Glioblastoma Multiforme: Correlation between molecular cancer subtypes and selected clinical features<BR>(primary solid tumor cohort)

Glioblastoma Multiforme: Correlation between molecular cancer subtypes and selected clinical features
(primary solid tumor cohort)

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

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 5 clinical features across 564 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 3 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 'CN CNMF'. 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 6 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 6 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 5 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	RADIATIONS RADIATION REGIMENINDICATION
Statistical Tests	logrank test	ANOVA	Chi-square test	ANOVA	Chi-square test
mRNA CNMF subtypes	0.367 (1.00)	0.0257 (1.00)	0.497 (1.00)	0.839 (1.00)	0.259 (1.00)
mRNA cHierClus subtypes	0.161 (1.00)	0.0276 (1.00)	0.563 (1.00)	0.487 (1.00)	0.122 (1.00)
miR CNMF subtypes	0.00835 (0.401)	0.101 (1.00)	0.508 (1.00)	0.943 (1.00)	0.638 (1.00)
miR cHierClus subtypes	0.0178 (0.839)	0.000872 (0.0427)	0.119 (1.00)	0.785 (1.00)	0.758 (1.00)
CN CNMF	0.0332 (1.00)	1.16e-06 (5.8e-05)	0.488 (1.00)	0.871 (1.00)	0.1 (1.00)
METHLYATION CNMF	0.619 (1.00)	0.148 (1.00)	0.125 (1.00)	0.945 (1.00)	0.549 (1.00)
RPPA CNMF subtypes	0.856 (1.00)	0.285 (1.00)	0.188 (1.00)	0.418 (1.00)	0.614 (1.00)
RPPA cHierClus subtypes	0.964 (1.00)	0.457 (1.00)	0.528 (1.00)	0.438 (1.00)	0.303 (1.00)
RNAseq CNMF subtypes	0.371 (1.00)	0.15 (1.00)	0.307 (1.00)	0.755 (1.00)	0.19 (1.00)
RNAseq cHierClus subtypes	0.35 (1.00)	0.349 (1.00)	0.0251 (1.00)	0.469 (1.00)	0.957 (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
Number of samples	177	172	170

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.367 (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	519	403	0.1 - 127.6 (9.9)
subtype1	177	145	0.2 - 127.6 (10.0)
subtype2	172	129	0.2 - 108.8 (9.2)
subtype3	170	129	0.1 - 92.6 (10.7)

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 = 0.0257 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	519	57.7 (14.5)
subtype1	177	57.3 (12.8)
subtype2	172	55.8 (16.4)
subtype3	170	60.0 (13.7)

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.497 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	204	315
subtype1	70	107
subtype2	62	110
subtype3	72	98

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 = 0.839 (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	137	77.5 (15.0)
subtype2	126	77.3 (13.0)
subtype3	126	76.5 (15.0)

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

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

P value = 0.259 (Fisher's exact test), Q value = 1

Table S6. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	348	171
subtype1	119	58
subtype2	108	64
subtype3	121	49

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

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S7. Get Full Table Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	223	127	169

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.161 (logrank test), Q value = 1

Table S8. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	519	403	0.1 - 127.6 (9.9)
subtype1	223	182	0.1 - 90.6 (10.4)
subtype2	127	94	0.1 - 92.6 (9.8)
subtype3	169	127	0.2 - 127.6 (9.4)

Figure S6. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.0276 (ANOVA), Q value = 1

Table S9. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	519	57.7 (14.5)
subtype1	223	57.7 (13.4)
subtype2	127	60.3 (13.8)
subtype3	169	55.7 (16.0)

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

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.563 (Fisher's exact test), Q value = 1

Table S10. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	204	315
subtype1	90	133
subtype2	53	74
subtype3	61	108

Figure S8. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.487 (ANOVA), Q value = 1

Table S11. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	389	77.1 (14.4)
subtype1	165	77.6 (14.4)
subtype2	98	75.6 (15.4)
subtype3	126	77.6 (13.5)

Figure S9. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

P value = 0.122 (Fisher's exact test), Q value = 1

Table S12. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	348	171
subtype1	156	67
subtype2	89	38
subtype3	103	66

Figure S10. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #3: 'miR CNMF subtypes'

Table S13. Get Full Table Description of clustering approach #3: 'miR CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	144	159	74	105

'miR CNMF subtypes' versus 'Time to Death'

P value = 0.00835 (logrank test), Q value = 0.4

Table S14. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	482	379	0.1 - 127.6 (10.3)
subtype1	144	116	0.1 - 51.3 (10.6)
subtype2	159	124	0.1 - 127.6 (10.6)
subtype3	74	57	0.1 - 53.8 (8.4)
subtype4	105	82	0.1 - 92.6 (10.8)

Figure S11. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'miR CNMF subtypes' versus 'AGE'

P value = 0.101 (ANOVA), Q value = 1

Table S15. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	482	57.5 (14.6)
subtype1	144	59.7 (11.4)
subtype2	159	55.5 (17.0)
subtype3	74	57.9 (15.3)
subtype4	105	57.4 (13.7)

Figure S12. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #2: 'AGE'

'miR CNMF subtypes' versus 'GENDER'

P value = 0.508 (Fisher's exact test), Q value = 1

Table S16. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	186	296
subtype1	56	88
subtype2	68	91
subtype3	26	48
subtype4	36	69

Figure S13. 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 S17. 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 S14. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

P value = 0.638 (Fisher's exact test), Q value = 1

Table S18. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	327	155
subtype1	102	42
subtype2	108	51
subtype3	46	28
subtype4	71	34

Figure S15. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #4: 'miR cHierClus subtypes'

Table S19. Get Full Table Description of clustering approach #4: 'miR cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	170	180	132

'miR cHierClus subtypes' versus 'Time to Death'

P value = 0.0178 (logrank test), Q value = 0.84

Table S20. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	482	379	0.1 - 127.6 (10.3)
subtype1	170	137	0.1 - 92.6 (9.8)
subtype2	180	145	0.1 - 127.6 (10.0)
subtype3	132	97	0.1 - 108.8 (10.7)

Figure S16. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'miR cHierClus subtypes' versus 'AGE'

P value = 0.000872 (ANOVA), Q value = 0.043

Table S21. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	482	57.5 (14.6)
subtype1	170	59.2 (12.6)
subtype2	180	58.9 (13.3)
subtype3	132	53.5 (17.6)

Figure S17. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'miR cHierClus subtypes' versus 'GENDER'

P value = 0.119 (Fisher's exact test), Q value = 1

Table S22. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	186	296
subtype1	58	112
subtype2	80	100
subtype3	48	84

Figure S18. 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 S23. 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 S19. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

P value = 0.758 (Fisher's exact test), Q value = 1

Table S24. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	327	155
subtype1	113	57
subtype2	121	59
subtype3	93	39

Figure S20. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #5: 'CN CNMF'

Table S25. Get Full Table Description of clustering approach #5: 'CN CNMF'

Cluster Labels	1	2	3
Number of samples	159	210	175

'CN CNMF' versus 'Time to Death'

P value = 0.0332 (logrank test), Q value = 1

Table S26. Clustering Approach #5: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	544	411	0.1 - 127.6 (9.6)
subtype1	159	125	0.1 - 127.6 (9.1)
subtype2	210	160	0.1 - 77.6 (9.9)
subtype3	175	126	0.1 - 108.8 (10.3)

Figure S21. Get High-res Image Clustering Approach #5: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

'CN CNMF' versus 'AGE'

P value = 1.16e-06 (ANOVA), Q value = 5.8e-05

Table S27. Clustering Approach #5: 'CN CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	544	57.8 (14.3)
subtype1	159	59.1 (14.7)
subtype2	210	60.6 (10.6)
subtype3	175	53.3 (16.6)

Figure S22. Get High-res Image Clustering Approach #5: 'CN CNMF' versus Clinical Feature #2: 'AGE'

'CN CNMF' versus 'GENDER'

P value = 0.488 (Fisher's exact test), Q value = 1

Table S28. Clustering Approach #5: 'CN CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	215	329
subtype1	68	91
subtype2	77	133
subtype3	70	105

Figure S23. Get High-res Image Clustering Approach #5: 'CN CNMF' versus Clinical Feature #3: 'GENDER'

'CN CNMF' versus 'KARNOFSKY.PERFORMANCE.SCORE'

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

Table S29. Clustering Approach #5: 'CN CNMF' 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 S24. Get High-res Image Clustering Approach #5: 'CN CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'CN CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.1 (Fisher's exact test), Q value = 1

Table S30. Clustering Approach #5: 'CN CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	369	175
subtype1	98	61
subtype2	144	66
subtype3	127	48

Figure S25. Get High-res Image Clustering Approach #5: 'CN CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #6: 'METHLYATION CNMF'

Table S31. Get Full Table Description of clustering approach #6: 'METHLYATION CNMF'

Cluster Labels	1	2	3	4
Number of samples	34	19	26	27

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.619 (logrank test), Q value = 1

Table S32. 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	34	17	0.8 - 58.8 (5.4)
subtype2	19	8	1.6 - 20.9 (8.3)
subtype3	26	17	0.1 - 47.9 (7.9)
subtype4	27	11	1.4 - 31.3 (7.5)

Figure S26. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.148 (ANOVA), Q value = 1

Table S33. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	106	61.2 (11.5)
subtype1	34	61.3 (14.1)
subtype2	19	64.8 (8.0)
subtype3	26	62.5 (8.5)
subtype4	27	57.3 (11.8)

Figure S27. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.125 (Fisher's exact test), Q value = 1

Table S34. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	47	59
subtype1	13	21
subtype2	5	14
subtype3	13	13
subtype4	16	11

Figure S28. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

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

P value = 0.945 (ANOVA), Q value = 1

Table S35. 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	13	76.2 (16.6)
subtype3	22	75.0 (15.0)
subtype4	22	77.7 (11.9)

Figure S29. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

P value = 0.549 (Fisher's exact test), Q value = 1

Table S36. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	74	32
subtype1	26	8
subtype2	12	7
subtype3	16	10
subtype4	20	7

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

Clustering Approach #7: 'RPPA CNMF subtypes'

Table S37. Get Full Table Description of clustering approach #7: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	58	60	43	49

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.856 (logrank test), Q value = 1

Table S38. 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	58	42	0.1 - 53.2 (8.1)
subtype2	60	38	0.2 - 108.8 (6.5)
subtype3	43	27	0.1 - 43.2 (9.1)
subtype4	49	30	0.2 - 47.9 (6.3)

Figure S31. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.285 (ANOVA), Q value = 1

Table S39. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	210	59.9 (14.1)
subtype1	58	59.5 (13.6)
subtype2	60	57.4 (16.9)
subtype3	43	61.8 (12.1)
subtype4	49	62.0 (12.0)

Figure S32. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.188 (Fisher's exact test), Q value = 1

Table S40. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	85	125
subtype1	17	41
subtype2	26	34
subtype3	18	25
subtype4	24	25

Figure S33. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.418 (ANOVA), Q value = 1

Table S41. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	168	75.6 (15.3)
subtype1	47	75.1 (15.7)
subtype2	50	76.4 (16.5)
subtype3	31	78.7 (11.5)
subtype4	40	72.8 (15.8)

Figure S34. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

P value = 0.614 (Fisher's exact test), Q value = 1

Table S42. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	150	60
subtype1	40	18
subtype2	43	17
subtype3	34	9
subtype4	33	16

Figure S35. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #8: 'RPPA cHierClus subtypes'

Table S43. Get Full Table Description of clustering approach #8: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	97	72	41

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.964 (logrank test), Q value = 1

Table S44. 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	97	62	0.2 - 108.8 (6.5)
subtype2	72	45	0.1 - 47.9 (8.7)
subtype3	41	30	1.2 - 53.2 (8.3)

Figure S36. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.457 (ANOVA), Q value = 1

Table S45. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	210	59.9 (14.1)
subtype1	97	59.9 (15.6)
subtype2	72	61.2 (12.1)
subtype3	41	57.8 (13.6)

Figure S37. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.528 (Fisher's exact test), Q value = 1

Table S46. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	85	125
subtype1	43	54
subtype2	28	44
subtype3	14	27

Figure S38. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.438 (ANOVA), Q value = 1

Table S47. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	168	75.6 (15.3)
subtype1	78	74.5 (14.9)
subtype2	57	77.7 (15.0)
subtype3	33	74.5 (16.8)

Figure S39. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

P value = 0.303 (Fisher's exact test), Q value = 1

Table S48. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	150	60
subtype1	65	32
subtype2	56	16
subtype3	29	12

Figure S40. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #9: 'RNAseq CNMF subtypes'

Table S49. Get Full Table Description of clustering approach #9: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4	5	6
Number of samples	37	32	28	24	6	25

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.371 (logrank test), Q value = 1

Table S50. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	152	99	0.2 - 54.0 (8.7)
subtype1	37	23	0.2 - 54.0 (8.8)
subtype2	32	22	0.4 - 29.0 (7.2)
subtype3	28	19	2.5 - 47.9 (11.3)
subtype4	24	15	0.2 - 31.3 (5.3)
subtype5	6	3	1.1 - 40.4 (5.8)
subtype6	25	17	1.2 - 33.1 (9.9)

Figure S41. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.15 (ANOVA), Q value = 1

Table S51. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	152	60.1 (13.2)
subtype1	37	56.0 (16.1)
subtype2	32	60.5 (13.0)
subtype3	28	59.2 (10.4)
subtype4	24	60.8 (13.3)
subtype5	6	63.0 (10.7)
subtype6	25	65.4 (10.8)

Figure S42. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.307 (Chi-square test), Q value = 1

Table S52. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	54	98
subtype1	11	26
subtype2	15	17
subtype3	8	20
subtype4	7	17
subtype5	1	5
subtype6	12	13

Figure S43. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.755 (ANOVA), Q value = 1

Table S53. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	114	75.6 (14.4)
subtype1	27	77.4 (12.3)
subtype2	24	74.2 (11.0)
subtype3	24	77.1 (16.0)
subtype4	16	76.9 (12.5)
subtype5	5	68.0 (26.8)
subtype6	18	73.9 (17.2)

Figure S44. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

P value = 0.19 (Chi-square test), Q value = 1

Table S54. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	97	55
subtype1	26	11
subtype2	19	13
subtype3	22	6
subtype4	11	13
subtype5	3	3
subtype6	16	9

Figure S45. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #10: 'RNAseq cHierClus subtypes'

Table S55. Get Full Table Description of clustering approach #10: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3	4	5	6
Number of samples	31	3	32	23	19	44

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.35 (logrank test), Q value = 1

Table S56. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	152	99	0.2 - 54.0 (8.7)
subtype1	31	19	0.2 - 54.0 (9.4)
subtype2	3	2	0.4 - 28.9 (4.2)
subtype3	32	21	0.2 - 31.3 (6.7)
subtype4	23	15	0.9 - 33.1 (7.3)
subtype5	19	12	1.1 - 47.9 (13.3)
subtype6	44	30	0.9 - 40.4 (8.3)

Figure S46. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.349 (ANOVA), Q value = 1

Table S57. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	152	60.1 (13.2)
subtype1	31	56.0 (17.2)
subtype2	3	53.7 (26.3)
subtype3	32	59.6 (12.8)
subtype4	23	62.0 (12.0)
subtype5	19	62.9 (7.8)
subtype6	44	61.6 (11.6)

Figure S47. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.0251 (Chi-square test), Q value = 1

Table S58. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	54	98
subtype1	9	22
subtype2	2	1
subtype3	5	27
subtype4	7	16
subtype5	9	10
subtype6	22	22

Figure S48. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.469 (ANOVA), Q value = 1

Table S59. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	114	75.6 (14.4)
subtype1	23	76.1 (12.3)
subtype2	2	70.0 (14.1)
subtype3	23	78.7 (14.2)
subtype4	16	78.1 (14.7)
subtype5	16	70.6 (19.1)
subtype6	34	74.7 (13.3)

Figure S49. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

P value = 0.957 (Chi-square test), Q value = 1

Table S60. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	97	55
subtype1	22	9
subtype2	2	1
subtype3	19	13
subtype4	14	9
subtype5	12	7
subtype6	28	16

Figure S50. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: '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 = 564
Number of clustering approaches = 10
Number of selected clinical features = 5
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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