Download Clinical and Translational applications of transcriptome sequencing

Using Mate-Pair Next Generation
Sequencing (MP-Seq) To Study
HPVs Roles in Cancer
Development
David I Smith, Ph.D.
Professor and Consultant
Department of Laboratory Medicine and Pathology
Chairman of the Technology Assessment Group
Center for Individualized Medicine
Mayo Clinic
Incidence trends of HNSCC in the
United States
2
Risk Factors
• Smoking and drinking
• 6-7th decade of life, prolonged exposure
• All sites
• HPV (16,18)
• Oropharyngeal squamous cell carcinoma (OPSCC)
(30-90% of patients)
• Younger patients (<50 years)
• Lack traditional risk factors
• Chemo/Radiation Sensitive
HPV
•
•
•
•
Large family of DNA viruses
Approximately 8 Kb genomes
Low risk HPVs cause papillomas
High risk HPVs associated with cancer
development
• Key oncogenes are HPV E6 (targets p53)
E7 (degrades pRB)
What is the role of HPV in
OPSCC?
• It is just assumed that HPV plays a similar
role as it does in cervical cancer.
• So how does HPV cause cervical cancer?
What does HPV do at the site of
integration and is the site of
integration important?
• What is important? Just that HPV is integrated so that E6
and E7 can be overexpressed?
• Different cervical cancers have different integration sites.
Appeared “random”. General model for cervical cancer is
that the sites of integration are unimportant.
• Are there genes that are possibly targeted by the
integrations?
• Does HPV integration do more than just insert HPV at a
position within the genome?
HPV Integrations
• We used RSO-PCR to identify sites of
integration in HPV16 and HPV18-positive
cervical cancers
• 50% of HPV16 integrations were within one of
the common fragile sites (CFSs)
• 65% of HPV18 integrations were within these
sites. Hot-spot around c-myc
• HeLa WGS revealed that this HPV18-positive
cervical cancer has HPV integrated near c-myc
(which is surrounded by two CFSs), and
amplification of HPV at the site of integration
Common Fragile Sites (CFSs)
• Highly unstable chromosomal regions found in all
individuals
• At least 89 distributed throughout the human genome
• Hot-spots for rearrangements in many different cancers
FRA3B, FRA16D, FRA6E
• Three most frequently expressed CFSs
• FRA3B spans 4 Mbs, FRA16D spans 2
Mbs, FRA6E spans 3 Mbs
• FHIT is a 1.5 Mb gene in FRA3B
• WWOX is a 1.1 Mb gene in FRA16D
• PARK2 is a 1.3 Mb gene in FRA6E
• All three are important tumor suppressors
involved in many different cancers
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5
6
7
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10
11
12
13
14
15
16
17
18
19
20
21
22
23
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25
26
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Gene Name
CNTNAP2
DMD
CSMD1
LRP1B
CTNNA3
NRXN3
A2BP
DAB-1
PDE4D
FHIT
KIAA1680
GPC5
GRID2
DLG2
AIP1
DPP10
Parkin
ILIRAPL1
PRKG1
EB-1
CSMD3
IL1RAPL2
AUTS2
DCC
GPC6
CDH13
ERBB4
ACCN1
CTNNA2
WD repeat
DKFZp686H
PTPRT
WWOX
NRXN1
IGSF4D
CDH12
PAR3L
PTPRN2
SOX5
TCBA1
LARGEST HUMAN GENES
Chromosome
Size
Exons/FPT
7q35
2304258
25/8107
Xp21.1
2092287
79/13957
8p23.2
2056709
70/11580
2q22.1
1900275
91/16556
10q21.3
1775996
18/3024
14q24.3
1691449
21/6356
16p13.2
1691217
16/2279
1p32.3
1548827
21/2683
5q11.2
1513407
17/2465
3p14.2
1499181
9/1095
4q22.1
1474315
11/5833
13q31.3
1468199
8/2588
4q22.3
1467842
16/3024
11q14.1
1463760
23/3071
7q21.11
1436474
21/6795
2q14.1
1402038
26/4905
6q26
1379130
12/2960
Xp21.2
1368379
11/2722
10q21.1
1302704
18/2213
12q23.1
1248678
26/3750
8q23.2
1213952
69/12486
Xq22.3
1200827
11/2985
7q11.22
1193536
19/5972
18q21.1
1190131
29/4608
13q31.3
1176822
9/2731
16q23.2
1169565
15/3926
2q34
1156473
28/5484
17q11.2
1143718
10/2748
2p12
1135782
18/3853
2q24
1126043
16/2132
11q25
1117478
8/6830
20q12
1117144
32/12680
16q23.2
1113013
9/2264
2p16.3
1109951
21/8114
3p12.1
1109105
10/3315
5p14.3
1102578
15/4167
2q33.3
1069815
23/4176
7q36.3
1048712
22/4735
12p12.1
1030095
18/4492
6q22.31
1021499
8/3183
CFS
FRA7I
FRAXB
FRA2F
FRA10D
FRA1B
FRA3B
FRA4F
FRA4F
FRA11F
FRA6E (6q26)
FRAXB
FRA16D (16q23.2)
HPV Integrations in Cervical
Cancer
• HeLa and 8q24.13 (between FRA8C and
FRAD)
• Several integrations observed in FHIT
• We’ve detected 2 integrations in LRP1B
• Other large genes have HPV integrations
• However, integrations occur throughout
the genome and not too many “hot-spots”
HPV and Other Cancers
• Dramatic increase in HPV-positive OPSCCs in
the past two decades (now 80-90% of OPSCCs
from the Mayo Clinic are HPV-positive)
• 85% of anal cancers, and 50% of penile, vulvar
and vaginal cancers are HPV-positive
• Does HPV trigger other cancers by a
mechanism similar to its’ key role in the
development of cervical cancer?
HPV and OPSCC
• Much more complex than cervical cancer
• Many patients have a history of smoking/drinking
and their tumors are also HPV-positive
• Not all HPV-positive OPSCCs are the same as
some have very low E6/E7 expression (latent
infections?) while others have high HPV E6/E7
expression
HPV and OPSCC Clinically
• HPV-positive OPSCC do better clinically than HPVnegative OPSCCs
• Current clinical tests for the presence of HPV are now
part of the clinical management of OPSCC patients
• De-escalation of therapies for HPV-positive OPSCCs?
• IHC for p16 (which is elevated in most HPV-positive
OPSCCs)
• Newer assays measure HPV E6/E7 expression (to
differentiate between high and low expression of these
oncoproteins)
Role of HPV in OPSCCs?
• Identical to cervical cancer?
• Most likely not
• Is HPV integrated in most HPV-positive
OPSCCs?
• When integrated what is the site of integration in
HPV?
• Is there any specificity to where HPV integrates
into the human genome?
• Common fragile sites and their large genes?
Characterizing HPV Integration
in OPSCC
• Various older ways to do this
• (1) RS-PCR
• (2) Measure E6/E7 expression versus E2
expression (based upon the idea that
when integrated E2 is disrupted)
• (3) in situ hybridization looking for signals
in interphase nuclei
• (4) Next generation sequencing
NGS to Identify HPV
Integrations
• Whole genome sequencing- Would require 100+ Gbs of
genome sequencing. Expensive and bioinformatically
challenging
• RNAseq looking for fused transcripts between HPV and
human genes. Only useful if HPV integrates close to a
gene and makes a fused transcript
• Is there a better way to do this that is both cheaper and
easier?
Mate-Pair Sequencing
(MP-Seq)
• Powerful technology to analyze genomes
• Break genomes into 3-5 Kb pieces
• Biotin label the ends and then circularize
(Illumina strategy)
• Break into small pieces and then capture
the two ends as single piece
• Prepare libraries to do paired-end
sequencing
Advantages of MP-Seq
• Sequence 100 bp from the ends of each matepair
• Even though you only sequence 200 bp, you are
inferring information about the 5 Kb (or whatever
size mate-pairs you use) between the two ends.
Known as bridge coverage
• With 3-5 Gbs of genome sequence can obtain a
high resolution examination of genome-wide
alterations (and also potentially identify HPVhuman junctions)
Mate Pair Genomic DNA Library
Isolate DNA
Fragment
Select 5-kb Fragments and
End-label with Biotin
Bio
*
* Bio
5 kb
Circularize
Fragment
*
*
*
Select Biotinlabeled Fragments
*
*
*
500 bp
Mate Pair Next Gen Sequencing
Biotin-labeled
Fragments
*
*
*
*
*
Next Gen Sequencing
*
*
*
*
*
500 bp
*
25
kb
...ACCGT
*
Map to Reference Genome
5 kb
TTGCA...
Genome Coverage
1/6 lane Illumina
7x coverage
Mate Pair Sequencing of
OPSCC
• 28 HPV-positive OPSCCs
• Construct 5 Kb mate-pair libraries
• Bar-code and sequence 4-6/lane on
Illumina HiSeq 2500s
• Obtain 70-100 million reads/library
New Algorithms
• Sarah Johnson and George VasmatzisBiomarker Discovery Program of CIM
• Better ways to examine the data
• Concatenated all viral genomes together to
make a new artificial chromosome (which they
call chromosome 29)
• New algorithms to detect novel junctions
(caused by inter- and intra-chromosomal
translocations)
• Copy number variation tool added
611A
OP64055
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Human_papillomavirus_type_16_1
GRCh38_Viral
(15;29)(q11.2;NA)
BIMAv3
Validation of Integration Events
• Analyze all the mate-pairs indicating a
potential HPV integration event
• Construct PCR primers as close to the
putative integration as possible
• Optimize PCR to generate a single
fragment
• Sanger sequence to validate
Fig. 1
Patient 4T
Patient 4T
Human chr14q33 Integration site HPV16 E1
Human chr12p13 Integration site HPV16 E5
Patient 17T
Patient 10T
Human chr1p36 Integration site HPV16 E6
Human chr4q22
Integration site
HPV26 L2
Validated HPV integrations in OPSCC
Patient ID
HPV
HPV bridged coverage
Validated Chr location
Genes or adjacent genes
HPV integration region
621T
HPV26+
54
4q22.3
PDLIM5
HPV26 L2
601T
HPV16+
1038
14q24.3
5' c-FOS…..3' JDB2
HPV16 E1(nt@1707)
601T
HPV16+
1038
12p13.31
5' LTBR…..3' CD27-AS1
HPV16 E5(nt@3916)
614A
HPV16+
3040.7
2q24.2
5' ITGB6……… 3' RBMS1
HPV16 E1(nt@1693)
687C
HPV16+
2963.8
1p36.11
5' WASF2…….. 3'AHDC1
HPV16 L2 (nt@4580)
687C
HPV16+
2963.8
2p16.3
FOXO11……. 3'FOXN2
HPV16 E6(nt@219); L1(nt@7650)
687C
HPV16+
2963.8
4q22.1
5' CCSER1…….3' GRID2
HPV16L6 (nt@474)
687C
HPV16+
2963.8
8p23.2
CSMD1
HPV16 L2 (nt@5032)
721A
HPV16+
4360.8
16q22.1
HSF4
HPV16E1(nt@1350) +/-
677T
HPV16+
48
1p3611
PADI2…….PADI1
HPV16 E6(nt@441)
688B
HPV16+
82.7
7q31.1, 7q36.3
FOXP2(7q31.1)
HPV16L1 (nt@7022)
655B
HPV16+
89.8
4q34.3
5'LOC285500 isoform X3…….3' teneurin-3
HPV16L1 (nt@6059)
Results
•
•
•
•
•
•
Only 8/28 (30%) of HPV-positive OPSCCs had HPV integrated.
0/12 integration events occurred in HPV E2
6/8 OPSCCs with HPV integration had a single integration. One had two
HPV integrations and one had 4.
Some samples had HPV integration but still a high episomal copy number of
HPV also present
HPVs role in OPSCC may be very different from its’ role in cervical cancer.
Most OPSCCs are driven by HPV in the episomal form!
Not all HPV-positive OPSCCs are the same
What Else Does Mate-Pair
Sequencing Give You?
• Sequencing just 3-5 Gbs of an MP-seq library
still provides detailed information about changes
in copy number across a cancer genome. Much
higher resolution than aCGH
• Also identifies inter- and intra-chromosomal
translocations. Each OPSCC had between 10
and 70 novel junctions from translocation events
MP-Seq and Translocations
• Examine data for inter- and intrachromosomal translocations
• Each sample had multiple such
alterations. Range from 9-70
• Where do these occur?
• What genes are at or near such events?
Translocations and Common
Fragile Sites
• Of the 284 chromosomal translocation
events that occur with high to mid
confidence, 146 (51.4%) occur in or
directly adjacent to bands containing
common fragile sites
CSMD1
• 4 of the 14 tumor samples have either HPV16
integration or an intra-chromosomal
translocation at CSMD1
• CSMD1- CUB and sushi multiple domain gene,
2 Mbs gene, 3rd largest human gene
• Located at 8p23.2 (same band as FRA8C)
• Loss of CSMD1 associated with poor prognosis
in CRC. Exhibits antitumor activity through
activation of the Smad pathway
Other Big Genes With Multiple
Translocation Events
– PDE4D
– LRP1B
– LARGE
– PTPRD
– PTPRG
794,083 bases
1,900,279 bases
760,616 bases
1,718,478 bases
736,045 bases
5q11.2
Near FRA2K
Near FRA22B
9p24.1
Near FRA3B
Digital Droplet PCR (ddPCR)
• Powerful technology to identify rare events (for
example, RainDance can produce 10 million
droplets).
• Circulating tumor DNA in the blood is
representative of the state of a growing tumor.
Post surgery this clears rapidly (if the tumor is
completely excised).
• Liquid biopsy to measure proportion of tumor
DNA in blood (by measuring a tumor-specific
marker, such as a translocation detected by
mate-pair sequencing).
Problem with ddPCR for cancer
screening
• Most cancers are not like CML (where
85% of them have the BCR-ABL
translocation), or colorectal cancer (where
80% have a specific mutation in K-ras)
• Difficult to find a small number of
alterations (either mutations and/or
translocations) for detecting unknown
cancers
Problem with ddPCR for cancer
screening
• Most cancers are not like CML (where
85% of them have the BCR-ABL
translocation), or colorectal cancer (where
80% have a specific mutation in K-ras)
• Difficult to find a small number of
alterations (either mutations and/or
translocations) for detecting unknown
cancers
Solution?
• Identify cancer- and patient-specific
alterations first
• Use 3-5 Gbs of MP-Seq.
• Identifies novel junctions
• Also may give information about that
specific cancer
• Each OPSCC had many alterations which
would be ideal of ddPCR
ddPCR To Monitor Therapy
• Identify novel junctions (either HPV
integration, translocation or
insertion/deletion)
• Construct PCR primers for these cancerspecific alterations (also patient-specific)
• Do ddPCR on blood samples to monitor
patient post-surgery as well as post other
treatment modalities
Acknowledgements
David I. Smith Lab
Ge Gao, Ph.D.
Otolaryngology
Vivian Wang, Ph.D.
Kerry Olson, MD
Jan Kasperbauer, MD
Eric Moore, MD
Erik Thorland, Ph.D.
Matt Ferber, Ph.D.
Nicole Tombers
Analysis
Sarah Johnson, Ph.D.
George Vasmatzis,
Ph.D.
Anatomic Pathology
Joaquin Garcia, MD
Radiation Oncology
Daniel Ma, M.D.