Correlation between aggregated molecular cancer subtypes and selected clinical features

Glioblastoma Multiforme (Primary solid tumor)

15 January 2014 | analyses__2014_01_15

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 (2014): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1BR8QPF

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 575 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 correlate to 'HISTOLOGICAL.TYPE'.
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 do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'AGE'.
3 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	Chi-square test	Fisher's exact test
mRNA CNMF subtypes	0.0517 (1.00)	0.013 (0.716)	0.399 (1.00)	0.861 (1.00)	0.0024 (0.142)	0.0689 (1.00)
mRNA cHierClus subtypes	0.0944 (1.00)	0.0808 (1.00)	0.611 (1.00)	0.806 (1.00)	0.0746 (1.00)	0.182 (1.00)
miR CNMF subtypes	0.00437 (0.253)	0.0648 (1.00)	0.68 (1.00)	0.85 (1.00)	0.431 (1.00)	0.441 (1.00)
miR cHierClus subtypes	0.355 (1.00)	0.056 (1.00)	0.0302 (1.00)	0.978 (1.00)	0.0375 (1.00)	0.934 (1.00)
Copy Number Ratio CNMF subtypes	0.00779 (0.444)	4.64e-07 (2.78e-05)	0.514 (1.00)	0.946 (1.00)	0.0118 (0.66)	0.602 (1.00)
METHLYATION CNMF	0.577 (1.00)	0.115 (1.00)	0.406 (1.00)	0.722 (1.00)	0.661 (1.00)	0.475 (1.00)
RPPA CNMF subtypes	0.272 (1.00)	0.233 (1.00)	0.222 (1.00)	0.403 (1.00)	0.439 (1.00)	0.313 (1.00)
RPPA cHierClus subtypes	0.281 (1.00)	0.46 (1.00)	0.625 (1.00)	0.695 (1.00)	0.743 (1.00)	0.49 (1.00)
RNAseq CNMF subtypes	0.224 (1.00)	0.0368 (1.00)	0.0673 (1.00)	0.576 (1.00)	0.491 (1.00)	0.833 (1.00)
RNAseq cHierClus subtypes	0.656 (1.00)	0.0255 (1.00)	0.0877 (1.00)	0.32 (1.00)	0.115 (1.00)	0.887 (1.00)

Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels	1	2	3	4
Number of samples	154	97	156	118

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.0517 (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	525	433	0.1 - 127.6 (10.4)
subtype1	154	136	0.1 - 127.6 (9.2)
subtype2	97	76	0.2 - 108.8 (10.6)
subtype3	156	123	0.1 - 92.6 (11.3)
subtype4	118	98	0.2 - 91.7 (9.8)

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 = 0.013 (ANOVA), Q value = 0.72

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

	nPatients	Mean (Std.Dev)
ALL	525	57.7 (14.6)
subtype1	154	58.4 (12.4)
subtype2	97	54.2 (17.4)
subtype3	156	60.0 (13.3)
subtype4	118	56.5 (15.7)

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.399 (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	320
subtype1	59	95
subtype2	41	56
subtype3	66	90
subtype4	39	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 = 0.861 (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	75	76.5 (11.0)
subtype3	119	76.5 (15.2)
subtype4	82	77.6 (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 = 0.0024 (Chi-square test), Q value = 0.14

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	6	20	499
subtype1	1	8	145
subtype2	0	7	90
subtype3	0	4	152
subtype4	5	1	112

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 = 0.0689 (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	360	165
subtype1	106	48
subtype2	68	29
subtype3	116	40
subtype4	70	48

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. Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	119	177	229

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.0944 (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	525	433	0.1 - 127.6 (10.4)
subtype1	119	95	0.1 - 92.6 (11.2)
subtype2	177	144	0.2 - 127.6 (9.9)
subtype3	229	194	0.1 - 91.0 (10.0)

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 = 0.0808 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	525	57.7 (14.6)
subtype1	119	59.9 (13.8)
subtype2	177	56.0 (16.2)
subtype3	229	57.8 (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 = 0.611 (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	320
subtype1	49	70
subtype2	64	113
subtype3	92	137

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 = 0.806 (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	92	76.6 (15.9)
subtype2	127	77.8 (11.9)
subtype3	170	76.9 (15.2)

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 = 0.0746 (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	6	20	499
subtype1	0	5	114
subtype2	5	4	168
subtype3	1	11	217

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 = 0.182 (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	360	165
subtype1	85	34
subtype2	112	65
subtype3	163	66

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. Description of clustering approach #3: 'miR CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	165	182	90	123

'miR CNMF subtypes' versus 'Time to Death'

P value = 0.00437 (logrank test), Q value = 0.25

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	560	448	0.1 - 127.6 (9.9)
subtype1	165	136	0.1 - 91.0 (10.3)
subtype2	182	141	0.1 - 127.6 (10.4)
subtype3	90	72	0.1 - 53.8 (8.4)
subtype4	123	99	0.1 - 92.6 (11.5)

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 = 0.0648 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	560	57.9 (14.3)
subtype1	165	59.8 (11.6)
subtype2	182	55.7 (16.6)
subtype3	90	58.5 (14.6)
subtype4	123	58.2 (13.7)

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

'miR CNMF subtypes' versus 'GENDER'

P value = 0.68 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	218	342
subtype1	63	102
subtype2	76	106
subtype3	36	54
subtype4	43	80

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 = 0.85 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	419	77.4 (14.6)
subtype1	125	76.6 (15.4)
subtype2	131	77.3 (14.6)
subtype3	72	78.1 (13.6)
subtype4	91	78.1 (14.2)

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 = 0.431 (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	8	20	532
subtype1	2	4	159
subtype2	1	8	173
subtype3	3	5	82
subtype4	2	3	118

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 = 0.441 (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	389	171
subtype1	118	47
subtype2	131	51
subtype3	57	33
subtype4	83	40

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. Description of clustering approach #4: 'miR cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	178	188	194

'miR cHierClus subtypes' versus 'Time to Death'

P value = 0.355 (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	560	448	0.1 - 127.6 (9.9)
subtype1	178	139	0.1 - 127.6 (10.3)
subtype2	188	148	0.1 - 108.8 (9.4)
subtype3	194	161	0.1 - 92.6 (10.3)

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 = 0.056 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	560	57.9 (14.3)
subtype1	178	58.6 (13.5)
subtype2	188	55.9 (16.4)
subtype3	194	59.2 (12.7)

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

'miR cHierClus subtypes' versus 'GENDER'

P value = 0.0302 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	218	342
subtype1	83	95
subtype2	70	118
subtype3	65	129

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 = 0.978 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	419	77.4 (14.6)
subtype1	133	77.5 (15.5)
subtype2	141	77.4 (14.3)
subtype3	145	77.2 (14.1)

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 = 0.0375 (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	8	20	532
subtype1	0	9	169
subtype2	5	9	174
subtype3	3	2	189

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 = 0.934 (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	389	171
subtype1	125	53
subtype2	131	57
subtype3	133	61

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. Description of clustering approach #5: 'Copy Number Ratio CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	149	219	185

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

P value = 0.00779 (logrank test), Q value = 0.44

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	553	445	0.1 - 127.6 (9.9)
subtype1	149	124	0.1 - 92.6 (9.3)
subtype2	219	177	0.1 - 77.6 (10.4)
subtype3	185	144	0.2 - 127.6 (10.6)

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 = 4.64e-07 (ANOVA), Q value = 2.8e-05

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

	nPatients	Mean (Std.Dev)
ALL	553	57.8 (14.4)
subtype1	149	59.8 (14.2)
subtype2	219	60.4 (10.9)
subtype3	185	53.2 (17.0)

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 = 0.514 (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	335
subtype1	64	85
subtype2	81	138
subtype3	73	112

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 = 0.946 (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	408	77.4 (14.7)
subtype1	106	77.0 (14.2)
subtype2	170	77.6 (15.4)
subtype3	132	77.3 (14.4)

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 = 0.0118 (Chi-square test), Q value = 0.66

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	10	18	525
subtype1	0	4	145
subtype2	2	5	212
subtype3	8	9	168

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 = 0.602 (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	382	171
subtype1	98	51
subtype2	154	65
subtype3	130	55

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. Description of clustering approach #6: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	32	44	32

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.577 (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	108	58	0.1 - 58.8 (7.4)
subtype1	32	17	0.8 - 58.8 (5.8)
subtype2	44	26	0.1 - 47.9 (8.0)
subtype3	32	15	1.2 - 50.5 (7.6)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.115 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	108	60.9 (12.0)
subtype1	32	60.2 (13.8)
subtype2	44	63.6 (8.4)
subtype3	32	57.8 (13.8)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.406 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	47	61
subtype1	12	20
subtype2	18	26
subtype3	17	15

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

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

P value = 0.722 (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	22	77.7 (18.0)
subtype2	33	74.8 (15.6)
subtype3	25	77.6 (12.0)

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 = 0.661 (Chi-square test), Q value = 1

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

nPatients	GLIOBLASTOMA MULTIFORME (GBM)	TREATED PRIMARY GBM	UNTREATED PRIMARY (DE NOVO) GBM
ALL	2	1	105
subtype1	0	0	32
subtype2	1	1	42
subtype3	1	0	31

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 = 0.475 (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	78	30
subtype1	25	7
subtype2	29	15
subtype3	24	8

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. 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 = 0.272 (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	150	0.1 - 108.8 (8.1)
subtype1	57	46	0.1 - 53.2 (8.7)
subtype2	60	40	0.2 - 108.8 (7.4)
subtype3	44	29	0.1 - 46.2 (9.3)
subtype4	49	35	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 = 0.313 (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	156	54
subtype1	42	15
subtype2	44	16
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. 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 = 0.281 (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	150	0.1 - 108.8 (8.1)
subtype1	64	45	0.1 - 47.9 (9.2)
subtype2	35	29	0.3 - 53.2 (7.9)
subtype3	111	76	0.2 - 108.8 (6.7)

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 = 0.625 (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 = 0.49 (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	156	54
subtype1	51	13
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. Description of clustering approach #9: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	47	70	35

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.224 (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	152	113	0.2 - 54.0 (9.1)
subtype1	47	33	0.2 - 54.0 (8.8)
subtype2	70	52	0.9 - 47.9 (9.8)
subtype3	35	28	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 = 0.0368 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	152	59.7 (13.5)
subtype1	47	55.6 (16.6)
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 = 0.0673 (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	99
subtype1	14	33
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 = 0.491 (Chi-square test), Q value = 1

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

nPatients	GLIOBLASTOMA MULTIFORME (GBM)	TREATED PRIMARY GBM	UNTREATED PRIMARY (DE NOVO) GBM
ALL	1	1	150
subtype1	1	0	46
subtype2	0	1	69
subtype3	0	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 = 0.833 (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	102	50
subtype1	32	15
subtype2	48	22
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. Description of clustering approach #10: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	69	22	61

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.656 (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	152	113	0.2 - 54.0 (9.1)
subtype1	69	48	0.4 - 40.4 (8.8)
subtype2	22	19	1.1 - 47.9 (10.2)
subtype3	61	46	0.2 - 54.0 (9.1)

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 = 0.0255 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	152	59.7 (13.5)
subtype1	69	62.1 (11.5)
subtype2	22	62.3 (10.5)
subtype3	61	56.1 (15.8)

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.0877 (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	99
subtype1	29	40
subtype2	9	13
subtype3	15	46

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 = 0.115 (Chi-square test), Q value = 1

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

nPatients	GLIOBLASTOMA MULTIFORME (GBM)	TREATED PRIMARY GBM	UNTREATED PRIMARY (DE NOVO) GBM
ALL	1	1	150
subtype1	0	0	69
subtype2	0	1	21
subtype3	1	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 = 0.887 (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	102	50
subtype1	46	23
subtype2	14	8
subtype3	42	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.merged_data.txt
Number of patients = 575
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

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

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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