1. DNA Methylation and Cancer
Shen-Chih Chang, Ph.D
Epi 243
May 14, 2009

2. Presentation Outline Epigenetics and DNA methylation
DNA methylation and Cancer
Techniques of measuring DNA methylation
Methylation-Specific PCR (MSP)
Selected results on lung and head and neck cancer
MethyLight Taqman real-time Methylation Assay
Selected results on bladder cancer
Selected results on liver cancer

3. Epigenetics
The study of heritable changes of DNA, not involving changes in DNA sequence, that regulate gene expression.
Classic genetics alone cannot explain the diversity of phenotypes within a population.
Identical twins or cloned animals have different phenotypes and susceptibilities to diseases.
Epigenetics provides additional instructions on how, where, and when the genetic information should be used.
Epigenetics controls gene expression in two main ways:
Chemically alteration of DNA: DNA methylation
Modification of histones: chromatin structure modulation

4. Epigenetic Mechanisms
Qiu J, 2006

5. Mapping chromosomal regions with differential DNA methylation in Monozygous twins by using comparative genomic hybridization for methylated DNA
Mapping chromosomal regions with differential DNA methylation in MZ twins by using comparative genomic hybridization for methylated DNA. Competitive hybridization onto normal metaphase chromosomes of the AIMS products generated from 3- and 50-year-old twin pairs. Examples of the hybridization of chromosomes 1, 3, 12, and 17 are displayed.
The 50-year-old twin pair shows abundant changes in the pattern of DNA methylation observed by the presence of green and red signals that indicate hypermethylation and hypomethylation events, whereas the 3-year-old twins have a very similar distribution of DNA methylation indicated by the presence of the yellow color obtained by equal amounts of the green and red dyes. Significant DNA methylation changes are indicated as thick red and green blocks in the ideograms.
Fraga M. F. et.al. PNAS 2005;102:
©2005 by National Academy of Sciences

6. DNA Methylation Chemical modification of DNA
Addition of a methyl group to the number 5 carbon of the cytosine, to convert cytosine to 5-methylcytosine.
In humans, DNA methylation occurs in a cytosine which is immediately followed by a guanine (dinucleotide CpGs).
(The CpG notation is used to distinguish a cytosine followed by guanine from a cytosine base paired to a guanine).

7. CpG Sites and CpG islands
CpG sites are not randomly distributed in the genome - the frequency of CpG sites in human genomes is 1%, which is less than the expected (~4-6%).
Around 60-90% of CpGs are methylated in mammals. DNA methylation frequently occurs in repeated sequences, and may help to suppress junk DNA and prevent chromosomal instability.
Unmethylated CpGs are grouped in clusters called “CpG islands” which tend to be located in the promoter regions of many genes.

8. Function of DNA Methylation
In humans, DNA is methylated by three enzymes, DNA methyltransferase 1, 3a, and 3b (DNMT1, DNMT3a, DNMT3b).
DNMT3a and 3b are the de novo methyltransferases that set up DNA methylation patterns early in development.
DNMT1 is the maintenance methyltransferase that is responsible for copying DNA methylation patterns to the daughter strands during DNA replication.
DNA methylation is important in:
Transcriptional gene silencing
Maintain genome stability
Embryonic development
Genomic imprinting
X chromosome inactivation

9. DNA Methylation and Cancer
Hypomethylation – decreased methylation levels
A lower level of DNA methylation in tumors as compared to their normal-tissue counterparts was one of the first epigenetic alterations to be found in human cancer. (Feinberg AP, et al., 1983).
Global hypomethylation of DNA sequences that are normally heavily methylated may result in
Chromosomal instability
Increased transcription from transposable elements
An elevated mutation rate due to mitotic recombination
Promoter hypomethylation of proto-oncogenes will activate the repressed gene expression

10. DNA Methylation and Cancer
Hypermethylation – increased methylation levels
Promoter hypermethylation can suppress gene expression in two ways:
Methylated DNA may itself impede the binding of transcriptional proteins to the gene
Methylated DNA may be bound by proteins which can modify histones to form compact, inactive chromatin.
Promoter hypermethylation of tumor-suppressor genes is a major event in the origin of many cancers.
The profiles of hypermethylation of the CpG islands are specific to the cancer type.

11. Baylin et al. 2001; Jones et al. 2002

12. Laird PW, 1997

13. Das PM 2004

14. Das PM 2004

15. Factors associated with DNA Methylation
Aging
Nutrient intake
Genetic polymorphisms
Metal exposure
Tobacco Smoking
Alcohol Drinking

16. Application of DNA Methylation Measurement
Early diagnosis –
Detection of CpG-island hypermethylation in biological fluids (serum/plasma)
Prognosis –
Hypemethylation of specific genes
Whole DNA methylation profiles
Prediction –
CpG island hypermethylation as a marker of response to chemotherapy
Prevention –
Developing DNMTs inhibitors as chemopreventive drugs to reactive silenced genes

18. Techniques of Measuring Gene-Specific Hypermethylation
Methylation Specific PCR (MSP)
MethyLight Taqman Real-Time Methylation Assay

19. Methylation Specific PCR (MSP)
DNA Modification
C U
CM C
Two set of primers
Methylated
Unmethylated
Positive control (Universal Methylated DNA)
Negative control (H2O)

20. Results from the MSP p16 GSTP1 MGMT
These are the results from the MSP. “+” is the positive control; they should show a band with “M” primer but not with “U” primer. “-” is the negative control; they should not show anything either with “M” nor with “U” primer to make sure that both primers are not containment. As a results, sample 486 is defined to be “Yes” in the p16 hypermethylation whereas sample 487 is defined to be “No”.

21. Selected Results on Lung and Head and Neck Cancer
Shu-Chun Chuang, Ph.D
Aim:
To evaluate the associations between lung and head and neck cancer and promoter-region methylation of selected genes, including P16INK4a, MGMT , and GSTP1 genes in buccal cell DNA in a population-based case-control study in Los Angeles county.

22. Materials and Methods
Study design: population-based case-control study
Subject selection criteria: Cases were newly diagnosed and pathologically confirmed. Controls were matched to cases on age, gender, and neighborhood.
Eligibility:
Current resident of Los Angeles County
18-65 during the observation period,
either speak English or Spanish or have translators available
have no other primary cancers (cases)
have no history of lung or head and neck cancers (controls)
Biological samples: buccal cell samples were collected during the interview
This is a population-based case-control study. Cases were newly diagnosed and pathologically confirmed from a Cancer Surveillance Program for the Los Angeles County. Controls were matched to cases on age (within 10 years old), sex, and neighborhood. Cases were current resident of Los Angeles County, years old during the observation period: , either speak English or Spanish or translator available, and no history of lung and head and neck cancers. All participants were interviewed by trained interviewers; ml cell samples were collected after the interview.

23. Response Rate
The response rate was 68% for control, but it is a little bit lower for cases. In lung cancer cases, the reasons for non-participation were die before we contacted them (25%), unwilling to participate (16%), and incorrect address (14%). In head and neck cancer cases, they are unwilling to participate (21%), incorrect address (18%), die or too ill to participate (14%). The buccal cell response rates are good. Except for oral and pharyngeal cancer cases, the response rates are almost 90%.

24. Main Effect of p16 Hypermethylation
Controls
N (%)
Lung
Head and Neck
Crude OR
(95% CI)
Adjusted OR1
Adjusted OR2 No
769 (84)
433 (81)
1.00
293 (84)
Yes
146 (16)
100 (19)
1.22 ( )
1.31 ( )
57 (16)
1.03 ( )
1.03 ( )
Adjusted for age, sex, race, and pack-years of smoking.
Adjusted for age, sex, race, pack-years of smoking, and drink-years of alcohol consumption

25. Main Effect of GSTP1 Hypermethylation
Controls
N (%)
Lung
Head and Neck
Crude OR
(95% CI)
Adjusted OR1
Adjusted OR2 No
675 (88)
354 (85)
1.00
254 (86)
Yes
96 (12)
61 (15)
1.21 ( )
1.17 ( )
40 (14)
1.11 ( )
1.04 ( )
Adjusted for age, sex, race, and pack-years of smoking.
Adjusted for age, sex, race, pack-years of smoking, and drink-years of alcohol consumption

26. Main Effect of MGMT Hypermethylation
Controls
N (%)
Lung
Head and Neck
Crude OR
(95% CI)
Adjusted OR1
Adjusted OR2 No
721 (82)
380 (77)
1.00
250 (76)
Yes
157 (18)
112 (23)
1.35 ( )
1.19 ( )
78 (24)
1.43 ( )
1.34 ( )
Adjusted for age, sex, race, and pack-years of smoking.
Adjusted for age, sex, race, pack-years of smoking, and drink-years of alcohol consumption
Stratified by Site
CDH1 is expressed in all epithelial cells acting as an adhesion molecule. Inactivation of CDH1 induces the impairment of cell adhesiveness and proliferation and results in tumor progression.
MGMT protects cells from DNA damage cause by mutagenic agents leading to alkylation at O6-guanine.
MGMT
Hypermethylation
Controls
N (%)
Pharynx
Crude OR
(95% CI)
Adjusted OR No
721 (82)
38 (68)
1.00
Yes
157 (18)
18 (32)
2.18 ( )
2.00 ( )
Adjusted for age, sex, race, pack-years of smoking, and drink-years of alcohol consumption

27. MethyLight Taqman Methylation Assay

28. Real-Time PCR
Real-time PCR is a PCR-based method using fluorescent molecules to directly measure the reaction while amplification is taking place.
Data are collected throughout the PCR process rather than the end of the process.
It measures the point in time when amplification of a target is first detected during cycling rather than by the amount of target accumulated at the end of PCR.
Can be used to achieve both qualitative and quantitative measurements.

29. Traditional Standard PCR5’ 3’ 5’ 3’ 5’ 3’ 5’ 3’
Denaturation
Primer Annealing
Elongation
Taq 5’ 3’ 5’ 5’ 5’ 3’
Taq
Repeat
In theory, product accumulation is proportional to 2n,
where n is the number of amplification cycle repeats

30. However, in reality...

31. Limitation in standard PCR
Amplification is exponential, but the exponential increase is limited:
Theoretical
A linear increase follows exponential
Eventually plateaus
plateau
Real Life
linear
Log Target DNA
Geometric
Cycle #

32. Standard PCR as endpoint
Identical reactions will have very different final amounts of fluorescence at endpoint

33. Real-Time PCR
The point at which the fluorescence rises appreciably above
threshold is called CT
Identical reactions will have identical CT values
Threshold CT

34. How to measure DNA concentration?

35. How to measure DNA concentration?

36. Setup for MethyLight MSP
Modified DNA as templates
Methylation sequence specific forward and backward primers
Taqman Probes: 5’-FAM TEMRA-3’
Taqman Universal PCR Master Mix
Negative control contains PCR reagents but without DNA – ddH2O
Replicate wells -- using two or more replicate reactions per sample to ensure statistical significance.

37. Setup for MethyLight MSP
A calibrator -- The sample used as the basis for comparative results.
A universal methylated positive control was used in this study as a calibrator.
An endogenous control gene -- A gene present at a consistent expression level in all experimental samples. An endogenous gene is used as an internal control of the difference amount of input DNA.
ACTB gene without CpG dinucleotides was used as endogenous control gene in this study.

38. Setup for MethyLight MSP
Endogenous control gene; others are all target genes
Calibrator
Negative control

39. Analyzing Relative Quantification Data
∆∆CT Method
∆CT (sample) = CT (marker)- CT (ACTB)
∆CT (calibrator) = CT (marker)- CT (ACTB)
∆∆CT = ∆CT (sample) - ∆CT (calibrator)
Relative quantification of methylated 5’-cytosine = E(-∆∆CT)
E: efficiency of amplification
Assumption: E = 2 for both marker and endogenous gene

40. Analyzing Relative Quantification Data -- Amplification Plot (linear plot of reporter signal vs cycle number) --
negative control
ACTB gene

41. Analyzing Relative Quantification Data -- Amplification plot of positive control--
linear plot of reporter signal vs cycle number
logarithmic plot of baseline-corrected reporter signal vs. cycle number

42. Analyzing Relative Quantification Data -- Amplification plot of p16 gene hypermethylation--
After adjusting baseline and threshold, software automatically calculates relative quantity (RQ) of the sample compared to the calibrator
logarithmic plot of baseline-corrected reporter signal vs. cycle number

43. Selected Results on Bladder Cancer
Yu-Ching Kelly Yang, Ph.D
Aim:
To evaluate the associations between promoter hypermethylation status of genes involved in bladder tumorigenesis (including P16INK4a, P14ARF, APC, CDH1, RASSF1A, MGMT, and GSTP1) in WBC, NBC, CIS, and, cancer tissues from 73 bladder cancer patients.

44. Materials and Methods Hospital-based case-only study
Memorial Sloan-Kettering Cancer Center
Recruitment period: Oct 1993 to June 1997
Cases selection criteria:
Newly diagnosed and pathologically confirmed bladder cancer cases
In stable medical condition
Have lived in the US for at least one year
Fresh bladder tissues were obtained from radical cystectomy
This is a population-based case-control study. Cases were newly diagnosed and pathologically confirmed from a Cancer Surveillance Program for the Los Angeles County. Controls were matched to cases on age (within 10 years old), sex, and neighborhood. Cases were current resident of Los Angeles County, years old during the observation period: , either speak English or Spanish or translator available, and no history of lung and head and neck cancers. All participants were interviewed by trained interviewers; ml cell samples were collected after the interview.

45. Study Population Patients with tissue blocks N = 152
Only have cancerous tissues
N = 33
Did not conform to
pathological criteria
N = 46
Patients with cancerous tissue and
non-cancerous tissue (NBC)
N = 73

46. Proportion of methylation detected in seven tumor-related genes in white blood cells (WBC), in situ (CIS), non-cancerous (NBC), and cancerous tissue specimens of the bladder cancer patients.
WBC
CIS
NBC
Cancer
Gene
analyzed
Methylation detected
P16
0/11
0.00
0/6
0/73
21/73
0.29
APC
3/6
0.5
42/73
0.58
MGMT
3/73
0.04
RASSF1A
1/11
0.09
2/6
0.33
10/73
0.14
28/73
0.38
CDH1
2/11
0.18
4/6
0.67
22/73
0.30
45/73
0.62
GSTP1
1/73
0.01
2/73
0.03
ARF
WBC:white blood cell; CIS: carcinoma in situ; NBC: epithelium showing no remarkable histological change
No DNA methylation of the P14ARF gene was detected in any specimen sample analyzed.
Although the CDH1 and RASSF1A genes were methylated even in WBC, methylation of other genes was never detected in WBC samples.
All genes except the P14ARF gene were methylated in bladder cancer tissues.
The prevalence of DNA methylation of the APC, CDH1, and RASSF1A increased progressively from WBC through NBC to cancer tissues, whereas DNA methylation of the P16INK4A and MGMT were prominent only in cancer tissues and never in NBC.

47. The mean and standard deviation of average number of methylated genes in white blood cells (WBC), carcinoma in situ (CIS), epithelium showing no remarkable histological change (NBC) and cancer tissues of bladder cancer patients
The average number of methylated genes was higher in NBC, CIS, and cancer tissue than that in WBC (p=0.05, 0.03, and <0.01, respectively).
Compared with NBC, the number of methylated genes was higher in cancer tissue (p<0.01).
However, there was no obvious difference in the average number of methylated genes between CIS and cancer tissues.

48. The association between hypermethylation in promoter-region of tumor-related genes and environmental exposures, including cigarette smoking and alcohol drinking
we found a marginal reverse association between cigarette smoking and hypermethylation in the P16INK4A gene

49. The association between hypermethylation in promoter-region of six tumor-related gene and clinicopathological factors
The prevalence of DNA methylation of at least one gene and the number of methylated genes increased significantly in tumors with vascular invasion than tumors without vascular invasion (p=0.008 and 0.01, respectively). We also found a strong association between vascular invasion and hypermethylation in the CDH1 gene (p=0.032).

50. The association between hypermethylation in six tumor-related genes and survival time
No obvious association was found between the favorable patient’s prognoses and the MI

51. Selected Results on Liver Cancer
Shen-Chih Chang, Ph.D
Aim:
To evaluate the associations between HCC and promoter-region methylation of selected genes, including APC, CDH1, P16INK4a, and MGMT genes in peripheral blood DNA in a Chinese population. Also, to examine the associations of hypermethylation with age, gender, tobacco smoking, and alcohol consumption.

52. Materials and Methods Population-based case-control study
Taixing, China
Recruitment period: Jan 1, 2000 to Jun 30, 2000
Cases selection criteria:
Newly diagnosed liver cancer cases
Age 25-70
Have no history of any previous diagnosis of cancer
Have lived in Taixing for at least 10 years
A group of healthy population controls were frequency-matched (on age and gender) to cases with a control-to-case ratio of 2:3 from the general population in Taixing (one common control group to three case groups)
Epidemiology data collection
Face-to-face interview
Blood samples collected during interview
This is a population-based case-control study. Cases were newly diagnosed and pathologically confirmed from a Cancer Surveillance Program for the Los Angeles County. Controls were matched to cases on age (within 10 years old), sex, and neighborhood. Cases were current resident of Los Angeles County, years old during the observation period: , either speak English or Spanish or translator available, and no history of lung and head and neck cancers. All participants were interviewed by trained interviewers; ml cell samples were collected after the interview.

53. Study Population 358 incident liver cancer cases were diagnosed
204 (57%) recruited
199 blood samples
194 DNA extracted
464 potential controls were identified
415 (90%) recruited
410 blood samples
393 DNA extracted

54. Associations between promoter hypermethylation of APC, CDH1, MGMT, and P16 gene and HCC
Case
(%)
Control (%)
Crude OR
(95% CI)
Adjusted OR1
Adjusted OR2
APC -
181 (95.6)
332 (94.1)
1.00 +
8 (4.2)
21 (6.0)
0.70 (0.30, 1.61)
0.52 (0.19, 1.41)
0.70 (0.24, 2.08)
p-value
0.3998
0.1981
0.5233
CDH1
100 (52.9)
173 (49.4)
89 (47.1)
177 (50.6)
0.87 (0.61, 1.24)
0.93 (0.61, 1.42)
1.05 (0.65, 1.68)
0.4406
0.7287
0.8548
MGMT
190 (100.0)
351 (100.0)
0 (0.0)
P16
348 (99.4)
2 (0.6)
No. of methylated genes
None
99 (52.4)
171 (48.9) 1
83 (43.9)
160 (45.7)
0.90 (0.62, 1.29)
0.98 (0.63, 1.51)
1.04 (0.64, 1.69) 2
7 (3.7)
19 (5.4)
0.64 (0.26, 1.57)
0.50 (0.17, 1.47)
0.78 (0.24, 2.59)
1: Adjusted on age, gender, BMI, education, smoking pack-years, alcohol drinking, HBsAg, and plasma AFB1-albumin adducts
2: Further adjusted on plasma levels of folate, vitamin B12, and homocysteine

55. Associations between promoter hypermethylation of APC and CDH1 gene and HCC, stratified on plasma folate levels
≦12.76 nM
>12.76 nM
Case/
Control
Crude OR
(95% CI)
Adjusted OR*
APC -
93/169
1.00
84/159 +
2/10
0.36 (0.08, 1.69)
0.20 (0.04, 1.06)
6/11
1.03 (0.37, 2.89)
1.01 (0.26, 3.86)
p-value
0.1974
0.0583
0.9513
0.9920
CDH1
48/82
49/89
47/96
0.84 (0.51, 1.38)
0.98 (0.54, 1.77)
41/79
0.94 (0.56, 1.58)
0.82 (0.43, 1.59)
0.4825
0.9412
0.8218
0.5653
APC+CDH1 (continuous)
0.78 (0.50, 1.22)
0.81 (0.48, 1.34)
0.96 (0.63, 1.47)
0.87 (0.50, 1.51)
0.2727
0.4084
0.8646
0.6294
*Adjusted on age, gender, BMI, education, smoking pack-years, alcohol drinking, HBsAg, and plasma AFB1-albumin adducts

56. Associations between promoter hypermethylation of APC and CDH1 gene and HCC, stratified on plasma vitamin B12 levels
≦ pM
> pM
Case/
Control
Crude OR
(95% CI)
Adjusted OR*
APC -
33/160
1.00
145/166 +
1/15
0.32 (0.04, 2.53)
0.24 (0.03, 2.04)
7/6
1.34 (0.44, 4.07)
1.62 (0.39, 6.69)
p-value
0.2824
0.1922
0.6096
0.5060
CDH1
19/79
80/92
15/94
0.66 (0.32, 1.39)
0.78 (0.33, 1.82)
72/79
1.05 (0.68, 1.62)
1.05 (0.59, 1.85)
0.2773
0.5628
0.8334
0.8726
APC+CDH1 (continuous)
0.64 (0.34, 1.21)
0.68 (0.34, 1.36)
1.07 (0.73, 1.58)
1.10 (0.67, 1.83)
0.1728
0.2781
0.7185
0.7060
*Adjusted on age, gender, BMI, education, smoking pack-years, alcohol drinking, HBsAg, and plasma AFB1-albumin adducts

57. Associations between promoter hypermethylation of APC and CDH1 gene and HCC, stratified on plasma homocysteine levels
≦9.50 µM
>9.50 µM
Case/
Control
Crude OR
(95% CI)
Adjusted OR*
APC -
79/168
1.00
100/161 +
4/10
0.85 (0.26, 2.80)
0.95 (0.24, 3.81)
4/11
0.59 (0.18, 1.89)
0.26 (0.05, 1.23)
p-value
0.7899
0.9468
0.3706
0.0890
CDH1
44/92
55/79
39/84
0.97 (0.58, 1.64)
1.00 (0.52, 1.92)
49/92
0.77 (0.47, 1.25)
0.75 (0.41, 1.39)
0.9115
0.9938
0.2825
0.3625
APC+CDH1 (continuous)
0.96 (0.61, 1.49)
1.00 (0.58, 1.70)
0.76 (0.50, 1.16)
0.67 (0.39, 1.15)
0.8393
0.9842
0.2020
0.1427
*Adjusted on age, gender, BMI, education, smoking pack-years, alcohol drinking, HBsAg, and plasma AFB1-albumin adducts

58. Associations between promoter hypermethylation of APC and CDH1 gene and age, gender, smoking, and alcohol drinking habits in the control group
Hypermethylation
APC
CDH1
No. of methylated genes No
(N, %)
Yes P* 1 2
Age
< 55
124 (37.4)
8 (38.1)
0.95
70 (40.5)
60 (33.9)
0.20
69 (40.4)
54 (33.8)
7 (36.8)
0.46
≥ 55
208 (62.7)
13 (61.9)
103 (59.5)
117 (66.1)
102 (59.7)
106 (66.3)
12 (63.2)
Gender
Female
104 (31.3)
6 (28.6)
0.79
54 (31.2)
54 (30.5)
0.89
53 (31.0)
50 (31.3)
5 (26.3)
0.91
Male
228 (68.7)
15 (71.4)
119 (68.8)
123 (69.5)
118 (69.0)
110 (68.8)
14 (73.7)
Smoking
Never
173 (52.3)
12 (57.1)
0.66
99 (57.6)
84 (47.5)
0.06
97 (57.1)
76 (47.5)
10 (52.6)
0.22
Ever
158 (47.7)
9 (42.9)
73 (42.4)
93 (52.5)
73 (42.9)
84 (52.5)
9 (47.4)
Alcohol
Drinking
168 (51.1)
0.47
90 (52.6)
85 (48.0)
0.39
88 (52.1)
80 (50.0)
0.45
161 (48.9)
81 (47.4)
92 (52.0)
81 (47.9)
*P-value from χ2 tests or Fisher’s exact test

59. Compare MSP and MethyLight
Methylation Specific PCR
MethyLight Methylation assay
Advantage
Inexpensive
Easy to perform
Less prone to human error
Faster, more efficient than MSP
More specific by adding Taqman probe
Disadvantage
Prone to human error
Easily get contaminated
Labor-intensive
Higher expenses for equipment maintenance

60. Other Techniques Gene-Specific DNA Methylation Global DNA Methylation
Sequencing
Microarray
High-resolution Melting Method (Roche)
BeadChip Technology (Illumina)
Global DNA Methylation
Liquid Chromatography-Mass Spectrometry
ELISA-based global methylation analysis assay (Sigma Aldrich)

61. References http://www.appliedbiosystems.com
Eads CA., et al. MethyLight: a high-throughput assay to measure DNA methylation. Nucleic Acids Res., 28: e32, 2000.
Esteller M. Epigenetics in cancer. New England Journal of Medicine, 358: , 2008
Qiu J. Epigenetics: unfinished symphony. Nature, 441: , 2006.
Zeschniqk M., et al. A novel real-time PCR assay for quantitative analysis of methylated alleles (QAMA): analysis of the retinoblastoma locus. Nucleic Acids Res., 7: 3125, 2004.

62. Thank you!