Ns126:Calendar/NOTES/2016-2-1
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Cancer Specific Methylation haplotype region show increased MHL in the plasma
- From Dr. Zhang
- I think I can rationalize the difficulty in detecting tumor haplotypes in plasma at a sufficient high level of sensitivity and specificity.
- We are sequencing the plasma samples with very limited input DNA, equivalent to only a few hundred copies per sample. With the additional major loss of material during bisulfite conversion and library construction, we are probably sampling one or two dozens of molecules per sample. On the other hand, the fraction of tumor DNA in plasma is typically in the range of 0.1% or lower. So if there are 1,000 regions that has tumor-specific haplotypes at 0.1% level and we are looking at 10 unique molecules per region, we would expect to see one tumor haplotypes in only 10 of 1,000 regions. If you move to the next sample, you would see another 10 regions. The chance of finding a set of common regions where we can consistently detect tumor haplotypes is extremely low, at least at the sample size of <100 that we have.
- Along this line of thoughts, random forest is not going to be the right approach for such data. Our best bet is to compare data from tumor tissues and normal plasma, identify a set of genomic regions that are clean in plasma and contain certain level of methylated haplotypes in tumor, then ask whether these regions have an elevated level of MHL.
- Dr. Zhang's Round 1 analysis: http://genome-tech.ucsd.edu/LabNotes/index.php/Kun:LabNotes/MONOD/2014-8-12#5._Searching_for_primary_tumor_specific_haplotypes
- Merge BAM files of primary cancer, cancer plasma and normal plasma
- LMS screening in normal plasma samples.
- Identify tumor-specific haplotype regions (primary solid tissue vs normal plasma)
- Calculate the cumulation/average MHL for these region (tumor specific) for a plasma sample.
- Then assign a probability for the plasma is belong to 'cancer' or 'normal' as well as which tissue-of-origin?
- A panel of canonical cancer-related methylation region, and a random genomic region as control)?
Round 1: MHL in imputed,normalized merge data which prediction analysis missed
- 1, Find out regions that contain HMHs in primary tumor but not in N37 normal tissues or normal plasma.
scp shg047@genome-miner.ucsd.edu:/home/kunzhang/CpgMIP/MONOD/Data/ cat PC-P-*.methHapCounts.txt | ../scripts/get_tumor_specific_HMH_regions.pl N37_ten_tissues_NC-P-ALL_RRBS-dRRBS.methHapCounts.txt > PC-P_N37_plasma_specific_HMH_regions.bed & cat 7-P-*.methHapCounts.txt | ../scripts/get_tumor_specific_HMH_regions.pl N37_ten_tissues_NC-P-ALL_RRBS-dRRBS.methHapCounts.txt > LC-P_N37_plasma_specific_HMH_regions.bed & cat 6-P-*.methHapCounts.txt | ../scripts/get_tumor_specific_HMH_regions.pl N37_ten_tissues_NC-P-ALL_RRBS-dRRBS.methHapCounts.txt > CRC-P_N37_plasma_specific_HMH_regions.bed &
samtools view -H 6-P-10.sorted.clipped.bam > header.sam samtools merge -h header.sam 6-P-10.sorted.clipped.bam 6-P-1.sorted.clipped.bam 6-P-2.sorted.clipped.bam 6-P-3.sorted.clipped.bam 6-P-4.sorted.clipped.bam 6-P-5.sorted.clipped.bam 6-P-6.sorted.clipped.bam 6-P-7.sorted.clipped.bam 6-P-8.sorted.clipped.bam 6-P-9.sorted.clipped.bam
- 1,define clean genomic region in normal plasma (low methylation region)
704 haplotype regions were identified as the cancer specific(solid tissue) and were clean in the normal plasma samples. File:Figure 4D.txt
- 2 Calculate the cumulation/average MHL for these region (tumor specific) for a plasma sample.
- MHL distribution for above defined regions
- CP: cancer plasma, CT: cancer tissue, NP: normal plasma
- 3. assign a probability for the plasma is belong to 'cancer' or 'normal' as well as which tissue-of-origin?
Colon: 740 > myData type mean sd n sem me 1 CP 0.303570259 0.04889567 740 0.0184808266 2.149566e+00 2 NP 0.001489827 0.00036459 740 0.0001378021 9.520868e+11 3 TP 0.004369450 0.01452305 740 0.0054891956 2.759810e+00
Lung: 740 > myData type mean sd n sem me 1 CP 0.310171625 0.05194363 740 0.0196328452 2.137493e+00 2 NP 0.001489827 0.00036459 740 0.0001378021 9.520868e+11 3 TP 0.008964632 0.02154434 740 0.0081429953 2.444373e+00
Round 2: Tunor specific Haplotype regions called by Dr.Zhang's script
- Dinh shared her alignment Bam file
- Calling haploinfo and identify cancer specific hapltype region with Dr.Zhang's previous script
- Change name to unified format 6-P, 7-P, PC-P code
# merge hapInfo cd /home/shg047/monod/hapinfo cat /home/shg047/monod/rrbs_kun/6-P-*.hapInfo.txt > ../mixHap/6-P-ALL.hapInfo.txt cat /home/shg047/monod/rrbs_kun/7-P-*.hapInfo.txt > ../mixHap/7-P-ALL.hapInfo.txt cat /home/shg047/monod/rrbs_kun/PC-P-*.hapInfo.txt > ../mixHap/PC-P-ALL.hapInfo.txt cat /home/shg047/monod/rrbs_kun/NC-P-*.hapInfo.txt > ../mixHap/NC-P-ALL.hapInfo.txt cat /home/shg047/monod/rrbs_kun/6-T-*.hapInfo.txt > ../mixHap/6-T-ALL.hapInfo.txt cat /home/shg047/monod/rrbs_kun/7-T-*.hapInfo.txt > ../mixHap/7-T-ALL.hapInfo.txt cat /home/shg047/monod/rrbs_kun/PC-T-*.hapInfo.txt > ../mixHap/PC-T-ALL.hapInfo.txt # find NP-P-high LOD target cd /home/shg047/monod/mixHap perl /home/shg047/monod/bin/mixMethHapAnalysis_19Aug14.pl NC-P-ALL.hapInfo.txt 6-T-ALL.hapInfo.txt 6-P-ALL.hapInfo.txt /home/kunzhang/CpgMIP/MONOD/Public_data/WB_middle_age_UMR.BED.txt |sort -k 2,2nr > RRBS_target_LOD_CC.txt perl /home/shg047/monod/bin/mixMethHapAnalysis_19Aug14.pl NC-P-ALL.hapInfo.txt 7-T-ALL.hapInfo.txt 7-P-ALL.hapInfo.txt /home/kunzhang/CpgMIP/MONOD/Public_data/WB_middle_age_UMR.BED.txt |sort -k 2,2nr > RRBS_target_LOD_LC.txt perl /home/shg047/monod/bin/mixMethHapAnalysis_19Aug14.pl NC-P-ALL.hapInfo.txt PC-T-ALL.hapInfo.txt PC-P-ALL.hapInfo.txt /home/kunzhang/CpgMIP/MONOD/Public_data/WB_middle_age_UMR.BED.txt |sort -k 2,2nr > RRBS_target_LOD_PC.txt ./find_NC-P_high-LOD_targets.pl > NC-P_high-LOD_targets.txt
- Then what about Dr. Zhang's result in Phase 1 Plasma dataset
perl /home/shg047/monod/bin/mixMethHapAnalysis_19Aug14.pl NC-P-ALL.hapInfo.txt 6-T-ALL.hapInfo.txt 6-P-ALL.hapInfo.txt /home/shg047/monod/mixHap/zhang/WB_middle_age_UMR.BED.txt| sort -k 2,2nr > /home/shg047/monod/mixHap/zhang/RRBS_targets_LOD_CC.txt perl /home/shg047/monod/bin/mixMethHapAnalysis_19Aug14.pl NC-P-ALL.hapInfo.txt 7-T-ALL.hapInfo.txt 7-P-ALL.hapInfo.txt /home/shg047/monod/mixHap/zhang/WB_middle_age_UMR.BED.txt| sort -k 2,2nr > /home/shg047/monod/mixHap/zhang/RRBS_targets_LOD_LC.txt perl /home/shg047/monod/bin/mixMethHapAnalysis_19Aug14.pl NC-P-ALL.hapInfo.txt PC-T-ALL.hapInfo.txt PC-P-ALL.hapInfo.txt /home/shg047/monod/mixHap/zhang/WB_middle_age_UMR.BED.txt| sort -k 2,2nr > /home/shg047/monod/mixHap/zhang/RRBS_targets_LOD_PC.txt cd /home/shg047/monod/mixHap/zhang
Round 3: Tumor specific MHBs in patients plasma: normal tissue (NT), normal plasma (NT), cancer tissue(CT), cancer plasma(CP)
First Time Update: 01/03/2016
- requirement: mhl<0.1 in normal while mhl>0.5 in cancer tissue. other cut-off were tried, results were similar.
- lung cancer (3 normal tissue, 5 cancer tissues, 26 normal plasma, 30 lung cancer plasma)
** MHL matrix: /home/shg047/monod/rrbs_kun/MOND.MHL.txt ** Find tumor specific MHBs and calculate MHL in lung cancer patients plasmas ** lung cancer tumor specific MHBs:File:Lung.mixhap.mhl.in.plsma.txt in /home/shg047/oasis/monod/mixHap/lung.mixhap.mhl.in.plsma.txt
- colon cancer(3 normal tissue, 5 cancer tissues, 26 normal plasma, 29 colon cancer plasma)
** MHL matrix: /home/shg047/monod/rrbs_kun/MOND.MHL.txt ** Find tumor specific MHBs and calculate MHL in colon cancer patients plasmas ** colon cancer tumor specific MHBs:File:Colon.mixhap.mhl.in.plsma.txt /home/shg047/oasis/monod/mixHap/colon.mixhap.mhl.in.plsma.txt
- pancreatic cancer(3 normal tissue, 5 cancer tissues, 26 normal plasma, 10 pancreatic cancer plasma)
** MHL matrix: /home/shg047/monod/rrbs_kun/MOND.MHL.txt ** Find tumor specific MHBs and calculate MHL in pancreatic cancer patients plasmas ** pranceatic cancer tumor specific MHBs:File:Pancreatic.mixhap.mhl.in.plsma.txt in /home/shg047/oasis/monod/mixHap/pancreatic.mixhap.mhl.in.plsma.txt
- MHL in NT,NP,CT,CP:File:7CB.tm.png
Second Time: Update: 10/03/2016
- Dr. Zhang want to check the heatmap to validate the result is solid. I made the heatmap plot by excel since there are some "NA" in the dataset.
cd /media/LTS_33T/SG_LTS33T/monod/mixHap Rscript colon.mixhap.plasma.R Rscript lung.mixhap.plasma.R Rscript pancreatic.mixhap.plasma.R
File:Matrix PlasmaHighMHL.xlsx File:Matrix PlasmaHighMHL.xlsx File:Matrix PlasmaHighMHL.xlsx
- Achieve the genomic regions to above excel files (bed file)
cd /home/shg047/oasis/monod/mixHap cut -f 1 colon.data.plsma-hypoall.txt > colon.hypermhl.plasma.txt cut -f 1 lung.data.plsma.hypoall.txt > lung.hypermhl.plasma.txt cut -f 1 pancreatic.data.plsma.hypoall.txt > pancreatic.hypermhl.plasma.txt
File:Colon.hypermhl.plasma.txt # 1367 File:Lung.hypermhl.plasma.txt # 784 File:Pancreatic.hypermhl.plasma.txt # 608
- File:34D5.tm.png
- Obvously, The MHL for the cancer plasma is not as expected as Dr. Zhang. There are large number of high MHL regions correspond to solid cancer tissues.
- Then I checked the MHL distribution in all the samples of MONOD project. I found the overall MHL in cancer plasma were higher than some other samples as the following figure shown (click and zoom out)
- File:887.tm.png
- Then Dr. Zhang want to collect regions which are hyper in cancer tissue however hypo in all the normal samples (N37+SALK). Again, the MHL was as the following figure:
- File:45A7.tm.png
- The boxplot for MHL distribution in 4 categories.
- File:6697.tm.png
Third Time: Update: 11/03/2016
- Check this result with another way
- re-check the hapinfo, mhl data for several samples
- Heatmap(excel) files were attached hereFile:Matrix PlasmaHighMHL.xlsx.
- Position, MHL and Hapinfo check
- File:Xzdieapdead.png
- Find 4 loci to check it again.
cd /home/kunzhang/CpgMIP/MONOD/Data/1407-combined_RRBS/mld_blocks_stringent_hapInfo grep 8054556 7-P-2.mld_blocks_r2-0.5.hapInfo.txt chr17:8054556-8054584 CCCCC 1 8054556,8054568,8054579,8054581,8054584
cd /home/shg047/monod/hapinfo grep 8054556 7-P-14.hapInfo.txt chr17:8054556-8054584 CCCCC 1 8054556,8054568,8054579,8054581,8054584
cd /home/shg047/monod/hapinfo grep 8054556 7-P-22.hapInfo.txt chr17:8054556-8054579 CCC 1 8054556,8054568,8054579
cd /home/shg047/monod/hapinfo grep 95947328 7-P-11.hapInfo.txt chr9:95947328-95947356 CCCCC 2 95947328,95947337,95947340,95947345,95947356 chr9:95947328-95947356 TTTTT 1 95947328,95947337,95947340,95947345,95947356
cd /home/shg047/monod/hapinfo grep 100204208 PC-T-2.hapInfo.txt chr14:100204208-100204257 CCT 1 100204208,100204221,100204232
cd /home/shg047/monod/hapinfo grep 100204208 PC-P-2.hapInfo.txt chr14:100204208-100204257 CT 1 100204250,100204257
- You will find all the hapinfo were correctly match to MHL in File:Matrix PlasmaHighMHL.xlsx
- You can try check any postion with the following command:
cd /home/shg047/monod/hapinfo grep loci hapinfo.file
- Also You can try to check MHL in hapinfo you created several month ago. These two version MHL don't have big different. I just let longer haplotype as I said before, such as CCCCCCCCC
cd /home/kunzhang/CpgMIP/MONOD/Data/1407-combined_RRBS/mld_blocks_stringent_hapInfo grep loci hapinfo.file
Round 4: Check the Batch 1 and Batch 2 samples
- Dr.Zhang found the signficant difference between batch 1 and batch 2 samples.
- Dinh check the difference between phase I and phase II samples -
- (1) bisulfite conversion rate (quantified by Lambda DNA spike-in) were between 98.54%-99.40% for phase I and lower between 96.61%-98.63% for phase II.
- (2) According to Noi's wikinotes, the ChargeSwitch gDNA serum kit was used to extract DNA from plasma in phase I and we used QIAmp plasma nucleic acid kit in phase II.
- Then how about the methylation level difference between batch 1 and batch 2
- Select same loci check the methylation level difference between batch 1 and batch 2
- Use same loci to do the clust analysis.
- Do we have clinical information to the samples?
- Dinh found no batch effect between phase 1 and phase 2 as the following.
cd /home/shg047/monod/mixHap/hapinfo/ Rscript methylationlevel_MHL_batch1_batch2.R
- File:32E9.tm.png
- box-plot for all the samples is as the following (methylation level)
- File:199.tm.png
- Density plot is as the following(methylation level):
- File:DF.tm.png
- MHL Difference between Batch 1 and Batch 2 sets
- File:3573.tm.png
*# genome-miner cd /home/shg047/monod/hapinfo less -S 6-P-1.hapInfo.txt less -S 6-P-11.hapInfo.txt
- Obviously, the distribution of the methylation status in the reads caused such difference.
- File:3A97.tm.png
- Question: Is there any golden standard RRBS/prior distribution for cancer plasma??