This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.
Testing the association between subtypes identified by 5 different clustering approaches and 7 clinical features across 527 patients, 7 significant findings detected with P value < 0.05.
-
CNMF clustering analysis on array-based mRNA expression data identified 3 subtypes that correlate to 'AGE'.
-
Consensus hierarchical clustering analysis on array-based mRNA expression data identified 3 subtypes that correlate to 'AGE'.
-
CNMF clustering analysis on array-based miR expression data identified 4 subtypes that correlate to 'Time to Death'.
-
Consensus hierarchical clustering analysis on array-based miR expression data identified 3 subtypes that correlate to 'Time to Death' and 'AGE'.
-
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death' and 'AGE'.
Table 1. Get Full Table Overview of the association between subtypes identified by 5 different clustering approaches and 7 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 7 significant findings detected.
Clinical Features |
Statistical Tests |
mRNA CNMF subtypes |
mRNA cHierClus subtypes |
miR CNMF subtypes |
miR cHierClus subtypes |
METHLYATION CNMF |
Time to Death | logrank test | 0.157 | 0.056 | 0.000614 | 0.00455 | 0.000342 |
AGE | ANOVA | 0.0257 | 0.0276 | 0.101 | 0.000872 | 2.71e-09 |
GENDER | Fisher's exact test | 0.497 | 0.563 | 0.555 | 0.14 | 0.963 |
KARNOFSKY PERFORMANCE SCORE | ANOVA | 0.839 | 0.487 | 0.943 | 0.785 | 0.109 |
HISTOLOGICAL TYPE | Fisher's exact test | 0.0668 | 0.67 | 0.256 | 0.264 | 0.859 |
RADIATIONS RADIATION REGIMENINDICATION | Fisher's exact test | 0.176 | 0.0901 | 0.423 | 0.944 | 0.187 |
NEOADJUVANT THERAPY | Fisher's exact test | 0.547 | 0.538 | 0.844 | 0.866 | 0.215 |
Table S1. Get Full Table Description of clustering approach #1: 'mRNA CNMF subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 177 | 172 | 170 |
P value = 0.157 (logrank test)
Table S2. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 519 | 403 | 0.1 - 127.6 (9.9) |
subtype1 | 177 | 145 | 0.2 - 127.6 (10.0) |
subtype2 | 172 | 129 | 0.2 - 108.8 (9.2) |
subtype3 | 170 | 129 | 0.1 - 92.6 (10.7) |
Figure S1. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

P value = 0.0257 (ANOVA)
Table S3. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 519 | 57.7 (14.5) |
subtype1 | 177 | 57.3 (12.8) |
subtype2 | 172 | 55.8 (16.4) |
subtype3 | 170 | 60.0 (13.7) |
Figure S2. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #2: 'AGE'

P value = 0.497 (Fisher's exact test)
Table S4. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 204 | 315 |
subtype1 | 70 | 107 |
subtype2 | 62 | 110 |
subtype3 | 72 | 98 |
Figure S3. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

P value = 0.839 (ANOVA)
Table S5. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 389 | 77.1 (14.4) |
subtype1 | 137 | 77.5 (15.0) |
subtype2 | 126 | 77.3 (13.0) |
subtype3 | 126 | 76.5 (15.0) |
Figure S4. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.0668 (Fisher's exact test)
Table S6. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'
nPatients | TREATED PRIMARY GBM | UNTREATED PRIMARY (DE NOVO) GBM |
---|---|---|
ALL | 361 | 20 |
subtype1 | 124 | 12 |
subtype2 | 121 | 3 |
subtype3 | 116 | 5 |
Figure S5. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

P value = 0.176 (Fisher's exact test)
Table S7. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 359 | 160 |
subtype1 | 125 | 52 |
subtype2 | 110 | 62 |
subtype3 | 124 | 46 |
Figure S6. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.547 (Fisher's exact test)
Table S8. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 312 | 207 |
subtype1 | 111 | 66 |
subtype2 | 98 | 74 |
subtype3 | 103 | 67 |
Figure S7. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

Table S9. Get Full Table Description of clustering approach #2: 'mRNA cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 223 | 127 | 169 |
P value = 0.056 (logrank test)
Table S10. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 519 | 403 | 0.1 - 127.6 (9.9) |
subtype1 | 223 | 182 | 0.1 - 90.6 (10.4) |
subtype2 | 127 | 94 | 0.1 - 92.6 (9.8) |
subtype3 | 169 | 127 | 0.2 - 127.6 (9.4) |
Figure S8. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

P value = 0.0276 (ANOVA)
Table S11. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 519 | 57.7 (14.5) |
subtype1 | 223 | 57.7 (13.4) |
subtype2 | 127 | 60.3 (13.8) |
subtype3 | 169 | 55.7 (16.0) |
Figure S9. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

P value = 0.563 (Fisher's exact test)
Table S12. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 204 | 315 |
subtype1 | 90 | 133 |
subtype2 | 53 | 74 |
subtype3 | 61 | 108 |
Figure S10. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

P value = 0.487 (ANOVA)
Table S13. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 389 | 77.1 (14.4) |
subtype1 | 165 | 77.6 (14.4) |
subtype2 | 98 | 75.6 (15.4) |
subtype3 | 126 | 77.6 (13.5) |
Figure S11. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.67 (Fisher's exact test)
Table S14. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'
nPatients | TREATED PRIMARY GBM | UNTREATED PRIMARY (DE NOVO) GBM |
---|---|---|
ALL | 361 | 20 |
subtype1 | 160 | 11 |
subtype2 | 85 | 4 |
subtype3 | 116 | 5 |
Figure S12. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

P value = 0.0901 (Fisher's exact test)
Table S15. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 359 | 160 |
subtype1 | 161 | 62 |
subtype2 | 92 | 35 |
subtype3 | 106 | 63 |
Figure S13. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.538 (Fisher's exact test)
Table S16. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 312 | 207 |
subtype1 | 139 | 84 |
subtype2 | 77 | 50 |
subtype3 | 96 | 73 |
Figure S14. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

Table S17. Get Full Table Description of clustering approach #3: 'miR CNMF subtypes'
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 144 | 159 | 74 | 105 |
P value = 0.000614 (logrank test)
Table S18. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 482 | 379 | 0.1 - 127.6 (10.3) |
subtype1 | 144 | 116 | 0.1 - 51.3 (10.6) |
subtype2 | 159 | 124 | 0.1 - 127.6 (10.6) |
subtype3 | 74 | 57 | 0.1 - 53.8 (8.4) |
subtype4 | 105 | 82 | 0.1 - 92.6 (10.8) |
Figure S15. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

P value = 0.101 (ANOVA)
Table S19. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 482 | 57.5 (14.6) |
subtype1 | 144 | 59.7 (11.4) |
subtype2 | 159 | 55.5 (17.0) |
subtype3 | 74 | 57.9 (15.3) |
subtype4 | 105 | 57.4 (13.7) |
Figure S16. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #2: 'AGE'

P value = 0.555 (Fisher's exact test)
Table S20. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 187 | 295 |
subtype1 | 56 | 88 |
subtype2 | 68 | 91 |
subtype3 | 27 | 47 |
subtype4 | 36 | 69 |
Figure S17. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #3: 'GENDER'

P value = 0.943 (ANOVA)
Table S21. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 368 | 77.6 (14.1) |
subtype1 | 112 | 77.6 (14.4) |
subtype2 | 114 | 77.5 (14.1) |
subtype3 | 62 | 76.9 (14.4) |
subtype4 | 80 | 78.4 (13.8) |
Figure S18. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.256 (Fisher's exact test)
Table S22. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'
nPatients | TREATED PRIMARY GBM | UNTREATED PRIMARY (DE NOVO) GBM |
---|---|---|
ALL | 325 | 20 |
subtype1 | 102 | 3 |
subtype2 | 106 | 9 |
subtype3 | 50 | 5 |
subtype4 | 67 | 3 |
Figure S19. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

P value = 0.423 (Fisher's exact test)
Table S23. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 337 | 145 |
subtype1 | 105 | 39 |
subtype2 | 113 | 46 |
subtype3 | 46 | 28 |
subtype4 | 73 | 32 |
Figure S20. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.844 (Fisher's exact test)
Table S24. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 289 | 193 |
subtype1 | 85 | 59 |
subtype2 | 92 | 67 |
subtype3 | 46 | 28 |
subtype4 | 66 | 39 |
Figure S21. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

Table S25. Get Full Table Description of clustering approach #4: 'miR cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 170 | 180 | 132 |
P value = 0.00455 (logrank test)
Table S26. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 482 | 379 | 0.1 - 127.6 (10.3) |
subtype1 | 170 | 137 | 0.1 - 92.6 (9.8) |
subtype2 | 180 | 145 | 0.1 - 127.6 (10.0) |
subtype3 | 132 | 97 | 0.1 - 108.8 (10.7) |
Figure S22. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

P value = 0.000872 (ANOVA)
Table S27. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 482 | 57.5 (14.6) |
subtype1 | 170 | 59.2 (12.6) |
subtype2 | 180 | 58.9 (13.3) |
subtype3 | 132 | 53.5 (17.6) |
Figure S23. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #2: 'AGE'

P value = 0.14 (Fisher's exact test)
Table S28. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 187 | 295 |
subtype1 | 59 | 111 |
subtype2 | 80 | 100 |
subtype3 | 48 | 84 |
Figure S24. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

P value = 0.785 (ANOVA)
Table S29. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 368 | 77.6 (14.1) |
subtype1 | 129 | 78.3 (13.5) |
subtype2 | 136 | 77.1 (15.5) |
subtype3 | 103 | 77.4 (13.1) |
Figure S25. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.264 (Fisher's exact test)
Table S30. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'
nPatients | TREATED PRIMARY GBM | UNTREATED PRIMARY (DE NOVO) GBM |
---|---|---|
ALL | 325 | 20 |
subtype1 | 121 | 4 |
subtype2 | 121 | 9 |
subtype3 | 83 | 7 |
Figure S26. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

P value = 0.944 (Fisher's exact test)
Table S31. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 337 | 145 |
subtype1 | 118 | 52 |
subtype2 | 125 | 55 |
subtype3 | 94 | 38 |
Figure S27. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.866 (Fisher's exact test)
Table S32. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 289 | 193 |
subtype1 | 103 | 67 |
subtype2 | 105 | 75 |
subtype3 | 81 | 51 |
Figure S28. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

Table S33. Get Full Table Description of clustering approach #5: 'METHLYATION CNMF'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 81 | 124 | 75 |
P value = 0.000342 (logrank test)
Table S34. Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 280 | 208 | 0.1 - 127.6 (10.0) |
subtype1 | 81 | 57 | 0.1 - 92.6 (10.0) |
subtype2 | 124 | 98 | 0.1 - 77.6 (9.3) |
subtype3 | 75 | 53 | 0.2 - 127.6 (12.4) |
Figure S29. Get High-res Image Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

P value = 2.71e-09 (ANOVA)
Table S35. Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 280 | 57.5 (14.9) |
subtype1 | 81 | 57.1 (11.9) |
subtype2 | 124 | 62.7 (12.6) |
subtype3 | 75 | 49.5 (17.5) |
Figure S30. Get High-res Image Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

P value = 0.963 (Fisher's exact test)
Table S36. Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 114 | 166 |
subtype1 | 34 | 47 |
subtype2 | 50 | 74 |
subtype3 | 30 | 45 |
Figure S31. Get High-res Image Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

P value = 0.109 (ANOVA)
Table S37. Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 213 | 75.4 (15.0) |
subtype1 | 63 | 77.1 (16.8) |
subtype2 | 93 | 72.9 (14.9) |
subtype3 | 57 | 77.4 (12.3) |
Figure S32. Get High-res Image Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.859 (Fisher's exact test)
Table S38. Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'
nPatients | TREATED PRIMARY GBM | UNTREATED PRIMARY (DE NOVO) GBM |
---|---|---|
ALL | 123 | 19 |
subtype1 | 34 | 6 |
subtype2 | 54 | 7 |
subtype3 | 35 | 6 |
Figure S33. Get High-res Image Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

P value = 0.187 (Fisher's exact test)
Table S39. Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 209 | 71 |
subtype1 | 65 | 16 |
subtype2 | 86 | 38 |
subtype3 | 58 | 17 |
Figure S34. Get High-res Image Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.215 (Fisher's exact test)
Table S40. Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 183 | 97 |
subtype1 | 57 | 24 |
subtype2 | 74 | 50 |
subtype3 | 52 | 23 |
Figure S35. Get High-res Image Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

-
Cluster data file = GBM.mergedcluster.txt
-
Clinical data file = GBM.clin.merged.picked.txt
-
Number of patients = 527
-
Number of clustering approaches = 5
-
Number of selected clinical features = 7
-
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 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
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
This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.