This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.
Testing the association between subtypes identified by 14 different clustering approaches and 9 clinical features across 1107 patients, 61 significant findings detected with P value < 0.05 and Q value < 0.25.
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CNMF clustering analysis on array-based mRNA expression data identified 4 subtypes that correlate to 'HISTOLOGICAL_TYPE'.
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Consensus hierarchical clustering analysis on array-based mRNA expression data identified 4 subtypes that correlate to 'Time to Death', 'YEARS_TO_BIRTH', and 'HISTOLOGICAL_TYPE'.
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CNMF clustering analysis on array-based miR expression data identified 4 subtypes that correlate to 'Time to Death'.
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Consensus hierarchical clustering analysis on array-based miR expression data identified 3 subtypes that do not correlate to any clinical features.
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3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'TUMOR_TISSUE_SITE', 'RADIATION_THERAPY', 'KARNOFSKY_PERFORMANCE_SCORE', 'HISTOLOGICAL_TYPE', 'RACE', and 'ETHNICITY'.
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4 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'TUMOR_TISSUE_SITE', 'RADIATION_THERAPY', 'KARNOFSKY_PERFORMANCE_SCORE', 'HISTOLOGICAL_TYPE', and 'RACE'.
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CNMF clustering analysis on RPPA data identified 3 subtypes that correlate to 'YEARS_TO_BIRTH', 'TUMOR_TISSUE_SITE', 'GENDER', 'RADIATION_THERAPY', and 'HISTOLOGICAL_TYPE'.
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Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that correlate to 'YEARS_TO_BIRTH', 'TUMOR_TISSUE_SITE', 'RADIATION_THERAPY', and 'HISTOLOGICAL_TYPE'.
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CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'TUMOR_TISSUE_SITE', 'RADIATION_THERAPY', 'KARNOFSKY_PERFORMANCE_SCORE', 'HISTOLOGICAL_TYPE', and 'ETHNICITY'.
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Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'TUMOR_TISSUE_SITE', 'RADIATION_THERAPY', 'KARNOFSKY_PERFORMANCE_SCORE', and 'HISTOLOGICAL_TYPE'.
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4 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'Time to Death', 'RADIATION_THERAPY', and 'HISTOLOGICAL_TYPE'.
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3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'RADIATION_THERAPY', 'KARNOFSKY_PERFORMANCE_SCORE', and 'HISTOLOGICAL_TYPE'.
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3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'Time to Death', 'GENDER', 'RADIATION_THERAPY', 'KARNOFSKY_PERFORMANCE_SCORE', 'HISTOLOGICAL_TYPE', and 'ETHNICITY'.
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4 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'RADIATION_THERAPY', 'KARNOFSKY_PERFORMANCE_SCORE', and 'HISTOLOGICAL_TYPE'.
Table 1. Get Full Table Overview of the association between subtypes identified by 14 different clustering approaches and 9 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 61 significant findings detected.
Clinical Features |
Time to Death |
YEARS TO BIRTH |
TUMOR TISSUE SITE |
GENDER |
RADIATION THERAPY |
KARNOFSKY PERFORMANCE SCORE |
HISTOLOGICAL TYPE |
RACE | ETHNICITY |
Statistical Tests | logrank test | Kruskal-Wallis (anova) | Fisher's exact test | Fisher's exact test | Fisher's exact test | Kruskal-Wallis (anova) | Fisher's exact test | Fisher's exact test | Fisher's exact test |
mRNA CNMF subtypes |
0.0522 (0.106) |
0.0788 (0.153) |
0.397 (0.511) |
0.519 (0.629) |
0.474 (0.586) |
0.00448 (0.0111) |
0.833 (0.938) |
0.123 (0.222) |
|
mRNA cHierClus subtypes |
0.00017 (0.000562) |
0.00252 (0.00675) |
0.092 (0.175) |
0.227 (0.349) |
0.515 (0.629) |
0.0295 (0.0677) |
0.589 (0.7) |
0.684 (0.788) |
|
miR CNMF subtypes |
0.00335 (0.00845) |
0.38 (0.494) |
0.651 (0.767) |
0.351 (0.481) |
0.861 (0.948) |
0.363 (0.482) |
0.203 (0.324) |
0.855 (0.948) |
|
miR cHierClus subtypes |
0.194 (0.317) |
0.127 (0.224) |
0.154 (0.261) |
0.463 (0.578) |
0.278 (0.398) |
0.284 (0.398) |
0.193 (0.317) |
0.676 (0.788) |
|
Copy Number Ratio CNMF subtypes |
0 (0) |
4.25e-77 (8.93e-76) |
1e-05 (3.94e-05) |
0.222 (0.345) |
1e-05 (3.94e-05) |
3.28e-20 (4.13e-19) |
1e-05 (3.94e-05) |
0.0348 (0.0769) |
0.00083 (0.00238) |
METHLYATION CNMF |
0 (0) |
1.5e-46 (2.69e-45) |
1e-05 (3.94e-05) |
0.898 (0.975) |
1e-05 (3.94e-05) |
5.93e-11 (4.98e-10) |
1e-05 (3.94e-05) |
0.0002 (0.000646) |
0.128 (0.224) |
RPPA CNMF subtypes |
0.151 (0.261) |
0.041 (0.0861) |
0.00239 (0.00655) |
0.0357 (0.0777) |
0.0081 (0.0196) |
0.278 (0.398) |
0.00308 (0.00792) |
0.284 (0.398) |
0.745 (0.846) |
RPPA cHierClus subtypes |
0.0528 (0.106) |
0.00266 (0.00699) |
0.0008 (0.00234) |
0.202 (0.324) |
0.0114 (0.027) |
0.104 (0.19) |
3e-05 (0.000111) |
0.357 (0.482) |
0.942 (1.00) |
RNAseq CNMF subtypes |
0 (0) |
3.33e-33 (4.67e-32) |
1e-05 (3.94e-05) |
0.294 (0.408) |
1e-05 (3.94e-05) |
1.49e-16 (1.7e-15) |
1e-05 (3.94e-05) |
0.38 (0.494) |
0.0431 (0.089) |
RNAseq cHierClus subtypes |
0 (0) |
4.19e-44 (6.6e-43) |
1e-05 (3.94e-05) |
0.362 (0.482) |
1e-05 (3.94e-05) |
5.38e-15 (5.65e-14) |
1e-05 (3.94e-05) |
0.176 (0.295) |
0.242 (0.362) |
MIRSEQ CNMF |
0.0123 (0.0287) |
0.917 (0.987) |
0.688 (0.788) |
0.00231 (0.00647) |
0.0929 (0.175) |
1e-05 (3.94e-05) |
0.273 (0.398) |
0.437 (0.55) |
|
MIRSEQ CHIERARCHICAL |
6.06e-14 (5.88e-13) |
1.78e-08 (1.4e-07) |
0.283 (0.398) |
1e-05 (3.94e-05) |
0.000222 (7e-04) |
1e-05 (3.94e-05) |
0.561 (0.674) |
0.231 (0.351) |
|
MIRseq Mature CNMF subtypes |
0.000268 (0.000824) |
0.0547 (0.108) |
0.0323 (0.0726) |
5e-05 (0.00018) |
0.0393 (0.0838) |
3e-05 (0.000111) |
0.865 (0.948) |
0.00072 (0.00216) |
|
MIRseq Mature cHierClus subtypes |
0 (0) |
1.6e-12 (1.44e-11) |
0.097 (0.18) |
0.00015 (0.000511) |
5.43e-05 (0.00019) |
1e-05 (3.94e-05) |
0.205 (0.324) |
0.432 (0.55) |
Table S1. Description of clustering approach #1: 'mRNA CNMF subtypes'
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 154 | 97 | 156 | 118 |
P value = 0.0522 (logrank test), Q value = 0.11
Table S2. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 525 | 447 | 0.1 - 127.6 (12.2) |
subtype1 | 154 | 138 | 0.1 - 127.6 (10.6) |
subtype2 | 97 | 81 | 0.2 - 115.9 (14.5) |
subtype3 | 156 | 128 | 0.1 - 94.8 (13.8) |
subtype4 | 118 | 100 | 0.2 - 91.8 (12.6) |
Figure S1. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D1V1.png)
P value = 0.0788 (Kruskal-Wallis (anova)), Q value = 0.15
Table S3. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 525 | 57.7 (14.6) |
subtype1 | 154 | 58.4 (12.4) |
subtype2 | 97 | 54.2 (17.4) |
subtype3 | 156 | 60.0 (13.3) |
subtype4 | 118 | 56.5 (15.7) |
Figure S2. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
![](D1V2.png)
P value = 0.397 (Fisher's exact test), Q value = 0.51
Table S4. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 205 | 320 |
subtype1 | 59 | 95 |
subtype2 | 41 | 56 |
subtype3 | 66 | 90 |
subtype4 | 39 | 79 |
Figure S3. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'GENDER'
![](D1V4.png)
P value = 0.519 (Fisher's exact test), Q value = 0.63
Table S5. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 70 | 435 |
subtype1 | 21 | 127 |
subtype2 | 14 | 79 |
subtype3 | 16 | 134 |
subtype4 | 19 | 95 |
Figure S4. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'
![](D1V5.png)
P value = 0.474 (Kruskal-Wallis (anova)), Q value = 0.59
Table S6. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 396 | 77.1 (14.6) |
subtype1 | 115 | 77.6 (15.8) |
subtype2 | 76 | 76.6 (11.0) |
subtype3 | 120 | 76.1 (15.4) |
subtype4 | 85 | 78.6 (14.7) |
Figure S5. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'
![](D1V6.png)
P value = 0.00448 (Fisher's exact test), Q value = 0.011
Table S7. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'
nPatients | GLIOBLASTOMA MULTIFORME (GBM) | TREATED PRIMARY GBM | UNTREATED PRIMARY (DE NOVO) GBM |
---|---|---|---|
ALL | 6 | 20 | 499 |
subtype1 | 1 | 8 | 145 |
subtype2 | 0 | 7 | 90 |
subtype3 | 0 | 4 | 152 |
subtype4 | 5 | 1 | 112 |
Figure S6. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'
![](D1V7.png)
P value = 0.833 (Fisher's exact test), Q value = 0.94
Table S8. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'RACE'
nPatients | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE |
---|---|---|---|
ALL | 13 | 31 | 462 |
subtype1 | 4 | 12 | 135 |
subtype2 | 2 | 7 | 84 |
subtype3 | 5 | 7 | 139 |
subtype4 | 2 | 5 | 104 |
Figure S7. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'RACE'
![](D1V8.png)
P value = 0.123 (Fisher's exact test), Q value = 0.22
Table S9. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #9: 'ETHNICITY'
nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO |
---|---|---|
ALL | 12 | 438 |
subtype1 | 3 | 131 |
subtype2 | 5 | 68 |
subtype3 | 3 | 135 |
subtype4 | 1 | 104 |
Figure S8. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #9: 'ETHNICITY'
![](D1V9.png)
Table S10. Description of clustering approach #2: 'mRNA cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 153 | 107 | 103 | 162 |
P value = 0.00017 (logrank test), Q value = 0.00056
Table S11. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 525 | 447 | 0.1 - 127.6 (12.2) |
subtype1 | 153 | 138 | 0.1 - 91.0 (12.2) |
subtype2 | 107 | 80 | 0.2 - 115.9 (14.7) |
subtype3 | 103 | 87 | 0.1 - 94.8 (13.8) |
subtype4 | 162 | 142 | 0.1 - 127.6 (10.4) |
Figure S9. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
![](D2V1.png)
P value = 0.00252 (Kruskal-Wallis (anova)), Q value = 0.0068
Table S12. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 525 | 57.7 (14.6) |
subtype1 | 153 | 56.9 (13.6) |
subtype2 | 107 | 52.9 (17.9) |
subtype3 | 103 | 60.6 (12.1) |
subtype4 | 162 | 59.8 (13.7) |
Figure S10. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
![](D2V2.png)
P value = 0.092 (Fisher's exact test), Q value = 0.17
Table S13. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 205 | 320 |
subtype1 | 51 | 102 |
subtype2 | 44 | 63 |
subtype3 | 50 | 53 |
subtype4 | 60 | 102 |
Figure S11. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'GENDER'
![](D2V4.png)
P value = 0.227 (Fisher's exact test), Q value = 0.35
Table S14. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 70 | 435 |
subtype1 | 17 | 131 |
subtype2 | 13 | 91 |
subtype3 | 11 | 88 |
subtype4 | 29 | 125 |
Figure S12. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'
![](D2V5.png)
P value = 0.515 (Kruskal-Wallis (anova)), Q value = 0.63
Table S15. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 396 | 77.1 (14.6) |
subtype1 | 118 | 78.1 (15.4) |
subtype2 | 84 | 77.6 (11.6) |
subtype3 | 81 | 75.2 (15.6) |
subtype4 | 113 | 77.3 (15.1) |
Figure S13. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'
![](D2V6.png)
P value = 0.0295 (Fisher's exact test), Q value = 0.068
Table S16. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'
nPatients | GLIOBLASTOMA MULTIFORME (GBM) | TREATED PRIMARY GBM | UNTREATED PRIMARY (DE NOVO) GBM |
---|---|---|---|
ALL | 6 | 20 | 499 |
subtype1 | 0 | 11 | 142 |
subtype2 | 1 | 3 | 103 |
subtype3 | 0 | 3 | 100 |
subtype4 | 5 | 3 | 154 |
Figure S14. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'
![](D2V7.png)
P value = 0.589 (Fisher's exact test), Q value = 0.7
Table S17. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'RACE'
nPatients | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE |
---|---|---|---|
ALL | 13 | 31 | 462 |
subtype1 | 4 | 13 | 130 |
subtype2 | 4 | 6 | 95 |
subtype3 | 3 | 4 | 93 |
subtype4 | 2 | 8 | 144 |
Figure S15. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'RACE'
![](D2V8.png)
P value = 0.684 (Fisher's exact test), Q value = 0.79
Table S18. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'ETHNICITY'
nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO |
---|---|---|
ALL | 12 | 438 |
subtype1 | 5 | 124 |
subtype2 | 1 | 91 |
subtype3 | 2 | 90 |
subtype4 | 4 | 133 |
Figure S16. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'ETHNICITY'
![](D2V9.png)
Table S19. Description of clustering approach #3: 'miR CNMF subtypes'
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 165 | 182 | 90 | 124 |
P value = 0.00335 (logrank test), Q value = 0.0084
Table S20. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 561 | 466 | 0.1 - 127.6 (12.2) |
subtype1 | 165 | 141 | 0.1 - 91.0 (11.3) |
subtype2 | 182 | 147 | 0.1 - 127.6 (13.7) |
subtype3 | 90 | 74 | 0.4 - 65.3 (9.3) |
subtype4 | 124 | 104 | 0.1 - 120.6 (12.8) |
Figure S17. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D3V1.png)
P value = 0.38 (Kruskal-Wallis (anova)), Q value = 0.49
Table S21. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 561 | 57.9 (14.3) |
subtype1 | 165 | 59.8 (11.6) |
subtype2 | 182 | 55.7 (16.6) |
subtype3 | 90 | 58.5 (14.6) |
subtype4 | 124 | 58.1 (13.6) |
Figure S18. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
![](D3V2.png)
P value = 0.651 (Fisher's exact test), Q value = 0.77
Table S22. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #4: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 218 | 343 |
subtype1 | 63 | 102 |
subtype2 | 76 | 106 |
subtype3 | 36 | 54 |
subtype4 | 43 | 81 |
Figure S19. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #4: 'GENDER'
![](D3V4.png)
P value = 0.351 (Fisher's exact test), Q value = 0.48
Table S23. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 73 | 465 |
subtype1 | 19 | 141 |
subtype2 | 21 | 153 |
subtype3 | 17 | 70 |
subtype4 | 16 | 101 |
Figure S20. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'
![](D3V5.png)
P value = 0.861 (Kruskal-Wallis (anova)), Q value = 0.95
Table S24. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 428 | 77.4 (14.7) |
subtype1 | 129 | 76.1 (15.5) |
subtype2 | 132 | 77.4 (14.8) |
subtype3 | 74 | 78.2 (13.5) |
subtype4 | 93 | 78.3 (14.6) |
Figure S21. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'
![](D3V6.png)
P value = 0.363 (Fisher's exact test), Q value = 0.48
Table S25. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'
nPatients | GLIOBLASTOMA MULTIFORME (GBM) | TREATED PRIMARY GBM | UNTREATED PRIMARY (DE NOVO) GBM |
---|---|---|---|
ALL | 9 | 20 | 532 |
subtype1 | 2 | 4 | 159 |
subtype2 | 1 | 8 | 173 |
subtype3 | 3 | 5 | 82 |
subtype4 | 3 | 3 | 118 |
Figure S22. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'
![](D3V7.png)
P value = 0.203 (Fisher's exact test), Q value = 0.32
Table S26. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #8: 'RACE'
nPatients | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE |
---|---|---|---|
ALL | 13 | 30 | 493 |
subtype1 | 3 | 13 | 140 |
subtype2 | 7 | 9 | 157 |
subtype3 | 1 | 1 | 85 |
subtype4 | 2 | 7 | 111 |
Figure S23. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #8: 'RACE'
![](D3V8.png)
P value = 0.855 (Fisher's exact test), Q value = 0.95
Table S27. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #9: 'ETHNICITY'
nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO |
---|---|---|
ALL | 12 | 459 |
subtype1 | 5 | 134 |
subtype2 | 4 | 153 |
subtype3 | 1 | 71 |
subtype4 | 2 | 101 |
Figure S24. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #9: 'ETHNICITY'
![](D3V9.png)
Table S28. Description of clustering approach #4: 'miR cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 302 | 129 | 130 |
P value = 0.194 (logrank test), Q value = 0.32
Table S29. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 561 | 466 | 0.1 - 127.6 (12.2) |
subtype1 | 302 | 252 | 0.1 - 120.6 (12.6) |
subtype2 | 129 | 107 | 0.1 - 92.7 (10.8) |
subtype3 | 130 | 107 | 0.1 - 127.6 (11.8) |
Figure S25. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
![](D4V1.png)
P value = 0.127 (Kruskal-Wallis (anova)), Q value = 0.22
Table S30. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 561 | 57.9 (14.3) |
subtype1 | 302 | 56.5 (15.9) |
subtype2 | 129 | 59.0 (11.5) |
subtype3 | 130 | 60.0 (12.5) |
Figure S26. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
![](D4V2.png)
P value = 0.154 (Fisher's exact test), Q value = 0.26
Table S31. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #4: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 218 | 343 |
subtype1 | 110 | 192 |
subtype2 | 48 | 81 |
subtype3 | 60 | 70 |
Figure S27. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #4: 'GENDER'
![](D4V4.png)
P value = 0.463 (Fisher's exact test), Q value = 0.58
Table S32. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 73 | 465 |
subtype1 | 44 | 245 |
subtype2 | 13 | 109 |
subtype3 | 16 | 111 |
Figure S28. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'
![](D4V5.png)
P value = 0.278 (Kruskal-Wallis (anova)), Q value = 0.4
Table S33. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 428 | 77.4 (14.7) |
subtype1 | 227 | 78.5 (14.0) |
subtype2 | 101 | 76.5 (15.6) |
subtype3 | 100 | 75.5 (15.4) |
Figure S29. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'
![](D4V6.png)
P value = 0.284 (Fisher's exact test), Q value = 0.4
Table S34. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'
nPatients | GLIOBLASTOMA MULTIFORME (GBM) | TREATED PRIMARY GBM | UNTREATED PRIMARY (DE NOVO) GBM |
---|---|---|---|
ALL | 9 | 20 | 532 |
subtype1 | 6 | 13 | 283 |
subtype2 | 3 | 2 | 124 |
subtype3 | 0 | 5 | 125 |
Figure S30. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'
![](D4V7.png)
P value = 0.193 (Fisher's exact test), Q value = 0.32
Table S35. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #8: 'RACE'
nPatients | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE |
---|---|---|---|
ALL | 13 | 30 | 493 |
subtype1 | 7 | 10 | 272 |
subtype2 | 3 | 11 | 113 |
subtype3 | 3 | 9 | 108 |
Figure S31. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #8: 'RACE'
![](D4V8.png)
P value = 0.676 (Fisher's exact test), Q value = 0.79
Table S36. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #9: 'ETHNICITY'
nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO |
---|---|---|
ALL | 12 | 459 |
subtype1 | 6 | 249 |
subtype2 | 2 | 109 |
subtype3 | 4 | 101 |
Figure S32. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #9: 'ETHNICITY'
![](D4V9.png)
Table S37. Description of clustering approach #5: 'Copy Number Ratio CNMF subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 529 | 539 | 15 |
P value = 0 (logrank test), Q value = 0
Table S38. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 1079 | 591 | 0.0 - 211.2 (15.7) |
subtype1 | 528 | 432 | 0.1 - 127.6 (12.1) |
subtype2 | 536 | 148 | 0.0 - 211.2 (21.4) |
subtype3 | 15 | 11 | 1.8 - 92.7 (14.9) |
Figure S33. Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D5V1.png)
P value = 4.25e-77 (Kruskal-Wallis (anova)), Q value = 8.9e-76
Table S39. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 1082 | 50.8 (15.8) |
subtype1 | 529 | 59.7 (11.9) |
subtype2 | 538 | 41.9 (14.0) |
subtype3 | 15 | 54.1 (15.7) |
Figure S34. Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
![](D5V2.png)
P value = 1e-05 (Fisher's exact test), Q value = 3.9e-05
Table S40. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'
nPatients | BRAIN | CENTRAL NERVOUS SYSTEM |
---|---|---|
ALL | 571 | 512 |
subtype1 | 466 | 63 |
subtype2 | 92 | 447 |
subtype3 | 13 | 2 |
Figure S35. Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'
![](D5V3.png)
P value = 0.222 (Fisher's exact test), Q value = 0.35
Table S41. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 452 | 631 |
subtype1 | 207 | 322 |
subtype2 | 239 | 300 |
subtype3 | 6 | 9 |
Figure S36. Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'GENDER'
![](D5V4.png)
P value = 1e-05 (Fisher's exact test), Q value = 3.9e-05
Table S42. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 258 | 767 |
subtype1 | 70 | 433 |
subtype2 | 186 | 321 |
subtype3 | 2 | 13 |
Figure S37. Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'
![](D5V5.png)
P value = 3.28e-20 (Kruskal-Wallis (anova)), Q value = 4.1e-19
Table S43. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 730 | 81.3 (14.7) |
subtype1 | 385 | 76.7 (15.4) |
subtype2 | 334 | 86.5 (11.9) |
subtype3 | 11 | 82.7 (14.2) |
Figure S38. Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'
![](D5V6.png)
P value = 1e-05 (Fisher's exact test), Q value = 3.9e-05
Table S44. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'
nPatients | ASTROCYTOMA | GLIOBLASTOMA MULTIFORME (GBM) | OLIGOASTROCYTOMA | OLIGODENDROGLIOMA | TREATED PRIMARY GBM | UNTREATED PRIMARY (DE NOVO) GBM |
---|---|---|---|---|---|---|
ALL | 194 | 28 | 129 | 189 | 18 | 525 |
subtype1 | 39 | 19 | 12 | 12 | 10 | 437 |
subtype2 | 153 | 9 | 117 | 177 | 7 | 76 |
subtype3 | 2 | 0 | 0 | 0 | 1 | 12 |
Figure S39. Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'
![](D5V7.png)
P value = 0.0348 (Fisher's exact test), Q value = 0.077
Table S45. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'RACE'
nPatients | AMERICAN INDIAN OR ALASKA NATIVE | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE |
---|---|---|---|---|
ALL | 1 | 21 | 71 | 957 |
subtype1 | 1 | 8 | 47 | 451 |
subtype2 | 0 | 13 | 24 | 491 |
subtype3 | 0 | 0 | 0 | 15 |
Figure S40. Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'RACE'
![](D5V8.png)
P value = 0.00083 (Fisher's exact test), Q value = 0.0024
Table S46. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'ETHNICITY'
nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO |
---|---|---|
ALL | 44 | 919 |
subtype1 | 9 | 440 |
subtype2 | 35 | 466 |
subtype3 | 0 | 13 |
Figure S41. Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'ETHNICITY'
![](D5V9.png)
Table S47. Description of clustering approach #6: 'METHLYATION CNMF'
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 184 | 237 | 64 | 166 |
P value = 0 (logrank test), Q value = 0
Table S48. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 647 | 211 | 0.0 - 211.2 (18.5) |
subtype1 | 183 | 119 | 0.2 - 211.2 (11.6) |
subtype2 | 236 | 48 | 0.0 - 172.8 (24.6) |
subtype3 | 64 | 24 | 0.1 - 146.1 (18.7) |
subtype4 | 164 | 20 | 0.1 - 182.3 (21.8) |
Figure S42. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'
![](D6V1.png)
P value = 1.5e-46 (Kruskal-Wallis (anova)), Q value = 2.7e-45
Table S49. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 650 | 46.4 (14.9) |
subtype1 | 184 | 59.3 (11.5) |
subtype2 | 237 | 37.9 (11.0) |
subtype3 | 64 | 44.2 (16.8) |
subtype4 | 165 | 45.2 (12.4) |
Figure S43. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
![](D6V2.png)
P value = 1e-05 (Fisher's exact test), Q value = 3.9e-05
Table S50. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'
nPatients | BRAIN | CENTRAL NERVOUS SYSTEM |
---|---|---|
ALL | 136 | 515 |
subtype1 | 118 | 66 |
subtype2 | 8 | 229 |
subtype3 | 10 | 54 |
subtype4 | 0 | 166 |
Figure S44. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'
![](D6V3.png)
P value = 0.898 (Fisher's exact test), Q value = 0.97
Table S51. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #4: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 287 | 364 |
subtype1 | 79 | 105 |
subtype2 | 104 | 133 |
subtype3 | 31 | 33 |
subtype4 | 73 | 93 |
Figure S45. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #4: 'GENDER'
![](D6V4.png)
P value = 1e-05 (Fisher's exact test), Q value = 3.9e-05
Table S52. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #5: 'RADIATION_THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 204 | 401 |
subtype1 | 25 | 143 |
subtype2 | 66 | 160 |
subtype3 | 25 | 32 |
subtype4 | 88 | 66 |
Figure S46. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #5: 'RADIATION_THERAPY'
![](D6V5.png)
P value = 5.93e-11 (Kruskal-Wallis (anova)), Q value = 5e-10
Table S53. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 402 | 84.5 (13.8) |
subtype1 | 123 | 77.9 (15.1) |
subtype2 | 151 | 87.7 (11.8) |
subtype3 | 33 | 82.4 (13.5) |
subtype4 | 95 | 88.6 (11.8) |
Figure S47. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'
![](D6V6.png)
P value = 1e-05 (Fisher's exact test), Q value = 3.9e-05
Table S54. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'
nPatients | ASTROCYTOMA | GLIOBLASTOMA MULTIFORME (GBM) | OLIGOASTROCYTOMA | OLIGODENDROGLIOMA | TREATED PRIMARY GBM | UNTREATED PRIMARY (DE NOVO) GBM |
---|---|---|---|---|---|---|
ALL | 194 | 19 | 130 | 191 | 1 | 116 |
subtype1 | 42 | 14 | 10 | 14 | 1 | 103 |
subtype2 | 125 | 3 | 70 | 34 | 0 | 5 |
subtype3 | 21 | 2 | 15 | 18 | 0 | 8 |
subtype4 | 6 | 0 | 35 | 125 | 0 | 0 |
Figure S48. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'
![](D6V7.png)
P value = 2e-04 (Fisher's exact test), Q value = 0.00065
Table S55. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #8: 'RACE'
nPatients | AMERICAN INDIAN OR ALASKA NATIVE | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE |
---|---|---|---|---|
ALL | 1 | 8 | 45 | 580 |
subtype1 | 0 | 2 | 25 | 150 |
subtype2 | 0 | 2 | 8 | 225 |
subtype3 | 1 | 0 | 6 | 55 |
subtype4 | 0 | 4 | 6 | 150 |
Figure S49. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #8: 'RACE'
![](D6V8.png)
P value = 0.128 (Fisher's exact test), Q value = 0.22
Table S56. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #9: 'ETHNICITY'
nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO |
---|---|---|
ALL | 33 | 542 |
subtype1 | 3 | 138 |
subtype2 | 14 | 207 |
subtype3 | 4 | 53 |
subtype4 | 12 | 144 |
Figure S50. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #9: 'ETHNICITY'
![](D6V9.png)
Table S57. Description of clustering approach #7: 'RPPA CNMF subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 229 | 207 | 222 |
P value = 0.151 (logrank test), Q value = 0.26
Table S58. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 654 | 265 | 0.0 - 211.2 (16.4) |
subtype1 | 229 | 91 | 0.0 - 211.2 (15.5) |
subtype2 | 204 | 66 | 0.1 - 154.4 (15.2) |
subtype3 | 221 | 108 | 0.1 - 182.3 (17.8) |
Figure S51. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D7V1.png)
P value = 0.041 (Kruskal-Wallis (anova)), Q value = 0.086
Table S59. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 657 | 48.6 (15.8) |
subtype1 | 229 | 49.2 (15.5) |
subtype2 | 207 | 46.4 (16.0) |
subtype3 | 221 | 49.9 (15.8) |
Figure S52. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
![](D7V2.png)
P value = 0.00239 (Fisher's exact test), Q value = 0.0065
Table S60. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'
nPatients | BRAIN | CENTRAL NERVOUS SYSTEM |
---|---|---|
ALL | 230 | 428 |
subtype1 | 91 | 138 |
subtype2 | 53 | 154 |
subtype3 | 86 | 136 |
Figure S53. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'
![](D7V3.png)
P value = 0.0357 (Fisher's exact test), Q value = 0.078
Table S61. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 282 | 376 |
subtype1 | 84 | 145 |
subtype2 | 90 | 117 |
subtype3 | 108 | 114 |
Figure S54. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'GENDER'
![](D7V4.png)
P value = 0.0081 (Fisher's exact test), Q value = 0.02
Table S62. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 181 | 428 |
subtype1 | 59 | 149 |
subtype2 | 73 | 121 |
subtype3 | 49 | 158 |
Figure S55. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'
![](D7V5.png)
P value = 0.278 (Kruskal-Wallis (anova)), Q value = 0.4
Table S63. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 415 | 81.3 (15.0) |
subtype1 | 144 | 81.5 (15.6) |
subtype2 | 123 | 82.5 (14.8) |
subtype3 | 148 | 80.2 (14.6) |
Figure S56. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'
![](D7V6.png)
P value = 0.00308 (Fisher's exact test), Q value = 0.0079
Table S64. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'
nPatients | ASTROCYTOMA | GLIOBLASTOMA MULTIFORME (GBM) | OLIGOASTROCYTOMA | OLIGODENDROGLIOMA | TREATED PRIMARY GBM | UNTREATED PRIMARY (DE NOVO) GBM |
---|---|---|---|---|---|---|
ALL | 147 | 17 | 114 | 167 | 3 | 210 |
subtype1 | 51 | 5 | 36 | 51 | 2 | 84 |
subtype2 | 38 | 4 | 47 | 69 | 0 | 49 |
subtype3 | 58 | 8 | 31 | 47 | 1 | 77 |
Figure S57. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'
![](D7V7.png)
P value = 0.284 (Fisher's exact test), Q value = 0.4
Table S65. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'RACE'
nPatients | AMERICAN INDIAN OR ALASKA NATIVE | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE |
---|---|---|---|---|
ALL | 1 | 12 | 45 | 576 |
subtype1 | 0 | 3 | 18 | 202 |
subtype2 | 1 | 3 | 9 | 189 |
subtype3 | 0 | 6 | 18 | 185 |
Figure S58. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'RACE'
![](D7V8.png)
P value = 0.745 (Fisher's exact test), Q value = 0.85
Table S66. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'ETHNICITY'
nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO |
---|---|---|
ALL | 29 | 571 |
subtype1 | 12 | 198 |
subtype2 | 8 | 187 |
subtype3 | 9 | 186 |
Figure S59. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'ETHNICITY'
![](D7V9.png)
Table S67. Description of clustering approach #8: 'RPPA cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 239 | 256 | 163 |
P value = 0.0528 (logrank test), Q value = 0.11
Table S68. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 654 | 265 | 0.0 - 211.2 (16.4) |
subtype1 | 238 | 116 | 0.1 - 211.2 (15.9) |
subtype2 | 256 | 98 | 0.0 - 182.3 (17.0) |
subtype3 | 160 | 51 | 0.1 - 154.4 (16.1) |
Figure S60. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
![](D8V1.png)
P value = 0.00266 (Kruskal-Wallis (anova)), Q value = 0.007
Table S69. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 657 | 48.6 (15.8) |
subtype1 | 239 | 49.9 (15.9) |
subtype2 | 255 | 49.7 (15.7) |
subtype3 | 163 | 44.8 (15.3) |
Figure S61. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
![](D8V2.png)
P value = 8e-04 (Fisher's exact test), Q value = 0.0023
Table S70. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'
nPatients | BRAIN | CENTRAL NERVOUS SYSTEM |
---|---|---|
ALL | 230 | 428 |
subtype1 | 88 | 151 |
subtype2 | 104 | 152 |
subtype3 | 38 | 125 |
Figure S62. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'
![](D8V3.png)
P value = 0.202 (Fisher's exact test), Q value = 0.32
Table S71. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 282 | 376 |
subtype1 | 104 | 135 |
subtype2 | 100 | 156 |
subtype3 | 78 | 85 |
Figure S63. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'GENDER'
![](D8V4.png)
P value = 0.0114 (Fisher's exact test), Q value = 0.027
Table S72. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 181 | 428 |
subtype1 | 56 | 167 |
subtype2 | 66 | 170 |
subtype3 | 59 | 91 |
Figure S64. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'
![](D8V5.png)
P value = 0.104 (Kruskal-Wallis (anova)), Q value = 0.19
Table S73. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 415 | 81.3 (15.0) |
subtype1 | 146 | 79.2 (16.7) |
subtype2 | 171 | 81.9 (13.8) |
subtype3 | 98 | 83.7 (14.0) |
Figure S65. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'
![](D8V6.png)
P value = 3e-05 (Fisher's exact test), Q value = 0.00011
Table S74. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'
nPatients | ASTROCYTOMA | GLIOBLASTOMA MULTIFORME (GBM) | OLIGOASTROCYTOMA | OLIGODENDROGLIOMA | TREATED PRIMARY GBM | UNTREATED PRIMARY (DE NOVO) GBM |
---|---|---|---|---|---|---|
ALL | 147 | 17 | 114 | 167 | 3 | 210 |
subtype1 | 61 | 6 | 33 | 57 | 2 | 80 |
subtype2 | 61 | 10 | 43 | 48 | 1 | 93 |
subtype3 | 25 | 1 | 38 | 62 | 0 | 37 |
Figure S66. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'
![](D8V7.png)
P value = 0.357 (Fisher's exact test), Q value = 0.48
Table S75. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'RACE'
nPatients | AMERICAN INDIAN OR ALASKA NATIVE | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE |
---|---|---|---|---|
ALL | 1 | 12 | 45 | 576 |
subtype1 | 0 | 4 | 19 | 206 |
subtype2 | 0 | 5 | 20 | 222 |
subtype3 | 1 | 3 | 6 | 148 |
Figure S67. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'RACE'
![](D8V8.png)
P value = 0.942 (Fisher's exact test), Q value = 1
Table S76. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'ETHNICITY'
nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO |
---|---|---|
ALL | 29 | 571 |
subtype1 | 10 | 208 |
subtype2 | 11 | 220 |
subtype3 | 8 | 143 |
Figure S68. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'ETHNICITY'
![](D8V9.png)
Table S77. Description of clustering approach #9: 'RNAseq CNMF subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 239 | 235 | 193 |
P value = 0 (logrank test), Q value = 0
Table S78. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 664 | 239 | 0.0 - 211.2 (18.0) |
subtype1 | 238 | 161 | 0.1 - 211.2 (12.6) |
subtype2 | 234 | 39 | 0.0 - 182.3 (24.3) |
subtype3 | 192 | 39 | 0.1 - 172.8 (21.8) |
Figure S69. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D9V1.png)
P value = 3.33e-33 (Kruskal-Wallis (anova)), Q value = 4.7e-32
Table S79. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 666 | 46.8 (15.1) |
subtype1 | 239 | 56.2 (14.3) |
subtype2 | 235 | 39.6 (11.5) |
subtype3 | 192 | 43.8 (14.1) |
Figure S70. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
![](D9V2.png)
P value = 1e-05 (Fisher's exact test), Q value = 3.9e-05
Table S80. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'
nPatients | BRAIN | CENTRAL NERVOUS SYSTEM |
---|---|---|
ALL | 152 | 515 |
subtype1 | 147 | 92 |
subtype2 | 2 | 233 |
subtype3 | 3 | 190 |
Figure S71. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'
![](D9V3.png)
P value = 0.294 (Fisher's exact test), Q value = 0.41
Table S81. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 283 | 384 |
subtype1 | 97 | 142 |
subtype2 | 95 | 140 |
subtype3 | 91 | 102 |
Figure S72. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'GENDER'
![](D9V4.png)
P value = 1e-05 (Fisher's exact test), Q value = 3.9e-05
Table S82. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 209 | 415 |
subtype1 | 37 | 185 |
subtype2 | 93 | 130 |
subtype3 | 79 | 100 |
Figure S73. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'
![](D9V5.png)
P value = 1.49e-16 (Kruskal-Wallis (anova)), Q value = 1.7e-15
Table S83. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 424 | 83.6 (14.1) |
subtype1 | 172 | 77.2 (14.7) |
subtype2 | 143 | 89.3 (11.0) |
subtype3 | 109 | 86.3 (12.5) |
Figure S74. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'
![](D9V6.png)
P value = 1e-05 (Fisher's exact test), Q value = 3.9e-05
Table S84. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'
nPatients | ASTROCYTOMA | GLIOBLASTOMA MULTIFORME (GBM) | OLIGOASTROCYTOMA | OLIGODENDROGLIOMA | TREATED PRIMARY GBM | UNTREATED PRIMARY (DE NOVO) GBM |
---|---|---|---|---|---|---|
ALL | 194 | 1 | 130 | 191 | 1 | 150 |
subtype1 | 64 | 0 | 14 | 14 | 1 | 146 |
subtype2 | 83 | 1 | 71 | 79 | 0 | 1 |
subtype3 | 47 | 0 | 45 | 98 | 0 | 3 |
Figure S75. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'
![](D9V7.png)
P value = 0.38 (Fisher's exact test), Q value = 0.49
Table S85. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'RACE'
nPatients | AMERICAN INDIAN OR ALASKA NATIVE | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE |
---|---|---|---|---|
ALL | 1 | 13 | 31 | 611 |
subtype1 | 1 | 6 | 16 | 214 |
subtype2 | 0 | 4 | 7 | 220 |
subtype3 | 0 | 3 | 8 | 177 |
Figure S76. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'RACE'
![](D9V8.png)
P value = 0.0431 (Fisher's exact test), Q value = 0.089
Table S86. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'ETHNICITY'
nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO |
---|---|---|
ALL | 35 | 574 |
subtype1 | 6 | 205 |
subtype2 | 18 | 198 |
subtype3 | 11 | 171 |
Figure S77. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'ETHNICITY'
![](D9V9.png)
Table S87. Description of clustering approach #10: 'RNAseq cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 208 | 190 | 269 |
P value = 0 (logrank test), Q value = 0
Table S88. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 664 | 239 | 0.0 - 211.2 (18.0) |
subtype1 | 208 | 151 | 0.1 - 133.7 (11.5) |
subtype2 | 189 | 39 | 0.0 - 211.2 (25.0) |
subtype3 | 267 | 49 | 0.1 - 182.3 (21.4) |
Figure S78. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
![](D10V1.png)
P value = 4.19e-44 (Kruskal-Wallis (anova)), Q value = 6.6e-43
Table S89. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 666 | 46.8 (15.1) |
subtype1 | 208 | 58.7 (12.9) |
subtype2 | 190 | 38.3 (11.1) |
subtype3 | 268 | 43.5 (13.5) |
Figure S79. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
![](D10V2.png)
P value = 1e-05 (Fisher's exact test), Q value = 3.9e-05
Table S90. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'
nPatients | BRAIN | CENTRAL NERVOUS SYSTEM |
---|---|---|
ALL | 152 | 515 |
subtype1 | 139 | 69 |
subtype2 | 9 | 181 |
subtype3 | 4 | 265 |
Figure S80. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'
![](D10V3.png)
P value = 0.362 (Fisher's exact test), Q value = 0.48
Table S91. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 283 | 384 |
subtype1 | 84 | 124 |
subtype2 | 76 | 114 |
subtype3 | 123 | 146 |
Figure S81. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'GENDER'
![](D10V4.png)
P value = 1e-05 (Fisher's exact test), Q value = 3.9e-05
Table S92. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 209 | 415 |
subtype1 | 34 | 158 |
subtype2 | 49 | 130 |
subtype3 | 126 | 127 |
Figure S82. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'
![](D10V5.png)
P value = 5.38e-15 (Kruskal-Wallis (anova)), Q value = 5.6e-14
Table S93. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 424 | 83.6 (14.1) |
subtype1 | 147 | 76.2 (15.1) |
subtype2 | 125 | 87.7 (11.5) |
subtype3 | 152 | 87.4 (11.9) |
Figure S83. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'
![](D10V6.png)
P value = 1e-05 (Fisher's exact test), Q value = 3.9e-05
Table S94. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'
nPatients | ASTROCYTOMA | GLIOBLASTOMA MULTIFORME (GBM) | OLIGOASTROCYTOMA | OLIGODENDROGLIOMA | TREATED PRIMARY GBM | UNTREATED PRIMARY (DE NOVO) GBM |
---|---|---|---|---|---|---|
ALL | 194 | 1 | 130 | 191 | 1 | 150 |
subtype1 | 44 | 0 | 13 | 12 | 1 | 138 |
subtype2 | 100 | 1 | 52 | 29 | 0 | 8 |
subtype3 | 50 | 0 | 65 | 150 | 0 | 4 |
Figure S84. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'
![](D10V7.png)
P value = 0.176 (Fisher's exact test), Q value = 0.3
Table S95. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'RACE'
nPatients | AMERICAN INDIAN OR ALASKA NATIVE | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE |
---|---|---|---|---|
ALL | 1 | 13 | 31 | 611 |
subtype1 | 1 | 4 | 16 | 185 |
subtype2 | 0 | 3 | 7 | 178 |
subtype3 | 0 | 6 | 8 | 248 |
Figure S85. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'RACE'
![](D10V8.png)
P value = 0.242 (Fisher's exact test), Q value = 0.36
Table S96. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'ETHNICITY'
nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO |
---|---|---|
ALL | 35 | 574 |
subtype1 | 6 | 174 |
subtype2 | 12 | 165 |
subtype3 | 17 | 235 |
Figure S86. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'ETHNICITY'
![](D10V9.png)
Table S97. Description of clustering approach #11: 'MIRSEQ CNMF'
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 137 | 106 | 185 | 83 |
P value = 0.0123 (logrank test), Q value = 0.029
Table S98. Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 508 | 120 | 0.0 - 211.2 (20.9) |
subtype1 | 136 | 30 | 0.0 - 145.1 (20.0) |
subtype2 | 106 | 38 | 0.1 - 211.2 (20.8) |
subtype3 | 184 | 37 | 0.1 - 156.2 (21.0) |
subtype4 | 82 | 15 | 0.1 - 182.3 (22.8) |
Figure S87. Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'
![](D11V1.png)
P value = 0.917 (Kruskal-Wallis (anova)), Q value = 0.99
Table S99. Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 510 | 43.0 (13.4) |
subtype1 | 137 | 42.4 (13.4) |
subtype2 | 106 | 42.8 (13.1) |
subtype3 | 184 | 43.2 (13.8) |
subtype4 | 83 | 43.7 (13.1) |
Figure S88. Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
![](D11V2.png)
P value = 0.688 (Fisher's exact test), Q value = 0.79
Table S100. Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #4: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 230 | 281 |
subtype1 | 59 | 78 |
subtype2 | 44 | 62 |
subtype3 | 89 | 96 |
subtype4 | 38 | 45 |
Figure S89. Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #4: 'GENDER'
![](D11V4.png)
P value = 0.00231 (Fisher's exact test), Q value = 0.0065
Table S101. Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #5: 'RADIATION_THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 184 | 292 |
subtype1 | 44 | 81 |
subtype2 | 27 | 76 |
subtype3 | 73 | 99 |
subtype4 | 40 | 36 |
Figure S90. Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #5: 'RADIATION_THERAPY'
![](D11V5.png)
P value = 0.0929 (Kruskal-Wallis (anova)), Q value = 0.17
Table S102. Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 305 | 86.6 (12.6) |
subtype1 | 83 | 86.4 (12.6) |
subtype2 | 71 | 84.4 (12.7) |
subtype3 | 104 | 87.1 (12.7) |
subtype4 | 47 | 89.4 (11.9) |
Figure S91. Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'
![](D11V6.png)
P value = 1e-05 (Fisher's exact test), Q value = 3.9e-05
Table S103. Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'
nPatients | ASTROCYTOMA | OLIGOASTROCYTOMA | OLIGODENDROGLIOMA |
---|---|---|---|
ALL | 193 | 127 | 191 |
subtype1 | 69 | 36 | 32 |
subtype2 | 65 | 22 | 19 |
subtype3 | 54 | 50 | 81 |
subtype4 | 5 | 19 | 59 |
Figure S92. Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'
![](D11V7.png)
P value = 0.273 (Fisher's exact test), Q value = 0.4
Table S104. Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #8: 'RACE'
nPatients | AMERICAN INDIAN OR ALASKA NATIVE | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE |
---|---|---|---|---|
ALL | 1 | 8 | 21 | 471 |
subtype1 | 1 | 1 | 10 | 122 |
subtype2 | 0 | 2 | 3 | 100 |
subtype3 | 0 | 2 | 6 | 174 |
subtype4 | 0 | 3 | 2 | 75 |
Figure S93. Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #8: 'RACE'
![](D11V8.png)
P value = 0.437 (Fisher's exact test), Q value = 0.55
Table S105. Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #9: 'ETHNICITY'
nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO |
---|---|---|
ALL | 32 | 445 |
subtype1 | 8 | 119 |
subtype2 | 4 | 93 |
subtype3 | 12 | 165 |
subtype4 | 8 | 68 |
Figure S94. Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #9: 'ETHNICITY'
![](D11V9.png)
Table S106. Description of clustering approach #12: 'MIRSEQ CHIERARCHICAL'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 222 | 186 | 103 |
P value = 6.06e-14 (logrank test), Q value = 5.9e-13
Table S107. Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 508 | 120 | 0.0 - 211.2 (20.9) |
subtype1 | 220 | 40 | 0.0 - 182.3 (25.0) |
subtype2 | 185 | 35 | 0.1 - 172.8 (22.6) |
subtype3 | 103 | 45 | 0.1 - 211.2 (16.8) |
Figure S95. Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'
![](D12V1.png)
P value = 1.78e-08 (Kruskal-Wallis (anova)), Q value = 1.4e-07
Table S108. Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 510 | 43.0 (13.4) |
subtype1 | 222 | 40.0 (11.5) |
subtype2 | 185 | 42.6 (13.8) |
subtype3 | 103 | 50.0 (13.8) |
Figure S96. Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
![](D12V2.png)
P value = 0.283 (Fisher's exact test), Q value = 0.4
Table S109. Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 230 | 281 |
subtype1 | 91 | 131 |
subtype2 | 90 | 96 |
subtype3 | 49 | 54 |
Figure S97. Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'GENDER'
![](D12V4.png)
P value = 1e-05 (Fisher's exact test), Q value = 3.9e-05
Table S110. Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'RADIATION_THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 184 | 292 |
subtype1 | 91 | 118 |
subtype2 | 76 | 96 |
subtype3 | 17 | 78 |
Figure S98. Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'RADIATION_THERAPY'
![](D12V5.png)
P value = 0.000222 (Kruskal-Wallis (anova)), Q value = 7e-04
Table S111. Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 305 | 86.6 (12.6) |
subtype1 | 139 | 88.8 (11.1) |
subtype2 | 105 | 87.0 (12.5) |
subtype3 | 61 | 80.8 (14.3) |
Figure S99. Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'
![](D12V6.png)
P value = 1e-05 (Fisher's exact test), Q value = 3.9e-05
Table S112. Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'
nPatients | ASTROCYTOMA | OLIGOASTROCYTOMA | OLIGODENDROGLIOMA |
---|---|---|---|
ALL | 193 | 127 | 191 |
subtype1 | 76 | 67 | 79 |
subtype2 | 48 | 44 | 94 |
subtype3 | 69 | 16 | 18 |
Figure S100. Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'
![](D12V7.png)
P value = 0.561 (Fisher's exact test), Q value = 0.67
Table S113. Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'RACE'
nPatients | AMERICAN INDIAN OR ALASKA NATIVE | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE |
---|---|---|---|---|
ALL | 1 | 8 | 21 | 471 |
subtype1 | 0 | 3 | 7 | 208 |
subtype2 | 0 | 3 | 8 | 170 |
subtype3 | 1 | 2 | 6 | 93 |
Figure S101. Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'RACE'
![](D12V8.png)
P value = 0.231 (Fisher's exact test), Q value = 0.35
Table S114. Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'ETHNICITY'
nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO |
---|---|---|
ALL | 32 | 445 |
subtype1 | 17 | 188 |
subtype2 | 12 | 164 |
subtype3 | 3 | 93 |
Figure S102. Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'ETHNICITY'
![](D12V9.png)
Table S115. Description of clustering approach #13: 'MIRseq Mature CNMF subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 185 | 161 | 161 |
P value = 0.000268 (logrank test), Q value = 0.00082
Table S116. Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 504 | 120 | 0.0 - 211.2 (20.9) |
subtype1 | 185 | 61 | 0.0 - 182.3 (19.1) |
subtype2 | 159 | 24 | 0.1 - 169.8 (25.0) |
subtype3 | 160 | 35 | 0.1 - 211.2 (20.2) |
Figure S103. Get High-res Image Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D13V1.png)
P value = 0.0547 (Kruskal-Wallis (anova)), Q value = 0.11
Table S117. Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 506 | 43.0 (13.4) |
subtype1 | 185 | 44.4 (14.3) |
subtype2 | 161 | 40.7 (11.7) |
subtype3 | 160 | 43.8 (13.7) |
Figure S104. Get High-res Image Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
![](D13V2.png)
P value = 0.0323 (Fisher's exact test), Q value = 0.073
Table S118. Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 229 | 278 |
subtype1 | 70 | 115 |
subtype2 | 76 | 85 |
subtype3 | 83 | 78 |
Figure S105. Get High-res Image Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'GENDER'
![](D13V4.png)
P value = 5e-05 (Fisher's exact test), Q value = 0.00018
Table S119. Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 183 | 290 |
subtype1 | 45 | 126 |
subtype2 | 72 | 78 |
subtype3 | 66 | 86 |
Figure S106. Get High-res Image Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'
![](D13V5.png)
P value = 0.0393 (Kruskal-Wallis (anova)), Q value = 0.084
Table S120. Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 304 | 86.7 (12.6) |
subtype1 | 104 | 85.0 (13.2) |
subtype2 | 106 | 89.2 (10.8) |
subtype3 | 94 | 85.6 (13.3) |
Figure S107. Get High-res Image Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'
![](D13V6.png)
P value = 3e-05 (Fisher's exact test), Q value = 0.00011
Table S121. Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'
nPatients | ASTROCYTOMA | OLIGOASTROCYTOMA | OLIGODENDROGLIOMA |
---|---|---|---|
ALL | 191 | 126 | 190 |
subtype1 | 95 | 43 | 47 |
subtype2 | 50 | 41 | 70 |
subtype3 | 46 | 42 | 73 |
Figure S108. Get High-res Image Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'
![](D13V7.png)
P value = 0.865 (Fisher's exact test), Q value = 0.95
Table S122. Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'RACE'
nPatients | AMERICAN INDIAN OR ALASKA NATIVE | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE |
---|---|---|---|---|
ALL | 1 | 8 | 21 | 467 |
subtype1 | 1 | 2 | 9 | 169 |
subtype2 | 0 | 4 | 6 | 147 |
subtype3 | 0 | 2 | 6 | 151 |
Figure S109. Get High-res Image Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'RACE'
![](D13V8.png)
P value = 0.00072 (Fisher's exact test), Q value = 0.0022
Table S123. Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'ETHNICITY'
nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO |
---|---|---|
ALL | 32 | 441 |
subtype1 | 6 | 165 |
subtype2 | 20 | 128 |
subtype3 | 6 | 148 |
Figure S110. Get High-res Image Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'ETHNICITY'
![](D13V9.png)
Table S124. Description of clustering approach #14: 'MIRseq Mature cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 90 | 138 | 190 | 89 |
P value = 0 (logrank test), Q value = 0
Table S125. Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 504 | 120 | 0.0 - 211.2 (20.9) |
subtype1 | 89 | 14 | 0.0 - 145.1 (23.7) |
subtype2 | 137 | 22 | 0.1 - 172.8 (26.0) |
subtype3 | 189 | 37 | 0.1 - 211.2 (22.3) |
subtype4 | 89 | 47 | 0.1 - 133.7 (15.7) |
Figure S111. Get High-res Image Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
![](D14V1.png)
P value = 1.6e-12 (Kruskal-Wallis (anova)), Q value = 1.4e-11
Table S126. Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 506 | 43.0 (13.4) |
subtype1 | 90 | 36.9 (11.3) |
subtype2 | 138 | 42.4 (12.0) |
subtype3 | 189 | 42.0 (13.0) |
subtype4 | 89 | 52.3 (13.8) |
Figure S112. Get High-res Image Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
![](D14V2.png)
P value = 0.097 (Fisher's exact test), Q value = 0.18
Table S127. Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 229 | 278 |
subtype1 | 35 | 55 |
subtype2 | 54 | 84 |
subtype3 | 96 | 94 |
subtype4 | 44 | 45 |
Figure S113. Get High-res Image Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'GENDER'
![](D14V4.png)
P value = 0.00015 (Fisher's exact test), Q value = 0.00051
Table S128. Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 183 | 290 |
subtype1 | 32 | 51 |
subtype2 | 60 | 72 |
subtype3 | 77 | 101 |
subtype4 | 14 | 66 |
Figure S114. Get High-res Image Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'
![](D14V5.png)
P value = 5.43e-05 (Kruskal-Wallis (anova)), Q value = 0.00019
Table S129. Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 304 | 86.7 (12.6) |
subtype1 | 56 | 87.3 (11.7) |
subtype2 | 91 | 90.1 (10.5) |
subtype3 | 104 | 86.8 (12.6) |
subtype4 | 53 | 79.8 (14.3) |
Figure S115. Get High-res Image Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'
![](D14V6.png)
P value = 1e-05 (Fisher's exact test), Q value = 3.9e-05
Table S130. Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'
nPatients | ASTROCYTOMA | OLIGOASTROCYTOMA | OLIGODENDROGLIOMA |
---|---|---|---|
ALL | 191 | 126 | 190 |
subtype1 | 49 | 26 | 15 |
subtype2 | 36 | 37 | 65 |
subtype3 | 45 | 51 | 94 |
subtype4 | 61 | 12 | 16 |
Figure S116. Get High-res Image Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'
![](D14V7.png)
P value = 0.205 (Fisher's exact test), Q value = 0.32
Table S131. Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'RACE'
nPatients | AMERICAN INDIAN OR ALASKA NATIVE | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE |
---|---|---|---|---|
ALL | 1 | 8 | 21 | 467 |
subtype1 | 0 | 0 | 2 | 86 |
subtype2 | 0 | 4 | 5 | 127 |
subtype3 | 0 | 2 | 7 | 177 |
subtype4 | 1 | 2 | 7 | 77 |
Figure S117. Get High-res Image Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'RACE'
![](D14V8.png)
P value = 0.432 (Fisher's exact test), Q value = 0.55
Table S132. Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'ETHNICITY'
nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO |
---|---|---|
ALL | 32 | 441 |
subtype1 | 7 | 73 |
subtype2 | 11 | 116 |
subtype3 | 11 | 172 |
subtype4 | 3 | 80 |
Figure S118. Get High-res Image Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'ETHNICITY'
![](D14V9.png)
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Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/GBMLGG-TP/20148947/GBMLGG-TP.mergedcluster.txt
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Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/GBMLGG-TP/19775190/GBMLGG-TP.merged_data.txt
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Number of patients = 1107
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Number of clustering approaches = 14
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Number of selected clinical features = 9
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Exclude small clusters that include fewer than K patients, K = 3
consensus non-negative matrix factorization clustering approach (Brunet et al. 2004)
Resampling-based clustering method (Monti et al. 2003)
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
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
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.
In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.