This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.
Testing the association between subtypes identified by 10 different clustering approaches and 7 clinical features across 80 patients, 13 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 5 subtypes that correlate to 'DAYS_TO_DEATH_OR_LAST_FUP' and 'PATHOLOGY_T_STAGE'.
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Consensus hierarchical clustering analysis on array-based mRNA expression data identified 4 subtypes that correlate to 'DAYS_TO_DEATH_OR_LAST_FUP' and 'RADIATION_THERAPY'.
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5 subtypes identified in current cancer cohort by 'LINCRNA CNMF'. These subtypes correlate to 'DAYS_TO_DEATH_OR_LAST_FUP'.
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4 subtypes identified in current cancer cohort by 'LINCRNA CHIERARCHICAL'. These subtypes correlate to 'DAYS_TO_DEATH_OR_LAST_FUP'.
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CNMF clustering analysis on array-based miR expression data identified 5 subtypes that correlate to 'DAYS_TO_DEATH_OR_LAST_FUP'.
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Consensus hierarchical clustering analysis on array-based miR expression data identified 5 subtypes that correlate to 'DAYS_TO_DEATH_OR_LAST_FUP' and 'YEARS_TO_BIRTH'.
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3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'DAYS_TO_DEATH_OR_LAST_FUP'.
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6 subtypes identified in current cancer cohort by 'Copy Number Threshold CNMF subtypes'. These subtypes correlate to 'DAYS_TO_DEATH_OR_LAST_FUP'.
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CNMF clustering analysis on methylation data identified 4 subtypes that correlate to 'DAYS_TO_DEATH_OR_LAST_FUP'.
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3 subtypes identified in current cancer cohort by 'METHYLATION CHIERARCHICAL'. These subtypes correlate to 'DAYS_TO_DEATH_OR_LAST_FUP'.
Table 1. Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 7 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 13 significant findings detected.
Clinical Features |
DAYS TO DEATH OR LAST FUP |
YEARS TO BIRTH |
PATHOLOGIC STAGE |
PATHOLOGY T STAGE |
PATHOLOGY M STAGE |
GENDER |
RADIATION THERAPY |
Statistical Tests | logrank test | Kruskal-Wallis (anova) | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test |
mRNA CNMF subtypes |
0.000583 (0.00453) |
0.394 (0.541) |
0.0529 (0.248) |
0.027 (0.172) |
0.287 (0.472) |
0.783 (0.879) |
0.386 (0.541) |
mRNA cHierClus subtypes |
8.91e-07 (3.12e-05) |
0.291 (0.472) |
0.15 (0.438) |
0.309 (0.48) |
0.13 (0.403) |
0.791 (0.879) |
0.0497 (0.248) |
LINCRNA CNMF |
0.0428 (0.248) |
0.06 (0.262) |
0.158 (0.443) |
0.271 (0.472) |
0.929 (0.971) |
0.966 (0.994) |
0.27 (0.472) |
LINCRNA CHIERARCHICAL |
4.57e-05 (0.000496) |
0.223 (0.472) |
0.287 (0.472) |
0.244 (0.472) |
0.25 (0.472) |
0.78 (0.879) |
0.183 (0.462) |
miR CNMF subtypes |
1.79e-06 (4.17e-05) |
0.285 (0.472) |
0.231 (0.472) |
0.292 (0.472) |
0.053 (0.248) |
0.444 (0.556) |
0.889 (0.943) |
miR cHierClus subtypes |
5.2e-06 (7.28e-05) |
0.00464 (0.0325) |
0.332 (0.484) |
0.292 (0.472) |
0.461 (0.556) |
0.327 (0.484) |
0.439 (0.556) |
Copy Number Ratio CNMF subtypes |
2.39e-06 (4.19e-05) |
0.0867 (0.32) |
0.201 (0.472) |
0.43 (0.556) |
0.0658 (0.271) |
0.83 (0.908) |
0.429 (0.556) |
Copy Number Threshold CNMF subtypes |
0.000237 (0.00207) |
0.0745 (0.29) |
0.682 (0.796) |
0.394 (0.541) |
0.112 (0.374) |
0.327 (0.484) |
0.297 (0.472) |
Methylation CNMF subtypes |
6.68e-08 (4.67e-06) |
0.0928 (0.325) |
0.448 (0.556) |
0.642 (0.762) |
0.185 (0.462) |
0.246 (0.472) |
0.879 (0.943) |
METHYLATION CHIERARCHICAL |
4.96e-05 (0.000496) |
0.133 (0.403) |
0.293 (0.472) |
0.456 (0.556) |
0.173 (0.462) |
1 (1.00) |
1 (1.00) |
Table S1. Description of clustering approach #1: 'mRNA CNMF subtypes'
Cluster Labels | 1 | 2 | 3 | 4 | 5 |
---|---|---|---|---|---|
Number of samples | 11 | 11 | 29 | 21 | 8 |
P value = 0.000583 (logrank test), Q value = 0.0045
Table S2. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 79 | 22 | 0.1 - 85.5 (26.1) |
subtype1 | 10 | 5 | 1.3 - 49.7 (18.2) |
subtype2 | 11 | 4 | 0.4 - 61.2 (31.1) |
subtype3 | 29 | 1 | 0.2 - 85.5 (27.6) |
subtype4 | 21 | 7 | 1.4 - 82.2 (21.0) |
subtype5 | 8 | 5 | 0.1 - 45.3 (23.8) |
Figure S1. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

P value = 0.394 (Kruskal-Wallis (anova)), Q value = 0.54
Table S3. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 79 | 61.5 (14.0) |
subtype1 | 10 | 67.4 (15.6) |
subtype2 | 11 | 59.8 (14.1) |
subtype3 | 29 | 57.8 (15.0) |
subtype4 | 21 | 64.7 (12.8) |
subtype5 | 8 | 62.1 (8.7) |
Figure S2. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

P value = 0.0529 (Fisher's exact test), Q value = 0.25
Table S4. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
nPatients | STAGE IIA | STAGE IIB | STAGE IIIA | STAGE IIIB | STAGE IIIC | STAGE IV |
---|---|---|---|---|---|---|
ALL | 11 | 27 | 25 | 10 | 1 | 4 |
subtype1 | 1 | 2 | 3 | 2 | 1 | 1 |
subtype2 | 0 | 3 | 4 | 3 | 0 | 0 |
subtype3 | 9 | 12 | 6 | 2 | 0 | 0 |
subtype4 | 0 | 7 | 9 | 3 | 0 | 2 |
subtype5 | 1 | 3 | 3 | 0 | 0 | 1 |
Figure S3. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

P value = 0.027 (Kruskal-Wallis (anova)), Q value = 0.17
Table S5. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
T2 | T3 | T4 | |
---|---|---|---|
ALL | 13 | 32 | 34 |
subtype1 | 1 | 3 | 6 |
subtype2 | 2 | 5 | 4 |
subtype3 | 9 | 14 | 6 |
subtype4 | 0 | 7 | 14 |
subtype5 | 1 | 3 | 4 |
Figure S4. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

P value = 0.287 (Fisher's exact test), Q value = 0.47
Table S6. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'
nPatients | CLASS0 | CLASS1 |
---|---|---|
ALL | 51 | 4 |
subtype1 | 8 | 1 |
subtype2 | 8 | 0 |
subtype3 | 17 | 0 |
subtype4 | 14 | 2 |
subtype5 | 4 | 1 |
Figure S5. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

P value = 0.783 (Fisher's exact test), Q value = 0.88
Table S7. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 34 | 45 |
subtype1 | 6 | 4 |
subtype2 | 4 | 7 |
subtype3 | 12 | 17 |
subtype4 | 8 | 13 |
subtype5 | 4 | 4 |
Figure S6. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'GENDER'

P value = 0.386 (Fisher's exact test), Q value = 0.54
Table S8. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 75 | 3 |
subtype1 | 8 | 1 |
subtype2 | 10 | 1 |
subtype3 | 28 | 1 |
subtype4 | 21 | 0 |
subtype5 | 8 | 0 |
Figure S7. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'

Table S9. Description of clustering approach #2: 'mRNA cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 15 | 15 | 27 | 23 |
P value = 8.91e-07 (logrank test), Q value = 3.1e-05
Table S10. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 79 | 22 | 0.1 - 85.5 (26.1) |
subtype1 | 15 | 9 | 0.1 - 45.3 (18.9) |
subtype2 | 15 | 2 | 0.4 - 61.2 (27.6) |
subtype3 | 27 | 1 | 0.2 - 85.5 (38.7) |
subtype4 | 22 | 10 | 1.3 - 49.7 (19.8) |
Figure S8. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

P value = 0.291 (Kruskal-Wallis (anova)), Q value = 0.47
Table S11. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 79 | 61.5 (14.0) |
subtype1 | 15 | 63.2 (12.9) |
subtype2 | 15 | 60.9 (14.0) |
subtype3 | 27 | 57.4 (14.7) |
subtype4 | 22 | 65.9 (13.3) |
Figure S9. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

P value = 0.15 (Fisher's exact test), Q value = 0.44
Table S12. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
nPatients | STAGE IIA | STAGE IIB | STAGE IIIA | STAGE IIIB | STAGE IIIC | STAGE IV |
---|---|---|---|---|---|---|
ALL | 11 | 27 | 25 | 10 | 1 | 4 |
subtype1 | 1 | 6 | 4 | 1 | 1 | 2 |
subtype2 | 1 | 5 | 4 | 4 | 0 | 0 |
subtype3 | 8 | 10 | 7 | 2 | 0 | 0 |
subtype4 | 1 | 6 | 10 | 3 | 0 | 2 |
Figure S10. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

P value = 0.309 (Kruskal-Wallis (anova)), Q value = 0.48
Table S13. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
T2 | T3 | T4 | |
---|---|---|---|
ALL | 13 | 32 | 34 |
subtype1 | 2 | 5 | 8 |
subtype2 | 2 | 7 | 6 |
subtype3 | 8 | 11 | 8 |
subtype4 | 1 | 9 | 12 |
Figure S11. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

P value = 0.13 (Fisher's exact test), Q value = 0.4
Table S14. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'
nPatients | CLASS0 | CLASS1 |
---|---|---|
ALL | 51 | 4 |
subtype1 | 9 | 2 |
subtype2 | 10 | 0 |
subtype3 | 18 | 0 |
subtype4 | 14 | 2 |
Figure S12. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

P value = 0.791 (Fisher's exact test), Q value = 0.88
Table S15. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 34 | 45 |
subtype1 | 8 | 7 |
subtype2 | 6 | 9 |
subtype3 | 12 | 15 |
subtype4 | 8 | 14 |
Figure S13. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'GENDER'

P value = 0.0497 (Fisher's exact test), Q value = 0.25
Table S16. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 75 | 3 |
subtype1 | 13 | 1 |
subtype2 | 13 | 2 |
subtype3 | 27 | 0 |
subtype4 | 22 | 0 |
Figure S14. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'

Table S17. Description of clustering approach #3: 'LINCRNA CNMF'
Cluster Labels | 1 | 2 | 3 | 4 | 5 |
---|---|---|---|---|---|
Number of samples | 18 | 7 | 28 | 11 | 16 |
P value = 0.0428 (logrank test), Q value = 0.25
Table S18. Clustering Approach #3: 'LINCRNA CNMF' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 79 | 22 | 0.1 - 85.5 (26.1) |
subtype1 | 18 | 10 | 0.1 - 61.2 (24.7) |
subtype2 | 7 | 1 | 0.2 - 27.0 (15.1) |
subtype3 | 28 | 7 | 1.4 - 82.2 (24.3) |
subtype4 | 10 | 3 | 1.3 - 85.5 (39.7) |
subtype5 | 16 | 1 | 0.6 - 52.0 (34.4) |
Figure S15. Get High-res Image Clustering Approach #3: 'LINCRNA CNMF' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

P value = 0.06 (Kruskal-Wallis (anova)), Q value = 0.26
Table S19. Clustering Approach #3: 'LINCRNA CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 79 | 61.5 (14.0) |
subtype1 | 18 | 62.9 (11.4) |
subtype2 | 7 | 61.3 (15.0) |
subtype3 | 28 | 64.2 (12.9) |
subtype4 | 10 | 67.0 (14.3) |
subtype5 | 16 | 52.0 (15.0) |
Figure S16. Get High-res Image Clustering Approach #3: 'LINCRNA CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

P value = 0.158 (Fisher's exact test), Q value = 0.44
Table S20. Clustering Approach #3: 'LINCRNA CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
nPatients | STAGE IIA | STAGE IIB | STAGE IIIA | STAGE IIIB | STAGE IIIC | STAGE IV |
---|---|---|---|---|---|---|
ALL | 11 | 27 | 25 | 10 | 1 | 4 |
subtype1 | 1 | 5 | 7 | 2 | 1 | 1 |
subtype2 | 2 | 3 | 0 | 2 | 0 | 0 |
subtype3 | 1 | 9 | 12 | 4 | 0 | 2 |
subtype4 | 2 | 3 | 2 | 2 | 0 | 1 |
subtype5 | 5 | 7 | 4 | 0 | 0 | 0 |
Figure S17. Get High-res Image Clustering Approach #3: 'LINCRNA CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

P value = 0.271 (Kruskal-Wallis (anova)), Q value = 0.47
Table S21. Clustering Approach #3: 'LINCRNA CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
T2 | T3 | T4 | |
---|---|---|---|
ALL | 13 | 32 | 34 |
subtype1 | 2 | 7 | 9 |
subtype2 | 2 | 3 | 2 |
subtype3 | 2 | 10 | 16 |
subtype4 | 2 | 4 | 4 |
subtype5 | 5 | 8 | 3 |
Figure S18. Get High-res Image Clustering Approach #3: 'LINCRNA CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

P value = 0.929 (Fisher's exact test), Q value = 0.97
Table S22. Clustering Approach #3: 'LINCRNA CNMF' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'
nPatients | CLASS0 | CLASS1 |
---|---|---|
ALL | 51 | 4 |
subtype1 | 12 | 1 |
subtype2 | 5 | 0 |
subtype3 | 20 | 2 |
subtype4 | 6 | 1 |
subtype5 | 8 | 0 |
Figure S19. Get High-res Image Clustering Approach #3: 'LINCRNA CNMF' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

P value = 0.966 (Fisher's exact test), Q value = 0.99
Table S23. Clustering Approach #3: 'LINCRNA CNMF' versus Clinical Feature #6: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 34 | 45 |
subtype1 | 9 | 9 |
subtype2 | 3 | 4 |
subtype3 | 12 | 16 |
subtype4 | 4 | 6 |
subtype5 | 6 | 10 |
Figure S20. Get High-res Image Clustering Approach #3: 'LINCRNA CNMF' versus Clinical Feature #6: 'GENDER'

P value = 0.27 (Fisher's exact test), Q value = 0.47
Table S24. Clustering Approach #3: 'LINCRNA CNMF' versus Clinical Feature #7: 'RADIATION_THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 75 | 3 |
subtype1 | 15 | 2 |
subtype2 | 7 | 0 |
subtype3 | 28 | 0 |
subtype4 | 10 | 0 |
subtype5 | 15 | 1 |
Figure S21. Get High-res Image Clustering Approach #3: 'LINCRNA CNMF' versus Clinical Feature #7: 'RADIATION_THERAPY'

Table S25. Description of clustering approach #4: 'LINCRNA CHIERARCHICAL'
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 21 | 24 | 20 | 15 |
P value = 4.57e-05 (logrank test), Q value = 5e-04
Table S26. Clustering Approach #4: 'LINCRNA CHIERARCHICAL' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 79 | 22 | 0.1 - 85.5 (26.1) |
subtype1 | 20 | 11 | 0.1 - 61.2 (23.7) |
subtype2 | 24 | 1 | 0.2 - 82.2 (24.3) |
subtype3 | 20 | 9 | 1.4 - 45.9 (19.8) |
subtype4 | 15 | 1 | 20.8 - 85.5 (39.8) |
Figure S22. Get High-res Image Clustering Approach #4: 'LINCRNA CHIERARCHICAL' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

P value = 0.223 (Kruskal-Wallis (anova)), Q value = 0.47
Table S27. Clustering Approach #4: 'LINCRNA CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 79 | 61.5 (14.0) |
subtype1 | 20 | 64.8 (12.5) |
subtype2 | 24 | 59.6 (13.4) |
subtype3 | 20 | 65.2 (13.2) |
subtype4 | 15 | 55.4 (16.4) |
Figure S23. Get High-res Image Clustering Approach #4: 'LINCRNA CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

P value = 0.287 (Fisher's exact test), Q value = 0.47
Table S28. Clustering Approach #4: 'LINCRNA CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
nPatients | STAGE IIA | STAGE IIB | STAGE IIIA | STAGE IIIB | STAGE IIIC | STAGE IV |
---|---|---|---|---|---|---|
ALL | 11 | 27 | 25 | 10 | 1 | 4 |
subtype1 | 1 | 6 | 6 | 3 | 1 | 2 |
subtype2 | 5 | 8 | 6 | 5 | 0 | 0 |
subtype3 | 1 | 6 | 9 | 2 | 0 | 2 |
subtype4 | 4 | 7 | 4 | 0 | 0 | 0 |
Figure S24. Get High-res Image Clustering Approach #4: 'LINCRNA CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

P value = 0.244 (Kruskal-Wallis (anova)), Q value = 0.47
Table S29. Clustering Approach #4: 'LINCRNA CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
T2 | T3 | T4 | |
---|---|---|---|
ALL | 13 | 32 | 34 |
subtype1 | 2 | 8 | 10 |
subtype2 | 6 | 8 | 10 |
subtype3 | 1 | 8 | 11 |
subtype4 | 4 | 8 | 3 |
Figure S25. Get High-res Image Clustering Approach #4: 'LINCRNA CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

P value = 0.25 (Fisher's exact test), Q value = 0.47
Table S30. Clustering Approach #4: 'LINCRNA CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'
nPatients | CLASS0 | CLASS1 |
---|---|---|
ALL | 51 | 4 |
subtype1 | 12 | 2 |
subtype2 | 19 | 0 |
subtype3 | 12 | 2 |
subtype4 | 8 | 0 |
Figure S26. Get High-res Image Clustering Approach #4: 'LINCRNA CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

P value = 0.78 (Fisher's exact test), Q value = 0.88
Table S31. Clustering Approach #4: 'LINCRNA CHIERARCHICAL' versus Clinical Feature #6: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 34 | 45 |
subtype1 | 9 | 11 |
subtype2 | 12 | 12 |
subtype3 | 7 | 13 |
subtype4 | 6 | 9 |
Figure S27. Get High-res Image Clustering Approach #4: 'LINCRNA CHIERARCHICAL' versus Clinical Feature #6: 'GENDER'

P value = 0.183 (Fisher's exact test), Q value = 0.46
Table S32. Clustering Approach #4: 'LINCRNA CHIERARCHICAL' versus Clinical Feature #7: 'RADIATION_THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 75 | 3 |
subtype1 | 17 | 2 |
subtype2 | 24 | 0 |
subtype3 | 20 | 0 |
subtype4 | 14 | 1 |
Figure S28. Get High-res Image Clustering Approach #4: 'LINCRNA CHIERARCHICAL' versus Clinical Feature #7: 'RADIATION_THERAPY'

Table S33. Description of clustering approach #5: 'miR CNMF subtypes'
Cluster Labels | 1 | 2 | 3 | 4 | 5 |
---|---|---|---|---|---|
Number of samples | 13 | 21 | 18 | 24 | 4 |
P value = 1.79e-06 (logrank test), Q value = 4.2e-05
Table S34. Clustering Approach #5: 'miR CNMF subtypes' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 79 | 22 | 0.1 - 85.5 (26.1) |
subtype1 | 13 | 6 | 0.4 - 61.2 (28.7) |
subtype2 | 21 | 12 | 0.1 - 45.3 (18.9) |
subtype3 | 17 | 2 | 0.2 - 85.5 (26.2) |
subtype4 | 24 | 0 | 0.2 - 82.2 (32.8) |
subtype5 | 4 | 2 | 1.3 - 27.0 (14.1) |
Figure S29. Get High-res Image Clustering Approach #5: 'miR CNMF subtypes' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

P value = 0.285 (Kruskal-Wallis (anova)), Q value = 0.47
Table S35. Clustering Approach #5: 'miR CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 79 | 61.5 (14.0) |
subtype1 | 13 | 60.4 (15.9) |
subtype2 | 21 | 65.2 (11.6) |
subtype3 | 17 | 61.2 (12.6) |
subtype4 | 24 | 57.4 (15.9) |
subtype5 | 4 | 72.2 (3.1) |
Figure S30. Get High-res Image Clustering Approach #5: 'miR CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

P value = 0.231 (Fisher's exact test), Q value = 0.47
Table S36. Clustering Approach #5: 'miR CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
nPatients | STAGE IIA | STAGE IIB | STAGE IIIA | STAGE IIIB | STAGE IIIC | STAGE IV |
---|---|---|---|---|---|---|
ALL | 11 | 27 | 25 | 10 | 1 | 4 |
subtype1 | 0 | 3 | 5 | 2 | 1 | 1 |
subtype2 | 1 | 8 | 7 | 3 | 0 | 2 |
subtype3 | 2 | 8 | 6 | 1 | 0 | 0 |
subtype4 | 7 | 7 | 7 | 3 | 0 | 0 |
subtype5 | 1 | 1 | 0 | 1 | 0 | 1 |
Figure S31. Get High-res Image Clustering Approach #5: 'miR CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

P value = 0.292 (Kruskal-Wallis (anova)), Q value = 0.47
Table S37. Clustering Approach #5: 'miR CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
T2 | T3 | T4 | |
---|---|---|---|
ALL | 13 | 32 | 34 |
subtype1 | 0 | 7 | 6 |
subtype2 | 2 | 7 | 12 |
subtype3 | 3 | 9 | 5 |
subtype4 | 7 | 8 | 9 |
subtype5 | 1 | 1 | 2 |
Figure S32. Get High-res Image Clustering Approach #5: 'miR CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

P value = 0.053 (Fisher's exact test), Q value = 0.25
Table S38. Clustering Approach #5: 'miR CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'
nPatients | CLASS0 | CLASS1 |
---|---|---|
ALL | 51 | 4 |
subtype1 | 9 | 1 |
subtype2 | 12 | 2 |
subtype3 | 13 | 0 |
subtype4 | 16 | 0 |
subtype5 | 1 | 1 |
Figure S33. Get High-res Image Clustering Approach #5: 'miR CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

P value = 0.444 (Fisher's exact test), Q value = 0.56
Table S39. Clustering Approach #5: 'miR CNMF subtypes' versus Clinical Feature #6: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 34 | 45 |
subtype1 | 6 | 7 |
subtype2 | 6 | 15 |
subtype3 | 10 | 7 |
subtype4 | 10 | 14 |
subtype5 | 2 | 2 |
Figure S34. Get High-res Image Clustering Approach #5: 'miR CNMF subtypes' versus Clinical Feature #6: 'GENDER'

P value = 0.889 (Fisher's exact test), Q value = 0.94
Table S40. Clustering Approach #5: 'miR CNMF subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 75 | 3 |
subtype1 | 11 | 1 |
subtype2 | 20 | 1 |
subtype3 | 17 | 0 |
subtype4 | 23 | 1 |
subtype5 | 4 | 0 |
Figure S35. Get High-res Image Clustering Approach #5: 'miR CNMF subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'

Table S41. Description of clustering approach #6: 'miR cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 | 4 | 5 |
---|---|---|---|---|---|
Number of samples | 21 | 20 | 8 | 17 | 14 |
P value = 5.2e-06 (logrank test), Q value = 7.3e-05
Table S42. Clustering Approach #6: 'miR cHierClus subtypes' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 79 | 22 | 0.1 - 85.5 (26.1) |
subtype1 | 20 | 12 | 0.1 - 49.7 (13.4) |
subtype2 | 20 | 7 | 2.1 - 61.2 (32.0) |
subtype3 | 8 | 1 | 0.2 - 85.5 (23.2) |
subtype4 | 17 | 1 | 0.2 - 82.2 (35.0) |
subtype5 | 14 | 1 | 0.4 - 44.3 (27.0) |
Figure S36. Get High-res Image Clustering Approach #6: 'miR cHierClus subtypes' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

P value = 0.00464 (Kruskal-Wallis (anova)), Q value = 0.032
Table S43. Clustering Approach #6: 'miR cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 79 | 61.5 (14.0) |
subtype1 | 20 | 68.6 (13.4) |
subtype2 | 20 | 61.0 (9.9) |
subtype3 | 8 | 59.8 (14.5) |
subtype4 | 17 | 64.1 (15.8) |
subtype5 | 14 | 50.2 (11.1) |
Figure S37. Get High-res Image Clustering Approach #6: 'miR cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

P value = 0.332 (Fisher's exact test), Q value = 0.48
Table S44. Clustering Approach #6: 'miR cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
nPatients | STAGE IIA | STAGE IIB | STAGE IIIA | STAGE IIIB | STAGE IIIC | STAGE IV |
---|---|---|---|---|---|---|
ALL | 11 | 27 | 25 | 10 | 1 | 4 |
subtype1 | 1 | 8 | 6 | 1 | 1 | 3 |
subtype2 | 1 | 6 | 7 | 4 | 0 | 1 |
subtype3 | 0 | 5 | 2 | 1 | 0 | 0 |
subtype4 | 5 | 5 | 4 | 3 | 0 | 0 |
subtype5 | 4 | 3 | 6 | 1 | 0 | 0 |
Figure S38. Get High-res Image Clustering Approach #6: 'miR cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

P value = 0.292 (Kruskal-Wallis (anova)), Q value = 0.47
Table S45. Clustering Approach #6: 'miR cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
T2 | T3 | T4 | |
---|---|---|---|
ALL | 13 | 32 | 34 |
subtype1 | 1 | 10 | 9 |
subtype2 | 3 | 7 | 10 |
subtype3 | 0 | 5 | 3 |
subtype4 | 5 | 7 | 5 |
subtype5 | 4 | 3 | 7 |
Figure S39. Get High-res Image Clustering Approach #6: 'miR cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

P value = 0.461 (Fisher's exact test), Q value = 0.56
Table S46. Clustering Approach #6: 'miR cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'
nPatients | CLASS0 | CLASS1 |
---|---|---|
ALL | 51 | 4 |
subtype1 | 14 | 3 |
subtype2 | 10 | 1 |
subtype3 | 6 | 0 |
subtype4 | 11 | 0 |
subtype5 | 10 | 0 |
Figure S40. Get High-res Image Clustering Approach #6: 'miR cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

P value = 0.327 (Fisher's exact test), Q value = 0.48
Table S47. Clustering Approach #6: 'miR cHierClus subtypes' versus Clinical Feature #6: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 34 | 45 |
subtype1 | 11 | 9 |
subtype2 | 5 | 15 |
subtype3 | 3 | 5 |
subtype4 | 9 | 8 |
subtype5 | 6 | 8 |
Figure S41. Get High-res Image Clustering Approach #6: 'miR cHierClus subtypes' versus Clinical Feature #6: 'GENDER'

P value = 0.439 (Fisher's exact test), Q value = 0.56
Table S48. Clustering Approach #6: 'miR cHierClus subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 75 | 3 |
subtype1 | 19 | 0 |
subtype2 | 18 | 2 |
subtype3 | 8 | 0 |
subtype4 | 17 | 0 |
subtype5 | 13 | 1 |
Figure S42. Get High-res Image Clustering Approach #6: 'miR cHierClus subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'

Table S49. Description of clustering approach #7: 'Copy Number Ratio CNMF subtypes'
Cluster Labels | 1 | 2 | 3 | 4 | 5 |
---|---|---|---|---|---|
Number of samples | 18 | 21 | 37 | 2 | 2 |
P value = 2.39e-06 (logrank test), Q value = 4.2e-05
Table S50. Clustering Approach #7: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 75 | 22 | 0.1 - 85.5 (25.0) |
subtype1 | 17 | 9 | 1.6 - 49.7 (19.6) |
subtype2 | 21 | 11 | 0.1 - 45.3 (21.0) |
subtype3 | 36 | 2 | 0.2 - 85.5 (29.7) |
Figure S43. Get High-res Image Clustering Approach #7: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

P value = 0.0867 (Kruskal-Wallis (anova)), Q value = 0.32
Table S51. Clustering Approach #7: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 75 | 61.6 (14.1) |
subtype1 | 17 | 64.3 (14.0) |
subtype2 | 21 | 66.3 (12.0) |
subtype3 | 37 | 57.6 (14.5) |
Figure S44. Get High-res Image Clustering Approach #7: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

P value = 0.201 (Fisher's exact test), Q value = 0.47
Table S52. Clustering Approach #7: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
nPatients | STAGE IIA | STAGE IIB | STAGE IIIA | STAGE IIIB | STAGE IIIC | STAGE IV |
---|---|---|---|---|---|---|
ALL | 11 | 27 | 22 | 9 | 1 | 4 |
subtype1 | 1 | 5 | 7 | 2 | 0 | 2 |
subtype2 | 1 | 8 | 5 | 3 | 1 | 2 |
subtype3 | 9 | 14 | 10 | 4 | 0 | 0 |
Figure S45. Get High-res Image Clustering Approach #7: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

P value = 0.43 (Kruskal-Wallis (anova)), Q value = 0.56
Table S53. Clustering Approach #7: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
T2 | T3 | T4 | |
---|---|---|---|
ALL | 13 | 30 | 32 |
subtype1 | 2 | 6 | 9 |
subtype2 | 2 | 8 | 11 |
subtype3 | 9 | 16 | 12 |
Figure S46. Get High-res Image Clustering Approach #7: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

P value = 0.0658 (Fisher's exact test), Q value = 0.27
Table S54. Clustering Approach #7: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'
nPatients | CLASS0 | CLASS1 |
---|---|---|
ALL | 50 | 4 |
subtype1 | 12 | 2 |
subtype2 | 12 | 2 |
subtype3 | 26 | 0 |
Figure S47. Get High-res Image Clustering Approach #7: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

P value = 0.83 (Fisher's exact test), Q value = 0.91
Table S55. Clustering Approach #7: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 34 | 41 |
subtype1 | 9 | 8 |
subtype2 | 9 | 12 |
subtype3 | 16 | 21 |
Figure S48. Get High-res Image Clustering Approach #7: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'GENDER'

P value = 0.429 (Fisher's exact test), Q value = 0.56
Table S56. Clustering Approach #7: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 71 | 3 |
subtype1 | 16 | 0 |
subtype2 | 19 | 2 |
subtype3 | 36 | 1 |
Figure S49. Get High-res Image Clustering Approach #7: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'

Table S57. Description of clustering approach #8: 'Copy Number Threshold CNMF subtypes'
Cluster Labels | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
---|---|---|---|---|---|---|---|---|
Number of samples | 16 | 7 | 22 | 12 | 2 | 14 | 5 | 2 |
P value = 0.000237 (logrank test), Q value = 0.0021
Table S58. Clustering Approach #8: 'Copy Number Threshold CNMF subtypes' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 75 | 21 | 0.1 - 85.5 (25.0) |
subtype1 | 15 | 7 | 1.6 - 49.7 (19.6) |
subtype2 | 7 | 3 | 1.4 - 32.3 (13.6) |
subtype3 | 21 | 2 | 0.2 - 85.5 (26.1) |
subtype4 | 12 | 0 | 0.6 - 82.2 (36.4) |
subtype6 | 14 | 6 | 0.1 - 61.2 (36.9) |
subtype7 | 5 | 3 | 2.2 - 36.6 (24.0) |
Figure S50. Get High-res Image Clustering Approach #8: 'Copy Number Threshold CNMF subtypes' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

P value = 0.0745 (Kruskal-Wallis (anova)), Q value = 0.29
Table S59. Clustering Approach #8: 'Copy Number Threshold CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 75 | 61.7 (14.0) |
subtype1 | 15 | 64.3 (16.3) |
subtype2 | 7 | 73.4 (11.3) |
subtype3 | 22 | 61.2 (14.4) |
subtype4 | 12 | 55.2 (11.3) |
subtype6 | 14 | 59.1 (11.8) |
subtype7 | 5 | 63.4 (13.9) |
Figure S51. Get High-res Image Clustering Approach #8: 'Copy Number Threshold CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

P value = 0.682 (Fisher's exact test), Q value = 0.8
Table S60. Clustering Approach #8: 'Copy Number Threshold CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
nPatients | STAGE IIA | STAGE IIB | STAGE IIIA | STAGE IIIB | STAGE IIIC | STAGE IV |
---|---|---|---|---|---|---|
ALL | 10 | 26 | 23 | 10 | 1 | 4 |
subtype1 | 1 | 5 | 5 | 2 | 0 | 2 |
subtype2 | 0 | 2 | 2 | 1 | 1 | 1 |
subtype3 | 5 | 8 | 6 | 3 | 0 | 0 |
subtype4 | 3 | 4 | 3 | 2 | 0 | 0 |
subtype6 | 0 | 6 | 5 | 2 | 0 | 0 |
subtype7 | 1 | 1 | 2 | 0 | 0 | 1 |
Figure S52. Get High-res Image Clustering Approach #8: 'Copy Number Threshold CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

P value = 0.394 (Kruskal-Wallis (anova)), Q value = 0.54
Table S61. Clustering Approach #8: 'Copy Number Threshold CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
T2 | T3 | T4 | |
---|---|---|---|
ALL | 12 | 31 | 32 |
subtype1 | 2 | 6 | 7 |
subtype2 | 1 | 1 | 5 |
subtype3 | 5 | 10 | 7 |
subtype4 | 3 | 4 | 5 |
subtype6 | 0 | 9 | 5 |
subtype7 | 1 | 1 | 3 |
Figure S53. Get High-res Image Clustering Approach #8: 'Copy Number Threshold CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

P value = 0.112 (Fisher's exact test), Q value = 0.37
Table S62. Clustering Approach #8: 'Copy Number Threshold CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'
nPatients | CLASS0 | CLASS1 |
---|---|---|
ALL | 48 | 4 |
subtype1 | 11 | 2 |
subtype2 | 5 | 1 |
subtype3 | 16 | 0 |
subtype4 | 8 | 0 |
subtype6 | 6 | 0 |
subtype7 | 2 | 1 |
Figure S54. Get High-res Image Clustering Approach #8: 'Copy Number Threshold CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

P value = 0.327 (Fisher's exact test), Q value = 0.48
Table S63. Clustering Approach #8: 'Copy Number Threshold CNMF subtypes' versus Clinical Feature #6: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 32 | 43 |
subtype1 | 6 | 9 |
subtype2 | 1 | 6 |
subtype3 | 11 | 11 |
subtype4 | 4 | 8 |
subtype6 | 6 | 8 |
subtype7 | 4 | 1 |
Figure S55. Get High-res Image Clustering Approach #8: 'Copy Number Threshold CNMF subtypes' versus Clinical Feature #6: 'GENDER'

P value = 0.297 (Fisher's exact test), Q value = 0.47
Table S64. Clustering Approach #8: 'Copy Number Threshold CNMF subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 72 | 2 |
subtype1 | 14 | 0 |
subtype2 | 6 | 1 |
subtype3 | 22 | 0 |
subtype4 | 12 | 0 |
subtype6 | 13 | 1 |
subtype7 | 5 | 0 |
Figure S56. Get High-res Image Clustering Approach #8: 'Copy Number Threshold CNMF subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'

Table S65. Description of clustering approach #9: 'Methylation CNMF subtypes'
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 28 | 13 | 16 | 23 |
P value = 6.68e-08 (logrank test), Q value = 4.7e-06
Table S66. Clustering Approach #9: 'Methylation CNMF subtypes' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 79 | 22 | 0.1 - 85.5 (26.1) |
subtype1 | 28 | 17 | 0.1 - 45.3 (19.3) |
subtype2 | 13 | 1 | 0.2 - 85.5 (27.5) |
subtype3 | 15 | 2 | 1.3 - 82.2 (31.8) |
subtype4 | 23 | 2 | 0.2 - 46.8 (27.6) |
Figure S57. Get High-res Image Clustering Approach #9: 'Methylation CNMF subtypes' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

P value = 0.0928 (Kruskal-Wallis (anova)), Q value = 0.32
Table S67. Clustering Approach #9: 'Methylation CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 79 | 61.5 (14.0) |
subtype1 | 28 | 66.9 (12.1) |
subtype2 | 13 | 60.8 (12.9) |
subtype3 | 15 | 60.0 (13.2) |
subtype4 | 23 | 56.4 (15.8) |
Figure S58. Get High-res Image Clustering Approach #9: 'Methylation CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

P value = 0.448 (Fisher's exact test), Q value = 0.56
Table S68. Clustering Approach #9: 'Methylation CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
nPatients | STAGE IIA | STAGE IIB | STAGE IIIA | STAGE IIIB | STAGE IIIC | STAGE IV |
---|---|---|---|---|---|---|
ALL | 11 | 27 | 25 | 10 | 1 | 4 |
subtype1 | 1 | 10 | 8 | 3 | 1 | 4 |
subtype2 | 2 | 4 | 5 | 2 | 0 | 0 |
subtype3 | 2 | 4 | 7 | 2 | 0 | 0 |
subtype4 | 6 | 9 | 5 | 3 | 0 | 0 |
Figure S59. Get High-res Image Clustering Approach #9: 'Methylation CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

P value = 0.642 (Kruskal-Wallis (anova)), Q value = 0.76
Table S69. Clustering Approach #9: 'Methylation CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
T2 | T3 | T4 | |
---|---|---|---|
ALL | 13 | 32 | 34 |
subtype1 | 2 | 11 | 15 |
subtype2 | 2 | 6 | 5 |
subtype3 | 3 | 6 | 6 |
subtype4 | 6 | 9 | 8 |
Figure S60. Get High-res Image Clustering Approach #9: 'Methylation CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

P value = 0.185 (Fisher's exact test), Q value = 0.46
Table S70. Clustering Approach #9: 'Methylation CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'
nPatients | CLASS0 | CLASS1 |
---|---|---|
ALL | 51 | 4 |
subtype1 | 18 | 4 |
subtype2 | 6 | 0 |
subtype3 | 11 | 0 |
subtype4 | 16 | 0 |
Figure S61. Get High-res Image Clustering Approach #9: 'Methylation CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

P value = 0.246 (Fisher's exact test), Q value = 0.47
Table S71. Clustering Approach #9: 'Methylation CNMF subtypes' versus Clinical Feature #6: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 34 | 45 |
subtype1 | 11 | 17 |
subtype2 | 5 | 8 |
subtype3 | 10 | 5 |
subtype4 | 8 | 15 |
Figure S62. Get High-res Image Clustering Approach #9: 'Methylation CNMF subtypes' versus Clinical Feature #6: 'GENDER'

P value = 0.879 (Fisher's exact test), Q value = 0.94
Table S72. Clustering Approach #9: 'Methylation CNMF subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 75 | 3 |
subtype1 | 25 | 2 |
subtype2 | 13 | 0 |
subtype3 | 15 | 0 |
subtype4 | 22 | 1 |
Figure S63. Get High-res Image Clustering Approach #9: 'Methylation CNMF subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'

Table S73. Description of clustering approach #10: 'METHYLATION CHIERARCHICAL'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 40 | 15 | 25 |
P value = 4.96e-05 (logrank test), Q value = 5e-04
Table S74. Clustering Approach #10: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 79 | 22 | 0.1 - 85.5 (26.1) |
subtype1 | 39 | 19 | 0.1 - 61.2 (19.9) |
subtype2 | 15 | 2 | 0.2 - 85.5 (31.8) |
subtype3 | 25 | 1 | 0.2 - 46.8 (27.5) |
Figure S64. Get High-res Image Clustering Approach #10: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

P value = 0.133 (Kruskal-Wallis (anova)), Q value = 0.4
Table S75. Clustering Approach #10: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 79 | 61.5 (14.0) |
subtype1 | 39 | 64.9 (12.8) |
subtype2 | 15 | 60.9 (13.0) |
subtype3 | 25 | 56.7 (15.4) |
Figure S65. Get High-res Image Clustering Approach #10: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

P value = 0.293 (Fisher's exact test), Q value = 0.47
Table S76. Clustering Approach #10: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
nPatients | STAGE IIA | STAGE IIB | STAGE IIIA | STAGE IIIB | STAGE IIIC | STAGE IV |
---|---|---|---|---|---|---|
ALL | 11 | 27 | 25 | 10 | 1 | 4 |
subtype1 | 2 | 13 | 14 | 4 | 1 | 4 |
subtype2 | 2 | 5 | 5 | 3 | 0 | 0 |
subtype3 | 7 | 9 | 6 | 3 | 0 | 0 |
Figure S66. Get High-res Image Clustering Approach #10: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

P value = 0.456 (Kruskal-Wallis (anova)), Q value = 0.56
Table S77. Clustering Approach #10: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
T2 | T3 | T4 | |
---|---|---|---|
ALL | 13 | 32 | 34 |
subtype1 | 4 | 16 | 19 |
subtype2 | 2 | 7 | 6 |
subtype3 | 7 | 9 | 9 |
Figure S67. Get High-res Image Clustering Approach #10: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

P value = 0.173 (Fisher's exact test), Q value = 0.46
Table S78. Clustering Approach #10: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'
nPatients | CLASS0 | CLASS1 |
---|---|---|
ALL | 51 | 4 |
subtype1 | 24 | 4 |
subtype2 | 11 | 0 |
subtype3 | 16 | 0 |
Figure S68. Get High-res Image Clustering Approach #10: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

P value = 1 (Fisher's exact test), Q value = 1
Table S79. Clustering Approach #10: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #6: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 34 | 45 |
subtype1 | 17 | 22 |
subtype2 | 6 | 9 |
subtype3 | 11 | 14 |
Figure S69. Get High-res Image Clustering Approach #10: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #6: 'GENDER'

P value = 1 (Fisher's exact test), Q value = 1
Table S80. Clustering Approach #10: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #7: 'RADIATION_THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 75 | 3 |
subtype1 | 36 | 2 |
subtype2 | 15 | 0 |
subtype3 | 24 | 1 |
Figure S70. Get High-res Image Clustering Approach #10: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #7: 'RADIATION_THERAPY'

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Cluster data file = /cromwell_root/fc-f5144117-2d5a-42c2-8998-5b38e52db5d9/9d897bdb-7a9a-423a-ad17-06f28ae98803/aggregate_clusters_workflow/1d8bac82-5a1d-4cd2-aeb1-8ddd0e1e7a20/call-aggregate_clusters/TCGA-UVM-TP.mergedcluster.txt
-
Clinical data file = /cromwell_root/fc-2289d790-de74-4808-9b0a-cefafc34d859/0d7c7dcf-18e0-4b2d-afc0-a0b2ee1e45ff/preprocess_clinical_workflow/70152ac6-f707-4277-8d60-8770b1b366c6/call-preprocess_clinical/TCGA-UVM-TP.clin.merged.picked.txt
-
Number of patients = 80
-
Number of clustering approaches = 10
-
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 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.