ERG GRIN2A IRF4 MGA PARP1 RAF1 SMARCD1 TSHR

BARD1 CDKN2B ESCO2 GSK3B IRF8 MGAM PAX5 RARA SMC1A TYK2

BCL10 CDKN2C ESR1 GTF2I IRS2 MITF PBRM1 RB1 SMC3 U2AF1

MSK-IMPACT Heme variant calling strategy

Blood

Saliva

and/or Nail

Variant Call Set

Unmatched variant calling

Variant Call Set with genotypes in normal(s)

Remove variants based on:

• Similar VAF around 50%

• CH characteristics

MSK-IMPACT Heme : example

Maria Arcila

RNAseq : Archer

Ryma Benayed

Archer Pan-Heme PanelABL1 BTK CHD1 EBF1 HOXA10 KLF2 MYBL1 PAX5 PYRY8 SOX11

ABL2 CALR CHIC2 EIF4A1 HOXA9 KMT2A MYC PBX1 RAB29 SRSF2

AICDA CARD11 CIITA ENTPD1 ID4 KRAS MYD88 PDCD1 RAG1 STAT3

AKT3 CBFB CREB3L2 EPOR IDH1 LIMD1 MYH11 PDCD1LG2 RAG2 STAT5B

ALK CBL CREBBP ERG IDH2 LMO1 NEK6 PDGFRA RANBP1 STAT6

ASB13 CCDC50 CRLF2 ETV6 IKZF1 LMO2 NF1 PDGFRB RARA STIL

ASXL1 CCND1 CSF1R EXOC2 IKZF2 LRMP NFKB1 PHF6 RBM15 STRBP

BATF3 CCND2 CSF3R EZH2 IKZF3 LYL1 NFKB2 PICALM RHOA TAL1

BAX CCND3 CTLA4 FAM216A IL16 LZTS1 NME1 PIM1 ROS1 TCF3

BCL11B CD274 CYB5R2 FBXW7 IL7R MAL NOTCH1 PIM2 RUNX1 TFG

BCL2 CD44 DCK FGFR1 IRF4 MALT1 NOTCH2 PLCG1 RUNX1T1 TLX1

BCL2A1 CD79B DEK FGFR2 IRF8 MAML3 NPM1 PLCG2 S1PR2 TLX3

BCL3 CDC25A DENND3 FGFR3 ITPKB MECOM NRAS PML SEMA6A TNFRSF13B

BCL6 CDK6 DLEU1 FLT3 JAK1 MKL1 NT5C2 PPAT SERPINA9 TNFSF4

BCR CDKN2A DNM2 FOXP1 JAK2 MLF1 NTRK3 PRDM16 SETBP1 TP63

BIRC3 CDKN2B DNMT3A FUT8 JAK3 MLLT10 NUP214 PRKAR2B SETD2 TYK2

BLNK CEBPA DNMT3B GATA1 KAT6A MLLT4 NUP98 PTK2B SF3B1 U2AF1

BMF CEBPD DNTT GATA2 KDM6A MME P2RY8 PTPN1 SH2B3 WT1

BMP7 CEBPE DUSP22 GLIS2 KIAA0101 MPL PAG1 PTPN11 SH3BP5 XPO1

BRAF CEBPG E2F2 GNAS KIT MUC1 PAICS PYCR1 SLC29A1 ZCCHC7

Ryma Benayed

Archer Heme : Example

Lymphotrack: Clonality assessment

Replaces conventional assays

Allow identification of the full range of clonal populations

high-resolution picture of the spectrum of immunity found in lymphoid

malignancies.

Determine specific DNA sequence of clonal rearrangements

Detect clonal events hidden in a polyclonal distribution

Track residual disease – low level and MRD

Define initial behaviors of clonal tumor populations, suppression or re-

emergence of these populations following treatment

For B cell processes - Examine Somatic Hypermutation (SHM) as a

prognostic marker

Maria Arcila

CATCTGGATACACCTTCACCAGCTACTATATGCACTGGGTGCGACAGGCCCCTGGACAAGGGCTTGAGTGGATGGGAATAATCAACCCTAGTGGTGGTAGCACAAGCTACGCACAGAAGTTCCAGGGCAGAGTCACCATGACCAGGGACACGTCCACGAGCACAGTCTACATGGAGCTGAGCAGCCTGAGATCTGAGGACACGGCCGTGTATTACTGTGCTAGAGATCAGTGGCTACCTCTCAACTACTTTGACTACTGGGGCCAGGGAACCCT

1 Core biopsy, porta hepatis

Very limited tissue – CLL/SLL

GCCTCAGGATTCTCCTTTAGTAGCTATGGCATGAGCTGGGTCCGCCAGGCTCCAGGGAAGGGGCTGGAGTGAGTGGCACATATCTGGAATGATGGAAGTCAGAAATACTATGCAGACTCTGTGAAGGGCCGATTCACAATCTCCGAGACAATTCTAAGAGCATGCTCTATCTGCAAATGGACAGTCTGAAAGCTAAGGACACGGCCATGTATTACTGTACCCCTTATTATGATTACGTTTGGGGGAGTTATCGTTATACCCACAGCATCACACGGTCCATCAGAAACCCATGCCACAGCCCTCCCCGCAGGGGACCGCCGCGTGCCATGTTACGATTTTGATCGAGGACACAGCGCCATGGGTATGGTGGCTACTGGGACCTACTTTGACTACTGGGGCCAGGGAACCCT

CACTGTCTTTGGTGGCTCCATCAGTACTTACTACTGGAGCTGGATCCGGCAGCCCCCAGGGAAGGGACTGGAGTGGATTGGGTACATCTATGACAGTGGGAGGACCAACTCCAACCCCTCCCTCAGGAGTCGAGTCACCATAATAGGAGACACGTCCAAGAACCAGATCTCCCTGAGGTTGAGCTCTGTGACCGCTGCGGACACGGCCGTTTATTACTGTGCGAGCACCAAGCGTAGAGAAGTTGCTGACGACTACTACTTCTACTACATGGACGTTTGGGGCAAAGGGACCAC

3 2

Future technologies: Molecular Barcoding

Molecular barcoding for detecting very low allele fraction variants

Schmitt et al. PNAS, 2012

Increases sensitivity of mutation calling significantly with reduced background mitation

rates

Data standardization / sharingWhile labs adopt existing or upcoming NGS based technologies, handling the massive

amounts of data generated is one of the biggest challenges to keep in mind

Harmonization of tumor types

Ritika Kundra, Niki Schultz

Needs of the clinical lab

What we needed for manual review of clinical cases?

• Track certain meta-data elements for the sample

• Review mutations at the raw data level (.bam files)

• Review copy number alterations and structural variants

• Track sample failures, repeats, multiple samples from the same patient

• Share data with the institute (clinicians + researchers)

MPath NGS

• MySQL based backend

database

• History tables for tracking

changes in data structures

• SQL Alchemy based data

objects

• Flask based front-end

application

• Interactivity based on

Angular, JQuery, D3

• Authentication for security

MPath

Aijaz Syed, Anoop Balakrishnan, John Ziegler

Ideals of Data Democratization

Genomic

Data

Clinicians Researchers

Decision

Making

Clinical Trial

Accruement

Cohort level

data

Raw data

files

Data sharing with ResearchersCohort level data

MPath

nightly data

update via APIs

de-identified data

cBio Portal

Data sharing with ResearchersCohort level data: cBio Portal

De-identification via DMP ID

P-00001234-T02-IM5

patient id

corresponding to MRN

sample counter

assay identifier

IM3 : MSK-IMPACT v3IM5 : MSK-IMPACT v5AR1 : MSK-Archer v1RD1 : RDTS v1IH1 : MSK-IMPACTHeme v1

Data sharing with ResearchersRaw data sharing

Challenges:

• Allow researchers to run their own algorithms, analyses on the data without

PHI

• Allow researchers to combine clinical with data generated in the research

setting

• Make sure different teams are not working on the same project without realizing

itSolution:

• Create a de-identified .bam file repository, updated nightly

• Require researchers to register their projects with the Data Usage Committee

• Require researchers to register their projects with IRB via data protocols

Acknowledgements

Aijaz

Syed

Mustafa

Syed

Jack

Birnbaum

John

Ziegler

Anoop

Balakrishnan

Shruti

Madur

Raghu

Chandramohan

Liu (Tony)

Zhen

Ryan

PtashkinAbhinita

Mohantya

Ronak

ShahSumit

Middha

Gowtham

Jayakumaran

Hyunjae Ryan

Kim

Meera

Prasad

Clinical Bioinformatics team

Acknowledgements

Clinical BioinformaticsAijaz Syed

Sumit Middha

Raghu Chandramohan

Abhinita Mohanty

Ryan Ptashkin

Meera Prasad

Mustafa Syed

Tony Liu

Jack Birnbaum

John Ziegler

Anoop Balakrishnan

Gowtham Jayakumaran

Aaron Dack

Anita Bowman

Jason Hweei

Mohammad Haque

Shruti Madur

Ronak Shah

Molecular DiagnosticsMarc Ladanyi

Maria Arcila

Ryma Benayed

Khedoudja Nafa

Liying Zhang

Laetitia Borsu

Meera Hameed

Snjezana Dogan

Dara Ross

Jaclyn Hechtman

Diana Mandelker

Lulu Wang

Jinjuan Yao

Deborah DeLair

Talia Mitchell

Angela Yannes

Justyna Sadowska

CMODavid Solit

Michael Berger

Agnes Viale

Barry Taylor

Niki Schultz

Nick Socci

Eder Paraiso

Julia Rudolph

Knowledge SystemsNiki Schultz

JJ Gao

Benjamin Gross

Yichao Sun

Hongxin Zhang

Fred Criscuolo

Dong Li

Ritika Kundra

Annice Chen

Debyani Chakravarty

Sarah Phillips

MSKCCRoss Levine

Jose Baselga

David Hyman

David Klimstra

Craig Thompson

Paul Sabbatini

Melissa Pessin

John Petrini

Chris Sander

Mark Robson

Mike Eubank

Stu Gardos

Roy Cambria

Dalicia Reales

Farmer Family Foundation

Marie Josée and Henry R. Kravis

Cycle for Survival

Geoffrey Beene Cancer Research Center

Justyna Sadowska

Jacklyn Casanova

Anna Plentsova

Iwona Kiecka

Julie Son

Lisa Stewart

Josh Somar

Tamim Malbari

Christine England

George Jour

Navid Sadri

Ken Tian

Keith Killian

Juan Gomez-Gelvez

Deepu Alex

Caleb Ho

Carlos Pagan

the present and future of next-generation sequencing in ... present and future of next-generation...

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