Advances in Translational Research Choi.pdf · UBE2C Ubiquitin-conjugating enzyme E2C 20q13.12 PRC1...
Transcript of Advances in Translational Research Choi.pdf · UBE2C Ubiquitin-conjugating enzyme E2C 20q13.12 PRC1...
Advances in Translational
Research
Yoon-La Choi
Department of Pathology
Samsung Medical Center
GBCC 2013. 12, Seoul
Agenda
• Molecular classification, Multigene prognostic predictor
• Genomic landscape, Genomic sequencing to search “actionable” driver
• Patient-derived xenograft (PDX) model for pre-clinical assessment
Molecular classification, Multigene prognostic predictor
Commercially Available
Multigene Signature
Reis-Filho and Pusztal, 2011
Expression Signatures: Current Status
• Which signature(s)?
- Now commercially available: MammaPrint, OncotypeDx, Genomic
grade index, Intrinsic subtype(PAM50), others
- Coming soon: Others
• Which patients?
- All signatures more useful for assessing prognosis in ER+
cancers (i.e., luminal) than in ER- cancers (i.e., HER2 and basal
molecular types)
• PAM50 Risk of Recurrence (ROR) Score provided more prognostic information in endocrine-treated, ER+ node-, patients than OncotypeDx recurrence score, especially in HER2-group
- More patients scored as high risk and fewer as intermediate risk
• OncotypeDx Recurrence Score commonly used to identify patients with ER+ breast cancer who could possibly be spared cytotoxic therapy(i.e., those with low recurrence score)
• None has yet been validated in prospective clinical trials TALORx (OncotypeDx)
MINDACT (MammaPrint)
RxPONDER (OncotypeDx)
JCO 2013
• The genes are rarely overlapped among multi-gene signatures even with the same purpose of prognostic prediction of early breast cancer.
• Similar prognostic power even with different gene collection.
• The power of prognostic prediction does not increase with summation or combination.
• They use similar biologic characteristic of genes.
• Proliferation genes are the common driving force in all prognostic signatures.
Large-size discovery data set (>1,000)
Need of analytic tool, combining of biologic and statistical significance
Characteristics of Multi-gene signatures
Breast cancer prognostic marker development process
Data Set Collection and Analysis Simulation with candidate
genes
Roche Light Cycler 480 based Kit Retrospective Study Product and Central Lab
Ensel Oh, Breast Cancer Res Treat 2011
imm
unity
pro
life
ration
Proliferation
p1
p2
p3
i1
i2
i3
p1 p2 p3
Symbol Name Location
p-genes (proliferation related)
UBE2C Ubiquitin-conjugating enzyme E2C 20q13.12
PRC1 Protein regulator of cytokinesis1 15q26.1
CCNB2 Cyclin B2 15q21.3
CDC20 Cell division cycle 20 homologur 1p34.1
i-genes (immunity related)
CD3D CD3d molecule, delta (CD3-TCR complex) 11q23
CD52 CD52 molecule 1p36
CCL19 Chemokine (C-C motif) ligand 19 9p13
TRBV20-1 T-cell receptor beta variable 20-1 7q34
Poor
Good
Test Optimization
GenesWell TM CDx-BCT
□ Oncotype DX®
□ Survival Probability obtained from GenesWell™CDx
□ GenesWell™CDx
Linear Predictor(LP) = -aaa*p.mean + bbb*i.mean + ccc
Survival
Probability
If Recurrence Score is 30,
-> Chance Rate of Recurrence
within 10 year = 20%
: Chemotherapy
If LP is 4.9 (Patient 5)
5/10 Years Survival Probability =
98%,95%
: No Chemotherapy!
Comparative Advantages
1) Patent Position
- No Conflict of Prognostic
Biomarkers with those of
Oncotype DX
2) Automatic Quantification
System
- Short Analysis Time
- Safe Handling for Samples
- Higher Reproducibility
- Low Cost of Goods
- Reduce Labor Cost (1/10)
Genomic landscape, Genomic sequencing to search “actionable” driver
Nature 2013
Genomic Analysis
The New Frontier
• Identification of genomic alterations in tumors, particularly “driver alterations”
- mutations
- Copy number variations
- Translocations
• Variety of techniques (WGS, WES, WTS, Targeted NGS)
• Goal: To identify “actionable” targets in order to personalized therapy
Whole Genome Sequencing
Advantages Disadvantages
-Point mutations -CNVs -Small indels -Rearrangements -Somatic mutations in non-coding regions (promoters, enhances, and non-coding RNAs)
-Point mutations and indels: >30-fold haploid coverage -Rearrangements: >10-fold physical coverage -High cost -Complicated analysis
DNA sample
Randomly fragment genomic DNA
DNA fragment with adapters
DNA cluster
Sequencing
Fragmentation
Adapter Ligation
Amplification
Whole Exome Sequencing
Advantages Disadvantages
-High sequence coverage and less raw sequence and cost than WGS. -Point mutations -Small indels -CNAs (need further validation)
-Incomplete exome capturing -Limit to mutations in coding regions -Lack efficient methodology to detect structural variations
Whole Transcriptome Sequencing
Advantages Disadvantages
-Higher sequence coverage and less raw sequence and cost than WGS -Calling somatic point mutations -Identifying differently expressed genes -Detecting fusion genes
-Mutations in genes expressed at low levels is hampered owing to lack of statistical power -Failing to detect truncating mutations because of nonsense mutations
Targeted sequencing (Panel)
• The advent of NGS technology has enabled the development of cost-effective multi-gene sequencing panels.
Advantages Disadvantages
-Higher sequence coverage with clinical diagnostic level -Calling somatic point mutations -CAN, fusion gene -Custom-required design
-Incomplete target capturing -Lack efficient methodology to detect structural variations
Catalog cancer panel
Life Technologies Agilent Illumina
Item Ion AmpliSeq Cancer HotSpot Panel v2
HaloPlex Cancer Research Panel TruSeq Amplicon Cancer Panel
Input DNA 10 ng 225 ng 225 ng 250 ng
Gene 50 ea 47 ea 47 ea 48 ea
Target size ~10 Kb 9.937 Kb 9.937 Kb 35 Kb
Sequenceable size 94.607 Kb 83.204 Kb 70 Kb
Total amplicon 207 ea 3,092 ea 2,121 ea 212 ea
Amplicon size 154 bp 150 bp 150 bp 170~190 bp
Minimal order 8 rxn 16 rxn 96 rxn
Sequencer PGM
(314chip, 4plex) MiSeq
(300Mb, 6plex) PGM
(316chip, 4plex) MiSeq
(300Mb, 8plex)
Coverage 500 x 500 x 500x 500 x
Total cost /sample ~500 $ ~400 $ ~550$ ~350$
Clinical level NGS Cancer Panel
Clinical level NGS Cancer Panel
Title Foundation Medicine (USA)
PGDx (USA)
RainDance (USA)
Samsung Genome Institute, SMC
Gene 236 120 54 80 (500)
Sample amount >=50 ng gDNA ? >=250 ng gDNA >=50 ng gDNA
Sample type Fresh, FFPE Fresh, FFPE Fresh, FFPE Fresh, FFPE
Matched normal Not needed Needed Not needed Not needed
Mean read depth >250X >250X ? >500X
Turn around time 3 weeks 3 weeks ? 1 week
Price $5,800 ? ? <$1,000
Insurance Acquired Not acquired Not acquired Not acquired
Variant type SNP, INDEL, CNV, Chromosomal rearrangement
SNP, INDEL, CNV, Chromosomal rearrangement
SNP, INDEL SNP, INDEL, CNV, Chromosomal rearrangement
Sensitivity >99% at >=10% allele frequency
>=1% allele frequency
? >99% at>=1% allele frequency
Specificity >99% PPV ? ? >99% PPV
Comprehensive genome analysis
Bioinformatics Personal targets
• DB (in-house and public) • Tumor, normal, blood e.g. lung, stomach and colon
• Prediction of clinical outcomes
• Integration of clinical and genome information
• Targeted re-sequencing • WES/WTS • Single cell analysis on multiple serial samples
• Automated genome analysis
• Knowledge-based, customized analysis
High-precision genome analysis for N-of-1 trial
Genomic analysis of chemotherapy-refractory alveolar rhabdomyosarcoma
27-year-old male with abdominal wall mass
Multiple mass with
pleural seeding
VDC + IE for
3weeks
Near complete remission
(sample #1)
3month, chest wall
mass, Salvage VIP
Pleural biopsy
(sample #2)
Pleural effusion
(sample #3)
Malignant acites
(sample #4)
CDK4
Gene Name
Gene Description Exome sequencing (#2) Sanger sequencing
Allele (%) Variant Tumor(#1) Ascites(#3) Primary cell culture
(from #2)
SCN1B sodium channel, voltage-gated, type I, beta 52.4 c.218A>G:p.Y73C 218A>G 218A>G 218A>G
PPP1R3A protein phosphatase 1, regulatory subunit 3A 46.5 c.1941T>A:p.D647E 1941T>A 1941T>A 1941T>A
GRID2 glutamate receptor, ionotropic, delta 2 46.3 c.1944C>A:p.Y648X WT 1944C>A 1944C>A
APBA2 amyloid beta (A4) precursor protein-binding, family A, me
mber 2 43.4 c.266G>A:p.G89D WT 266G>A 266G>A
ZNF142 zinc finger protein 142 42.9 c.1138G>A:p.A380T WT 1138G>A 1138G>A
ZYG11A zyg-11 homolog A
(C. elegans) 41.9 c.1762G>T:p.E588X 1762G>T 1762G>T 1762G>T
RBFOX1 RNA binding protein, fox-1 homolog (C. elegans) 1 39.8 c.241C>A:p.H81N WT 241C>A 241C>A
TCF7L1 transcription factor 7-like 1 (T-cell specific, HMG-box)
31.6 c.1006GA:p.V336M WT WT WT
TEX13B testis expressed 13B 22.2 c.406C>T:p.L136F WT WT WT
DSCAML1 Down syndrome cell adhesion molecule like 1 13.5 c.3110T>G:p.L1037R WT WT WT
Whole exome sequencing
SCN1B PPP1R3A ZYG11A
SCN1B PPP1R3A ZYG11A GRID2 APBA2 ZNF142 RBFOX1
SCN1B PPP1R3A ZYG11A GRID2 APBA2 ZNF142 RBFOX1 TCF7L1 TEX13B
DSCAML1
SCN1B PPP1R3A ZYG11A GRID2 APBA2 ZNF142 RBFOX1 TCF7L1 TEX13B
DSCAML1
CDK inhibitor
Genomic Analysis
The New Frontier
• Caveats
- “Actionable” = Clinically useful
- Many tumors have multiple genetic alterations
- Intratumoral heterogeneity
- Genetic alterations frequently change during the course of tumor progression
Patient-derived xenograft model (AVATAR) for pre-
clinical assays
Tentler, J. J. et al. (2012) Nat. Rev. Clin. Oncol.
Establishment and Testing of PDX models
“Avatars” : mice or other animals with human tissue implanted onto them. (xenografts)
Need of AVATAR
• Which drugs are the most effective for the patient is difficult, and testing different drugs puts the patient at risk.
• Personalized xenograft model : a large number of cancer drugs can be tested for their effectiveness on a particular patient.
• Xenograft model often resembles the tumor in the patient better than cultured cancer.
Challenges and limitations
• Difficult to make xenografts. Success rates of tumor implantation depend according to cancer types, and it can be as low as <1% in prostate cancer and ~10% in breast cancer.
(~70% in GBM, ~60% in Lung cancer in SMC IRCR)
• Creation of xenograft models and drug screening take about 6-8 months
• Long-term collaboration and joint effort among clinicians, biologists, scientists, and patients.
Cancer Sample QC Genomic Data
Generation Analysis & Validation
Agilent 8x60K platform
Agilent 8x60K platform
1. Array CGH
2. Gene Expression
3. Mutation Profile : Cancer panel
STR Genotyping
Human-Mouse ALB Test
DNA, RNA Extraction
Pathology QC
0
20
40
60
80
100
Fail
Intermediate
Accept
Histologic QC
Histologic and Protein Characteristics of AVATAR
Case %
Acceptable DNA quality 198 95.0
No human DNA in xenograft mouse DNA 8 3.9
Mouse DNA contamination in human DNA 2 1.1
Total 208 100
Human Human+Mouse Mouse
Human – Mouse Test
STR Genotyping
TP53
KRAS PIK3CA
MLH1
FBXW7
CTNNB1
HRAS
APC
CDKN2A
STK11
BRAF
ERBB2 NRAS RB1
SMAD4 VHL
Ion Torrent Ampliseq Cancer Panel
Human
PDX
AVATAR mouse Passage 2
Patient
AVATAR mouse Passage 5
AVATAR mouse Passage 2
Patient
AVATAR mouse Passage 5
Xenograft specific
380 g
enes
133 samples aCGH Analysis
Removal of Xenograft-specific genes
breast gastric colon bladder pancreas
ovary lung brain meta
133 samples aCGH Analysis
Candidate target genes in breast cancer are maintained in PDX
Human
PDX
New drug therapeutic efficacy validation and lead optimization
• Whole-genome comparisons
• Structural and copy number aberrations were found to be retained with high fidelity.
• Variable numbers of PDX-specific somatic events were documented, although they were only rarely functionally significant.
• PDX models are an important resource for the search for genome-forward treatment options and capture endocrine-drug resistance etiologies that are not observed in standard cell lines.
Thank you..
Thanks to our patients and Samsung Medical Center Team
Samsung Medical Center Seok Jin Nam Jeong Eon Lee Young-Hyuck Im Yoen Hee Park Yong Bum Cho Jeeyun Lee Samsung Genome Institute Woogyang Park Institute for Refractory Cancer Research, SMC Do-Hyun Nam Kyeung Min Joo
Lab of CGMP, SMC Ensel Oh Yu Jin Kim Ji-Young Song Min Jung Sung Suzie Ahn Seoul National University Young-Kee Shin Ryong Nam Kim Gencurix Sang-Rae Cho