GBM - glioblastoma multiforme

Batch vs clinical traits

Number of clinical traits: 31

Number of batches based on DNA methylation data: 19

Relationship between batch and the center:

> table(batchID,two)
       two
batchID 02 06 08 12 14 15 16 19 26 27 28 32 41 74 76 81 87
   0186 25  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
   0199 17  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
   0218  0 28  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
   0242  0 53  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
   0279 34 17 15  0  0  0  0  0  0  0  0  0  0  0  0  0  0
   0287 26 16 15  0  0  0  0  0  0  0  0  0  0  0  0  0  0
   0297 13  0 19  0  0  0  0  0  0  0  0  0  0  0  0  0  0
   0314  2  7  6  5  0  0  0  0  0  0  0  0  0  0  0  0  0
   0337  0 10  0 16  0  1  0  0  0  0  0  0  0  0  0  0  0
   0392  0  7  0 10  6  0  5  0  0  0  0  0  0  0  0  0  0
   0521  0  4  0 12 12  3  7  6  3  0  0  0  0  0  0  0  0
   0595  0  4  0  4 13  0  1 10  1  4  9  0  0  0  0  0  0
   0788  5 12  0  0  3  0  0  0  0 13 10  4  0  0  0  0  0
   0915  0  0  0  9  2  0  0  9  0  0  0  5  4  0  0  0  0
   1228  0  1  0  0  2  0  1  3  1  0  0 15  4  0  0  0  0
   1481  0  5  0  3  0  0  0  0  6  0 11  1  0  0  9  0  0
   1697  0 13  0  0  1  1  0 10  1  0  3  2  1  0  5  1  1
   1844  0 10  0  0  5  0  0  1  2  0  1  0  1  6 10  1  0
   2004  0  7  0  0  3  0  0  4  0  0  0  0  0  0  0  0  0

Relationship between batch and clinical variable, significant correlations (entire table can be found here)

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GBM_clinical,DataType,NumberOfNAs,Test,Pvalue
year_of_initial_pathologic_diagnosis,integer,35,Kruskal-Wallis rank sum test,1.34E-64
pretreatment_history,factor,35,Pearson's Chi-squared test,2.98E-29
histological_type,factor,35,Pearson's Chi-squared test,6.11E-20
initial_pathologic_diagnosis_method,factor,37,Pearson's Chi-squared test,5.24E-15
vital_status,factor,36,Pearson's Chi-squared test,8.36E-15
hormonal_therapy,factor,59,Pearson's Chi-squared test,6.50E-14
targeted_molecular_therapy,factor,65,Pearson's Chi-squared test,5.80E-10
additional_pharmaceutical_therapy,factor,72,Pearson's Chi-squared test,4.19E-05
additional_drug_therapy,factor,73,Pearson's Chi-squared test,5.08E-05
days_to_last_followup,integer,35,Kruskal-Wallis rank sum test,2.90E-04
person_neoplasm_cancer_status,factor,88,Pearson's Chi-squared test,4.81E-04
additional_chemo_therapy,factor,106,Pearson's Chi-squared test,5.18E-03
days_to_death,integer,169,Kruskal-Wallis rank sum test,6.30E-03
days_to_birth,integer,35,Kruskal-Wallis rank sum test,1.13E-02
age_at_initial_pathologic_diagnosis,integer,35,Kruskal-Wallis rank sum test,1.20E-02

Survival vs batch

Code for automatic analysis of survival and correlation with clinical traits can be found here.

Kaplan Meier Curve and survival by batch:

Summary of the cox proportional hazards model can be found here, batch shows significant correlation with survival (Likelihood ratio test= 31 on 17 df, p=0.02; Wald test = 28.17 on 17 df, p=0.04297; Score (logrank) test = 29.48 on 17 df, p=0.03035)

I need to state here (and for all cancer types from TCGA that I have analyzed and will analyze) that the p-values for the association of the batches with the clinical traits correspond to ALL batches. However, actual DNA methylation (or other data) may not be available for all batches yet. For example, for GBM I have 9 batches for the downloaded 286 patients. I still see significant correlation between these batches and the clinical traits (p values might be bigger though. For example, correlation between histological type and batches is 3.9e-11).

DNA methylation

Downloaded the data in the last week of December, 27k, Level1, 294 patients. Weird format for the files, methylated and unmethylated probe intensities are in the first and second columns, different from the format that was used for other datasets.

Compared list of DNA methylation patients with the technical info, tech info is available for only 286 patients. Stick with those for the analysis.

Technical variables:

#Correlation of DNA methylation (real data) batches and the center:
> table(two,shortMeth$batchID)

two  0186 0199 0218 0392 0521 0595 0788 0915 1228
  02   25   17    0    0    0    0    5    0    0
  06    0    0   20    7    4    4   12    0    1
  12    0    0    0   10   12    4    0    9    0
  14    0    0    0    6   12   13    3    2    2
  15    0    0    0    0    3    0    0    0    0
  16    0    0    0    5    7    1    0    0    1
  19    0    0    0    0    6   10    0    9    3
  26    0    0    0    0    3    1    0    0    1
  27    0    0    0    0    0    4   13    0    0
  28    0    0    0    0    0    9   10    0    0
  32    0    0    0    0    0    0    4    5   15
  41    0    0    0    0    0    0    0    4    4
#Concentration can be probably treated as a continuous variable:
> shortMeth[1:10,3]
 [1] 0.15 ug/uL  0.15 ug/uL  0.140 ug/uL 0.14 ug/uL  0.14 ug/uL  0.140 ug/uL
 [7] 0.14 ug/uL  0.189 ug/uL 0.167 ug/uL 0.164 ug/uL
81 Levels: 0.071 ug/uL 0.12514558 ug/uL 0.134 ug/uL ... .19 ug/uL
> table(shortMeth$plate_column)

 1  2  3  4  5  6
66 68 64 43 24 21
> table(shortMeth$plate_row)

 A  B  C  D  E  F  G  H
37 36 37 38 37 35 35 31
> table(shortMeth$shortDay)

13-5-2009 13-9-2010 14-4-2010 18-9-2008 20-1-2010 24-8-2009 29-6-2007  3-5-2007
       47        27        29        28        47        46        20        17
 4-4-2007
       25
#Also, day of shipment is the same as the batch which I have seen before
> table(shortMeth$batchID,shortMeth$shortDay)

       13-5-2009 13-9-2010 14-4-2010 18-9-2008 20-1-2010 24-8-2009 29-6-2007
  0186         0         0         0         0         0         0         0
  0199         0         0         0         0         0         0         0
  0218         0         0         0         0         0         0        20
  0392         0         0         0        28         0         0         0
  0521        47         0         0         0         0         0         0
  0595         0         0         0         0         0        46         0
  0788         0         0         0         0        47         0         0
  0915         0         0        29         0         0         0         0
  1228         0        27         0         0         0         0         0

       3-5-2007 4-4-2007
  0186        0       25
  0199       17        0
  0218        0        0
  0392        0        0
  0521        0        0
  0595        0        0
  0788        0        0
  0915        0        0
  1228        0        0

Correlations with the first principal components:

batchID       amount concentration plate_column  plate_row     shortDay
V1 2.675388e-34 2.305474e-24  0.0000837995  0.002205121 0.33206728 2.675388e-34
V2 3.542743e-02 9.080686e-02  0.8088404440  0.160738153 0.28798646 3.542743e-02
V3 1.232019e-01 9.583923e-02  0.2891811969  0.711142850 0.04714336 1.232019e-01
V4 2.845743e-01 2.493076e-01  0.2479220305  0.353589457 0.45597681 2.845743e-01
V5 2.697899e-02 2.963406e-02  0.4862742532  0.956135668 0.51387714 2.697899e-02
V6 1.639557e-39 6.765313e-13  0.0005379532  0.397065857 0.73169940 1.639557e-39

Converting concentration to a continuous variable and correlating it with the principal components showed that it is correlation with PC1 (p-value = 1.117e-06) but not with PC2 (p-value = 0.5189).

Begin by removing batch: