SONG, Dong 5/26/2020
Wang Lab at HKUST (http://wang-lab.ust.hk/)
Cancer EvoLutionary analysis using LOngitudinal genomic data (CELLO) is a protocal for comprehensive analysis of longitudinal genomic sequencing data in cancer. It was originally developed by Jiguang Wang [1,2], and was then packed up by Biaobin Jiang and Dong Song in MATLAB and R seperately. This code was written in R.
The input SAVI report (input.savi.txt) consists of a list of genetic variants from 90 glioblastoma patients.
source('CELLO.R')savi.table <- mutRead("../../input.savi.txt",20,1,5)head(savi.table[,1:10])## chr pos ref alt Effect Effect_Impact
## 1 chr10 5248362 T C splice_donor_variant+intron_variant HIGH
## 2 chr10 27703023 A T missense_variant MODERATE
## 3 chr10 122263428 G A missense_variant MODERATE
## 4 chr11 209598 C T synonymous_variant LOW
## 5 chr11 20676292 TG T frameshift_variant HIGH
## 6 chr12 21422680 C T synonymous_variant LOW
## Functional_Class Codon_Change Amino_Acid_Change Amino_Acid_length
## 1 - - - 323
## 2 MISSENSE Tcc/Acc S53T 767
## 3 MISSENSE cGc/cAc R52H 271
## 4 SILENT tcC/tcT S108 537
## 5 - tgg/ W758 797
## 6 SILENT ccG/ccA P605 670
knownDriverGene <- c('LTBP4','PTPN11','NF1','RB1','PDGFRA','PIK3CG','PIK3R1','PIK3CA','PTEN','EGFR','IDH1','ATRX','TP53')
stats <- mutStats(savi.table, knownDriverGene,5,remove_LOW = TRUE)head(stats$mutNum.table)## Patients Primary Common Recurrent
## 1 R001 2 39 2
## 2 R002 15 36 12
## 3 R003 36 0 1
## 4 R004 1 36 3
## 5 R005 16 38 11
## 6 R006 2 4 118
head(stats$mutGenes.table)## LTBP4 PTPN11 NF1 RB1 PDGFRA PIK3CG PIK3R1 PIK3CA PTEN EGFR IDH1 ATRX TP53
## R001 "N" "N" "N" "N" "N" "N" "N" "C" "N" "N" "N" "N" "C"
## R002 "N" "N" "C" "N" "N" "C" "C" "N" "N" "N" "N" "N" "N"
## R003 "N" "N" "N" "N" "N" "N" "N" "N" "N" "N" "N" "N" "N"
## R004 "N" "N" "C" "N" "N" "N" "N" "C" "N" "N" "N" "N" "N"
## R005 "N" "N" "N" "N" "N" "N" "N" "C" "N" "P" "N" "N" "N"
## R006 "R" "N" "R" "N" "N" "N" "N" "R" "N" "N" "N" "N" "R"
mutLandscape(stats$mutNum.table,stats$mutGenes.table)
mutCorrelation(stats$mutGenes.table)
freq.table <- mutFrequency(savi.table, knownDriverGene,stats$mutGenes.table,5)
head(freq.table)## Primary Recurent Common
## LTBP4 0 9 0
## PTPN11 1 2 2
## NF1 4 12 10
## RB1 1 2 2
## PDGFRA 2 3 1
## PIK3CG 2 1 5
hm.table <- mutSignature(savi.table,15,350,1.3)
## P-value between Primary and NonHM Recurrence: 0.229260285986123
## P-value between Primary and HM Recurrence: 2.59649100398582e-06
## P-value between NonHM Recurrence and HM Recurrence: 4.38254782225301e-05
cluster.table <- mutTreeClustering(stats$mutNum.table)
switch.table <- mutSwitch(savi.table,knownDriverGene,5,20)
selGene <-c('LTBP4','IDH1','ATRX','TP53','NF1','MSH6','PIK3CG','PIK3R1','PIK3CA','PTEN','EGFR')
selStats <- mutStats(savi.table, selGene,5,remove_LOW = TRUE)
TEDG <- mutDirectedGraph(selStats$mutGenes.table)
TEDG$node.table## Gene P_CDF FC Occurrence
## 1 LTBP4 0.0625000 -2.5849625 9
## 2 IDH1 0.0078125 3.1699250 37
## 3 ATRX 0.1250000 1.8073549 35
## 4 TP53 0.5078125 0.8073549 74
## 5 NF1 0.3750000 -1.3219281 34
## 6 MSH6 0.0625000 -2.5849625 8
## 7 PIK3CG 0.2500000 -2.0000000 13
## 8 PIK3R1 1.0000000 0.0000000 10
## 9 PIK3CA 0.2500000 -2.0000000 17
## 10 PTEN 1.0000000 0.0000000 42
## 11 EGFR 0.3750000 1.3219281 26
TEDG$edge.table## geneA geneB weight label
## 1 "IDH1" "LTBP4" "3" "R027;R049;R055"
## 2 "ATRX" "LTBP4" "1" "R049"
## 3 "TP53" "LTBP4" "3" "R027;R049;R055"
## 4 "PTEN" "LTBP4" "1" "R039"
## 5 "EGFR" "LTBP4" "2" "R022;R039"
## 6 "IDH1" "TP53" "1" "R054"
## 7 "IDH1" "NF1" "5" "R043;R049;R051;R053;R055"
## 8 "IDH1" "MSH6" "3" "R027;R044;R051"
## 9 "IDH1" "PIK3R1" "1" "R051"
## 10 "IDH1" "PIK3CA" "2" "R048;R049"
## 11 "IDH1" "PTEN" "2" "R046;R051"
## 12 "IDH1" "EGFR" "1" "R027"
## 13 "ATRX" "TP53" "1" "R054"
## 14 "ATRX" "NF1" "5" "R043;R049;R051;R053;R077"
## 15 "ATRX" "MSH6" "2" "R044;R051"
## 16 "ATRX" "PIK3R1" "1" "R051"
## 17 "ATRX" "PIK3CA" "2" "R048;R049"
## 18 "EGFR" "ATRX" "1" "R039"
## 19 "TP53" "NF1" "7" "R034;R043;R049;R051;R053;R055;R077"
## 20 "TP53" "MSH6" "3" "R027;R044;R051"
## 21 "TP53" "PIK3CG" "1" "R042"
## 22 "TP53" "PIK3CA" "2" "R048;R049"
## 23 "TP53" "PTEN" "5" "R034;R038;R046;R051;R061"
## 24 "EGFR" "TP53" "2" "R022;R039"
## 25 "NF1" "PIK3CG" "1" "R042"
## 26 "PIK3R1" "NF1" "1" "R100"
## 27 "PTEN" "MSH6" "1" "R039"
## 28 "EGFR" "MSH6" "2" "R022;R039"
## 29 "PTEN" "PIK3CG" "1" "R042"
## 30 "PIK3R1" "PTEN" "1" "R024"
[1] Wang, J., Cazzato, E., Ladewig, E., Frattini, V., Rosenbloom, D. I., Zairis, S., … & Lee, J. K. (2016). Clonal evolution of glioblastoma under therapy. Nature Genetics, 48(7), 768-776.
[2] Wang, J., Khiabanian, H., Rossi, D., Fabbri, G., Gattei, V., Forconi, F., … & Pasqualucci, L. (2014). Tumor evolutionary directed graphs and the history of chronic lymphocytic leukemia. Elife, 3, e02869.