Correlate_Clinical_vs_Molecular_Signatures

Brain Lower Grade Glioma (Primary solid tumor)

22 February 2013 | analyses__2013_02_22

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): Correlate_Clinical_vs_Molecular_Signatures. Broad Institute of MIT and Harvard. doi:10.7908/C1BR8QDV

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 208 patients, 14 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 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'Time to Death', 'AGE', 'GENDER', and '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 5 subtypes that correlate to 'Time to Death', 'AGE', and 'HISTOLOGICAL.TYPE'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'HISTOLOGICAL.TYPE'.
4 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.

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, 14 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 (0.449)	0.757 (1.00)
Copy Number Ratio CNMF subtypes	1.62e-05 (0.000663)	6.39e-08 (2.81e-06)	0.00183 (0.0658)	0.977 (1.00)	2.47e-13 (1.18e-11)	0.0468 (1.00)
METHLYATION CNMF	1.19e-05 (0.000502)	2.76e-05 (0.00108)	0.0183 (0.584)	0.872 (1.00)	1.55e-11 (7.27e-10)	0.000172 (0.00654)
RNAseq CNMF subtypes	1.74e-05 (0.000695)	0.00125 (0.0464)	0.0091 (0.31)	0.327 (1.00)	8.84e-11 (4.07e-09)	0.0465 (1.00)
RNAseq cHierClus subtypes	0.0328 (0.952)	0.0419 (1.00)	0.444 (1.00)	0.245 (1.00)	2.28e-10 (1.03e-08)	0.0253 (0.759)
MIRSEQ CNMF	0.336 (1.00)	0.392 (1.00)	0.546 (1.00)	0.282 (1.00)	4.12e-07 (1.77e-05)	0.221 (1.00)
MIRSEQ CHIERARCHICAL	0.0778 (1.00)	0.566 (1.00)	0.873 (1.00)	0.0536 (1.00)	0.00492 (0.172)	0.25 (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 = 0.45

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: 'Copy Number Ratio CNMF subtypes'

Table S15. Get Full Table Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	85	48	74

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

P value = 1.62e-05 (logrank test), Q value = 0.00066

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	206	51	0.0 - 211.2 (13.4)
subtype1	85	14	0.1 - 156.2 (13.4)
subtype2	48	24	0.5 - 211.2 (11.9)
subtype3	73	13	0.0 - 182.3 (14.3)

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

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 6.39e-08 (ANOVA), Q value = 2.8e-06

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

	nPatients	Mean (Std.Dev)
ALL	207	43.1 (13.4)
subtype1	85	37.3 (11.8)
subtype2	48	50.1 (12.9)
subtype3	74	45.2 (12.9)

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

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.00183 (Fisher's exact test), Q value = 0.066

Table S18. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	89	118
subtype1	28	57
subtype2	31	17
subtype3	30	44

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

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

P value = 0.977 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	93	88.4 (10.5)
subtype1	37	88.6 (10.8)
subtype2	26	88.1 (7.5)
subtype3	30	88.3 (12.3)

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

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

P value = 2.47e-13 (Chi-square test), Q value = 1.2e-11

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

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	63	54	89
subtype1	33	30	21
subtype2	24	15	9
subtype3	6	9	59

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

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

P value = 0.0468 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	104	103
subtype1	46	39
subtype2	29	19
subtype3	29	45

Figure S18. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' 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 = 5e-04

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 = 0.0011

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 = 7.3e-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 = 0.0065

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	5
Number of samples	55	40	35	67	9

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 1.74e-05 (logrank test), Q value = 0.00069

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	205	51	0.0 - 211.2 (13.4)
subtype1	55	14	0.0 - 130.8 (16.5)
subtype2	40	20	0.1 - 211.2 (9.6)
subtype3	34	8	0.1 - 182.3 (15.8)
subtype4	67	8	0.1 - 156.2 (14.5)
subtype5	9	1	0.2 - 44.0 (14.3)

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 = 0.00125 (ANOVA), Q value = 0.046

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

	nPatients	Mean (Std.Dev)
ALL	206	43.2 (13.4)
subtype1	55	38.8 (11.7)
subtype2	40	48.1 (14.0)
subtype3	35	47.5 (12.5)
subtype4	67	42.5 (13.8)
subtype5	9	36.2 (10.5)

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

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.0091 (Chi-square test), Q value = 0.31

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

nPatients	FEMALE	MALE
ALL	89	117
subtype1	19	36
subtype2	26	14
subtype3	18	17
subtype4	24	43
subtype5	2	7

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 = 0.327 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	91	88.2 (10.5)
subtype1	32	90.9 (9.3)
subtype2	18	87.8 (5.5)
subtype3	16	86.9 (15.4)
subtype4	22	86.8 (9.9)
subtype5	3	80.0 (17.3)

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 = 8.84e-11 (Chi-square test), Q value = 4.1e-09

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

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	62	54	89
subtype1	20	20	15
subtype2	28	6	6
subtype3	2	5	28
subtype4	10	20	36
subtype5	2	3	4

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 = 0.0465 (Chi-square test), Q value = 1

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

nPatients	NO	YES
ALL	102	104
subtype1	35	20
subtype2	22	18
subtype3	12	23
subtype4	30	37
subtype5	3	6

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
Number of samples	32	108	66

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0328 (logrank test), Q value = 0.95

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	205	51	0.0 - 211.2 (13.4)
subtype1	31	8	0.1 - 182.3 (15.1)
subtype2	108	33	0.0 - 211.2 (12.9)
subtype3	66	10	0.1 - 156.2 (14.5)

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 = 0.0419 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	206	43.2 (13.4)
subtype1	32	48.6 (12.4)
subtype2	108	42.0 (13.2)
subtype3	66	42.5 (13.7)

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.444 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	89	117
subtype1	17	15
subtype2	46	62
subtype3	26	40

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 = 0.245 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	91	88.2 (10.5)
subtype1	16	86.9 (15.4)
subtype2	52	89.8 (9.0)
subtype3	23	85.7 (9.5)

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 = 2.28e-10 (Chi-square test), Q value = 1e-08

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

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	62	54	89
subtype1	1	4	27
subtype2	51	31	25
subtype3	10	19	37

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 = 0.0253 (Fisher's exact test), Q value = 0.76

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

nPatients	NO	YES
ALL	102	104
subtype1	10	22
subtype2	62	46
subtype3	30	36

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

Clustering Approach #7: 'MIRSEQ CNMF'

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

Cluster Labels	1	2	3	4
Number of samples	47	46	32	82

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.336 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	206	50	0.0 - 211.2 (13.4)
subtype1	47	12	0.0 - 117.4 (8.4)
subtype2	46	18	0.1 - 211.2 (16.7)
subtype3	32	7	0.1 - 182.3 (18.1)
subtype4	81	13	0.1 - 156.2 (12.4)

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

'MIRSEQ CNMF' versus 'AGE'

P value = 0.392 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	207	43.2 (13.4)
subtype1	47	41.8 (14.1)
subtype2	46	41.1 (13.0)
subtype3	32	44.9 (12.0)
subtype4	82	44.6 (13.7)

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

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.546 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	89	118
subtype1	21	26
subtype2	17	29
subtype3	17	15
subtype4	34	48

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

'MIRSEQ CNMF' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.282 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	92	88.5 (10.5)
subtype1	24	90.8 (8.8)
subtype2	19	87.9 (10.8)
subtype3	17	90.6 (10.3)
subtype4	32	85.9 (11.3)

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

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 4.12e-07 (Chi-square test), Q value = 1.8e-05

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

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	63	54	89
subtype1	22	13	11
subtype2	24	11	11
subtype3	3	5	24
subtype4	14	25	43

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

'MIRSEQ CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.221 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	103	104
subtype1	23	24
subtype2	29	17
subtype3	14	18
subtype4	37	45

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

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S50. Get Full Table Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3
Number of samples	83	12	112

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.0778 (logrank test), Q value = 1

Table S51. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	206	50	0.0 - 211.2 (13.4)
subtype1	82	13	0.1 - 211.2 (12.9)
subtype2	12	1	0.2 - 52.4 (10.9)
subtype3	112	36	0.0 - 182.3 (15.6)

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

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.566 (ANOVA), Q value = 1

Table S52. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	207	43.2 (13.4)
subtype1	83	44.3 (14.1)
subtype2	12	40.6 (13.5)
subtype3	112	42.7 (12.9)

Figure S44. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.873 (Fisher's exact test), Q value = 1

Table S53. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	89	118
subtype1	36	47
subtype2	6	6
subtype3	47	65

Figure S45. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.0536 (ANOVA), Q value = 1

Table S54. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	92	88.5 (10.5)
subtype1	33	86.1 (12.0)
subtype2	5	82.0 (13.0)
subtype3	54	90.6 (8.8)

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

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 0.00492 (Chi-square test), Q value = 0.17

Table S55. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	63	54	89
subtype1	14	22	47
subtype2	5	4	3
subtype3	44	28	39

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

'MIRSEQ CHIERARCHICAL' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.25 (Fisher's exact test), Q value = 1

Table S56. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	103	104
subtype1	36	47
subtype2	8	4
subtype3	59	53

Figure S48. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' 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 = 208
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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