Ns126:MONOD: Difference between revisions
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==Wiki Index== | ==Wiki Index== | ||
* Data analysis: re-defining bins based on methylation haplotype blocks: http://genome-tech.ucsd.edu/LabNotes/index.php/Kun:LabNotes/MONOD/2015-7-6 | * Data analysis: re-defining bins based on methylation haplotype blocks: http://genome-tech.ucsd.edu/LabNotes/index.php/Kun:LabNotes/MONOD/2015-7-6 | ||
* Round 5: screening of all Blood LMS clusters: http://genome-tech.ucsd.edu/LabNotes/index.php/Kun:LabNotes/MONOD/2014-8-4 | * Round 5: screening of all Blood LMS clusters: http://genome-tech.ucsd.edu/LabNotes/index.php/Kun:LabNotes/MONOD/2014-8-4 | ||
* Kun's key questions related to the MONOD project: http://genome-tech.ucsd.edu/LabNotes/index.php/Kun:LabNotes/MONOD/2014-10-8 | * Kun's key questions related to the MONOD project: http://genome-tech.ucsd.edu/LabNotes/index.php/Kun:LabNotes/MONOD/2014-10-8 | ||
* RRBS data analysis (mixHap): http://genome-tech.ucsd.edu/LabNotes/index.php/Kun:LabNotes/MONOD/2014-8-12#5._Searching_for_primary_tumor_specific_haplotypes | * RRBS data analysis (mixHap): http://genome-tech.ucsd.edu/LabNotes/index.php/Kun:LabNotes/MONOD/2014-8-12#5._Searching_for_primary_tumor_specific_haplotypes | ||
* Plasma RRBS BAM TO FASTQ: | |||
* Nimblegen capture of blood LMS regions: http://genome-tech.ucsd.edu/LabNotes/index.php/Kun:LabNotes/MONOD/2014-11-22 | * Nimblegen capture of blood LMS regions: http://genome-tech.ucsd.edu/LabNotes/index.php/Kun:LabNotes/MONOD/2014-11-22 | ||
* MHB HM450K beadchip: http://genome-tech.ucsd.edu/LabNotes/index.php/Shicheng:Calendar/NOTES/2015-6-26 | * MHB HM450K beadchip: http://genome-tech.ucsd.edu/LabNotes/index.php/Shicheng:Calendar/NOTES/2015-6-26 | ||
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* Methylation Array Based Biomarker: | * Methylation Array Based Biomarker: | ||
* [[Data Submission and Re-name]] | * [[Data Submission and Re-name]] | ||
* Dinh: Batch 3 Plasma RRBS library:[http://genome-tech.ucsd.edu/LabNotes/index.php/Dinh/Dinh_2016/NOTES/2016-3-23] | |||
*[[MONOD:Background]] | *[[MONOD:Background]] | ||
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* Read Length >= 75 or not? | * Read Length >= 75 or not? | ||
* WGBS or RRBS | * WGBS or RRBS | ||
* Dinh's BAM: /oasis/tscc/scratch/ddiep/Working | |||
=== Computational Script === | === Computational Script === | ||
* [[43 Samples in prediction model]] | |||
* bam to haploinfo | * bam to haploinfo | ||
* [[bam2hapinfo-V1.0.pl]] | * [[bam2hapinfo-V1.0.pl]] | ||
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perl [[~/bin/bismarkMappingStatistic.pl]] | perl [[~/bin/bismarkMappingStatistic.pl]] | ||
=== Bam to Hapinfo (Update: March/ | === Bam to Hapinfo (Update: March/29/2016)=== | ||
*bam and hapinfo files | *bam and hapinfo files | ||
* N37: /home/shg047/oasis/N37/ | * N37: /home/shg047/oasis/N37/sortBam | ||
* SALK: | ** cd /home/shg047/oasis/N37/hapinfo | ||
** perl ../[[renameN37.pl]] | |||
** rm ~/oasis/monod/hapinfo/N37* | |||
** cp N37* ~/oasis/monod/hapinfo/ | |||
* SALK: /home/shg047/oasis/SALK/bam | |||
*2 Colon (GSE46644): /home/shg047/oasis/Ziller2013 | |||
cd /media/LTS_60T/Dinh/WGBS_LTS33/Hg19/Tumor_WGBS/BAMfiles | |||
cd /oasis/tscc/scratch/ddiep/Ziller_BAMfiles | |||
GSM1204465 SRX332736 Colon primary tumor | |||
GSM1465024 SRX669642 HCT116 | |||
cd /home/shg047/oasis/Tumor-WGBS/HCT116-SRX669642/bam | |||
perl ~/bin/[[samInfoPrep4Bam2Hapinfo.pl]] .sorted.clipped.bam > ../Saminfo4bam2hapinfo.txt | |||
perl ~/bin/[[bam2hapInfo2PBS.pl]] ../Saminfo4bam2hapinfo.txt submit nonbismark | |||
cd /home/shg047/oasis/Tumor-WGBS/HCC-SRX332736 | |||
perl ~/bin/[[samInfoPrep4Bam2Hapinfo.pl]] .sorted.clipped.bam > ../Saminfo4bam2hapinfo.txt | |||
perl ~/bin/[[bam2hapInfo2PBS.pl]] ../Saminfo4bam2hapinfo.txt submit nonbismark | |||
* Blood: | * Blood: | ||
** ln -s /home/k4zhang/my_oasis_tscc/MONOD/whole_blood_WGBS/BAMfiles bam | ** ln -s /home/k4zhang/my_oasis_tscc/MONOD/whole_blood_WGBS/BAMfiles bam | ||
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=== Hapinfo to MHB (Update: March/16/2016)=== | === Hapinfo to MHB (Update: March/16/2016)=== | ||
* | |||
=== Hapinfo to MHL matrix=== | === Hapinfo to MHL matrix=== | ||
* Updated MHL Matrix: /home/shg047/oasis/monod/mhl/monod.mhl.List1.march29.txt | |||
* WB | * WB | ||
cd /home/shg047/oasis/AgeBlood/hapinfo | cd /home/shg047/oasis/AgeBlood/hapinfo | ||
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File:MONOD.08172015.png | File:MONOD.08172015.png | ||
</gallery> | </gallery> | ||
=== HMH=== | |||
*Indentify cancer assocated hyper-methylated haplotype | |||
cd /media/Home_Raid1/shg047/work/monod/hapinfo | |||
perl [[methHMH.pl]] 6-T-1.sorted.clipped.bam.hapInfo.txt 6-P-1.sorted.clipped.bam.hapInfo.txt [[excl.list.txt]] ./caHMH/caHMH-1 | |||
perl methHMH.pl 6-T-2.sorted.clipped.bam.hapInfo.txt 6-P-2.sorted.clipped.bam.hapInfo.txt excl.list.txt ./caHMH/caHMH-2 | |||
perl methHMH.pl 6-T-3.sorted.clipped.bam.hapInfo.txt 6-P-3.sorted.clipped.bam.hapInfo.txt excl.list.txt ./caHMH/caHMH-3 | |||
perl methHMH.pl 6-T-4.sorted.clipped.bam.hapInfo.txt 6-P-4.sorted.clipped.bam.hapInfo.txt excl.list.txt ./caHMH/caHMH-4 | |||
perl methHMH.pl 6-T-5.sorted.clipped.bam.hapInfo.txt 6-P-5.sorted.clipped.bam.hapInfo.txt excl.list.txt ./caHMH/caHMH-5 | |||
cat caHMH* > HMH.txt | |||
Rscript caHMH.R | |||
#>>>>>>>> | |||
caHMH.R | |||
#>>>>>>>> | |||
data<-read.table("HMH.txt",head=T,sep="\t",as.is=T) | |||
input<-data.matrix(data[,8:ncol(data)]) | |||
caHMH<-data[which(apply(input,1,function(x) sum(x)==0)),1] | |||
unique(as.character(caHMH)) | |||
write.table(unique(as.character(caHMH)),file="caHMH.rlt.txt",col.names=F,row.names=F,quote=F) | |||
#>>>>>>>> | |||
caHMH.sh | |||
#>>>>>>>> | |||
awk -F":|-" '{print $1,$2,$3,$1":"$2"-"$3}' OFS="\t" caHMH.rlt.txt > caHMH.bed | |||
bedtools intersect -wao -a caHMH.bed -b ~/work/db/hg19/hg19_refGene.bed | sort -k1,1n | |||
* Take one caHMH and validate with MHL values in all our samples (MHL matrix) | |||
# chr1 119543104 119543127 chr1:119543104-119543127 TBX15 | |||
setwd("/oasis/tscc/scratch/shg047/monod/hapinfo") | |||
load("MHL4.RData") | |||
data[match("chr1:119543104-119543127",rownames(data)),] | |||
# quite good, highly low methylated in >86% normal tissues and hyper-methylated in cancer related samples including 6-T, and parts of 6-P. \\ | |||
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===Cancer Diagnosis Performance based on Methylation Haplotype Loading of circulating free DNA methylation === | ===Cancer Diagnosis Performance based on Methylation Haplotype Loading of circulating free DNA methylation === | ||
? THINK ABOUT THE TISSUE SPECIFIC AND CANCER SPECIFIC? | |||
In this section, RRBS and Capseq dataset were taken as the discovery dataset and the BSPP dataset were used validate the performance of the biomarkers. For RRBS dataset, 68 cancer and 8 normal samples were included. 56046 regions were obtained by methylation haplotype loading algorithm (MHL) and 17263 regions were 100% un-methylated in 8 normal samples. 248 regions among 56046 were methylated in at least 50% cancer samples while 100% un-methylated in normal samples. The average length of the regions were 103bp (IQR=95bp, SD=126) and were located in the promoter region of 182 genes (2000bp up-stream of TSS). With the help of text mining, 21 genes of them were validated to be methylation relevant cancer related genes (Table). In order to evaluate the distinguish ability of DNA methylation from cancer to the normal samples, Random forest model were applied. In the procedure of RF model, the optimal number of variables tried at each split is131 (mtry) and number of trees (mtrees) makes no difference to the prediction accuracy from 250 to 7000. RF prediction model showed 1592 regions could provide positive ability to distinguish cancer samples from normal samples with sensitivity of 97.06%, specificity of 100% and accuracy of 97.37%. Furthermore, we found the top 206 most importance regions could take account of 80.0% contribution to the accurate prediction. And the random forest model based on top 206 regions with mtry of 14 and mtrees of 500 showed better performance with 100% sensitivity, 100% specificity and 100% accuracy. The result showed these 206 regions were high frequent hypermethylated in cancers (Mean=41.4%, SD=14.5%, IQR=22.4%). (Supplementary Table **) | In this section, RRBS and Capseq dataset were taken as the discovery dataset and the BSPP dataset were used validate the performance of the biomarkers. For RRBS dataset, 68 cancer and 8 normal samples were included. 56046 regions were obtained by methylation haplotype loading algorithm (MHL) and 17263 regions were 100% un-methylated in 8 normal samples. 248 regions among 56046 were methylated in at least 50% cancer samples while 100% un-methylated in normal samples. The average length of the regions were 103bp (IQR=95bp, SD=126) and were located in the promoter region of 182 genes (2000bp up-stream of TSS). With the help of text mining, 21 genes of them were validated to be methylation relevant cancer related genes (Table). In order to evaluate the distinguish ability of DNA methylation from cancer to the normal samples, Random forest model were applied. In the procedure of RF model, the optimal number of variables tried at each split is131 (mtry) and number of trees (mtrees) makes no difference to the prediction accuracy from 250 to 7000. RF prediction model showed 1592 regions could provide positive ability to distinguish cancer samples from normal samples with sensitivity of 97.06%, specificity of 100% and accuracy of 97.37%. Furthermore, we found the top 206 most importance regions could take account of 80.0% contribution to the accurate prediction. And the random forest model based on top 206 regions with mtry of 14 and mtrees of 500 showed better performance with 100% sensitivity, 100% specificity and 100% accuracy. The result showed these 206 regions were high frequent hypermethylated in cancers (Mean=41.4%, SD=14.5%, IQR=22.4%). (Supplementary Table **) | ||
For CapSeq dataset, 31 cancer 24 normal samples were included. 86206 regions were obtained by methylation haplotype loading algorithm (MHL), however, only 2205 regions were 100% un-methylated in 24 normal samples. Parameters of Random forest model were tuned and the optimal number of variables tried at each split is 92 (mtry) and number of trees (mtree) makes no difference to the prediction accuracy from 250 to 7000. Random forest algorithm showed 516 regions could provide positive ability to separate cancer samples from normal samples with sensitivity of 87.1%, specificity of 75.0% and accuracy of 81.26%. We found that the top 21 most importance regions could take account of 80.0% contribution for the classification. Therefore, the second round prediction process of random forest model based on top 21 regions with mtry of 7 and mtrees of 750 showed perfect classification with 96.77% sensitivity, 100% specificity and 98.18% accuracy. The average hyper-methylation frequency in cancer samples for above 516 and 21 regions 90.43% (SD=0.08, IQR=0.096) and 96.7% (SD=0.041, IQR=0.056), respectively. (Supplementary Table ** and supplementary Table **) | For CapSeq dataset, 31 cancer 24 normal samples were included. 86206 regions were obtained by methylation haplotype loading algorithm (MHL), however, only 2205 regions were 100% un-methylated in 24 normal samples. Parameters of Random forest model were tuned and the optimal number of variables tried at each split is 92 (mtry) and number of trees (mtree) makes no difference to the prediction accuracy from 250 to 7000. Random forest algorithm showed 516 regions could provide positive ability to separate cancer samples from normal samples with sensitivity of 87.1%, specificity of 75.0% and accuracy of 81.26%. We found that the top 21 most importance regions could take account of 80.0% contribution for the classification. Therefore, the second round prediction process of random forest model based on top 21 regions with mtry of 7 and mtrees of 750 showed perfect classification with 96.77% sensitivity, 100% specificity and 98.18% accuracy. The average hyper-methylation frequency in cancer samples for above 516 and 21 regions 90.43% (SD=0.08, IQR=0.096) and 96.7% (SD=0.041, IQR=0.056), respectively. (Supplementary Table ** and supplementary Table **) | ||
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All the assumption of we can used DNA methylation in free-cell circulating DNA methylation is based on the methylation status of solid cancers can be released into plasma without any specific selection, thus, the aberrant DNA methylation fragment or biomarkers identified in solid tissues can be used to be the target for cancer non-invasive diagnosis, screening or prognosis surveillance. We then constructed 15 genome-wide DNA methylation profiles for the paired plasmas and solid tissues from same cancer patient with RRBS assay to check whether DNA methylation fragments derived from cancer cells were released into the blood randomly or selectively. | All the assumption of we can used DNA methylation in free-cell circulating DNA methylation is based on the methylation status of solid cancers can be released into plasma without any specific selection, thus, the aberrant DNA methylation fragment or biomarkers identified in solid tissues can be used to be the target for cancer non-invasive diagnosis, screening or prognosis surveillance. We then constructed 15 genome-wide DNA methylation profiles for the paired plasmas and solid tissues from same cancer patient with RRBS assay to check whether DNA methylation fragments derived from cancer cells were released into the blood randomly or selectively. | ||
5 paired lung cancer, colon cancer and pancreatic cancer solid tissues and plasma were enrolled in this section and single-base methylome were established by RRBS as mentioned above. Correlation analysis based on 56046 MHL regions showed there is just weak correlation between genome-wide DNA methylation of solid tissues and plasmas. The average correlation between tissue and plasma was 0.50 (95%CI: 0.44-0.56)(Figure). What’s more, only about 28% (95%CI: 25%-31%) regions were simultaneously methylated both in tissues and plasma. Even considering different threshold to define the methylation status and transferring the continuous MHL to binary DNA methylation status, the maximum co-methylation ratio was still less than 63% (Figure). These evidence showed the dramatically significant heterogeneity between solid and plasma DNA methylation profile and provide the importance that we should validate all the biomarkers identified in solid tissues in cell-free circulating plasma DNA to guarantee the validity of the biomarkers. | 5 paired lung cancer, colon cancer and pancreatic cancer solid tissues and plasma were enrolled in this section and single-base methylome were established by RRBS as mentioned above. Correlation analysis based on 56046 MHL regions showed there is just weak correlation between genome-wide DNA methylation of solid tissues and plasmas. The average correlation between tissue and plasma was 0.50 (95%CI: 0.44-0.56)(Figure). What’s more, only about 28% (95%CI: 25%-31%) regions were simultaneously methylated both in tissues and plasma. Even considering different threshold to define the methylation status and transferring the continuous MHL to binary DNA methylation status, the maximum co-methylation ratio was still less than 63% (Figure). These evidence showed the dramatically significant heterogeneity between solid and plasma DNA methylation profile and provide the importance that we should validate all the biomarkers identified in solid tissues in cell-free circulating plasma DNA to guarantee the validity of the biomarkers. | ||
25204 regions which was methylated in sample pairs for at least one time were selected to evaluate the selectivity of hyper-methylated DNA fragments from solid tissues to plasma. The results showed the process of the releasing were non-random, with at least 1590 fragment were significantly prefer-selected (P<1.98*10-6, binomial test). These fragments were located in the regions of 1190 genes. Function enrichment analysis showed these genes were significantly associated with cancer relevant biological functions (Table), including embryonic morphogenesis, regulation of transcription, neuron differentiation, regionalization, tissue morphogenesis ,transcription factor activity, sequence-specific DNA binding ,transcription regulator activity. | 25204 regions which was methylated in sample pairs for at least one time were selected to evaluate the selectivity of hyper-methylated DNA fragments from solid tissues to plasma. The results showed the process of the releasing were non-random, with at least 1590 fragment were significantly prefer-selected (P<1.98*10-6, binomial test). These fragments were located in the regions of 1190 genes. Function enrichment analysis showed these genes were significantly associated with cancer relevant biological functions (Table), including embryonic morphogenesis, regulation of transcription, neuron differentiation, regionalization, tissue morphogenesis ,transcription factor activity, sequence-specific DNA binding ,transcription regulator activity. | ||
[[File:1155.tm.png|400px]] | |||
===Re-validation of the hyper-methylated fragments of plasma in public GEO dataset. === | ===Re-validation of the hyper-methylated fragments of plasma in public GEO dataset. === | ||
GSE56044 (124 lung cancer and 12 normal), GSE39279 (444 lung cancer), GSE52401 (244 normal lung), TCGA-lung cancer (), TCGA-colon cancer () and TCGA-pancreatic cancer () dataset were downloaded and summarized. | GSE56044 (124 lung cancer and 12 normal), GSE39279 (444 lung cancer), GSE52401 (244 normal lung), TCGA-lung cancer (), TCGA-colon cancer () and TCGA-pancreatic cancer () dataset were downloaded and summarized. | ||
=== Quantitatively estimation to the level of cancer DNA fractions in the plasma === | |||
By computationally mixing the sequencing reads from cancer tissues and whole blood samples (WBC), we created synthetic admixtures at various levels of tumor fraction. We found that MHL is 2-5 folder higher than the methylation level of individual CpG sites across the full range of tumor fractions. Remarkably, MHL provides additional gain of signal-to-noise ratio (mean divided by standard deviation) compared with AMF as the fraction of tumor DNA decreases below 10%, which is typical for clinical samples. We then took the individual plasma data sets, and predicted the tumor fraction based on the MHL distribution established by computational mixing. Except for a small number (N<5) of outliers, we observed significantly higher average MHL in cancer plasma than in normal plasma. Note that all Group II MHBs were selected without using any information from the plasma samples, and hence they should be generally applicable to other plasma samples. Interestingly, we also found that the estimated tumor DNA fraction were positive correlated with normalized cfDNA yield from the cancer patients (P<0.000023). | |||
===Quantitatively estimation to the level of cancer DNA fractions in the plasma=== | |||
Recent studies have demonstrated that epigenetic information imbedded in cfDNA has the potential for predicting tumor’s tissue-of-origin. Consistently, we found that tissue-of-origin derived methylation haplotypes were the most abundant fraction in cancer plasma. Here we asked whether a MHL-based framework and a set of targets derived from whole genome data would allow us to predict tissue-of-origin with quantifiable sensitivity and specificity, which is crucial for future clinical applications. We compiled 43 WGBS and RRBS data sets for 10 human normal tissues that have high cancer incident rate, and identified a set of 2,880 tissue-specific MHBs as the candidates. We then used these tissue-specific MHBs or subsets to predict the tissue-of-origin for the cancer plasma sample. We adopted the approach by counting the total number of tissue-specific MHBs in the plasma samples and comparing with all other tissues, in order to infer the most probable tissue-of-origin. At the cutoff of minimal 10 tissue-specific methylated haplotypes per tissue type, we observed an average 90% accuracy for mapping a data set from the primary tissue to its tissue type (Figure 5, middle). We then applied this method to the full set of plasma data from 59 cancer patients and 75 normal individuals, and achieved an average prediction accuracy of 82.8%, 88.5%, 91.2% for the plasma from colorectal cancer, lung cancer, and control plasma samples respectively with 5-fold cross-validation. For the incorrectly classified samples, we noticed that 4 out of 5 colorectal cancer plasma were from metastatic colorectal cancer patients while the fifth was in fact tubular adenoma. In the case of lung cancer, one misclassified sample came from a patient with benign fibrous tissue. Our invention demonstrate these biomarkers could be useful for tissue-of-origin mapping and other related applications. | |||
=== Tissue-of-origin mapping by normal, cancer and inflammation reference === | |||
* Dr. Zhang said maybe we can integrate cancer,normal and inflammation samples as the reference so that we can mapping the plasma to differnet tissue, different stage, just like the 'GPS' | |||
* Take the normal samples as the background and don't forget to do the cross-validation and then estimate each plasma to differenet refernece and then give the pvalue | |||
* Very important and interesting idea come out from Dr. Zhang again. Dr. Zhang said we can merge one disease reference and one normal reference and for that diease, we would get more strong signals,compared with single reference (10/16/2016). | |||
* differrent stastic Z-score, Baysian score. | |||
* kedney cancer as the reference since we have samples. | |||
==Discussion== | ==Discussion== | ||
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In the prediction section, top 62 regions could provide the distinguish accuracy of 86.87% which did not indicated Capseq method were worse than RRBS in the prediction ability. First capture, On the another side, when you will top 10 predictive regions, the separate ability of prediction model based on Capseq dataset could come up to the sensitivity of 90.32%, specificity of 95.83% and accuracy of 92.49%. However, small number of predictors would greatly decrease the robust or reproducibility of the prediction model and would bring biased inference in the process of clinical application of Capseq assay. | In the prediction section, top 62 regions could provide the distinguish accuracy of 86.87% which did not indicated Capseq method were worse than RRBS in the prediction ability. First capture, On the another side, when you will top 10 predictive regions, the separate ability of prediction model based on Capseq dataset could come up to the sensitivity of 90.32%, specificity of 95.83% and accuracy of 92.49%. However, small number of predictors would greatly decrease the robust or reproducibility of the prediction model and would bring biased inference in the process of clinical application of Capseq assay. | ||
63.4% MHL distance of BSPP were less than 200bp, indicating they are almost located in the same region/CpG island in human genome, therefore, we need merge these MHL region together to increase the sensitivity of the prediction. | 63.4% MHL distance of BSPP were less than 200bp, indicating they are almost located in the same region/CpG island in human genome, therefore, we need merge these MHL region together to increase the sensitivity of the prediction. | ||
=== Analysis influence from low coverage and differential coverage === | |||
* Alan also told me that our RRBS were not quite enriched | |||
* Jun told Dr. Zhang the coverage between plasma and tissue is quite different. | |||
* Then we need think about whether there will be influcence for the furture usage | |||
* Work after Dr. Zhang's previous analysis | |||
cd /media/NAS3_volume2/Kun/KZ_LTS33T/MONOD | |||
== to do list == | |||
*Prepare for better version: | |||
*(1) Perform additional quality filtering of our data, remove some low-quality samples, and repeat the analysis. | |||
*(2) Determine how low (percentage of tumor DNA in blood) can we go in tissue-of-origin mapping, and ctDNA detection, by computational mixing of tumor data with WB data at various ratios, and perform the analysis similar to Figure 4. | |||
*(3) Look out for additional WGBS data in the public database. | |||
*(4) Look further into the potential differences of the spreading pattern between adding and removing methyl groups. | |||
*(5) Try reference-free-deconvolution to detect tumor-DNA from plasma | |||
*(6) Cancer-specific methylation haplotype (here, we can include all the genomic region, not only in MHB) and then estimate the cancer fragment contribution. | |||
*(7) Any biological function for the interesting tissue-specific MHBs in the prediction model. | |||
*(8) The overlap between our prediction features and other studies? | |||
*(9) With our prediction model to predict some other public dataset and test the model stability. | |||
*(10) Dr. Zhang mentioned that we actually have more data, can we get more interesting result from these sequencing data: Yes. | |||
I just realized that we also have another set of data that wasn’t used in our NG paper. The data were generated with a different strategy, and my gut feeling is that it might have a higher unique read depth per sample than our RRBS data. Can you process them similar to what you did in Supp. Figures 11/12? | |||
/media/Home_Raid1/kunzhang/MONOD/150209_SN216/SeqCap/mld_blocks_July2015 | |||
http://genome-tech.ucsd.edu/LabNotes/index.php/Kun:LabNotes/MONOD/2015-3-4 | |||
http://genome-tech.ucsd.edu/LabNotes/index.php/Kun:LabNotes/MONOD/2015-7-6#Update_on_10.2F16.2F2015 | |||
Thanks! | |||
==Method and Materials== | ==Method and Materials== | ||
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*NSCLC samples and corresponding normal lung tissues for validation study in Chinese population were obtained from 150 patients who underwent pulmonary resection for primary NSCLC at Changhai Hospital, Shanghai, China. The study was approved by Fudan University and Changhai Hospital and Informed consents were obtained from the patients. Exclusion criteria included subjects with a family history of lung cancer, previous radiotherapy, and chemotherapy or adjuvant therapy before surgery. All tissues were immediately frozen at -80℃ after surgical resection. Histological examination and tumor-node-metastasis classification were conducted according to World Health Organization classification criteria (5) and AJCC Cancer Staging Manual, 7th Edition (6), respectively. Age, gender, smoking status, histology type, TNM stage and differentiation status were collected as the covariates when conducting association between DNA methylation and disease status. Smoking status was assigned to binary status: never and ever smoking. TNM stage was assigned to early stage (I and II) or late stage (III and IV) when it is necessary so that the sample size can be big enough to get the efficient statistic power. | *NSCLC samples and corresponding normal lung tissues for validation study in Chinese population were obtained from 150 patients who underwent pulmonary resection for primary NSCLC at Changhai Hospital, Shanghai, China. The study was approved by Fudan University and Changhai Hospital and Informed consents were obtained from the patients. Exclusion criteria included subjects with a family history of lung cancer, previous radiotherapy, and chemotherapy or adjuvant therapy before surgery. All tissues were immediately frozen at -80℃ after surgical resection. Histological examination and tumor-node-metastasis classification were conducted according to World Health Organization classification criteria (5) and AJCC Cancer Staging Manual, 7th Edition (6), respectively. Age, gender, smoking status, histology type, TNM stage and differentiation status were collected as the covariates when conducting association between DNA methylation and disease status. Smoking status was assigned to binary status: never and ever smoking. TNM stage was assigned to early stage (I and II) or late stage (III and IV) when it is necessary so that the sample size can be big enough to get the efficient statistic power. | ||
* Plasma | |||
** 10 PC-P, Noi: http://genome-tech.ucsd.edu/LabNotes/index.php/Noi/NOTES/2014-6-9 | |||
*CTT-frozen and CTT-FFPE RRBS library and sequencing [http://genome-tech.ucsd.edu/LabNotes/index.php/Noi/NOTES/2014-6-2] | *CTT-frozen and CTT-FFPE RRBS library and sequencing [http://genome-tech.ucsd.edu/LabNotes/index.php/Noi/NOTES/2014-6-2] | ||
** The aim of expriments on TT-frozen and CTT-FFPE | ** The aim of expriments on TT-frozen and CTT-FFPE | ||
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[[1360 tissue specific MHL in tissue cluster]] | [[1360 tissue specific MHL in tissue cluster]] | ||
=== LD in molecular level and pearson correlation in population level === | |||
[[LDvsCor.R]] | |||
[[File:1572.tm.png|400px]] | |||
=== Cancer specific methylation haplotype === | === Cancer specific methylation haplotype === | ||
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cp ../*-T* ./ | cp ../*-T* ./ | ||
cp ../*N37* ./ | cp ../*N37* ./ | ||
== More Thinking== | |||
1, Dr Jirun Peng proposed a method called MCTA-Seq to do plasma cell-free dna methylation sequencing, any advantage? | |||
==Acknowledgement== | ==Acknowledgement== | ||
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Can it be used on RRBS data? We can downsample high coverage data to lower the effect of clonal reads on the analysis. | Can it be used on RRBS data? We can downsample high coverage data to lower the effect of clonal reads on the analysis. | ||
Noi to check the input requirement for performing RRBS, can we perform RRBS in parallel on the test samples. | Noi to check the input requirement for performing RRBS, can we perform RRBS in parallel on the test samples. | ||
# intersting is if I can collect same marker which exist in hm450k, then our current work can be validate in large number of cancer smaples?? no, our sample is plasma, current is solid cancer smaples. | |||
==Reference== | ==Reference== |
Latest revision as of 18:00, 11 May 2017
Wiki Index[edit]
- Data analysis: re-defining bins based on methylation haplotype blocks: http://genome-tech.ucsd.edu/LabNotes/index.php/Kun:LabNotes/MONOD/2015-7-6
- Round 5: screening of all Blood LMS clusters: http://genome-tech.ucsd.edu/LabNotes/index.php/Kun:LabNotes/MONOD/2014-8-4
- Kun's key questions related to the MONOD project: http://genome-tech.ucsd.edu/LabNotes/index.php/Kun:LabNotes/MONOD/2014-10-8
- RRBS data analysis (mixHap): http://genome-tech.ucsd.edu/LabNotes/index.php/Kun:LabNotes/MONOD/2014-8-12#5._Searching_for_primary_tumor_specific_haplotypes
- Plasma RRBS BAM TO FASTQ:
- Nimblegen capture of blood LMS regions: http://genome-tech.ucsd.edu/LabNotes/index.php/Kun:LabNotes/MONOD/2014-11-22
- MHB HM450K beadchip: http://genome-tech.ucsd.edu/LabNotes/index.php/Shicheng:Calendar/NOTES/2015-6-26
- Methylation Haplotyping (Cancer detection): http://genome-tech.ucsd.edu/LabNotes/index.php/Dinh/Dinh_2014/NOTES/2014-8-8
- Layer Specific MHBs: http://genome-tech.ucsd.edu/LabNotes/index.php/Shicheng:Calendar/NOTES/2016-2-24
- Tumor specific MHBs with hyper-MHL in cancer plasma: http://genome-tech.ucsd.edu/LabNotes/index.php/Shicheng:Calendar/NOTES/2016-2-1
- Methylation Array Based Biomarker:
- Data Submission and Re-name
- Dinh: Batch 3 Plasma RRBS library:[1]
- MONOD:Background
Results[edit]
Library and Public dataset[edit]
- library = non-directional or directional?
- Single-end or Pired-end
- Phred score = 33 or 64?
- Read Length >= 75 or not?
- WGBS or RRBS
- Dinh's BAM: /oasis/tscc/scratch/ddiep/Working
Computational Script[edit]
- 43 Samples in prediction model
- bam to haploinfo
* bam2hapinfo-V1.0.pl * Debug:Update read strain determine function, March 2016: $strand<16 to $strand & 16 or 0x10 * update again: Now, bam2hapinfo.pl works for BAMs created by bismark and bisReadMapper.
- haploinfo to MHL
* hapinfo2mhl-V2016.pl
RD10 and 80bp up Region Definition[edit]
- Define WGBS frequently coverage genomic regions
- Read Dept >= 10
- Length >= 80bp
Merge all the Bam files: MergeAllBamFilesWithBash.sh[2] /home/kunzhang/softwares/bedtools-2.17.0/bin/bedtools genomecov -bg -split -ibam N37_10_tissue_pool_chr1.merged.bam> N37_10_tissue_pooled.chr1.genomecov.bed awk '$4>9 { print $1"\t"$2"\t"$3}' N37_10_tissue_pooled.chr1.genomecov.bed | /home/kunzhang/softwares/bedtools-2.17.0/bin/bedtools merge -d 10 -i - > N37_10_tissue_pooled.chr1.RD10.genomecov.bed awk '$3-$2>80 {print $1"\t"$2"\t"$3"\t"$3-$2+1}' N37_10_tissue_pooled.chr1.RD10.genomecov.bed > N37_10_tissue_pooled.chr1.RD10_80up.genomecov.bed
GWBS alignment with Bismark[edit]
- Alignment with Bismark in glean queue
*prepare the sample information manually, therefore, you can alignment single-end and paired-end BS-seq Fastq at same time. Sam_Config_File Format perl ~/bin/bismark2bamPBS.pl Sam_Config_File submit *check which alignment is failed and therefore re-submit the job again: perl ~/bin/bismarkAlignSucceedCheck.pl *check alignment ratio/coverage: perl ~/bin/bismarkMappingStatistic.pl
Bam to Hapinfo (Update: March/29/2016)[edit]
- bam and hapinfo files
* N37: /home/shg047/oasis/N37/sortBam ** cd /home/shg047/oasis/N37/hapinfo ** perl ../renameN37.pl ** rm ~/oasis/monod/hapinfo/N37* ** cp N37* ~/oasis/monod/hapinfo/ * SALK: /home/shg047/oasis/SALK/bam
- 2 Colon (GSE46644): /home/shg047/oasis/Ziller2013
cd /media/LTS_60T/Dinh/WGBS_LTS33/Hg19/Tumor_WGBS/BAMfiles cd /oasis/tscc/scratch/ddiep/Ziller_BAMfiles GSM1204465 SRX332736 Colon primary tumor GSM1465024 SRX669642 HCT116 cd /home/shg047/oasis/Tumor-WGBS/HCT116-SRX669642/bam perl ~/bin/samInfoPrep4Bam2Hapinfo.pl .sorted.clipped.bam > ../Saminfo4bam2hapinfo.txt perl ~/bin/bam2hapInfo2PBS.pl ../Saminfo4bam2hapinfo.txt submit nonbismark cd /home/shg047/oasis/Tumor-WGBS/HCC-SRX332736 perl ~/bin/samInfoPrep4Bam2Hapinfo.pl .sorted.clipped.bam > ../Saminfo4bam2hapinfo.txt perl ~/bin/bam2hapInfo2PBS.pl ../Saminfo4bam2hapinfo.txt submit nonbismark
- Blood:
** ln -s /home/k4zhang/my_oasis_tscc/MONOD/whole_blood_WGBS/BAMfiles bam ** cd /home/shg047/oasis/AgeBlood ** perl ~/bin/mergeBamByChrosome.pl /home/shg047/oasis/AgeBlood/bam /home/shg047/oasis/AgeBlood/mergeBam submit
- HCC:
- Plasma:
**Phase I: *** where is fastq? ***/oasis/tscc/scratch/ddiep/Working/Rerun_rrbs/BAMfiles ***/oasis/tscc/scratch/ddiep/Working/Rerun_rrbs/MethylFreq
**Phase II: *** where is fastq? *** /oasis/tscc/scratch/ddiep/Plasma_RRBS_151208/BAMfiles
**Phase III: ***
- Estellar: /home/shg047/oasis/Estellar2016
Hapinfo to Methylation LD Heatmap(Update: March/16/2016)[edit]
Hapinfo to MHB (Update: March/16/2016)[edit]
Hapinfo to MHL matrix[edit]
- Updated MHL Matrix: /home/shg047/oasis/monod/mhl/monod.mhl.List1.march29.txt
- WB
cd /home/shg047/oasis/AgeBlood/hapinfo echo "perl ~/bin/hapinfo2mhl.pl ./ > ../wb.mhl.march22.txt" | qsub -N wbc.hapinfo2mhl -q pdafm WB_hapinfo_mf.pbs WB_hapinfo_mhl.pbs
- Simulation: Figure 2 (manuscript-code and data).
cd /home/shg047/oasis/monod/test/hapinfo2mhl perl ~/bin/hapinfo2mhl.pl ./ cd /home/shg047/oasis/monod/test/hapinfo2mhb cat ../hapinfo2mhl/*hapInfo.txt > MergeInfo.txt perl ~/oasis/monod/bin/hapinfo2R2ByBed.pl rlt chr10:100027918-100027944 < MergeInfo.txt *File:36CF.tm.png File:6129.tm.pngFile:757E.tm.png
- Real Data
cd /oasis/tscc/scratch/shg047/AgeBlood/mhl * Relationship between MF and MHL in WB(Age) data: Rcode.R(Attention: here 1-9 haplotype were used. In Dr. Zhang's idea, only 3-7 haplotype were used in version 1 script) * smoothscatter.plot.monod.wb.R * File:Monod.3FD8.tm.pngFile:6229.tm.png
miss ratio: 43.63%: sum(is.na(data))/(nrow(data)*ncol(data))
Estellar2016
* Estellar: cd /home/shg047/oasis/Estellar2016/mergeHapinfo Rscript --vanilla ~/bin/matrixPlot.R -i "Estellar2016.mhl.march19.txt" -o "Esterllar2016"
Strategy for genome-wide DNA methylation sequencing for cancer and plasma samples[edit]
- Linkage disequilibrium (R2) of methylation status between pair-wise CpG loci within the methylation blocks
DNA methylation linkage disequilibrium inferred by the methylation status from the single read of the BS-seq were conducted in the present study. The R2 of pair-wise CpG loci within the methylation block were calculated
- Number of MHBs with R2
scp shg047@genome-miner:/home/kunzhang/CpgMIP/MONOD/Data/WGBS_data/mld_block_hapInfo_July2015/All_chromosomes_combined/* /home/shg047/oasis/monod/mhb/hapinfo
Methylation haplotype blocks were conservative in human tissues[edit]
- MONOD.08172015.png
HMH[edit]
- Indentify cancer assocated hyper-methylated haplotype
cd /media/Home_Raid1/shg047/work/monod/hapinfo perl methHMH.pl 6-T-1.sorted.clipped.bam.hapInfo.txt 6-P-1.sorted.clipped.bam.hapInfo.txt excl.list.txt ./caHMH/caHMH-1 perl methHMH.pl 6-T-2.sorted.clipped.bam.hapInfo.txt 6-P-2.sorted.clipped.bam.hapInfo.txt excl.list.txt ./caHMH/caHMH-2 perl methHMH.pl 6-T-3.sorted.clipped.bam.hapInfo.txt 6-P-3.sorted.clipped.bam.hapInfo.txt excl.list.txt ./caHMH/caHMH-3 perl methHMH.pl 6-T-4.sorted.clipped.bam.hapInfo.txt 6-P-4.sorted.clipped.bam.hapInfo.txt excl.list.txt ./caHMH/caHMH-4 perl methHMH.pl 6-T-5.sorted.clipped.bam.hapInfo.txt 6-P-5.sorted.clipped.bam.hapInfo.txt excl.list.txt ./caHMH/caHMH-5 cat caHMH* > HMH.txt Rscript caHMH.R #>>>>>>>> caHMH.R #>>>>>>>> data<-read.table("HMH.txt",head=T,sep="\t",as.is=T) input<-data.matrix(data[,8:ncol(data)]) caHMH<-data[which(apply(input,1,function(x) sum(x)==0)),1] unique(as.character(caHMH)) write.table(unique(as.character(caHMH)),file="caHMH.rlt.txt",col.names=F,row.names=F,quote=F) #>>>>>>>> caHMH.sh #>>>>>>>> awk -F":|-" '{print $1,$2,$3,$1":"$2"-"$3}' OFS="\t" caHMH.rlt.txt > caHMH.bed bedtools intersect -wao -a caHMH.bed -b ~/work/db/hg19/hg19_refGene.bed | sort -k1,1n
- Take one caHMH and validate with MHL values in all our samples (MHL matrix)
# chr1 119543104 119543127 chr1:119543104-119543127 TBX15 setwd("/oasis/tscc/scratch/shg047/monod/hapinfo") load("MHL4.RData") data[match("chr1:119543104-119543127",rownames(data)),] # quite good, highly low methylated in >86% normal tissues and hyper-methylated in cancer related samples including 6-T, and parts of 6-P. \\
- how to test the significance between PBMC and TCGA methylation haplotype blocks File:R20150818.txt
Cancer Diagnosis Performance based on Methylation Haplotype Loading of circulating free DNA methylation[edit]
? THINK ABOUT THE TISSUE SPECIFIC AND CANCER SPECIFIC? In this section, RRBS and Capseq dataset were taken as the discovery dataset and the BSPP dataset were used validate the performance of the biomarkers. For RRBS dataset, 68 cancer and 8 normal samples were included. 56046 regions were obtained by methylation haplotype loading algorithm (MHL) and 17263 regions were 100% un-methylated in 8 normal samples. 248 regions among 56046 were methylated in at least 50% cancer samples while 100% un-methylated in normal samples. The average length of the regions were 103bp (IQR=95bp, SD=126) and were located in the promoter region of 182 genes (2000bp up-stream of TSS). With the help of text mining, 21 genes of them were validated to be methylation relevant cancer related genes (Table). In order to evaluate the distinguish ability of DNA methylation from cancer to the normal samples, Random forest model were applied. In the procedure of RF model, the optimal number of variables tried at each split is131 (mtry) and number of trees (mtrees) makes no difference to the prediction accuracy from 250 to 7000. RF prediction model showed 1592 regions could provide positive ability to distinguish cancer samples from normal samples with sensitivity of 97.06%, specificity of 100% and accuracy of 97.37%. Furthermore, we found the top 206 most importance regions could take account of 80.0% contribution to the accurate prediction. And the random forest model based on top 206 regions with mtry of 14 and mtrees of 500 showed better performance with 100% sensitivity, 100% specificity and 100% accuracy. The result showed these 206 regions were high frequent hypermethylated in cancers (Mean=41.4%, SD=14.5%, IQR=22.4%). (Supplementary Table **) For CapSeq dataset, 31 cancer 24 normal samples were included. 86206 regions were obtained by methylation haplotype loading algorithm (MHL), however, only 2205 regions were 100% un-methylated in 24 normal samples. Parameters of Random forest model were tuned and the optimal number of variables tried at each split is 92 (mtry) and number of trees (mtree) makes no difference to the prediction accuracy from 250 to 7000. Random forest algorithm showed 516 regions could provide positive ability to separate cancer samples from normal samples with sensitivity of 87.1%, specificity of 75.0% and accuracy of 81.26%. We found that the top 21 most importance regions could take account of 80.0% contribution for the classification. Therefore, the second round prediction process of random forest model based on top 21 regions with mtry of 7 and mtrees of 750 showed perfect classification with 96.77% sensitivity, 100% specificity and 98.18% accuracy. The average hyper-methylation frequency in cancer samples for above 516 and 21 regions 90.43% (SD=0.08, IQR=0.096) and 96.7% (SD=0.041, IQR=0.056), respectively. (Supplementary Table ** and supplementary Table **) For BSPP dataset, 16 cancer plasma and 16 normal plasma samples were included. 36281 regions were obtained by methylation haplotype loading algorithm (MHL) and 4194 regions were 100% un-methylated in 16 normal samples. All the regions hypermethylated in cancer and 100% un-methylated in normal samples were collected from RRBS, SeqCap and BSPP dataset and 30 regions were found un-methylation in all the normal samples of the 3 dataset. These 30 regions could explain 93.75% cancers incidence and the specificity is 100%. The methylation frequency of these 30 regions ranged from 6.25% to 25% in free-cell circulating DNA while it is hypermethylated in 1.47%-44% RRBS cancer samples and 6.45%-77% SeqCap cancer samples. These 30 regions were located in the promoter or gene body of 24 genes, including FGF19, MEF2C-AS1, MEF2C, HABP2, AGO3, TAF15, ZWILCH, YTHDF3-AS1, ZNF213, C9orf114, C14orf28, ADAM11, CDKN2AIP, TMEM87B, CDKL3, MSH5, MSH5-SAPCD1, UBC, PCM1, PCK2, NR2F6, JADE2, KANK1 and MCM3. At least 12 genes have been demonstrated to be associated with human cancers with the annotation from NCBI (HABP2, FGF19, MEF2C, AGO3, CDKL3, MCM3, TAF15, PCK2, NR2F6, MSH5, ZWILCH and PCM1). For example, the FGF19-FGFR4 signaling axis has been implicated in the pathogenesis of several cancers in mice and potentially in humans (7) while HABP2 has been found to be hypermethylated in hepatocellular carcinomas cancer (8). AGO3 was found significant decreased in human liver cancer (9). KANK1 re-expression induced by 5-Aza-2'-deoxycytidine could suppresses nasopharyngeal carcinoma cell proliferation and promotes apoptosis (10). Whole-exome sequencing identifies mutated PCK2 associated with carcinoma cell proliferation in a hepatocellular carcinoma patient(11)
Genome-wide DNA methylation profile comparison between solid tissue and circulating free DNA methylation in plasma[edit]
All the assumption of we can used DNA methylation in free-cell circulating DNA methylation is based on the methylation status of solid cancers can be released into plasma without any specific selection, thus, the aberrant DNA methylation fragment or biomarkers identified in solid tissues can be used to be the target for cancer non-invasive diagnosis, screening or prognosis surveillance. We then constructed 15 genome-wide DNA methylation profiles for the paired plasmas and solid tissues from same cancer patient with RRBS assay to check whether DNA methylation fragments derived from cancer cells were released into the blood randomly or selectively. 5 paired lung cancer, colon cancer and pancreatic cancer solid tissues and plasma were enrolled in this section and single-base methylome were established by RRBS as mentioned above. Correlation analysis based on 56046 MHL regions showed there is just weak correlation between genome-wide DNA methylation of solid tissues and plasmas. The average correlation between tissue and plasma was 0.50 (95%CI: 0.44-0.56)(Figure). What’s more, only about 28% (95%CI: 25%-31%) regions were simultaneously methylated both in tissues and plasma. Even considering different threshold to define the methylation status and transferring the continuous MHL to binary DNA methylation status, the maximum co-methylation ratio was still less than 63% (Figure). These evidence showed the dramatically significant heterogeneity between solid and plasma DNA methylation profile and provide the importance that we should validate all the biomarkers identified in solid tissues in cell-free circulating plasma DNA to guarantee the validity of the biomarkers. 25204 regions which was methylated in sample pairs for at least one time were selected to evaluate the selectivity of hyper-methylated DNA fragments from solid tissues to plasma. The results showed the process of the releasing were non-random, with at least 1590 fragment were significantly prefer-selected (P<1.98*10-6, binomial test). These fragments were located in the regions of 1190 genes. Function enrichment analysis showed these genes were significantly associated with cancer relevant biological functions (Table), including embryonic morphogenesis, regulation of transcription, neuron differentiation, regionalization, tissue morphogenesis ,transcription factor activity, sequence-specific DNA binding ,transcription regulator activity.
File:1155.tm.png
Re-validation of the hyper-methylated fragments of plasma in public GEO dataset.[edit]
GSE56044 (124 lung cancer and 12 normal), GSE39279 (444 lung cancer), GSE52401 (244 normal lung), TCGA-lung cancer (), TCGA-colon cancer () and TCGA-pancreatic cancer () dataset were downloaded and summarized.
Quantitatively estimation to the level of cancer DNA fractions in the plasma[edit]
By computationally mixing the sequencing reads from cancer tissues and whole blood samples (WBC), we created synthetic admixtures at various levels of tumor fraction. We found that MHL is 2-5 folder higher than the methylation level of individual CpG sites across the full range of tumor fractions. Remarkably, MHL provides additional gain of signal-to-noise ratio (mean divided by standard deviation) compared with AMF as the fraction of tumor DNA decreases below 10%, which is typical for clinical samples. We then took the individual plasma data sets, and predicted the tumor fraction based on the MHL distribution established by computational mixing. Except for a small number (N<5) of outliers, we observed significantly higher average MHL in cancer plasma than in normal plasma. Note that all Group II MHBs were selected without using any information from the plasma samples, and hence they should be generally applicable to other plasma samples. Interestingly, we also found that the estimated tumor DNA fraction were positive correlated with normalized cfDNA yield from the cancer patients (P<0.000023).
Quantitatively estimation to the level of cancer DNA fractions in the plasma[edit]
Recent studies have demonstrated that epigenetic information imbedded in cfDNA has the potential for predicting tumor’s tissue-of-origin. Consistently, we found that tissue-of-origin derived methylation haplotypes were the most abundant fraction in cancer plasma. Here we asked whether a MHL-based framework and a set of targets derived from whole genome data would allow us to predict tissue-of-origin with quantifiable sensitivity and specificity, which is crucial for future clinical applications. We compiled 43 WGBS and RRBS data sets for 10 human normal tissues that have high cancer incident rate, and identified a set of 2,880 tissue-specific MHBs as the candidates. We then used these tissue-specific MHBs or subsets to predict the tissue-of-origin for the cancer plasma sample. We adopted the approach by counting the total number of tissue-specific MHBs in the plasma samples and comparing with all other tissues, in order to infer the most probable tissue-of-origin. At the cutoff of minimal 10 tissue-specific methylated haplotypes per tissue type, we observed an average 90% accuracy for mapping a data set from the primary tissue to its tissue type (Figure 5, middle). We then applied this method to the full set of plasma data from 59 cancer patients and 75 normal individuals, and achieved an average prediction accuracy of 82.8%, 88.5%, 91.2% for the plasma from colorectal cancer, lung cancer, and control plasma samples respectively with 5-fold cross-validation. For the incorrectly classified samples, we noticed that 4 out of 5 colorectal cancer plasma were from metastatic colorectal cancer patients while the fifth was in fact tubular adenoma. In the case of lung cancer, one misclassified sample came from a patient with benign fibrous tissue. Our invention demonstrate these biomarkers could be useful for tissue-of-origin mapping and other related applications.
Tissue-of-origin mapping by normal, cancer and inflammation reference[edit]
- Dr. Zhang said maybe we can integrate cancer,normal and inflammation samples as the reference so that we can mapping the plasma to differnet tissue, different stage, just like the 'GPS'
- Take the normal samples as the background and don't forget to do the cross-validation and then estimate each plasma to differenet refernece and then give the pvalue
- Very important and interesting idea come out from Dr. Zhang again. Dr. Zhang said we can merge one disease reference and one normal reference and for that diease, we would get more strong signals,compared with single reference (10/16/2016).
- differrent stastic Z-score, Baysian score.
- kedney cancer as the reference since we have samples.
Discussion[edit]
we found the top 289 most importance regions could take account of 80.0% contribution to the accurate prediction. Random forest algorithm showed 1585 regions could provide positive ability to distinguish cancer samples from normal samples while the top 286 most importance regions could take account of about 80.0% contribution, with sensitivity of 97.06%, specificity of 100% and accuracy of 97.37%. The second round prediction process of random forest model based on top 206 regions with mtry of 14 and mtrees of 500 showed 100% sensitivity, 100 specificity and 100% accuracy. One the other side, 248 regions were hypermethylated in at least 50% cancer samples. The average length of the regions were 103bp (IQR=95bp, SD=126) and were located in the promoter region of 182 genes (2000bp up-stream of TSS). With the help of text mining, 21 genes of them were validated to be methylation relevant cancer related genes (Table).
Next-generation methylation sequencing and quality control
Unique mappable reads
In the first step,
In the next step, genome-wide DNA methylation profiles of 30 samples including 15 solid cancer tissues (5 colon cancer, 5 lung cancer and 5 pancreatic cancer) and corresponding plasmas detected with reduced representation bisulfite sequencing (RRBS) were collected to discover the pattern of the shedding for the methylated DNA fragments from tissues to blood.
Task 1.
1, You need to filter all the hypermethylated fragement in cancer solid tissues and corresponding plasma circulating DNA while no methylation signals in health plasma. 2, and then validation these signals in TCGA database.
Task 2. Different method comparison.
Can it be used on RRBS data? We can downsample high coverage data to lower the effect of clonal reads on the analysis. Noi to check the input requirement for performing RRBS, can we perform RRBS in parallel on the test samples.
The optimal number of variables tried at each split is 16 (mtry) and number of trees (mtrees) makes no difference to the prediction accuracy from 500 to 7000. RF prediction model showed 1585 regions could provide positive ability to distinguish cancer samples from normal samples with high specificity of 100%, however, the sensitivity was only 25%, which indicated large number low predictive biomarkers were enrolled into the prediction model. After removed the most 60% lower informative regions in the random forest model the sensitivity was only 78%, (Informative regions from BSPP see supplementary Table *)
Therefore, we merged with the informative biomarkers identified in RRBS and Cap-seq dataset, eventual, there are 2 biomarkers were remained, including:
In the prediction section, top 62 regions could provide the distinguish accuracy of 86.87% which did not indicated Capseq method were worse than RRBS in the prediction ability. First capture, On the another side, when you will top 10 predictive regions, the separate ability of prediction model based on Capseq dataset could come up to the sensitivity of 90.32%, specificity of 95.83% and accuracy of 92.49%. However, small number of predictors would greatly decrease the robust or reproducibility of the prediction model and would bring biased inference in the process of clinical application of Capseq assay.
63.4% MHL distance of BSPP were less than 200bp, indicating they are almost located in the same region/CpG island in human genome, therefore, we need merge these MHL region together to increase the sensitivity of the prediction.
Analysis influence from low coverage and differential coverage[edit]
- Alan also told me that our RRBS were not quite enriched
- Jun told Dr. Zhang the coverage between plasma and tissue is quite different.
- Then we need think about whether there will be influcence for the furture usage
- Work after Dr. Zhang's previous analysis
cd /media/NAS3_volume2/Kun/KZ_LTS33T/MONOD
to do list[edit]
- Prepare for better version:
- (1) Perform additional quality filtering of our data, remove some low-quality samples, and repeat the analysis.
- (2) Determine how low (percentage of tumor DNA in blood) can we go in tissue-of-origin mapping, and ctDNA detection, by computational mixing of tumor data with WB data at various ratios, and perform the analysis similar to Figure 4.
- (3) Look out for additional WGBS data in the public database.
- (4) Look further into the potential differences of the spreading pattern between adding and removing methyl groups.
- (5) Try reference-free-deconvolution to detect tumor-DNA from plasma
- (6) Cancer-specific methylation haplotype (here, we can include all the genomic region, not only in MHB) and then estimate the cancer fragment contribution.
- (7) Any biological function for the interesting tissue-specific MHBs in the prediction model.
- (8) The overlap between our prediction features and other studies?
- (9) With our prediction model to predict some other public dataset and test the model stability.
- (10) Dr. Zhang mentioned that we actually have more data, can we get more interesting result from these sequencing data: Yes.
I just realized that we also have another set of data that wasn’t used in our NG paper. The data were generated with a different strategy, and my gut feeling is that it might have a higher unique read depth per sample than our RRBS data. Can you process them similar to what you did in Supp. Figures 11/12? /media/Home_Raid1/kunzhang/MONOD/150209_SN216/SeqCap/mld_blocks_July2015 http://genome-tech.ucsd.edu/LabNotes/index.php/Kun:LabNotes/MONOD/2015-3-4 http://genome-tech.ucsd.edu/LabNotes/index.php/Kun:LabNotes/MONOD/2015-7-6#Update_on_10.2F16.2F2015 Thanks!
Method and Materials[edit]
Clinical sample and DNA collection[edit]
- NSCLC samples and corresponding normal lung tissues for validation study in Chinese population were obtained from 150 patients who underwent pulmonary resection for primary NSCLC at Changhai Hospital, Shanghai, China. The study was approved by Fudan University and Changhai Hospital and Informed consents were obtained from the patients. Exclusion criteria included subjects with a family history of lung cancer, previous radiotherapy, and chemotherapy or adjuvant therapy before surgery. All tissues were immediately frozen at -80℃ after surgical resection. Histological examination and tumor-node-metastasis classification were conducted according to World Health Organization classification criteria (5) and AJCC Cancer Staging Manual, 7th Edition (6), respectively. Age, gender, smoking status, histology type, TNM stage and differentiation status were collected as the covariates when conducting association between DNA methylation and disease status. Smoking status was assigned to binary status: never and ever smoking. TNM stage was assigned to early stage (I and II) or late stage (III and IV) when it is necessary so that the sample size can be big enough to get the efficient statistic power.
- Plasma
- CTT-frozen and CTT-FFPE RRBS library and sequencing [3]
- The aim of expriments on TT-frozen and CTT-FFPE
- N37 samples with WGBS
Bam files were stored in TSCC server:
Mapping, bisulfite conversion rate[edit]
Average Methyaltion Level for Specific Genomic Regions[edit]
Obviously, we need compare the effect of MHL and average methylation level, especially in the same genomic regions. Here is how to get average methylation level for some genomic postions.
PileOMeth extract -r chr10:123-456 genome.fa alignments.bam
Pairwise R2 (LD) calcualtion within the methylation block from haploinfo files[edit]
- Calculate the pairwise R2 with perl script code
- prepare script to calculate LD R2 by genomic region Shicheng:haploinfo2LDR2.pl
- collect WGBS(WB,ES,Roadmap) haploinfo files.
- R script for LD block plot code
- relationship between R2 and distance (absolute and relative) code
- Haplotype and MHL
Number of MHBs with different R2 cut-off[edit]
cd /home/shg047/oasis/monod/hapinfo/WGBS qsub hapinfo2mhb.job
Umi-BSPP and Methylation Haplotype Loading (MHL)[edit]
Bioinformatics and Statistics[edit]
We need to look into each cancer individually on the list, maybe rank the cancer samples from most preferable to least preferable. Once we get optimistic results, we can further ask for samples from other centers. We cannot realistically ask specifically for each stage of cancer, although having samples from multiple stages might be better. prevalence treatment options for early detection (stage 1) current diagnostic methods We need to ask for buffy coat (1 tube) and matched serum samples (2 tubes) per patient We need to ask for primary tumor samples, they might be Formalin-fixed paraffin-embedded (FFPE) samples check Blueprint data to see how successful we were at capturing DNA purified from FFPE samples look into the kit for fixing degraded DNA Noi said we successfully captured with 50 ng converted DNA before, and it is possible to obtain this much from 2 tubes of serum samples, however, we can also look into the amplified DNA from Illumina (can we perform capture on these samples?)
RRBS option Dinh need to check the bayes classification algorithm Can it be used on RRBS data? We can downsample high coverage data to lower the effect of clonal reads on the analysis. Noi to check the input requirement for performing RRBS, can we perform RRBS in parallel on the test samples.
In the biomarker discover stage, the biomarker whose methylation were 100% un-methylated in normal plasmas were enrolled while any regions which were detected to be methylated in normal plasmas were filter out from the candidates. In the procedure of differential methylation test, regions whose variance in total samples were stage at lowest 30% quantile were removed to decrease the burden of multi-test correction. For the random forest prediction, optimal parameters were tuned before the tanning of the model the best number of tries and tress were determined by the grid search method with lowest out of beg prediction error. Intersection analysis of the genome position were conducted by BEDTOOLS and the regions whose distance less than 25bp were taken as the same biomarker region.
R packages of IRanges, Biostrings, stringr, randomForest, impute, rpart, e1071, biclust were used in the the statistic and bioinformatics analysis.
Potential biomarker based on published papers from NCBI were extracted for lung cancer (74 papers), colon cancer (8 papers) and pancreatic cancer (13 papers) with the strategy of title including “methylation” and corresponding cancer symbols as well as abstract including “Diagnosis”.
Gene Ontology Analysis[edit]
Cluster and Classification Analysis[edit]
1360 tissue specific MHL in tissue cluster
LD in molecular level and pearson correlation in population level[edit]
Cancer specific methylation haplotype[edit]
Dr. Zhang hope to visualize some explicit cancer specific methylation haplotype. I checked the wiki page for his previous anlaysis and conducted similar analysis based on paired cancer tissues, cancer plasma and normal plasma.
wiki page:[[6]]
Prepare SRA submission[edit]
cd /home/shg047/oasis/monod/hapinfo/SRA cp ../*-P-* ./ cp ../*-T* ./ cp ../*N37* ./
More Thinking[edit]
1, Dr Jirun Peng proposed a method called MCTA-Seq to do plasma cell-free dna methylation sequencing, any advantage?
Acknowledgement[edit]
Author’s Contribution[edit]
Can it be used on RRBS data? We can downsample high coverage data to lower the effect of clonal reads on the analysis. Noi to check the input requirement for performing RRBS, can we perform RRBS in parallel on the test samples.
- intersting is if I can collect same marker which exist in hm450k, then our current work can be validate in large number of cancer smaples?? no, our sample is plasma, current is solid cancer smaples.
Reference[edit]
1.Zhao Y, Sun J, Zhang H, Guo S, Gu J, Wang W, et al. High-frequency aberrantly methylated targets in pancreatic adenocarcinoma identified via global DNA methylation analysis using methylCap-seq. Clinical epigenetics. 2014;6(1):18. Epub 2014/10/03. 2.Siegel RL, Miller KD, Jemal A. Cancer statistics, 2015. CA: a cancer journal for clinicians. 2015;65(1):5-29. Epub 2015/01/07. 3.Hankey BF, Ries LA, Edwards BK. The surveillance, epidemiology, and end results program: a national resource. Cancer Epidemiol Biomarkers Prev. 1999;8(12):1117-21. Epub 1999/12/29. 4.Warton K, Lin V, Navin T, Armstrong NJ, Kaplan W, Ying K, et al. Methylation-capture and Next-Generation Sequencing of free circulating DNA from human plasma. BMC genomics. 2014;15:476. Epub 2014/06/16. 5.Gibbs AR, Thunnissen FB. Histological typing of lung and pleural tumours: third edition. J Clin Pathol. 2001;54(7):498-9. Epub 2001/06/29. 6.Edge SB, Compton CC. The American Joint Committee on Cancer: the 7th edition of the AJCC cancer staging manual and the future of TNM. Ann Surg Oncol. 2010;17(6):1471-4. Epub 2010/02/25. 7.Desnoyers LR, Pai R, Ferrando RE, Hotzel K, Le T, Ross J, et al. Targeting FGF19 inhibits tumor growth in colon cancer xenograft and FGF19 transgenic hepatocellular carcinoma models. Oncogene. 2008;27(1):85-97. Epub 2007/06/30. 8.Revill K, Wang T, Lachenmayer A, Kojima K, Harrington A, Li J, et al. Genome-wide methylation analysis and epigenetic unmasking identify tumor suppressor genes in hepatocellular carcinoma. Gastroenterology. 2013;145(6):1424-35 e1-25. Epub 2013/09/10. 9.Kitagawa N, Ojima H, Shirakihara T, Shimizu H, Kokubu A, Urushidate T, et al. Downregulation of the microRNA biogenesis components and its association with poor prognosis in hepatocellular carcinoma. Cancer science. 2013;104(5):543-51. Epub 2013/02/13. 10.Luo FY, Xiao S, Liu ZH, Zhang PF, Xiao ZQ, Tang CE. Kank1 reexpression induced by 5-Aza-2'-deoxycytidine suppresses nasopharyngeal carcinoma cell proliferation and promotes apoptosis. International journal of clinical and experimental pathology. 2015;8(2):1658-65. Epub 2015/05/15. 11.Liu YX, Zhang SF, Ji YH, Guo SJ, Wang GF, Zhang GW. Whole-exome sequencing identifies mutated PCK2 and HUWE1 associated with carcinoma cell proliferation in a hepatocellular carcinoma patient. Oncology letters. 2012;4(4):847-51. Epub 2012/12/04.