Correlation between molecular cancer subtypes and selected clinical features

Glioblastoma Multiforme (Primary solid tumor)

21 April 2013 | analyses__2013_04_21

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): Glioblastoma Multiforme (Primary solid tumor cohort) - 21 April 2013: Correlation between molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1RR1W69

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 '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 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	Fisher's exact test	ANOVA	Fisher's exact 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)
Copy Number Ratio CNMF subtypes	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	0.8 (1.00)	0.233 (1.00)	0.222 (1.00)	0.403 (1.00)	0.559 (1.00)
RPPA cHierClus subtypes	0.767 (1.00)	0.46 (1.00)	0.622 (1.00)	0.695 (1.00)	0.611 (1.00)
RNAseq CNMF subtypes	0.363 (1.00)	0.0725 (1.00)	0.0767 (1.00)	0.576 (1.00)	0.378 (1.00)
RNAseq cHierClus subtypes	0.731 (1.00)	0.045 (1.00)	0.107 (1.00)	0.32 (1.00)	0.973 (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: 'Copy Number Ratio CNMF subtypes'

Table S25. Get Full Table Description of clustering approach #5: 'Copy Number Ratio CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	159	210	175

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

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

Table S26. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' 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: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'Copy Number Ratio CNMF subtypes' versus 'AGE'

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

Table S27. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' 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: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

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

Table S28. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' 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: '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 S29. 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 S24. 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 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Table S30. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' 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: 'Copy Number Ratio CNMF subtypes' 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	57	60	44	49

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.8 (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	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 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 = 0.233 (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	57	59.2 (13.6)
subtype2	60	57.4 (16.9)
subtype3	44	61.5 (12.2)
subtype4	49	62.5 (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 = 0.222 (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	40
subtype2	26	34
subtype3	18	26
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 = 0.403 (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	46	75.0 (15.9)
subtype2	50	76.4 (16.5)
subtype3	32	78.8 (11.3)
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 = 0.559 (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	39	18
subtype2	43	17
subtype3	35	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	64	35	111

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.767 (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	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 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 = 0.46 (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	64	61.7 (12.1)
subtype2	35	58.4 (13.2)
subtype3	111	59.4 (15.4)

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

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.622 (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	25	39
subtype2	12	23
subtype3	48	63

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 = 0.695 (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	49	76.9 (14.2)
subtype2	26	73.8 (17.7)
subtype3	93	75.4 (15.3)

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 = 0.611 (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	48	16
subtype2	23	12
subtype3	79	32

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
Number of samples	46	70	35

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.363 (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	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 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 = 0.0725 (ANOVA), Q value = 1

Table S51. 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 S42. 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 S52. 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 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 = 0.576 (ANOVA), Q value = 1

Table S53. 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 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 = 0.378 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	96	55
subtype1	32	14
subtype2	45	25
subtype3	19	16

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
Number of samples	69	22	60

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.731 (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	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 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 = 0.045 (ANOVA), Q value = 1

Table S57. 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 S47. 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 S58. 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 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 = 0.32 (ANOVA), Q value = 1

Table S59. 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 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 = 0.973 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	96	55
subtype1	43	26
subtype2	14	8
subtype3	39	21

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

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] 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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