detection of low-frequent mitochondrial dna variants using … · 2019. 4. 10. · detection of...
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Detection of low-frequent mitochondrial DNA variants using SMRT sequencing
Marjolein J.A. Weerts
SMRT Leiden 2018 June 13
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Content
Mitochondrial DNA & liquid biopsy in oncology
Pitfalls when studying human mitochondrial DNA
Approach to detect low-frequent mitochondrial DNA variants using SMRT sequencing
Application in a pilot study
2
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Human mitochondrial DNA
1 * With the exception of aneuploid tumor cells and germ cells
Nuclear DNA
Mitochondrial DNA
Inheritance Maternal & Paternal Maternal
Structure Double stranded lineair
Double stranded circular
Size 3.1 Gb 16.6 kb
Cellular content 99 – 99.9% 1 – 0.1%
Copies/cell Two* Hundreds to Thousands
Gene density ~ 1 / 120 kb ~ 1 / 0.45 kb
Mutation rate ~10-fold higher
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Liquid biopsy in oncology
Less invasive than a regular biopsy! Blood-circulating cell-free DNA (cfDNA) Released by normal as well as tumor cells
Genetic information on the patient’s cancer (primary tumor ánd metastases)
JCO 32(6): 579-586, 2014
3
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Studying human mtDNA in cancer
HYPOTHESIS
The high mtDNA copy number per cell as well as the high mtDNA mutation rate make it worthwhile to explore the potential of tumor-specific cf-mtDNA variants as cancer marker in the blood of cancer patients
4
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Studying human mtDNA in cancer
HYPOTHESIS
The high mtDNA copy number per cell as well as the high mtDNA mutation rate make it worthwhile to explore the potential of tumor-specific cf-mtDNA variants as cancer marker in the blood of cancer patients
AIMS
Detection of low-frequent mtDNA variants
Highly mtDNA-specific detection of mtDNA variants
4
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Pitfalls when studying human mtDNA
High mutation rate
Heteroplasmy
Nuclear insertions of mitochondrial origin: NUMTs
5
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Pitfalls when studying human mtDNA
High mutation rate Several orders of magnitude higher than that of nDNA
Nearly 10,000 variable positions reported in databases (e.g. mitomap, mtDB)
6
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Pitfalls when studying human mtDNA
Heteroplasmy Genetically different mtDNA molecules within a single cell
Heteroplasmy patterns differ between tissues within an individual
7
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Pitfalls when studying human mtDNA
Nuclear insertions of mitochondrial origin: NUMTs Each 175 bp mtDNA segment: ~9.5 NUMT copies
Cancer cells contain somatic NUMT insertion events
NUMT similarity to mtDNA interferes with variant detection
9
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Studying human mtDNA in cancer
HYPOTHESIS
The high mtDNA copy number per cell as well as the high mtDNA mutation rate make it worthwhile to explore the potential of tumor-specific cf-mtDNA variants as cancer marker in the blood of cancer patients
AIMS
Detection of low-frequent mtDNA variants
Highly mtDNA-specific detection of mtDNA variants
10
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Highly mtDNA-specific detection of mtDNA variants
Minimize the interference of NUMTs:
pure mtDNA sample (devoid of nuclear DNA)
sequence large fragments SMRT sequencing (~80% of human NUMTs are < 500 bp mtDNA segments)
11 MJA Weerts et al., Scientific Reports 2018; 8:2261
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SMRT sequencing approach for mtDNA
12
1 • Amplification of mtDNA (15-18 PCR cycles)
• 9x 1700-3000 bp amplicons covering the entire mtDNA
2 • Purify & pool amplicons (equimolar) per sample
3 • Barcode each sample (5 PCR cycles), purify & pool samples
4 • Generate SMRTbell library
5 • Sequencing on RSII or Sequel
MJA Weerts et al., Scientific Reports 2018; 8:2261
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SMRT sequencing approach for mtDNA
12
1 • Amplification of mtDNA (15-18 PCR cycles)
• 9x 1700-3000 bp amplicons covering the entire mtDNA
2 • Purify & pool amplicons (equimolar) per sample
3 • Barcode each sample (5 PCR cycles), purify & pool samples
4 • Generate SMRTbell library
5 • Sequencing on RSII or Sequel
MJA Weerts et al., Scientific Reports 2018; 8:2261
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SMRT sequencing approach for mtDNA
12
1 • Amplification of mtDNA (15-18 PCR cycles)
• 9x 1700-3000 bp amplicons covering the entire mtDNA
2 • Purify & pool amplicons (equimolar) per sample
3 • Barcode each sample (5 PCR cycles), purify & pool samples
4 • Generate SMRTbell library
5 • Sequencing on RSII or Sequel
MJA Weerts et al., Scientific Reports 2018; 8:2261
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SMRT sequencing approach for mtDNA
12
1 • Amplification of mtDNA (15-18 PCR cycles)
• 9x 1700-3000 bp amplicons covering the entire mtDNA
2 • Purify & pool amplicons (equimolar) per sample
3 • Barcode each sample (5 PCR cycles), purify & pool samples
4 • Generate SMRTbell library
5 • Sequencing on RSII or Sequel
MJA Weerts et al., Scientific Reports 2018; 8:2261
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SMRT sequencing approach for mtDNA
12
1 • Amplification of mtDNA (15-18 PCR cycles)
• 9x 1700-3000 bp amplicons covering the entire mtDNA
2 • Purify & pool amplicons (equimolar) per sample
3 • Barcode each sample (5 PCR cycles), purify & pool samples
4 • Generate SMRTbell library
5 • Sequencing on RSII or Sequel
MJA Weerts et al., Scientific Reports 2018; 8:2261
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SMRT sequencing approach for mtDNA
13
6 • Generate circular consensus reads (CCS2 algorithm)
7 • Attribute reads using sample-specific barcode (TSSV)
8 • Select highly accurate CCS reads (QV>=99%, >= 5 passes)
9 • Trim reads (Cutadapt) and align to extended version or rCRS
(BWA-MEM)
10 • Call positions alternative to the reference sequence in pileup
(Rsamtools, min_nucleotide_depth=5)
MJA Weerts et al., Scientific Reports 2018; 8:2261
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SMRT sequencing approach for mtDNA
13
6 • Generate circular consensus reads (CCS2 algorithm)
7 • Attribute reads using sample-specific barcode (TSSV)
8 • Select highly accurate CCS reads (QV>=99%, >= 5 passes)
9 • Trim reads (Cutadapt) and align to extended version or rCRS
(BWA-MEM)
10 • Call positions alternative to the reference sequence in pileup
(Rsamtools, min_nucleotide_depth=5)
MJA Weerts et al., Scientific Reports 2018; 8:2261
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SMRT sequencing approach for mtDNA
13
6 • Generate circular consensus reads (CCS2 algorithm)
7 • Attribute reads using sample-specific barcode (TSSV)
8 • Select highly accurate CCS reads (QV>=99%, >= 5 passes)
9 • Trim reads (Cutadapt) and align to extended version or rCRS
(BWA-MEM)
10 • Call positions alternative to the reference sequence in pileup
(Rsamtools, min_nucleotide_depth=5)
MJA Weerts et al., Scientific Reports 2018; 8:2261
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SMRT sequencing approach for mtDNA
13
6 • Generate circular consensus reads (CCS2 algorithm)
7 • Attribute reads using sample-specific barcode (TSSV)
8 • Select highly accurate CCS reads (QV>=99%, >= 5 passes)
9 • Trim reads (Cutadapt) and align to extended version or rCRS
(BWA-MEM)
10 • Call positions alternative to the reference sequence in pileup
(Rsamtools, min_nucleotide_depth=5)
MJA Weerts et al., Scientific Reports 2018; 8:2261
compensate for mapping bias due to
circularity of the mitochondrial
genome
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SMRT sequencing approach for mtDNA
13
6 • Generate circular consensus reads (CCS2 algorithm)
7 • Attribute reads using sample-specific barcode (TSSV)
8 • Select highly accurate CCS reads (QV>=99%, >= 5 passes)
9 • Trim reads (Cutadapt) and align to extended version or rCRS
(BWA-MEM)
10 • Call positions alternative to the reference sequence in pileup
(Rsamtools, min_nucleotide_depth=5)
MJA Weerts et al., Scientific Reports 2018; 8:2261
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Detection of low-frequent mtDNA variants
14 MJA Weerts et al., Scientific Reports 2018; 8:2261
Determine detection limits empirically:
mixtures of the cell lines containing different mtDNA variants
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Detection of low-frequent mtDNA variants
14 MJA Weerts et al., Scientific Reports 2018; 8:2261
Determine detection limits empirically:
mixtures of the cell lines containing different mtDNA variants
Allele frequency
0% 0.001% 0.01% 0.1% 1% 10%
Digital PCR 0/2 0/2 1/2 1/2 2/2 2/2
UltraSEEK 0/7 0/7 0/7 2/7 7/7 7/7
SMRT seq 0/23 0/23 0/23 21/23 23/23 23/23 positions
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Studying human mtDNA in cancer
HYPOTHESIS
The high mtDNA copy number per cell as well as the high mtDNA mutation rate make it worthwhile to explore the potential of tumor-specific cf-mtDNA variants as cancer marker in the blood of cancer patients
10
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Tumor-specific cf-mtDNA variants as cancer marker in the blood of cancer patients?
15 MJA Weerts et al., Neoplasia. 2018; 20(7):687–696
Pilot study: SMRT sequence the entire mtDNA of eight cancer patients
Tumor, matched normal (tissue of origin!) and cfDNA
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Tumor-specific cf-mtDNA variants as cancer marker in the blood of cancer patients?
Patient 1
Primary breast cancer (2 cm) with lymph nodes involved
Neoadjuvant endocrine therapy
Surgery, radiation
Prolonged endocrine therapy
Disease recurrence after 4 months (bone, lung and liver metastases)
Second-line endocrine therapy
Disease progression
Chemotherapy
16 MJA Weerts et al., Neoplasia. 2018; 20(7):687–696
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Tumor-specific cf-mtDNA variants as cancer marker in the blood of cancer patients?
Patient 1
Primary breast cancer (2 cm) with lymph nodes involved
Neoadjuvant endocrine therapy
Surgery, radiation
Prolonged endocrine therapy
Disease recurrence after 4 months (bone, lung and liver metastases)
Second-line endocrine therapy
Disease progression
Chemotherapy
16
Normal mammary Primary tumor
cfDNA
cfDNA
MJA Weerts et al., Neoplasia. 2018; 20(7):687–696
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1200
G>A
5351
A>G
7368
T>C
664
G>A
6255
G>A
1310
3 G
>A
1629
8 T>
C
Primary tumor a 0 0 0 15% 44% 1.2% 99%
Primary tumor b 0 0 0 7.6% 38% 1.2% 99%
Mammary tissue a 0 0 0 0 0.8% 0 99%
Mammary tissue b 0 0 0 0 0 0 98%
cfDNA (pre surgery) 2.0% 1.1% 1.0% 0 0 0 99%
cfDNA (post chemo) 2.9% 0 0 0 0 0 99%
Tumor-specific cf-mtDNA variants as cancer marker in the blood of cancer patients?
Patient 1
17 MJA Weerts et al., Neoplasia. 2018; 20(7):687–696
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Tumor-specific cf-mtDNA variants as cancer marker in the blood of cancer patients?
Patient 1
Primary breast cancer (2 cm) with lymph nodes involved
Neoadjuvant endocrine therapy
Surgery, radiation
Prolonged endocrine therapy
Disease recurrence after 4 months (bone, lung and liver metastases)
Second-line endocrine therapy
Disease progression
Chemotherapy
18
Normal mammary Primary tumor
cfDNA
cfDNA
cfDNA
cfDNA cfDNA
MJA Weerts et al., Neoplasia. 2018; 20(7):687–696
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1200
G>A
5351
A>G
7368
T>C
664
G>A
6255
G>A
1310
3 G
>A
1629
8 T>
C
Primary tumor a 0 0 0 15% 44% 1.2% 99%
Primary tumor b 0 0 0 7.6% 38% 1.2% 99%
Mammary tissue a 0 0 0 0 0.8% 0 99%
Mammary tissue b 0 0 0 0 0 0 98%
cfDNA (pre surgery) 2.0% 1.1% 1.0% 0 0 0 99%
cfDNA (pre 2nd line) na na na 0 0.03% na na
cfDNA (pre chemo) na na na 0.06% 0.3% na na
cfDNA (post chemo) na na na 0 0 na na
cfDNA (post chemo) 2.9% 0 0 0 0 0 99%
Tumor-specific cf-mtDNA variants as cancer marker in the blood of cancer patients?
Patient 1
MJA Weerts et al., Neoplasia. 2018; 20(7):687–696
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Tumor-specific cf-mtDNA variants as cancer marker in the blood of cancer patients?
Patient 2
Colorectal cancer with synchronous hepatic metastases
Surgery
20 MJA Weerts et al., Neoplasia. 2018; 20(7):687–696
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Tumor-specific cf-mtDNA variants as cancer marker in the blood of cancer patients?
Patient 2
Colorectal cancer with synchronous hepatic metastases
Surgery
20
Normal colon Primary tumor cfDNA
Metastasis 1
Metastasis 2 Normal liver
MJA Weerts et al., Neoplasia. 2018; 20(7):687–696
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7078
G>A
1245
3 T>
C
1016
0 C
>T
1091
4 G
>A
66 G
>T
152
T>C
189
A>G
1614
7 C
>T
1614
8 C
>T
1924
T>C
2305
T>C
4048
G>A
1630
4 T>
C
60 T
>C
72 T
>C
Primary tumor 0 0 0 0 0 0 0 0 0 0.7%
9.9%
1.4%
99%
0 11%
Metastasis 1 0 0 0 11%
0 0 0 0 0 42%
34%
0 100%
0 6.2%
Metastasis 2 0 0 21%
1.4%
0 0 0 0 0.9%
35%
39%
0 100%
0.7%
5.6%
Colon tissue 0 0 0 0 2.5%
1.1%
0.4%
0 0 0 1.1%
0 99%
0 6.5%
Liver tissue 0 0 0 0 0 0.4%
1.1%
1.5%
1.2%
0 0 0 99%
3.4%
43%
cfDNA 1.2%
1.9%
0 0 0 0 0.2%
0 0 0 0 0 99%
0 0.2%
Tumor-specific cf-mtDNA variants as cancer marker in the blood of cancer patients?
Patient 2
MJA Weerts et al., Neoplasia. 2018; 20(7):687–696
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7078
G>A
1245
3 T>
C
1016
0 C
>T
1091
4 G
>A
66 G
>T
152
T>C
189
A>G
1614
7 C
>T
1614
8 C
>T
1924
T>C
2305
T>C
4048
G>A
1630
4 T>
C
60 T
>C
72 T
>C
Primary tumor 0 0 0 0 0 0 0 0 0 0.7%
9.9%
1.4%
99%
0 11%
Metastasis 1 0 0 0 11%
0 0 0 0 0 42%
34%
0 100%
0 6.2%
Metastasis 2 0 0 21%
1.4%
0 0 0 0 0.9%
35%
39%
0 100%
0.7%
5.6%
Colon tissue 0 0 0 0 2.5%
1.1%
0.4%
0 0 0 1.1%
0 99%
0 6.5%
Liver tissue 0 0 0 0 0 0.4%
1.1%
1.5%
1.2%
0 0 0 99%
3.4%
43%
cfDNA 1.2%
1.9%
0 0 0 0 0.2%
0 0 0 0 0 99%
0 0.2%
Tumor-specific cf-mtDNA variants as cancer marker in the blood of cancer patients?
Patient 2
MJA Weerts et al., Neoplasia. 2018; 20(7):687–696
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Studying human mtDNA in cancer
HYPOTHESIS
The high mtDNA copy number per cell as well as the high mtDNA mutation rate make it worthwhile to explore the potential of tumor-specific cf-mtDNA variants as cancer marker in the blood of cancer patients
22
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Studying human mtDNA in cancer
HYPOTHESIS
The high mtDNA copy number per cell as well as the high mtDNA mutation rate make it worthwhile to explore the potential of tumor-specific cf-mtDNA variants as cancer marker in the blood of cancer patients
CONCLUSIONS
SMRT sequencing suitable for low-frequent mtDNA single-nucleotide variant detection.
Tumor-specific mtDNA variants rarely detected as cfDNA.
22
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Acknowledgements
These results have been published
Weerts et al., Scientific Reports 2018; 8:2261 Weerts et al., Neoplasia. 2018; 20(7):687–696
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Erasmus MC Medical Oncology John Foekens
John Martens
Stefan Sleijfer
LUMC Leiden Genome Technology Center Rolf Vossen
Yahya Anvar
Philips Research Precision and
Decentralized Diagnostics Dianne van Strijp
Pieter-Jan van der Zaag
Eveline den Biezen – Timmermans
Anja van de Stolpe
contact: [email protected]