Glioblastoma Multiforme: Correlation between molecular cancer subtypes and selected clinical features
(primary solid tumor cohort)
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Overview
Introduction

This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.

Summary

Testing the association between subtypes identified by 10 different clustering approaches and 5 clinical features across 564 patients, 2 significant findings detected with P value < 0.05 and Q value < 0.25.

- CNMF clustering analysis on array-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.

- Consensus hierarchical clustering analysis on array-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.

- CNMF clustering analysis on array-based miR expression data identified 4 subtypes that do not correlate to any clinical features.

- Consensus hierarchical clustering analysis on array-based miR expression data identified 3 subtypes that correlate to 'AGE'.

- 3 subtypes identified in current cancer cohort by 'CN CNMF'. These subtypes correlate to 'AGE'.

- 4 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes do not correlate to any clinical features.

- CNMF clustering analysis on RPPA data identified 4 subtypes that do not correlate to any clinical features.

- Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that do not correlate to any clinical features.

- CNMF clustering analysis on sequencing-based mRNA expression data identified 6 subtypes that do not correlate to any clinical features.

- Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 6 subtypes that do not correlate to any clinical features.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 5 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 2 significant findings detected.

Clinical Features | Time to Death | AGE | GENDER | KARNOFSKY PERFORMANCE SCORE | RADIATIONS RADIATION REGIMENINDICATION
Statistical Tests | logrank test | ANOVA | Chi-square test | ANOVA | Chi-square test
mRNA CNMF subtypes | 0.367 (1.00) | 0.0257 (1.00) | 0.497 (1.00) | 0.839 (1.00) | 0.259 (1.00)
mRNA cHierClus subtypes | 0.161 (1.00) | 0.0276 (1.00) | 0.563 (1.00) | 0.487 (1.00) | 0.122 (1.00)
miR CNMF subtypes | 0.00835 (0.401) | 0.101 (1.00) | 0.508 (1.00) | 0.943 (1.00) | 0.638 (1.00)
miR cHierClus subtypes | 0.0178 (0.839) | 0.000872 (0.0427) | 0.119 (1.00) | 0.785 (1.00) | 0.758 (1.00)
CN CNMF | 0.0332 (1.00) | 1.16e-06 (5.8e-05) | 0.488 (1.00) | 0.871 (1.00) | 0.1 (1.00)
METHLYATION CNMF | 0.619 (1.00) | 0.148 (1.00) | 0.125 (1.00) | 0.945 (1.00) | 0.549 (1.00)
RPPA CNMF subtypes | 0.856 (1.00) | 0.285 (1.00) | 0.188 (1.00) | 0.418 (1.00) | 0.614 (1.00)
RPPA cHierClus subtypes | 0.964 (1.00) | 0.457 (1.00) | 0.528 (1.00) | 0.438 (1.00) | 0.303 (1.00)
RNAseq CNMF subtypes | 0.371 (1.00) | 0.15 (1.00) | 0.307 (1.00) | 0.755 (1.00) | 0.19 (1.00)
RNAseq cHierClus subtypes | 0.35 (1.00) | 0.349 (1.00) | 0.0251 (1.00) | 0.469 (1.00) | 0.957 (1.00)

Clustering Approach #1: 'mRNA CNMF subtypes'

Table S1. Get Full Table Description of clustering approach #1: 'mRNA CNMF subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 177 | 172 | 170

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.367 (logrank test), Q value = 1

Table S2. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 519 | 403 | 0.1 - 127.6 (9.9)
subtype1 | 177 | 145 | 0.2 - 127.6 (10.0)
subtype2 | 172 | 129 | 0.2 - 108.8 (9.2)
subtype3 | 170 | 129 | 0.1 - 92.6 (10.7)

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

'mRNA CNMF subtypes' versus 'AGE'

P value = 0.0257 (ANOVA), Q value = 1

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

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

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

'mRNA CNMF subtypes' versus 'GENDER'

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

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

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

Figure S3. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.839 (ANOVA), Q value = 1

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 389 | 77.1 (14.4)
subtype1 | 137 | 77.5 (15.0)
subtype2 | 126 | 77.3 (13.0)
subtype3 | 126 | 76.5 (15.0)

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

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

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

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

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

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

Clustering Approach #2: 'mRNA cHierClus subtypes'

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

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 223 | 127 | 169

'mRNA cHierClus subtypes' versus 'Time to Death'

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

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

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

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

'mRNA cHierClus subtypes' versus 'AGE'

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

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

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

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

'mRNA cHierClus subtypes' versus 'GENDER'

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Clustering Approach #3: 'miR CNMF subtypes'

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

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

'miR CNMF subtypes' versus 'Time to Death'

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

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

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

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

'miR CNMF subtypes' versus 'AGE'

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

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

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

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

'miR CNMF subtypes' versus 'GENDER'

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

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

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

Figure S13. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.943 (ANOVA), Q value = 1

Table S17. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 368 | 77.6 (14.1)
subtype1 | 112 | 77.6 (14.4)
subtype2 | 114 | 77.5 (14.1)
subtype3 | 62 | 76.9 (14.4)
subtype4 | 80 | 78.4 (13.8)

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

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

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

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

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

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

Clustering Approach #4: 'miR cHierClus subtypes'

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

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 170 | 180 | 132

'miR cHierClus subtypes' versus 'Time to Death'

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

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

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

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

'miR cHierClus subtypes' versus 'AGE'

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

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

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

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

'miR cHierClus subtypes' versus 'GENDER'

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

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

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

Figure S18. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.785 (ANOVA), Q value = 1

Table S23. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 368 | 77.6 (14.1)
subtype1 | 129 | 78.3 (13.5)
subtype2 | 136 | 77.1 (15.5)
subtype3 | 103 | 77.4 (13.1)

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

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

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

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

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

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

Clustering Approach #5: 'CN CNMF'

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

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 159 | 210 | 175

'CN CNMF' versus 'Time to Death'

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

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

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

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

'CN CNMF' versus 'AGE'

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

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

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

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

'CN CNMF' versus 'GENDER'

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

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

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

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

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

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

Table S29. Clustering Approach #5: 'CN CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 409 | 77.3 (14.7)
subtype1 | 113 | 76.8 (14.0)
subtype2 | 164 | 77.3 (15.7)
subtype3 | 132 | 77.8 (14.2)

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

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

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

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

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

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

Clustering Approach #6: 'METHLYATION CNMF'

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

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

'METHLYATION CNMF' versus 'Time to Death'

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

Table S32. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 106 | 53 | 0.1 - 58.8 (6.9)
subtype1 | 34 | 17 | 0.8 - 58.8 (5.4)
subtype2 | 19 | 8 | 1.6 - 20.9 (8.3)
subtype3 | 26 | 17 | 0.1 - 47.9 (7.9)
subtype4 | 27 | 11 | 1.4 - 31.3 (7.5)

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

'METHLYATION CNMF' versus 'AGE'

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

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

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

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

'METHLYATION CNMF' versus 'GENDER'

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

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

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

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

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

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

Table S35. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 80 | 76.5 (15.2)
subtype1 | 23 | 77.0 (17.9)
subtype2 | 13 | 76.2 (16.6)
subtype3 | 22 | 75.0 (15.0)
subtype4 | 22 | 77.7 (11.9)

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

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

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

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

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

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

Clustering Approach #7: 'RPPA CNMF subtypes'

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

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

'RPPA CNMF subtypes' versus 'Time to Death'

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

Table S38. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 210 | 137 | 0.1 - 108.8 (7.9)
subtype1 | 58 | 42 | 0.1 - 53.2 (8.1)
subtype2 | 60 | 38 | 0.2 - 108.8 (6.5)
subtype3 | 43 | 27 | 0.1 - 43.2 (9.1)
subtype4 | 49 | 30 | 0.2 - 47.9 (6.3)

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

'RPPA CNMF subtypes' versus 'AGE'

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

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

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

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

'RPPA CNMF subtypes' versus 'GENDER'

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Clustering Approach #8: 'RPPA cHierClus subtypes'

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

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 97 | 72 | 41

'RPPA cHierClus subtypes' versus 'Time to Death'

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

Table S44. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 210 | 137 | 0.1 - 108.8 (7.9)
subtype1 | 97 | 62 | 0.2 - 108.8 (6.5)
subtype2 | 72 | 45 | 0.1 - 47.9 (8.7)
subtype3 | 41 | 30 | 1.2 - 53.2 (8.3)

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

'RPPA cHierClus subtypes' versus 'AGE'

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

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

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

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

'RPPA cHierClus subtypes' versus 'GENDER'

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Clustering Approach #9: 'RNAseq CNMF subtypes'

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

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

'RNAseq CNMF subtypes' versus 'Time to Death'

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

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

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

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

'RNAseq CNMF subtypes' versus 'AGE'

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

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

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

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

'RNAseq CNMF subtypes' versus 'GENDER'

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Clustering Approach #10: 'RNAseq cHierClus subtypes'

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

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

'RNAseq cHierClus subtypes' versus 'Time to Death'

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

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

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

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

'RNAseq cHierClus subtypes' versus 'AGE'

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

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

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

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

'RNAseq cHierClus subtypes' versus 'GENDER'

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

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

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

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

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

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

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

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

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

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

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

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

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

Figure S50. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Methods & Data
Input

- Cluster data file = GBM-TP.mergedcluster.txt

- Clinical data file = GBM-TP.clin.merged.picked.txt

- Number of patients = 564

- Number of clustering approaches = 10

- Number of selected clinical features = 5

- Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches
CNMF clustering

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

Consensus hierarchical clustering

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

Survival analysis

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

ANOVA analysis

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

Fisher's exact test

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

Chi-square test

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

Q value calculation

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

Download Results

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

- 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)
[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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