Brain Lower Grade Glioma: Correlation between molecular cancer subtypes and selected clinical features
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, 22 significant findings detected with P value < 0.05.

- CNMF clustering analysis on array-based mRNA expression data identified 3 subtypes that correlate to 'HISTOLOGICAL.TYPE'.

- Consensus hierarchical clustering analysis on array-based mRNA expression data identified 3 subtypes that correlate to 'HISTOLOGICAL.TYPE'.

- 3 subtypes identified in current cancer cohort by 'CN CNMF'. These subtypes correlate to 'Time to Death', 'GENDER', 'HISTOLOGICAL.TYPE', and 'RADIATIONS.RADIATION.REGIMENINDICATION'.

- 4 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death', 'AGE', 'GENDER', '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', 'HISTOLOGICAL.TYPE', and 'RADIATIONS.RADIATION.REGIMENINDICATION'.

- CNMF clustering analysis on sequencing-based miR expression data identified 7 subtypes that correlate to 'AGE' and 'HISTOLOGICAL.TYPE'.

- Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'Time to Death' and '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 from statistical tests. Thresholded by P value < 0.05, 22 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 | 0.326 | 0.101 | 0.441 | 0.0226 | 0.384
mRNA cHierClus subtypes | 0.31 | 0.467 | 0.172 | 0.441 | 0.0136 | 0.757
CN CNMF | 0.0131 | 0.162 | 0.0354 | 0.0523 | 0.000129 | 0.0365
METHLYATION CNMF | 1.19e-05 | 2.76e-05 | 0.0183 | 0.872 | 1.55e-11 | 0.000172
RNAseq CNMF subtypes | 0.000486 | 0.0677 | 0.294 | 0.204 | 4.12e-06 | 0.00271
RNAseq cHierClus subtypes | 0.000316 | 0.0059 | 0.127 | 0.361 | 1.22e-05 | 0.0151
MIRseq CNMF subtypes | 0.324 | 0.00901 | 0.0591 | 0.294 | 0.000491 | 0.13
MIRseq cHierClus subtypes | 0.0326 | 0.804 | 0.693 | 0.852 | 0.0496 | 0.0781

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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

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.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

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)
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