Brain Lower Grade Glioma: Correlation between molecular cancer subtypes and selected clinical features
Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/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 7 clinical features across 146 patients, 16 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', 'HISTOLOGICAL.TYPE', and 'RADIATIONS.RADIATION.REGIMENINDICATION'.

- 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 3 subtypes that correlate to 'Time to Death' and 'HISTOLOGICAL.TYPE'.

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

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

- Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 4 subtypes that correlate to 'AGE', 'GENDER', 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 7 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 16 significant findings detected.

Clinical Features | Time to Death | AGE | GENDER | KARNOFSKY PERFORMANCE SCORE | HISTOLOGICAL TYPE | RADIATIONS RADIATION REGIMENINDICATION | NEOADJUVANT THERAPY
Statistical Tests | logrank test | ANOVA | Fisher's exact test | ANOVA | Chi-square test | Fisher's exact test | Fisher's exact test
mRNA CNMF subtypes | 0.0944 | 0.326 | 0.101 | 0.441 | 0.0226 | 0.384 | 0.883
mRNA cHierClus subtypes | 0.126 | 0.467 | 0.172 | 0.441 | 0.0136 | 0.757 | 0.685
CN CNMF | 0.00416 | 0.272 | 0.549 | 0.925 | 9.72e-08 | 0.00206 | 0.359
METHLYATION CNMF | 1.73e-06 | 1.38e-05 | 0.0696 | 0.845 | 3.33e-10 | 6.96e-05 | 0.459
RNAseq CNMF subtypes | 0.0355 | 0.0914 | 0.306 | 0.826 | 0.000403 | 0.201 | 0.661
RNAseq cHierClus subtypes | 0.0635 | 0.0294 | 0.214 | 0.842 | 0.000268 | 0.366 | 0.281
MIRseq CNMF subtypes | 0.195 | 0.285 | 0.172 | 0.719 | 0.469 | 0.79 | 0.102
MIRseq cHierClus subtypes | 0.112 | 0.0293 | 0.0238 | 0.729 | 0.00256 | 0.339 | 0.0789

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 = 0.0944 (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'

'mRNA CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.883 (Fisher's exact test)

Table S8. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 17 | 10
subtype1 | 5 | 4
subtype2 | 7 | 3
subtype3 | 5 | 3

Figure S7. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S9. 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 = 0.126 (logrank test)

Table S10. 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 S8. 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 S11. 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 S9. 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 S12. 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 S10. 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 S13. 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 S11. 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 S14. 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 S12. 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 S15. 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 S13. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'mRNA cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.685 (Fisher's exact test)

Table S16. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 17 | 10
subtype1 | 5 | 4
subtype2 | 4 | 3
subtype3 | 8 | 3

Figure S14. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

Clustering Approach #3: 'CN CNMF'

Table S17. Get Full Table Description of clustering approach #3: 'CN CNMF'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 49 | 42 | 54

'CN CNMF' versus 'Time to Death'

P value = 0.00416 (logrank test)

Table S18. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 145 | 48 | 0.0 - 211.2 (18.0)
subtype1 | 49 | 21 | 0.1 - 211.2 (13.3)
subtype2 | 42 | 16 | 0.4 - 156.2 (18.9)
subtype3 | 54 | 11 | 0.0 - 182.3 (24.1)

Figure S15. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

'CN CNMF' versus 'AGE'

P value = 0.272 (ANOVA)

Table S19. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 145 | 42.8 (13.0)
subtype1 | 49 | 44.1 (13.9)
subtype2 | 42 | 40.1 (12.2)
subtype3 | 54 | 43.8 (12.9)

Figure S16. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #2: 'AGE'

'CN CNMF' versus 'GENDER'

P value = 0.549 (Fisher's exact test)

Table S20. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 62 | 83
subtype1 | 24 | 25
subtype2 | 16 | 26
subtype3 | 22 | 32

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

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

P value = 0.925 (ANOVA)

Table S21. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 77 | 89.1 (10.3)
subtype1 | 27 | 89.3 (7.8)
subtype2 | 26 | 88.5 (10.8)
subtype3 | 24 | 89.6 (12.3)

Figure S18. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'CN CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 9.72e-08 (Chi-square test)

Table S22. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients | ASTROCYTOMA | OLIGOASTROCYTOMA | OLIGODENDROGLIOMA
--- | --- | --- | ---
ALL | 50 | 36 | 58
subtype1 | 21 | 18 | 9
subtype2 | 21 | 11 | 10
subtype3 | 8 | 7 | 39

Figure S19. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

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

P value = 0.00206 (Fisher's exact test)

Table S23. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 86 | 59
subtype1 | 33 | 16
subtype2 | 31 | 11
subtype3 | 22 | 32

Figure S20. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'CN CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.359 (Fisher's exact test)

Table S24. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 71 | 74
subtype1 | 20 | 29
subtype2 | 23 | 19
subtype3 | 28 | 26

Figure S21. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

Clustering Approach #4: 'METHLYATION CNMF'

Table S25. Get Full Table Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels | 1 | 2 | 3 | 4
--- | --- | --- | --- | ---
Number of samples | 63 | 23 | 16 | 38

'METHLYATION CNMF' versus 'Time to Death'

P value = 1.73e-06 (logrank test)

Table S26. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 140 | 44 | 0.0 - 211.2 (17.8)
subtype1 | 63 | 19 | 0.0 - 156.2 (23.3)
subtype2 | 23 | 15 | 0.9 - 211.2 (11.5)
subtype3 | 16 | 2 | 0.1 - 97.9 (14.3)
subtype4 | 38 | 8 | 0.1 - 182.3 (26.1)

Figure S22. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 1.38e-05 (ANOVA)

Table S27. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 140 | 42.6 (13.0)
subtype1 | 63 | 38.8 (11.4)
subtype2 | 23 | 51.7 (13.7)
subtype3 | 16 | 36.2 (13.0)
subtype4 | 38 | 46.0 (11.2)

Figure S23. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.0696 (Fisher's exact test)

Table S28. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 62 | 78
subtype1 | 22 | 41
subtype2 | 15 | 8
subtype3 | 6 | 10
subtype4 | 19 | 19

Figure S24. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

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

P value = 0.845 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 76 | 89.2 (10.3)
subtype1 | 38 | 90.0 (10.1)
subtype2 | 14 | 87.9 (5.8)
subtype3 | 6 | 86.7 (10.3)
subtype4 | 18 | 89.4 (13.5)

Figure S25. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 3.33e-10 (Chi-square test)

Table S30. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients | ASTROCYTOMA | OLIGOASTROCYTOMA | OLIGODENDROGLIOMA
--- | --- | --- | ---
ALL | 49 | 34 | 56
subtype1 | 27 | 21 | 14
subtype2 | 14 | 6 | 3
subtype3 | 6 | 5 | 5
subtype4 | 2 | 2 | 34

Figure S26. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

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

P value = 6.96e-05 (Fisher's exact test)

Table S31. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 83 | 57
subtype1 | 48 | 15
subtype2 | 16 | 7
subtype3 | 5 | 11
subtype4 | 14 | 24

Figure S27. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.459 (Fisher's exact test)

Table S32. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 70 | 70
subtype1 | 32 | 31
subtype2 | 12 | 11
subtype3 | 5 | 11
subtype4 | 21 | 17

Figure S28. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S33. Get Full Table Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 13 | 17 | 17

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.0355 (logrank test)

Table S34. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 47 | 24 | 1.2 - 211.2 (31.8)
subtype1 | 13 | 9 | 6.7 - 211.2 (16.8)
subtype2 | 17 | 8 | 6.8 - 94.5 (43.9)
subtype3 | 17 | 7 | 1.2 - 182.3 (47.9)

Figure S29. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.0914 (ANOVA)

Table S35. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 47 | 44.0 (12.3)
subtype1 | 13 | 46.4 (14.6)
subtype2 | 17 | 38.8 (7.4)
subtype3 | 17 | 47.3 (13.2)

Figure S30. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.306 (Fisher's exact test)

Table S36. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 25 | 22
subtype1 | 9 | 4
subtype2 | 7 | 10
subtype3 | 9 | 8

Figure S31. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.826 (ANOVA)

Table S37. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 27 | 89.6 (10.9)
subtype1 | 9 | 90.0 (5.0)
subtype2 | 10 | 88.0 (14.8)
subtype3 | 8 | 91.2 (11.3)

Figure S32. 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 = 0.000403 (Chi-square test)

Table S38. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients | ASTROCYTOMA | OLIGOASTROCYTOMA | OLIGODENDROGLIOMA
--- | --- | --- | ---
ALL | 15 | 14 | 18
subtype1 | 8 | 4 | 1
subtype2 | 7 | 6 | 4
subtype3 | 0 | 4 | 13

Figure S33. 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 = 0.201 (Fisher's exact test)

Table S39. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 33 | 14
subtype1 | 10 | 3
subtype2 | 14 | 3
subtype3 | 9 | 8

Figure S34. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.661 (Fisher's exact test)

Table S40. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 26 | 21
subtype1 | 7 | 6
subtype2 | 8 | 9
subtype3 | 11 | 6

Figure S35. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S41. Get Full Table Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 12 | 16 | 19

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0635 (logrank test)

Table S42. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 47 | 24 | 1.2 - 211.2 (31.8)
subtype1 | 12 | 8 | 6.7 - 211.2 (15.1)
subtype2 | 16 | 6 | 1.2 - 182.3 (43.1)
subtype3 | 19 | 10 | 6.8 - 114.0 (47.9)

Figure S36. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.0294 (ANOVA)

Table S43. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 47 | 44.0 (12.3)
subtype1 | 12 | 46.8 (15.2)
subtype2 | 16 | 48.6 (12.3)
subtype3 | 19 | 38.4 (7.8)

Figure S37. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.214 (Fisher's exact test)

Table S44. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 25 | 22
subtype1 | 9 | 3
subtype2 | 8 | 8
subtype3 | 8 | 11

Figure S38. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.842 (ANOVA)

Table S45. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 27 | 89.6 (10.9)
subtype1 | 8 | 90.0 (5.3)
subtype2 | 7 | 91.4 (12.1)
subtype3 | 12 | 88.3 (13.4)

Figure S39. 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 = 0.000268 (Chi-square test)

Table S46. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients | ASTROCYTOMA | OLIGOASTROCYTOMA | OLIGODENDROGLIOMA
--- | --- | --- | ---
ALL | 15 | 14 | 18
subtype1 | 7 | 4 | 1
subtype2 | 0 | 3 | 13
subtype3 | 8 | 7 | 4

Figure S40. 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 = 0.366 (Fisher's exact test)

Table S47. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 33 | 14
subtype1 | 9 | 3
subtype2 | 9 | 7
subtype3 | 15 | 4

Figure S41. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.281 (Fisher's exact test)

Table S48. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 26 | 21
subtype1 | 7 | 5
subtype2 | 11 | 5
subtype3 | 8 | 11

Figure S42. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

Clustering Approach #7: 'MIRseq CNMF subtypes'

Table S49. Get Full Table Description of clustering approach #7: 'MIRseq CNMF subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 22 | 25 | 20

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.195 (logrank test)

Table S50. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 67 | 27 | 0.1 - 211.2 (23.8)
subtype1 | 22 | 12 | 4.7 - 182.3 (19.4)
subtype2 | 25 | 9 | 0.1 - 134.3 (46.6)
subtype3 | 20 | 6 | 1.2 - 211.2 (16.3)

Figure S43. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.285 (ANOVA)

Table S51. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 67 | 43.1 (12.1)
subtype1 | 22 | 42.7 (11.2)
subtype2 | 25 | 40.8 (9.6)
subtype3 | 20 | 46.5 (15.2)

Figure S44. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.172 (Fisher's exact test)

Table S52. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 33 | 34
subtype1 | 14 | 8
subtype2 | 9 | 16
subtype3 | 10 | 10

Figure S45. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.719 (ANOVA)

Table S53. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 41 | 89.8 (9.6)
subtype1 | 13 | 89.2 (7.6)
subtype2 | 16 | 88.8 (12.6)
subtype3 | 12 | 91.7 (7.2)

Figure S46. 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 = 0.469 (Chi-square test)

Table S54. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients | ASTROCYTOMA | OLIGOASTROCYTOMA | OLIGODENDROGLIOMA
--- | --- | --- | ---
ALL | 24 | 20 | 23
subtype1 | 8 | 7 | 7
subtype2 | 10 | 9 | 6
subtype3 | 6 | 4 | 10

Figure S47. 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 = 0.79 (Fisher's exact test)

Table S55. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 48 | 19
subtype1 | 17 | 5
subtype2 | 17 | 8
subtype3 | 14 | 6

Figure S48. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.102 (Fisher's exact test)

Table S56. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 39 | 28
subtype1 | 9 | 13
subtype2 | 18 | 7
subtype3 | 12 | 8

Figure S49. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

Clustering Approach #8: 'MIRseq cHierClus subtypes'

Table S57. Get Full Table Description of clustering approach #8: 'MIRseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3 | 4
--- | --- | --- | --- | ---
Number of samples | 25 | 15 | 13 | 14

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.112 (logrank test)

Table S58. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 67 | 27 | 0.1 - 211.2 (23.8)
subtype1 | 25 | 9 | 0.1 - 134.3 (46.6)
subtype2 | 15 | 8 | 6.4 - 211.2 (13.4)
subtype3 | 13 | 6 | 4.7 - 114.0 (18.9)
subtype4 | 14 | 4 | 1.2 - 182.3 (24.1)

Figure S50. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.0293 (ANOVA)

Table S59. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 67 | 43.1 (12.1)
subtype1 | 25 | 40.1 (9.1)
subtype2 | 15 | 47.8 (12.4)
subtype3 | 13 | 38.0 (9.3)
subtype4 | 14 | 48.3 (15.6)

Figure S51. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.0238 (Fisher's exact test)

Table S60. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 33 | 34
subtype1 | 8 | 17
subtype2 | 11 | 4
subtype3 | 9 | 4
subtype4 | 5 | 9

Figure S52. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.729 (ANOVA)

Table S61. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 41 | 89.8 (9.6)
subtype1 | 16 | 88.8 (12.6)
subtype2 | 12 | 88.3 (5.8)
subtype3 | 8 | 92.5 (4.6)
subtype4 | 5 | 92.0 (13.0)

Figure S53. 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 = 0.00256 (Chi-square test)

Table S62. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients | ASTROCYTOMA | OLIGOASTROCYTOMA | OLIGODENDROGLIOMA
--- | --- | --- | ---
ALL | 24 | 20 | 23
subtype1 | 11 | 9 | 5
subtype2 | 9 | 4 | 2
subtype3 | 4 | 4 | 5
subtype4 | 0 | 3 | 11

Figure S54. 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 = 0.339 (Fisher's exact test)

Table S63. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 48 | 19
subtype1 | 17 | 8
subtype2 | 13 | 2
subtype3 | 10 | 3
subtype4 | 8 | 6

Figure S55. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.0789 (Fisher's exact test)

Table S64. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 39 | 28
subtype1 | 19 | 6
subtype2 | 9 | 6
subtype3 | 5 | 8
subtype4 | 6 | 8

Figure S56. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

Methods & Data
Input

- Cluster data file = LGG.mergedcluster.txt

- Clinical data file = LGG.clin.merged.picked.txt

- Number of patients = 146

- Number of clustering approaches = 8

- Number of selected clinical features = 7

- 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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