Correlation between aggregated molecular cancer subtypes and selected clinical features

Glioblastoma Multiforme (Primary solid tumor)

23 May 2013 | analyses__2013_05_23

Maintainer Information

Citation Information

Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)

Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1FN147T

Overview

Introduction

This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.

Summary

Testing the association between subtypes identified by 10 different clustering approaches and 6 clinical features across 573 patients, 2 significant findings detected with P value < 0.05 and Q value < 0.25.

CNMF clustering analysis on array-based mRNA expression data identified 4 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on array-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.
CNMF clustering analysis on array-based miR expression data identified 4 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on array-based miR expression data identified 3 subtypes that correlate to 'AGE'.
3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'AGE'.
4 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes do not correlate to any clinical features.
CNMF clustering analysis on RPPA data identified 4 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that do not correlate to any clinical features.
CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 6 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 2 significant findings detected.


Clinical Features	Time to Death	AGE	GENDER	KARNOFSKY PERFORMANCE SCORE	HISTOLOGICAL TYPE	RADIATIONS RADIATION REGIMENINDICATION
Statistical Tests	logrank test	ANOVA	Fisher's exact test	ANOVA	Fisher's exact test	Fisher's exact test
mRNA CNMF subtypes	0.0691 (1.00)	0.00554 (0.321)	0.437 (1.00)	0.888 (1.00)	0.0262 (1.00)	0.15 (1.00)
mRNA cHierClus subtypes	0.133 (1.00)	0.015 (0.84)	0.556 (1.00)	0.139 (1.00)	0.0952 (1.00)	0.112 (1.00)
miR CNMF subtypes	0.00772 (0.44)	0.11 (1.00)	0.465 (1.00)	0.943 (1.00)	0.257 (1.00)	0.355 (1.00)
miR cHierClus subtypes	0.0244 (1.00)	0.00151 (0.0892)	0.13 (1.00)	0.785 (1.00)	0.515 (1.00)	0.833 (1.00)
Copy Number Ratio CNMF subtypes	0.0729 (1.00)	1.85e-05 (0.00111)	0.512 (1.00)	0.871 (1.00)	0.0729 (1.00)	0.279 (1.00)
METHLYATION CNMF	0.894 (1.00)	0.163 (1.00)	0.359 (1.00)	0.954 (1.00)	0.16 (1.00)	0.345 (1.00)
RPPA CNMF subtypes	0.8 (1.00)	0.233 (1.00)	0.222 (1.00)	0.403 (1.00)	0.439 (1.00)	0.296 (1.00)
RPPA cHierClus subtypes	0.767 (1.00)	0.46 (1.00)	0.622 (1.00)	0.695 (1.00)	0.743 (1.00)	0.623 (1.00)
RNAseq CNMF subtypes	0.363 (1.00)	0.0725 (1.00)	0.0767 (1.00)	0.576 (1.00)	1 (1.00)	0.903 (1.00)
RNAseq cHierClus subtypes	0.731 (1.00)	0.045 (1.00)	0.107 (1.00)	0.32 (1.00)	0.146 (1.00)	0.712 (1.00)

Clustering Approach #1: 'mRNA CNMF subtypes'

Table S1. Get Full Table Description of clustering approach #1: 'mRNA CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	154	94	156	119

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.0691 (logrank test), Q value = 1

Table S2. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	523	407	0.1 - 127.6 (9.9)
subtype1	154	128	0.1 - 127.6 (9.1)
subtype2	94	71	0.2 - 108.8 (10.5)
subtype3	156	117	0.1 - 92.6 (10.8)
subtype4	119	91	0.2 - 91.7 (9.3)

Figure S1. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA CNMF subtypes' versus 'AGE'

P value = 0.00554 (ANOVA), Q value = 0.32

Table S3. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	523	57.8 (14.5)
subtype1	154	58.4 (12.4)
subtype2	94	53.7 (17.2)
subtype3	156	60.2 (13.3)
subtype4	119	56.8 (15.5)

Figure S2. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.437 (Fisher's exact test), Q value = 1

Table S4. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	205	318
subtype1	59	95
subtype2	39	55
subtype3	67	89
subtype4	40	79

Figure S3. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'mRNA CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.888 (ANOVA), Q value = 1

Table S5. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	389	77.1 (14.4)
subtype1	113	77.9 (15.5)
subtype2	73	76.7 (10.9)
subtype3	119	76.5 (15.2)
subtype4	84	77.4 (14.5)

Figure S4. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'mRNA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0262 (Chi-square test), Q value = 1

Table S6. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	GLIOBLASTOMA MULTIFORME (GBM)	TREATED PRIMARY GBM	UNTREATED PRIMARY (DE NOVO) GBM
ALL	4	20	499
subtype1	1	8	145
subtype2	0	7	87
subtype3	0	4	152
subtype4	3	1	115

Figure S5. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'mRNA CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.15 (Fisher's exact test), Q value = 1

Table S7. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	358	165
subtype1	105	49
subtype2	64	30
subtype3	116	40
subtype4	73	46

Figure S6. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S8. Get Full Table Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	131	172	220

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.133 (logrank test), Q value = 1

Table S9. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	523	407	0.1 - 127.6 (9.9)
subtype1	131	95	0.1 - 92.6 (9.7)
subtype2	172	132	0.2 - 127.6 (9.4)
subtype3	220	180	0.1 - 90.6 (10.3)

Figure S7. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.015 (ANOVA), Q value = 0.84

Table S10. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	523	57.8 (14.5)
subtype1	131	60.6 (13.7)
subtype2	172	55.8 (16.0)
subtype3	220	57.6 (13.5)

Figure S8. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.556 (Fisher's exact test), Q value = 1

Table S11. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	205	318
subtype1	56	75
subtype2	63	109
subtype3	86	134

Figure S9. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'mRNA cHierClus subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.139 (ANOVA), Q value = 1

Table S12. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	389	77.1 (14.4)
subtype1	100	74.9 (15.8)
subtype2	125	78.7 (12.4)
subtype3	164	77.3 (14.8)

Figure S10. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0952 (Chi-square test), Q value = 1

Table S13. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	GLIOBLASTOMA MULTIFORME (GBM)	TREATED PRIMARY GBM	UNTREATED PRIMARY (DE NOVO) GBM
ALL	4	20	499
subtype1	0	3	128
subtype2	3	4	165
subtype3	1	13	206

Figure S11. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'mRNA cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.112 (Fisher's exact test), Q value = 1

Table S14. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	358	165
subtype1	90	41
subtype2	108	64
subtype3	160	60

Figure S12. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #3: 'miR CNMF subtypes'

Table S15. Get Full Table Description of clustering approach #3: 'miR CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	146	159	76	107

'miR CNMF subtypes' versus 'Time to Death'

P value = 0.00772 (logrank test), Q value = 0.44

Table S16. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	488	385	0.1 - 127.6 (10.3)
subtype1	146	118	0.1 - 51.3 (10.6)
subtype2	159	124	0.1 - 127.6 (10.6)
subtype3	76	59	0.1 - 53.8 (8.4)
subtype4	107	84	0.1 - 92.6 (10.8)

Figure S13. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'miR CNMF subtypes' versus 'AGE'

P value = 0.11 (ANOVA), Q value = 1

Table S17. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	488	57.6 (14.7)
subtype1	146	59.6 (11.6)
subtype2	159	55.5 (17.0)
subtype3	76	58.2 (15.4)
subtype4	107	57.6 (13.9)

Figure S14. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #2: 'AGE'

'miR CNMF subtypes' versus 'GENDER'

P value = 0.465 (Fisher's exact test), Q value = 1

Table S18. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	188	300
subtype1	57	89
subtype2	68	91
subtype3	27	49
subtype4	36	71

Figure S15. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'miR CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.943 (ANOVA), Q value = 1

Table S19. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	368	77.6 (14.1)
subtype1	112	77.6 (14.4)
subtype2	114	77.5 (14.1)
subtype3	62	76.9 (14.4)
subtype4	80	78.4 (13.8)

Figure S16. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'miR CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.257 (Chi-square test), Q value = 1

Table S20. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	GLIOBLASTOMA MULTIFORME (GBM)	TREATED PRIMARY GBM	UNTREATED PRIMARY (DE NOVO) GBM
ALL	6	20	462
subtype1	2	3	141
subtype2	0	9	150
subtype3	2	5	69
subtype4	2	3	102

Figure S17. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'miR CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.355 (Fisher's exact test), Q value = 1

Table S21. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	336	152
subtype1	105	41
subtype2	112	47
subtype3	46	30
subtype4	73	34

Figure S18. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #4: 'miR cHierClus subtypes'

Table S22. Get Full Table Description of clustering approach #4: 'miR cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	173	181	134

'miR cHierClus subtypes' versus 'Time to Death'

P value = 0.0244 (logrank test), Q value = 1

Table S23. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	488	385	0.1 - 127.6 (10.3)
subtype1	173	140	0.1 - 92.6 (9.9)
subtype2	181	146	0.1 - 127.6 (9.9)
subtype3	134	99	0.1 - 108.8 (10.7)

Figure S19. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'miR cHierClus subtypes' versus 'AGE'

P value = 0.00151 (ANOVA), Q value = 0.089

Table S24. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	488	57.6 (14.7)
subtype1	173	59.1 (12.9)
subtype2	181	59.0 (13.4)
subtype3	134	53.7 (17.6)

Figure S20. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'miR cHierClus subtypes' versus 'GENDER'

P value = 0.13 (Fisher's exact test), Q value = 1

Table S25. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	188	300
subtype1	59	114
subtype2	80	101
subtype3	49	85

Figure S21. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'miR cHierClus subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.785 (ANOVA), Q value = 1

Table S26. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	368	77.6 (14.1)
subtype1	129	78.3 (13.5)
subtype2	136	77.1 (15.5)
subtype3	103	77.4 (13.1)

Figure S22. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'miR cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.515 (Chi-square test), Q value = 1

Table S27. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	GLIOBLASTOMA MULTIFORME (GBM)	TREATED PRIMARY GBM	UNTREATED PRIMARY (DE NOVO) GBM
ALL	6	20	462
subtype1	3	4	166
subtype2	1	9	171
subtype3	2	7	125

Figure S23. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'miR cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.833 (Fisher's exact test), Q value = 1

Table S28. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	336	152
subtype1	117	56
subtype2	124	57
subtype3	95	39

Figure S24. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #5: 'Copy Number Ratio CNMF subtypes'

Table S29. Get Full Table Description of clustering approach #5: 'Copy Number Ratio CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	159	212	181

'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.0729 (logrank test), Q value = 1

Table S30. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	552	419	0.1 - 127.6 (9.6)
subtype1	159	125	0.1 - 127.6 (9.1)
subtype2	212	162	0.1 - 77.6 (10.1)
subtype3	181	132	0.1 - 108.8 (9.9)

Figure S25. Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 1.85e-05 (ANOVA), Q value = 0.0011

Table S31. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	552	57.9 (14.4)
subtype1	159	59.1 (14.7)
subtype2	212	60.4 (10.8)
subtype3	181	53.9 (16.8)

Figure S26. Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.512 (Fisher's exact test), Q value = 1

Table S32. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	218	334
subtype1	68	91
subtype2	78	134
subtype3	72	109

Figure S27. Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'Copy Number Ratio CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.871 (ANOVA), Q value = 1

Table S33. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	409	77.3 (14.7)
subtype1	113	76.8 (14.0)
subtype2	164	77.3 (15.7)
subtype3	132	77.8 (14.2)

Figure S28. Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0729 (Chi-square test), Q value = 1

Table S34. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	GLIOBLASTOMA MULTIFORME (GBM)	TREATED PRIMARY GBM	UNTREATED PRIMARY (DE NOVO) GBM
ALL	8	18	526
subtype1	0	5	154
subtype2	2	5	205
subtype3	6	8	167

Figure S29. Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'Copy Number Ratio CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.279 (Fisher's exact test), Q value = 1

Table S35. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	381	171
subtype1	102	57
subtype2	149	63
subtype3	130	51

Figure S30. Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #6: 'METHLYATION CNMF'

Table S36. Get Full Table Description of clustering approach #6: 'METHLYATION CNMF'

Cluster Labels	1	2	3	4
Number of samples	35	27	17	27

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.894 (logrank test), Q value = 1

Table S37. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	106	53	0.1 - 58.8 (6.9)
subtype1	35	18	0.8 - 58.8 (5.4)
subtype2	27	14	0.5 - 20.9 (8.0)
subtype3	17	10	0.1 - 47.9 (8.0)
subtype4	27	11	1.4 - 31.3 (7.5)

Figure S31. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.163 (ANOVA), Q value = 1

Table S38. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	106	61.2 (11.5)
subtype1	35	61.3 (13.9)
subtype2	27	64.1 (8.2)
subtype3	17	62.6 (8.9)
subtype4	27	57.3 (11.8)

Figure S32. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.359 (Fisher's exact test), Q value = 1

Table S39. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	47	59
subtype1	13	22
subtype2	11	16
subtype3	7	10
subtype4	16	11

Figure S33. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

'METHLYATION CNMF' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.954 (ANOVA), Q value = 1

Table S40. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	80	76.5 (15.2)
subtype1	23	77.0 (17.9)
subtype2	19	75.3 (14.7)
subtype3	16	75.6 (16.7)
subtype4	22	77.7 (11.9)

Figure S34. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.16 (Fisher's exact test), Q value = 1

Table S41. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	TREATED PRIMARY GBM	UNTREATED PRIMARY (DE NOVO) GBM
ALL	1	105
subtype1	0	35
subtype2	0	27
subtype3	1	16
subtype4	0	27

Figure S35. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'METHLYATION CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.345 (Fisher's exact test), Q value = 1

Table S42. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	77	29
subtype1	28	7
subtype2	16	11
subtype3	13	4
subtype4	20	7

Figure S36. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #7: 'RPPA CNMF subtypes'

Table S43. Get Full Table Description of clustering approach #7: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	57	60	44	49

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.8 (logrank test), Q value = 1

Table S44. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	210	137	0.1 - 108.8 (7.9)
subtype1	57	41	0.1 - 53.2 (8.3)
subtype2	60	38	0.2 - 108.8 (6.5)
subtype3	44	27	0.1 - 43.2 (9.1)
subtype4	49	31	0.2 - 47.9 (6.1)

Figure S37. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.233 (ANOVA), Q value = 1

Table S45. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	210	59.9 (14.1)
subtype1	57	59.2 (13.6)
subtype2	60	57.4 (16.9)
subtype3	44	61.5 (12.2)
subtype4	49	62.5 (12.0)

Figure S38. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.222 (Fisher's exact test), Q value = 1

Table S46. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	85	125
subtype1	17	40
subtype2	26	34
subtype3	18	26
subtype4	24	25

Figure S39. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RPPA CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.403 (ANOVA), Q value = 1

Table S47. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	168	75.6 (15.3)
subtype1	46	75.0 (15.9)
subtype2	50	76.4 (16.5)
subtype3	32	78.8 (11.3)
subtype4	40	72.8 (15.8)

Figure S40. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.439 (Fisher's exact test), Q value = 1

Table S48. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	TREATED PRIMARY GBM	UNTREATED PRIMARY (DE NOVO) GBM
ALL	3	207
subtype1	2	55
subtype2	0	60
subtype3	0	44
subtype4	1	48

Figure S41. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'RPPA CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.296 (Fisher's exact test), Q value = 1

Table S49. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	155	55
subtype1	42	15
subtype2	43	17
subtype3	37	7
subtype4	33	16

Figure S42. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #8: 'RPPA cHierClus subtypes'

Table S50. Get Full Table Description of clustering approach #8: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	64	35	111

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.767 (logrank test), Q value = 1

Table S51. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	210	137	0.1 - 108.8 (7.9)
subtype1	64	42	0.1 - 47.9 (8.9)
subtype2	35	24	0.3 - 53.2 (7.9)
subtype3	111	71	0.2 - 108.8 (6.4)

Figure S43. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.46 (ANOVA), Q value = 1

Table S52. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	210	59.9 (14.1)
subtype1	64	61.7 (12.1)
subtype2	35	58.4 (13.2)
subtype3	111	59.4 (15.4)

Figure S44. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.622 (Fisher's exact test), Q value = 1

Table S53. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	85	125
subtype1	25	39
subtype2	12	23
subtype3	48	63

Figure S45. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RPPA cHierClus subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.695 (ANOVA), Q value = 1

Table S54. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	168	75.6 (15.3)
subtype1	49	76.9 (14.2)
subtype2	26	73.8 (17.7)
subtype3	93	75.4 (15.3)

Figure S46. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.743 (Fisher's exact test), Q value = 1

Table S55. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	TREATED PRIMARY GBM	UNTREATED PRIMARY (DE NOVO) GBM
ALL	3	207
subtype1	1	63
subtype2	1	34
subtype3	1	110

Figure S47. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'RPPA cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.623 (Fisher's exact test), Q value = 1

Table S56. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	155	55
subtype1	50	14
subtype2	26	9
subtype3	79	32

Figure S48. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #9: 'RNAseq CNMF subtypes'

Table S57. Get Full Table Description of clustering approach #9: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	46	70	35

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.363 (logrank test), Q value = 1

Table S58. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	151	98	0.2 - 54.0 (8.6)
subtype1	46	29	0.2 - 54.0 (8.8)
subtype2	70	45	0.9 - 47.9 (9.1)
subtype3	35	24	0.2 - 31.3 (6.0)

Figure S49. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.0725 (ANOVA), Q value = 1

Table S59. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	151	60.0 (13.2)
subtype1	46	56.3 (16.0)
subtype2	70	62.0 (10.6)
subtype3	35	60.9 (13.3)

Figure S50. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.0767 (Fisher's exact test), Q value = 1

Table S60. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	53	98
subtype1	14	32
subtype2	31	39
subtype3	8	27

Figure S51. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.576 (ANOVA), Q value = 1

Table S61. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	113	75.8 (14.4)
subtype1	36	74.2 (15.2)
subtype2	51	75.7 (14.2)
subtype3	26	78.1 (13.9)

Figure S52. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1 (Fisher's exact test), Q value = 1

Table S62. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	TREATED PRIMARY GBM	UNTREATED PRIMARY (DE NOVO) GBM
ALL	1	150
subtype1	0	46
subtype2	1	69
subtype3	0	35

Figure S53. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'RNAseq CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.903 (Fisher's exact test), Q value = 1

Table S63. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	100	51
subtype1	31	15
subtype2	47	23
subtype3	22	13

Figure S54. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #10: 'RNAseq cHierClus subtypes'

Table S64. Get Full Table Description of clustering approach #10: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	69	22	60

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.731 (logrank test), Q value = 1

Table S65. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	151	98	0.2 - 54.0 (8.6)
subtype1	69	44	0.4 - 40.4 (7.6)
subtype2	22	15	1.1 - 47.9 (7.1)
subtype3	60	39	0.2 - 54.0 (8.9)

Figure S55. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.045 (ANOVA), Q value = 1

Table S66. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	151	60.0 (13.2)
subtype1	69	62.1 (11.5)
subtype2	22	62.3 (10.5)
subtype3	60	56.7 (15.3)

Figure S56. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.107 (Fisher's exact test), Q value = 1

Table S67. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	53	98
subtype1	29	40
subtype2	9	13
subtype3	15	45

Figure S57. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq cHierClus subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.32 (ANOVA), Q value = 1

Table S68. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	113	75.8 (14.4)
subtype1	51	74.7 (14.6)
subtype2	18	72.8 (15.3)
subtype3	44	78.2 (13.7)

Figure S58. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.146 (Fisher's exact test), Q value = 1

Table S69. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	TREATED PRIMARY GBM	UNTREATED PRIMARY (DE NOVO) GBM
ALL	1	150
subtype1	0	69
subtype2	1	21
subtype3	0	60

Figure S59. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'RNAseq cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.712 (Fisher's exact test), Q value = 1

Table S70. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	100	51
subtype1	46	23
subtype2	13	9
subtype3	41	19

Figure S60. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Methods & Data

Input

Cluster data file = GBM-TP.mergedcluster.txt
Clinical data file = GBM-TP.clin.merged.picked.txt
Number of patients = 573
Number of clustering approaches = 10
Number of selected clinical features = 6
Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches

CNMF clustering

consensus non-negative matrix factorization clustering approach (Brunet et al. 2004)

Consensus hierarchical clustering

Resampling-based clustering method (Monti et al. 2003)

Survival analysis

For survival clinical features, the Kaplan-Meier survival curves of tumors with and without gene mutations were plotted and the statistical significance P values were estimated by logrank test (Bland and Altman 2004) using the 'survdiff' function in R

ANOVA analysis

For continuous numerical clinical features, one-way analysis of variance (Howell 2002) was applied to compare the clinical values between tumor subtypes using 'anova' function in R

Fisher's exact test

For binary clinical features, two-tailed Fisher's exact tests (Fisher 1922) were used to estimate the P values using the 'fisher.test' function in R

Chi-square test

For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.test' function in R

Q value calculation

For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.

Download Results

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

References

[1] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)

[2] Monti et al., Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data, Machine Learning 52(1):91-118 (2003)

[3] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)

[4] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)

[5] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)

[6] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)

[7] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)

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