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
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
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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
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
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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
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
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Pitfalls when studying human mtDNA
High mutation rate
Heteroplasmy
Nuclear insertions of mitochondrial origin: NUMTs
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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)
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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
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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
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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
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
SMRT sequencing approach for mtDNA
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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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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Normal mammary Primary tumor
cfDNA
cfDNA
MJA Weerts et al., Neoplasia. 2018; 20(7):687–696
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
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
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Normal mammary Primary tumor
cfDNA
cfDNA
cfDNA
cfDNA cfDNA
MJA Weerts et al., Neoplasia. 2018; 20(7):687–696
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
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
Tumor-specific cf-mtDNA variants as cancer marker in the blood of cancer patients?
Patient 2
Colorectal cancer with synchronous hepatic metastases
Surgery
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Normal colon Primary tumor cfDNA
Metastasis 1
Metastasis 2 Normal liver
MJA Weerts et al., Neoplasia. 2018; 20(7):687–696
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
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
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
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.
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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]