Brain Lower Grade Glioma: Correlation between molecular cancer subtypes and selected clinical features<BR>(primary solid tumor cohort)

Brain Lower Grade Glioma: 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 8 different clustering approaches and 6 clinical features across 179 patients, 12 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.
3 subtypes identified in current cancer cohort by 'CN CNMF'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.
4 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death', 'AGE', 'HISTOLOGICAL.TYPE', and 'RADIATIONS.RADIATION.REGIMENINDICATION'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'Time to Death', 'HISTOLOGICAL.TYPE', and 'RADIATIONS.RADIATION.REGIMENINDICATION'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'Time to Death', 'AGE', and 'HISTOLOGICAL.TYPE'.
CNMF clustering analysis on sequencing-based miR expression data identified 7 subtypes that correlate to 'HISTOLOGICAL.TYPE'.
Consensus hierarchical clustering analysis on sequencing-based miR 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 8 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, 12 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	0.247 (1.00)	0.326 (1.00)	0.101 (1.00)	0.441 (1.00)	0.0226 (0.699)	0.384 (1.00)
mRNA cHierClus subtypes	0.31 (1.00)	0.467 (1.00)	0.172 (1.00)	0.441 (1.00)	0.0136 (0.463)	0.757 (1.00)
CN CNMF	0.0131 (0.458)	0.162 (1.00)	0.0354 (1.00)	0.0523 (1.00)	0.000129 (0.00555)	0.0365 (1.00)
METHLYATION CNMF	1.19e-05 (0.00055)	2.76e-05 (0.00121)	0.0183 (0.584)	0.872 (1.00)	1.55e-11 (7.43e-10)	0.000172 (0.00723)
RNAseq CNMF subtypes	0.000486 (0.0194)	0.0677 (1.00)	0.294 (1.00)	0.204 (1.00)	4.12e-06 (0.000193)	0.00271 (0.103)
RNAseq cHierClus subtypes	0.000316 (0.013)	0.0059 (0.218)	0.127 (1.00)	0.361 (1.00)	1.22e-05 (0.00055)	0.0151 (0.497)
MIRseq CNMF subtypes	0.324 (1.00)	0.00901 (0.324)	0.0591 (1.00)	0.294 (1.00)	0.000491 (0.0194)	0.13 (1.00)
MIRseq cHierClus subtypes	0.0326 (0.978)	0.804 (1.00)	0.693 (1.00)	0.852 (1.00)	0.0496 (1.00)	0.0781 (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	9	10	8

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.247 (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	27	9	0.1 - 134.3 (46.6)
subtype1	9	4	10.6 - 130.8 (43.9)
subtype2	10	3	0.1 - 78.2 (36.5)
subtype3	8	2	14.4 - 134.3 (51.3)

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

'mRNA CNMF subtypes' versus 'AGE'

P value = 0.326 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	27	39.3 (9.1)
subtype1	9	39.2 (6.2)
subtype2	10	42.3 (7.6)
subtype3	8	35.8 (12.6)

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.101 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	9	18
subtype1	2	7
subtype2	6	4
subtype3	1	7

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 = 0.441 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	17	88.8 (12.2)
subtype1	7	84.3 (16.2)
subtype2	7	92.9 (7.6)
subtype3	3	90.0 (10.0)

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 = 0.0226 (Chi-square test), Q value = 0.7

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

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	10	9	8
subtype1	7	2	0
subtype2	2	3	5
subtype3	1	4	3

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 = 0.384 (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	19	8
subtype1	7	2
subtype2	8	2
subtype3	4	4

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

Clustering Approach #2: 'mRNA cHierClus subtypes'

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

Cluster Labels	1	2	3
Number of samples	9	7	11

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.31 (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	27	9	0.1 - 134.3 (46.6)
subtype1	9	4	10.6 - 130.8 (43.9)
subtype2	7	2	14.4 - 134.3 (52.4)
subtype3	11	3	0.1 - 78.2 (41.1)

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 = 0.467 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	27	39.3 (9.1)
subtype1	9	39.2 (6.2)
subtype2	7	36.0 (13.6)
subtype3	11	41.5 (7.6)

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

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.172 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	9	18
subtype1	2	7
subtype2	1	6
subtype3	6	5

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 = 0.441 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	17	88.8 (12.2)
subtype1	7	84.3 (16.2)
subtype2	3	90.0 (10.0)
subtype3	7	92.9 (7.6)

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 = 0.0136 (Chi-square test), Q value = 0.46

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

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	10	9	8
subtype1	7	2	0
subtype2	0	4	3
subtype3	3	3	5

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 = 0.757 (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	19	8
subtype1	7	2
subtype2	4	3
subtype3	8	3

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

Clustering Approach #3: 'CN CNMF'

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

Cluster Labels	1	2	3
Number of samples	59	50	69

'CN CNMF' versus 'Time to Death'

P value = 0.0131 (logrank test), Q value = 0.46

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	177	50	0.0 - 211.2 (14.7)
subtype1	59	22	0.1 - 211.2 (8.8)
subtype2	50	17	0.1 - 156.2 (18.2)
subtype3	68	11	0.0 - 182.3 (14.5)

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

'CN CNMF' versus 'AGE'

P value = 0.162 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	178	43.1 (13.4)
subtype1	59	45.8 (13.4)
subtype2	50	42.3 (12.5)
subtype3	69	41.5 (13.8)

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

'CN CNMF' versus 'GENDER'

P value = 0.0354 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	76	102
subtype1	33	26
subtype2	20	30
subtype3	23	46

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

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

P value = 0.0523 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	92	88.5 (10.5)
subtype1	34	91.2 (6.9)
subtype2	29	84.8 (13.3)
subtype3	29	89.0 (10.1)

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

'CN CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.000129 (Chi-square test), Q value = 0.0055

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

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	55	47	75
subtype1	27	19	12
subtype2	17	12	21
subtype3	11	16	42

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

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

P value = 0.0365 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	96	82
subtype1	35	24
subtype2	32	18
subtype3	29	40

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

Clustering Approach #4: 'METHLYATION CNMF'

Table S22. Get Full Table Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels	1	2	3	4
Number of samples	76	29	19	50

'METHLYATION CNMF' versus 'Time to Death'

P value = 1.19e-05 (logrank test), Q value = 0.00055

Table S23. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	173	46	0.0 - 211.2 (14.5)
subtype1	76	20	0.0 - 156.2 (17.5)
subtype2	29	16	0.1 - 211.2 (8.4)
subtype3	19	2	0.1 - 97.9 (10.6)
subtype4	49	8	0.1 - 182.3 (14.5)

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

'METHLYATION CNMF' versus 'AGE'

P value = 2.76e-05 (ANOVA), Q value = 0.0012

Table S24. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	174	43.1 (13.4)
subtype1	76	39.8 (12.2)
subtype2	29	50.4 (14.1)
subtype3	19	35.6 (12.3)
subtype4	50	46.6 (12.3)

Figure S20. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.0183 (Fisher's exact test), Q value = 0.58

Table S25. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	76	98
subtype1	28	48
subtype2	20	9
subtype3	6	13
subtype4	22	28

Figure S21. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

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

P value = 0.872 (ANOVA), Q value = 1

Table S26. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	91	88.6 (10.5)
subtype1	45	89.3 (10.5)
subtype2	17	87.1 (5.9)
subtype3	7	87.1 (9.5)
subtype4	22	88.6 (13.6)

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

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 1.55e-11 (Chi-square test), Q value = 7.4e-10

Table S27. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	54	46	73
subtype1	30	27	18
subtype2	17	7	5
subtype3	6	6	7
subtype4	1	6	43

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

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

P value = 0.000172 (Fisher's exact test), Q value = 0.0072

Table S28. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	93	81
subtype1	53	23
subtype2	17	12
subtype3	6	13
subtype4	17	33

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

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S29. Get Full Table Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	23	29	24	34

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.000486 (logrank test), Q value = 0.019

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	110	41	0.0 - 211.2 (19.0)
subtype1	23	16	4.1 - 211.2 (13.4)
subtype2	29	10	0.0 - 130.8 (25.9)
subtype3	24	7	1.2 - 182.3 (29.4)
subtype4	34	8	0.1 - 156.2 (18.2)

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

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.0677 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	110	42.9 (12.9)
subtype1	23	46.8 (14.3)
subtype2	29	39.5 (10.3)
subtype3	24	46.6 (10.8)
subtype4	34	40.7 (14.3)

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

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.294 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	50	60
subtype1	14	9
subtype2	11	18
subtype3	12	12
subtype4	13	21

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

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

P value = 0.204 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	58	88.6 (11.0)
subtype1	14	88.6 (5.3)
subtype2	17	92.9 (5.9)
subtype3	13	84.6 (18.1)
subtype4	14	87.1 (10.7)

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

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 4.12e-06 (Chi-square test), Q value = 0.00019

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

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	36	27	46
subtype1	15	5	3
subtype2	13	9	7
subtype3	1	3	20
subtype4	7	10	16

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

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

P value = 0.00271 (Fisher's exact test), Q value = 0.1

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

nPatients	NO	YES
ALL	66	44
subtype1	17	6
subtype2	23	6
subtype3	8	16
subtype4	18	16

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

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S36. Get Full Table Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	35	35	20	20

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.000316 (logrank test), Q value = 0.013

Table S37. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	110	41	0.0 - 211.2 (19.0)
subtype1	35	8	0.1 - 156.2 (17.4)
subtype2	35	12	0.0 - 130.8 (25.9)
subtype3	20	7	4.8 - 182.3 (31.2)
subtype4	20	14	4.1 - 211.2 (12.5)

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

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.0059 (ANOVA), Q value = 0.22

Table S38. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	110	42.9 (12.9)
subtype1	35	42.8 (14.3)
subtype2	35	37.7 (10.5)
subtype3	20	45.9 (10.6)
subtype4	20	49.5 (13.2)

Figure S32. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.127 (Fisher's exact test), Q value = 1

Table S39. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	50	60
subtype1	13	22
subtype2	13	22
subtype3	11	9
subtype4	13	7

Figure S33. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.361 (ANOVA), Q value = 1

Table S40. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	58	88.6 (11.0)
subtype1	14	84.3 (14.5)
subtype2	20	90.5 (11.0)
subtype3	12	90.8 (10.0)
subtype4	12	88.3 (5.8)

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

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1.22e-05 (Chi-square test), Q value = 0.00055

Table S41. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	36	27	46
subtype1	7	10	18
subtype2	15	11	8
subtype3	1	2	17
subtype4	13	4	3

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

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

P value = 0.0151 (Fisher's exact test), Q value = 0.5

Table S42. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	66	44
subtype1	17	18
subtype2	25	10
subtype3	8	12
subtype4	16	4

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

Clustering Approach #7: 'MIRseq CNMF subtypes'

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

Cluster Labels	1	2	3	4	5	6	7
Number of samples	23	29	19	20	47	31	9

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.324 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	177	49	0.0 - 211.2 (14.7)
subtype1	23	4	0.0 - 107.9 (6.0)
subtype2	29	12	0.1 - 134.3 (28.2)
subtype3	19	8	0.8 - 117.4 (16.5)
subtype4	19	4	0.1 - 94.3 (17.3)
subtype5	47	8	0.1 - 156.2 (8.8)
subtype6	31	12	1.2 - 211.2 (17.9)
subtype7	9	1	3.1 - 95.6 (15.3)

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

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.00901 (ANOVA), Q value = 0.32

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

	nPatients	Mean (Std.Dev)
ALL	178	43.3 (13.3)
subtype1	23	37.2 (10.9)
subtype2	29	42.0 (11.3)
subtype3	19	48.5 (12.9)
subtype4	20	49.4 (13.3)
subtype5	47	41.9 (14.0)
subtype6	31	46.1 (14.1)
subtype7	9	36.1 (11.2)

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

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.0591 (Chi-square test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	76	102
subtype1	5	18
subtype2	8	21
subtype3	11	8
subtype4	11	9
subtype5	21	26
subtype6	17	14
subtype7	3	6

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

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

P value = 0.294 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	91	88.6 (10.5)
subtype1	13	93.8 (5.1)
subtype2	16	86.9 (12.0)
subtype3	11	91.8 (6.0)
subtype4	9	90.0 (10.0)
subtype5	21	85.7 (10.3)
subtype6	18	87.8 (12.2)
subtype7	3	83.3 (20.8)

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

'MIRseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.000491 (Chi-square test), Q value = 0.019

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

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	55	47	75
subtype1	10	8	5
subtype2	17	6	6
subtype3	9	6	4
subtype4	2	3	15
subtype5	9	15	22
subtype6	7	7	17
subtype7	1	2	6

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

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

P value = 0.13 (Chi-square test), Q value = 1

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

nPatients	NO	YES
ALL	95	83
subtype1	9	14
subtype2	19	10
subtype3	11	8
subtype4	9	11
subtype5	21	26
subtype6	22	9
subtype7	4	5

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

Clustering Approach #8: 'MIRseq cHierClus subtypes'

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

Cluster Labels	1	2	3
Number of samples	7	94	77

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0326 (logrank test), Q value = 0.98

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	177	49	0.0 - 211.2 (14.7)
subtype1	7	1	7.1 - 21.0 (13.4)
subtype2	94	34	0.0 - 182.3 (17.0)
subtype3	76	14	0.1 - 211.2 (13.4)

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

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.804 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	178	43.3 (13.3)
subtype1	7	41.4 (13.1)
subtype2	94	42.9 (13.1)
subtype3	77	44.0 (13.8)

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

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.693 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	76	102
subtype1	2	5
subtype2	39	55
subtype3	35	42

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

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

P value = 0.852 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	91	88.6 (10.5)
subtype1	5	88.0 (4.5)
subtype2	48	89.2 (9.9)
subtype3	38	87.9 (11.9)

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

'MIRseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0496 (Chi-square test), Q value = 1

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

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	55	47	75
subtype1	3	1	3
subtype2	37	24	32
subtype3	15	22	40

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

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

P value = 0.0781 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	95	83
subtype1	5	2
subtype2	56	38
subtype3	34	43

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

Methods & Data

Input

Cluster data file = LGG-TP.mergedcluster.txt
Clinical data file = LGG-TP.clin.merged.picked.txt
Number of patients = 179
Number of clustering approaches = 8
Number of selected clinical features = 6
Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches

CNMF clustering

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

Consensus hierarchical clustering

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

Survival analysis

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

ANOVA analysis

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

Fisher's exact test

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

Chi-square test

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

Q value calculation

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

Download Results

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

References

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

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

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

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

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

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

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

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