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 15 clinical features across 484 patients, 58 significant findings detected with P value < 0.05 and Q value < 0.25.
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3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'AGE', 'NEOPLASM.DISEASESTAGE', and 'EXTRATHYROIDAL.EXTENSION'.
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3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.N.STAGE', 'HISTOLOGICAL.TYPE', 'EXTRATHYROIDAL.EXTENSION', and 'NUMBER.OF.LYMPH.NODES'.
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CNMF clustering analysis on RPPA data identified 3 subtypes that correlate to 'AGE', 'NEOPLASM.DISEASESTAGE', 'HISTOLOGICAL.TYPE', and 'EXTRATHYROIDAL.EXTENSION'.
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Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that correlate to 'AGE', 'PATHOLOGY.N.STAGE', 'HISTOLOGICAL.TYPE', and 'EXTRATHYROIDAL.EXTENSION'.
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CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.STAGE', 'PATHOLOGY.N.STAGE', 'HISTOLOGICAL.TYPE', 'EXTRATHYROIDAL.EXTENSION', and 'NUMBER.OF.LYMPH.NODES'.
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Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.STAGE', 'PATHOLOGY.N.STAGE', 'PATHOLOGY.M.STAGE', 'HISTOLOGICAL.TYPE', 'EXTRATHYROIDAL.EXTENSION', and 'NUMBER.OF.LYMPH.NODES'.
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3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.STAGE', 'PATHOLOGY.N.STAGE', 'PATHOLOGY.M.STAGE', 'HISTOLOGICAL.TYPE', 'EXTRATHYROIDAL.EXTENSION', and 'NUMBER.OF.LYMPH.NODES'.
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3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.STAGE', 'PATHOLOGY.N.STAGE', 'PATHOLOGY.M.STAGE', 'HISTOLOGICAL.TYPE', 'EXTRATHYROIDAL.EXTENSION', and 'NUMBER.OF.LYMPH.NODES'.
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3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.STAGE', 'PATHOLOGY.N.STAGE', 'PATHOLOGY.M.STAGE', 'HISTOLOGICAL.TYPE', 'EXTRATHYROIDAL.EXTENSION', and 'NUMBER.OF.LYMPH.NODES'.
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3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'AGE', 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.STAGE', 'PATHOLOGY.N.STAGE', 'PATHOLOGY.M.STAGE', 'HISTOLOGICAL.TYPE', 'EXTRATHYROIDAL.EXTENSION', and 'NUMBER.OF.LYMPH.NODES'.
Table 1. Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 15 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 58 significant findings detected.
Clinical Features |
Statistical Tests |
Copy Number Ratio CNMF subtypes |
METHLYATION CNMF |
RPPA CNMF subtypes |
RPPA cHierClus subtypes |
RNAseq CNMF subtypes |
RNAseq cHierClus subtypes |
MIRSEQ CNMF |
MIRSEQ CHIERARCHICAL |
MIRseq Mature CNMF subtypes |
MIRseq Mature cHierClus subtypes |
Time to Death | logrank test |
0.0594 (1.00) |
0.6 (1.00) |
0.573 (1.00) |
0.0612 (1.00) |
0.642 (1.00) |
0.792 (1.00) |
0.0313 (1.00) |
0.0599 (1.00) |
0.00454 (0.409) |
0.0219 (1.00) |
AGE | ANOVA |
2.75e-05 (0.00319) |
0.266 (1.00) |
0.00189 (0.179) |
0.000969 (0.0959) |
0.115 (1.00) |
0.325 (1.00) |
0.17 (1.00) |
0.0318 (1.00) |
0.0744 (1.00) |
0.00194 (0.183) |
NEOPLASM DISEASESTAGE | Chi-square test |
0.000296 (0.0316) |
1.7e-05 (0.00202) |
0.00174 (0.167) |
0.0094 (0.836) |
0.00012 (0.0134) |
1.68e-05 (0.00202) |
2.51e-06 (0.000316) |
1.45e-07 (1.88e-05) |
0.000356 (0.0374) |
9.18e-09 (1.25e-06) |
PATHOLOGY T STAGE | Chi-square test |
0.511 (1.00) |
0.00323 (0.297) |
0.00431 (0.392) |
0.0241 (1.00) |
7.31e-06 (0.000899) |
2.14e-05 (0.0025) |
0.000214 (0.0233) |
7.57e-05 (0.00863) |
0.000589 (0.06) |
2.84e-05 (0.00326) |
PATHOLOGY N STAGE | Fisher's exact test |
0.0844 (1.00) |
3.22e-13 (4.48e-11) |
0.0327 (1.00) |
0.000106 (0.0119) |
1.99e-14 (2.78e-12) |
4.18e-17 (5.98e-15) |
3.91e-12 (5.39e-10) |
1.43e-14 (2.01e-12) |
2.6e-10 (3.56e-08) |
9.2e-15 (1.31e-12) |
PATHOLOGY M STAGE | Chi-square test |
0.0405 (1.00) |
0.148 (1.00) |
0.248 (1.00) |
0.389 (1.00) |
0.0489 (1.00) |
0.000455 (0.0474) |
0.000721 (0.0728) |
0.00131 (0.128) |
0.000295 (0.0316) |
0.000476 (0.049) |
GENDER | Fisher's exact test |
0.275 (1.00) |
0.862 (1.00) |
0.725 (1.00) |
0.491 (1.00) |
0.949 (1.00) |
0.699 (1.00) |
0.833 (1.00) |
0.661 (1.00) |
0.623 (1.00) |
0.65 (1.00) |
HISTOLOGICAL TYPE | Chi-square test |
0.218 (1.00) |
5.05e-27 (7.28e-25) |
0.000105 (0.0119) |
1.04e-08 (1.41e-06) |
1.9e-32 (2.78e-30) |
3.42e-35 (5.06e-33) |
2.45e-34 (3.6e-32) |
7.71e-36 (1.15e-33) |
9.14e-30 (1.33e-27) |
1.57e-36 (2.35e-34) |
RADIATIONS RADIATION REGIMENINDICATION | Fisher's exact test |
0.323 (1.00) |
0.0885 (1.00) |
0.0102 (0.898) |
0.249 (1.00) |
0.0147 (1.00) |
0.193 (1.00) |
0.457 (1.00) |
0.0379 (1.00) |
0.0793 (1.00) |
0.0153 (1.00) |
RADIATIONEXPOSURE | Fisher's exact test |
0.15 (1.00) |
0.499 (1.00) |
1 (1.00) |
0.111 (1.00) |
0.93 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
0.808 (1.00) |
0.867 (1.00) |
EXTRATHYROIDAL EXTENSION | Chi-square test |
0.00251 (0.233) |
2.48e-06 (0.000315) |
0.000891 (0.0891) |
0.00108 (0.106) |
5.21e-08 (6.83e-06) |
4.19e-07 (5.41e-05) |
3.73e-08 (4.93e-06) |
3.4e-08 (4.53e-06) |
1.93e-06 (0.000247) |
2.14e-08 (2.87e-06) |
COMPLETENESS OF RESECTION | Chi-square test |
0.448 (1.00) |
0.172 (1.00) |
0.336 (1.00) |
0.854 (1.00) |
0.256 (1.00) |
0.41 (1.00) |
0.198 (1.00) |
0.261 (1.00) |
0.112 (1.00) |
0.0957 (1.00) |
NUMBER OF LYMPH NODES | ANOVA |
0.191 (1.00) |
0.000189 (0.0208) |
0.872 (1.00) |
0.52 (1.00) |
1.19e-05 (0.00144) |
9.51e-06 (0.00116) |
5.95e-06 (0.000738) |
4.57e-06 (0.000571) |
0.000237 (0.0256) |
1.89e-05 (0.00223) |
MULTIFOCALITY | Fisher's exact test |
0.314 (1.00) |
0.931 (1.00) |
0.0116 (1.00) |
0.214 (1.00) |
0.177 (1.00) |
0.304 (1.00) |
0.878 (1.00) |
0.916 (1.00) |
0.417 (1.00) |
0.818 (1.00) |
TUMOR SIZE | ANOVA |
0.0139 (1.00) |
0.609 (1.00) |
0.607 (1.00) |
0.605 (1.00) |
0.38 (1.00) |
0.0168 (1.00) |
0.0205 (1.00) |
0.135 (1.00) |
0.0299 (1.00) |
0.212 (1.00) |
Table S1. Description of clustering approach #1: 'Copy Number Ratio CNMF subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 31 | 376 | 75 |
P value = 0.0594 (logrank test), Q value = 1
Table S2. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 477 | 14 | 0.0 - 158.8 (14.8) |
subtype1 | 30 | 3 | 0.0 - 98.6 (14.0) |
subtype2 | 372 | 10 | 0.1 - 158.8 (15.1) |
subtype3 | 75 | 1 | 0.0 - 85.1 (14.5) |
Figure S1. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D1V1.png)
P value = 2.75e-05 (ANOVA), Q value = 0.0032
Table S3. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 482 | 47.1 (15.6) |
subtype1 | 31 | 59.4 (14.4) |
subtype2 | 376 | 46.2 (15.7) |
subtype3 | 75 | 46.9 (13.3) |
Figure S2. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'
![](D1V2.png)
P value = 0.000296 (Chi-square test), Q value = 0.032
Table S4. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'
nPatients | STAGE I | STAGE II | STAGE III | STAGE IV | STAGE IVA | STAGE IVC |
---|---|---|---|---|---|---|
ALL | 273 | 50 | 106 | 2 | 43 | 6 |
subtype1 | 8 | 10 | 9 | 1 | 2 | 1 |
subtype2 | 220 | 33 | 84 | 0 | 33 | 4 |
subtype3 | 45 | 7 | 13 | 1 | 8 | 1 |
Figure S3. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'
![](D1V3.png)
P value = 0.511 (Chi-square test), Q value = 1
Table S5. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'
nPatients | T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 139 | 160 | 160 | 21 |
subtype1 | 5 | 12 | 12 | 2 |
subtype2 | 109 | 120 | 129 | 16 |
subtype3 | 25 | 28 | 19 | 3 |
Figure S4. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'
![](D1V4.png)
P value = 0.0844 (Fisher's exact test), Q value = 1
Table S6. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'
nPatients | 0 | 1 |
---|---|---|
ALL | 218 | 217 |
subtype1 | 17 | 7 |
subtype2 | 164 | 177 |
subtype3 | 37 | 33 |
Figure S5. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'
![](D1V5.png)
P value = 0.0405 (Chi-square test), Q value = 1
Table S7. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'
nPatients | M0 | M1 | MX |
---|---|---|---|
ALL | 264 | 9 | 208 |
subtype1 | 9 | 1 | 21 |
subtype2 | 216 | 6 | 153 |
subtype3 | 39 | 2 | 34 |
Figure S6. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'
![](D1V6.png)
P value = 0.275 (Fisher's exact test), Q value = 1
Table S8. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 354 | 128 |
subtype1 | 19 | 12 |
subtype2 | 280 | 96 |
subtype3 | 55 | 20 |
Figure S7. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'
![](D1V7.png)
P value = 0.218 (Chi-square test), Q value = 1
Table S9. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'
nPatients | OTHER SPECIFY | THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL | THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED) | THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES) |
---|---|---|---|---|
ALL | 9 | 341 | 97 | 35 |
subtype1 | 0 | 20 | 10 | 1 |
subtype2 | 8 | 270 | 67 | 31 |
subtype3 | 1 | 51 | 20 | 3 |
Figure S8. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'
![](D1V8.png)
P value = 0.323 (Fisher's exact test), Q value = 1
Table S10. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 14 | 468 |
subtype1 | 2 | 29 |
subtype2 | 11 | 365 |
subtype3 | 1 | 74 |
Figure S9. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D1V9.png)
P value = 0.15 (Fisher's exact test), Q value = 1
Table S11. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'RADIATIONEXPOSURE'
nPatients | NO | YES |
---|---|---|
ALL | 407 | 17 |
subtype1 | 27 | 1 |
subtype2 | 311 | 16 |
subtype3 | 69 | 0 |
Figure S10. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'RADIATIONEXPOSURE'
![](D1V10.png)
P value = 0.00251 (Chi-square test), Q value = 0.23
Table S12. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'EXTRATHYROIDAL.EXTENSION'
nPatients | MINIMAL (T3) | MODERATE/ADVANCED (T4A) | NONE | VERY ADVANCED (T4B) |
---|---|---|---|---|
ALL | 126 | 16 | 322 | 1 |
subtype1 | 6 | 0 | 23 | 1 |
subtype2 | 105 | 15 | 243 | 0 |
subtype3 | 15 | 1 | 56 | 0 |
Figure S11. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'EXTRATHYROIDAL.EXTENSION'
![](D1V11.png)
P value = 0.448 (Chi-square test), Q value = 1
Table S13. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'
nPatients | R0 | R1 | R2 | RX |
---|---|---|---|---|
ALL | 371 | 47 | 4 | 29 |
subtype1 | 22 | 3 | 0 | 4 |
subtype2 | 293 | 38 | 3 | 18 |
subtype3 | 56 | 6 | 1 | 7 |
Figure S12. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'
![](D1V12.png)
P value = 0.191 (ANOVA), Q value = 1
Table S14. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 383 | 3.6 (6.2) |
subtype1 | 22 | 1.5 (3.7) |
subtype2 | 301 | 3.8 (6.5) |
subtype3 | 60 | 3.1 (5.4) |
Figure S13. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'
![](D1V13.png)
P value = 0.314 (Fisher's exact test), Q value = 1
Table S15. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'
nPatients | MULTIFOCAL | UNIFOCAL |
---|---|---|
ALL | 218 | 254 |
subtype1 | 12 | 19 |
subtype2 | 167 | 201 |
subtype3 | 39 | 34 |
Figure S14. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'
![](D1V14.png)
P value = 0.0139 (ANOVA), Q value = 1
Table S16. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 384 | 3.0 (1.6) |
subtype1 | 26 | 3.8 (1.7) |
subtype2 | 300 | 2.9 (1.6) |
subtype3 | 58 | 2.8 (1.4) |
Figure S15. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'
![](D1V15.png)
Table S17. Description of clustering approach #2: 'METHLYATION CNMF'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 149 | 79 | 256 |
P value = 0.6 (logrank test), Q value = 1
Table S18. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 479 | 14 | 0.0 - 158.8 (14.8) |
subtype1 | 147 | 3 | 0.0 - 132.4 (14.0) |
subtype2 | 76 | 4 | 0.0 - 157.2 (13.9) |
subtype3 | 256 | 7 | 0.1 - 158.8 (15.8) |
Figure S16. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'
![](D2V1.png)
P value = 0.266 (ANOVA), Q value = 1
Table S19. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 484 | 47.2 (15.6) |
subtype1 | 149 | 48.7 (15.1) |
subtype2 | 79 | 45.2 (16.4) |
subtype3 | 256 | 46.9 (15.6) |
Figure S17. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'
![](D2V2.png)
P value = 1.7e-05 (Chi-square test), Q value = 0.002
Table S20. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'
nPatients | STAGE I | STAGE II | STAGE III | STAGE IV | STAGE IVA | STAGE IVC |
---|---|---|---|---|---|---|
ALL | 274 | 51 | 106 | 2 | 43 | 6 |
subtype1 | 86 | 30 | 26 | 1 | 3 | 2 |
subtype2 | 51 | 2 | 18 | 1 | 7 | 0 |
subtype3 | 137 | 19 | 62 | 0 | 33 | 4 |
Figure S18. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'
![](D2V3.png)
P value = 0.00323 (Chi-square test), Q value = 0.3
Table S21. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'
nPatients | T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 140 | 161 | 160 | 21 |
subtype1 | 54 | 55 | 39 | 1 |
subtype2 | 26 | 24 | 26 | 2 |
subtype3 | 60 | 82 | 95 | 18 |
Figure S19. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'
![](D2V4.png)
P value = 3.22e-13 (Fisher's exact test), Q value = 4.5e-11
Table S22. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'
nPatients | 0 | 1 |
---|---|---|
ALL | 220 | 217 |
subtype1 | 98 | 27 |
subtype2 | 31 | 45 |
subtype3 | 91 | 145 |
Figure S20. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'
![](D2V5.png)
P value = 0.148 (Chi-square test), Q value = 1
Table S23. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'
nPatients | M0 | M1 | MX |
---|---|---|---|
ALL | 265 | 9 | 209 |
subtype1 | 70 | 3 | 75 |
subtype2 | 47 | 0 | 32 |
subtype3 | 148 | 6 | 102 |
Figure S21. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'
![](D2V6.png)
P value = 0.862 (Fisher's exact test), Q value = 1
Table S24. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 355 | 129 |
subtype1 | 108 | 41 |
subtype2 | 60 | 19 |
subtype3 | 187 | 69 |
Figure S22. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'
![](D2V7.png)
P value = 5.05e-27 (Chi-square test), Q value = 7.3e-25
Table S25. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'
nPatients | OTHER SPECIFY | THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL | THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED) | THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES) |
---|---|---|---|---|
ALL | 9 | 341 | 99 | 35 |
subtype1 | 2 | 70 | 77 | 0 |
subtype2 | 2 | 65 | 6 | 6 |
subtype3 | 5 | 206 | 16 | 29 |
Figure S23. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'
![](D2V8.png)
P value = 0.0885 (Fisher's exact test), Q value = 1
Table S26. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 14 | 470 |
subtype1 | 1 | 148 |
subtype2 | 2 | 77 |
subtype3 | 11 | 245 |
Figure S24. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D2V9.png)
P value = 0.499 (Fisher's exact test), Q value = 1
Table S27. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'RADIATIONEXPOSURE'
nPatients | NO | YES |
---|---|---|
ALL | 408 | 17 |
subtype1 | 121 | 6 |
subtype2 | 67 | 4 |
subtype3 | 220 | 7 |
Figure S25. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'RADIATIONEXPOSURE'
![](D2V10.png)
P value = 2.48e-06 (Chi-square test), Q value = 0.00031
Table S28. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'EXTRATHYROIDAL.EXTENSION'
nPatients | MINIMAL (T3) | MODERATE/ADVANCED (T4A) | NONE | VERY ADVANCED (T4B) |
---|---|---|---|---|
ALL | 126 | 16 | 324 | 1 |
subtype1 | 21 | 0 | 120 | 0 |
subtype2 | 20 | 1 | 54 | 1 |
subtype3 | 85 | 15 | 150 | 0 |
Figure S26. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'EXTRATHYROIDAL.EXTENSION'
![](D2V11.png)
P value = 0.172 (Chi-square test), Q value = 1
Table S29. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'
nPatients | R0 | R1 | R2 | RX |
---|---|---|---|---|
ALL | 373 | 47 | 4 | 29 |
subtype1 | 120 | 8 | 0 | 7 |
subtype2 | 60 | 8 | 0 | 4 |
subtype3 | 193 | 31 | 4 | 18 |
Figure S27. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'
![](D2V12.png)
P value = 0.000189 (ANOVA), Q value = 0.021
Table S30. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 384 | 3.6 (6.2) |
subtype1 | 104 | 1.4 (4.0) |
subtype2 | 67 | 4.2 (7.1) |
subtype3 | 213 | 4.4 (6.6) |
Figure S28. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'
![](D2V13.png)
P value = 0.931 (Fisher's exact test), Q value = 1
Table S31. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #14: 'MULTIFOCALITY'
nPatients | MULTIFOCAL | UNIFOCAL |
---|---|---|
ALL | 219 | 255 |
subtype1 | 68 | 78 |
subtype2 | 34 | 43 |
subtype3 | 117 | 134 |
Figure S29. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #14: 'MULTIFOCALITY'
![](D2V14.png)
P value = 0.609 (ANOVA), Q value = 1
Table S32. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #15: 'TUMOR.SIZE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 385 | 3.0 (1.6) |
subtype1 | 117 | 3.1 (1.6) |
subtype2 | 63 | 3.0 (1.6) |
subtype3 | 205 | 2.9 (1.5) |
Figure S30. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #15: 'TUMOR.SIZE'
![](D2V15.png)
Table S33. Description of clustering approach #3: 'RPPA CNMF subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 60 | 78 | 79 |
P value = 0.573 (logrank test), Q value = 1
Table S34. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 216 | 13 | 0.1 - 158.8 (17.4) |
subtype1 | 60 | 4 | 1.0 - 158.8 (16.4) |
subtype2 | 78 | 4 | 0.2 - 147.4 (21.1) |
subtype3 | 78 | 5 | 0.1 - 147.8 (14.5) |
Figure S31. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D3V1.png)
P value = 0.00189 (ANOVA), Q value = 0.18
Table S35. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 217 | 48.4 (16.8) |
subtype1 | 60 | 52.0 (15.2) |
subtype2 | 78 | 50.8 (16.0) |
subtype3 | 79 | 43.1 (17.5) |
Figure S32. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'
![](D3V2.png)
P value = 0.00174 (Chi-square test), Q value = 0.17
Table S36. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'
nPatients | STAGE I | STAGE II | STAGE III | STAGE IVA | STAGE IVC |
---|---|---|---|---|---|
ALL | 113 | 33 | 45 | 20 | 4 |
subtype1 | 30 | 11 | 14 | 2 | 1 |
subtype2 | 30 | 13 | 21 | 14 | 0 |
subtype3 | 53 | 9 | 10 | 4 | 3 |
Figure S33. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'
![](D3V3.png)
P value = 0.00431 (Chi-square test), Q value = 0.39
Table S37. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'
nPatients | T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 51 | 81 | 73 | 11 |
subtype1 | 22 | 22 | 15 | 0 |
subtype2 | 13 | 24 | 33 | 8 |
subtype3 | 16 | 35 | 25 | 3 |
Figure S34. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'
![](D3V4.png)
P value = 0.0327 (Fisher's exact test), Q value = 1
Table S38. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'
nPatients | 0 | 1 |
---|---|---|
ALL | 95 | 93 |
subtype1 | 33 | 18 |
subtype2 | 28 | 41 |
subtype3 | 34 | 34 |
Figure S35. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'
![](D3V5.png)
P value = 0.248 (Chi-square test), Q value = 1
Table S39. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'
nPatients | M0 | M1 | MX |
---|---|---|---|
ALL | 114 | 5 | 97 |
subtype1 | 37 | 1 | 21 |
subtype2 | 42 | 1 | 35 |
subtype3 | 35 | 3 | 41 |
Figure S36. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'
![](D3V6.png)
P value = 0.725 (Fisher's exact test), Q value = 1
Table S40. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 150 | 67 |
subtype1 | 42 | 18 |
subtype2 | 56 | 22 |
subtype3 | 52 | 27 |
Figure S37. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'
![](D3V7.png)
P value = 0.000105 (Chi-square test), Q value = 0.012
Table S41. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'
nPatients | OTHER SPECIFY | THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL | THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED) | THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES) |
---|---|---|---|---|
ALL | 2 | 153 | 52 | 10 |
subtype1 | 0 | 34 | 25 | 1 |
subtype2 | 1 | 54 | 14 | 9 |
subtype3 | 1 | 65 | 13 | 0 |
Figure S38. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'
![](D3V8.png)
P value = 0.0102 (Fisher's exact test), Q value = 0.9
Table S42. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 13 | 204 |
subtype1 | 0 | 60 |
subtype2 | 9 | 69 |
subtype3 | 4 | 75 |
Figure S39. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D3V9.png)
P value = 1 (Fisher's exact test), Q value = 1
Table S43. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONEXPOSURE'
nPatients | NO | YES |
---|---|---|
ALL | 181 | 11 |
subtype1 | 52 | 3 |
subtype2 | 66 | 4 |
subtype3 | 63 | 4 |
Figure S40. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONEXPOSURE'
![](D3V10.png)
P value = 0.000891 (Chi-square test), Q value = 0.089
Table S44. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'EXTRATHYROIDAL.EXTENSION'
nPatients | MINIMAL (T3) | MODERATE/ADVANCED (T4A) | NONE |
---|---|---|---|
ALL | 55 | 10 | 145 |
subtype1 | 13 | 0 | 47 |
subtype2 | 28 | 8 | 40 |
subtype3 | 14 | 2 | 58 |
Figure S41. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'EXTRATHYROIDAL.EXTENSION'
![](D3V11.png)
P value = 0.336 (Chi-square test), Q value = 1
Table S45. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'
nPatients | R0 | R1 | R2 | RX |
---|---|---|---|---|
ALL | 165 | 21 | 2 | 14 |
subtype1 | 47 | 6 | 1 | 5 |
subtype2 | 58 | 12 | 0 | 4 |
subtype3 | 60 | 3 | 1 | 5 |
Figure S42. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'
![](D3V12.png)
P value = 0.872 (ANOVA), Q value = 1
Table S46. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 167 | 3.4 (5.9) |
subtype1 | 43 | 3.8 (7.5) |
subtype2 | 64 | 3.2 (4.5) |
subtype3 | 60 | 3.5 (5.9) |
Figure S43. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'
![](D3V13.png)
P value = 0.0116 (Fisher's exact test), Q value = 1
Table S47. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'
nPatients | MULTIFOCAL | UNIFOCAL |
---|---|---|
ALL | 100 | 110 |
subtype1 | 35 | 22 |
subtype2 | 27 | 49 |
subtype3 | 38 | 39 |
Figure S44. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'
![](D3V14.png)
P value = 0.607 (ANOVA), Q value = 1
Table S48. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 186 | 3.3 (1.6) |
subtype1 | 51 | 3.2 (1.6) |
subtype2 | 65 | 3.4 (1.6) |
subtype3 | 70 | 3.2 (1.5) |
Figure S45. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'
![](D3V15.png)
Table S49. Description of clustering approach #4: 'RPPA cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 31 | 92 | 94 |
P value = 0.0612 (logrank test), Q value = 1
Table S50. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 216 | 13 | 0.1 - 158.8 (17.4) |
subtype1 | 31 | 4 | 1.1 - 147.4 (19.8) |
subtype2 | 91 | 3 | 0.1 - 147.8 (17.5) |
subtype3 | 94 | 6 | 0.3 - 158.8 (16.6) |
Figure S46. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
![](D4V1.png)
P value = 0.000969 (ANOVA), Q value = 0.096
Table S51. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 217 | 48.4 (16.8) |
subtype1 | 31 | 54.7 (16.1) |
subtype2 | 92 | 43.7 (16.2) |
subtype3 | 94 | 50.8 (16.5) |
Figure S47. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'
![](D4V2.png)
P value = 0.0094 (Chi-square test), Q value = 0.84
Table S52. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'
nPatients | STAGE I | STAGE II | STAGE III | STAGE IVA | STAGE IVC |
---|---|---|---|---|---|
ALL | 113 | 33 | 45 | 20 | 4 |
subtype1 | 8 | 9 | 8 | 6 | 0 |
subtype2 | 53 | 7 | 20 | 10 | 2 |
subtype3 | 52 | 17 | 17 | 4 | 2 |
Figure S48. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'
![](D4V3.png)
P value = 0.0241 (Chi-square test), Q value = 1
Table S53. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'
nPatients | T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 51 | 81 | 73 | 11 |
subtype1 | 6 | 11 | 10 | 4 |
subtype2 | 15 | 33 | 37 | 6 |
subtype3 | 30 | 37 | 26 | 1 |
Figure S49. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'
![](D4V4.png)
P value = 0.000106 (Fisher's exact test), Q value = 0.012
Table S54. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'
nPatients | 0 | 1 |
---|---|---|
ALL | 95 | 93 |
subtype1 | 13 | 11 |
subtype2 | 28 | 55 |
subtype3 | 54 | 27 |
Figure S50. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'
![](D4V5.png)
P value = 0.389 (Chi-square test), Q value = 1
Table S55. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'
nPatients | M0 | M1 | MX |
---|---|---|---|
ALL | 114 | 5 | 97 |
subtype1 | 14 | 0 | 17 |
subtype2 | 54 | 3 | 35 |
subtype3 | 46 | 2 | 45 |
Figure S51. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'
![](D4V6.png)
P value = 0.491 (Fisher's exact test), Q value = 1
Table S56. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 150 | 67 |
subtype1 | 24 | 7 |
subtype2 | 64 | 28 |
subtype3 | 62 | 32 |
Figure S52. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'
![](D4V7.png)
P value = 1.04e-08 (Chi-square test), Q value = 1.4e-06
Table S57. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'
nPatients | OTHER SPECIFY | THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL | THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED) | THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES) |
---|---|---|---|---|
ALL | 2 | 153 | 52 | 10 |
subtype1 | 1 | 15 | 14 | 1 |
subtype2 | 1 | 81 | 2 | 8 |
subtype3 | 0 | 57 | 36 | 1 |
Figure S53. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'
![](D4V8.png)
P value = 0.249 (Fisher's exact test), Q value = 1
Table S58. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 13 | 204 |
subtype1 | 3 | 28 |
subtype2 | 7 | 85 |
subtype3 | 3 | 91 |
Figure S54. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D4V9.png)
P value = 0.111 (Fisher's exact test), Q value = 1
Table S59. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONEXPOSURE'
nPatients | NO | YES |
---|---|---|
ALL | 181 | 11 |
subtype1 | 25 | 4 |
subtype2 | 75 | 4 |
subtype3 | 81 | 3 |
Figure S55. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONEXPOSURE'
![](D4V10.png)
P value = 0.00108 (Chi-square test), Q value = 0.11
Table S60. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'EXTRATHYROIDAL.EXTENSION'
nPatients | MINIMAL (T3) | MODERATE/ADVANCED (T4A) | NONE |
---|---|---|---|
ALL | 55 | 10 | 145 |
subtype1 | 8 | 4 | 19 |
subtype2 | 31 | 6 | 52 |
subtype3 | 16 | 0 | 74 |
Figure S56. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'EXTRATHYROIDAL.EXTENSION'
![](D4V11.png)
P value = 0.854 (Chi-square test), Q value = 1
Table S61. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'
nPatients | R0 | R1 | R2 | RX |
---|---|---|---|---|
ALL | 165 | 21 | 2 | 14 |
subtype1 | 26 | 2 | 0 | 3 |
subtype2 | 68 | 11 | 1 | 4 |
subtype3 | 71 | 8 | 1 | 7 |
Figure S57. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'
![](D4V12.png)
P value = 0.52 (ANOVA), Q value = 1
Table S62. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 167 | 3.4 (5.9) |
subtype1 | 24 | 2.6 (5.1) |
subtype2 | 74 | 4.0 (5.6) |
subtype3 | 69 | 3.1 (6.4) |
Figure S58. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'
![](D4V13.png)
P value = 0.214 (Fisher's exact test), Q value = 1
Table S63. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'
nPatients | MULTIFOCAL | UNIFOCAL |
---|---|---|
ALL | 100 | 110 |
subtype1 | 11 | 19 |
subtype2 | 40 | 49 |
subtype3 | 49 | 42 |
Figure S59. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'
![](D4V14.png)
P value = 0.605 (ANOVA), Q value = 1
Table S64. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 186 | 3.3 (1.6) |
subtype1 | 25 | 3.5 (1.6) |
subtype2 | 81 | 3.3 (1.5) |
subtype3 | 80 | 3.2 (1.6) |
Figure S60. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'
![](D4V15.png)
Table S65. Description of clustering approach #5: 'RNAseq CNMF subtypes'
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 150 | 50 | 117 | 159 |
P value = 0.642 (logrank test), Q value = 1
Table S66. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 471 | 14 | 0.0 - 158.8 (14.8) |
subtype1 | 149 | 4 | 0.0 - 132.4 (14.1) |
subtype2 | 48 | 1 | 0.1 - 157.2 (11.8) |
subtype3 | 116 | 2 | 0.1 - 158.8 (15.1) |
subtype4 | 158 | 7 | 0.1 - 155.5 (17.1) |
Figure S61. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D5V1.png)
P value = 0.115 (ANOVA), Q value = 1
Table S67. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 476 | 47.2 (15.6) |
subtype1 | 150 | 49.1 (15.5) |
subtype2 | 50 | 44.7 (17.4) |
subtype3 | 117 | 45.0 (14.6) |
subtype4 | 159 | 47.9 (15.8) |
Figure S62. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'
![](D5V2.png)
P value = 0.00012 (Chi-square test), Q value = 0.013
Table S68. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'
nPatients | STAGE I | STAGE II | STAGE III | STAGE IV | STAGE IVA | STAGE IVC |
---|---|---|---|---|---|---|
ALL | 268 | 51 | 105 | 2 | 42 | 6 |
subtype1 | 88 | 29 | 26 | 1 | 3 | 2 |
subtype2 | 33 | 1 | 12 | 0 | 4 | 0 |
subtype3 | 66 | 14 | 24 | 0 | 10 | 2 |
subtype4 | 81 | 7 | 43 | 1 | 25 | 2 |
Figure S63. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'
![](D5V3.png)
P value = 7.31e-06 (Chi-square test), Q value = 9e-04
Table S69. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'
nPatients | T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 136 | 159 | 158 | 21 |
subtype1 | 56 | 54 | 38 | 2 |
subtype2 | 20 | 13 | 14 | 2 |
subtype3 | 33 | 48 | 32 | 4 |
subtype4 | 27 | 44 | 74 | 13 |
Figure S64. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'
![](D5V4.png)
P value = 1.99e-14 (Fisher's exact test), Q value = 2.8e-12
Table S70. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'
nPatients | 0 | 1 |
---|---|---|
ALL | 216 | 214 |
subtype1 | 100 | 26 |
subtype2 | 17 | 33 |
subtype3 | 49 | 59 |
subtype4 | 50 | 96 |
Figure S65. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'
![](D5V5.png)
P value = 0.0489 (Chi-square test), Q value = 1
Table S71. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'
nPatients | M0 | M1 | MX |
---|---|---|---|
ALL | 261 | 9 | 205 |
subtype1 | 67 | 3 | 79 |
subtype2 | 34 | 0 | 16 |
subtype3 | 72 | 3 | 42 |
subtype4 | 88 | 3 | 68 |
Figure S66. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'
![](D5V6.png)
P value = 0.949 (Fisher's exact test), Q value = 1
Table S72. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 349 | 127 |
subtype1 | 108 | 42 |
subtype2 | 37 | 13 |
subtype3 | 88 | 29 |
subtype4 | 116 | 43 |
Figure S67. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'
![](D5V7.png)
P value = 1.9e-32 (Chi-square test), Q value = 2.8e-30
Table S73. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'
nPatients | OTHER SPECIFY | THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL | THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED) | THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES) |
---|---|---|---|---|
ALL | 9 | 334 | 99 | 34 |
subtype1 | 2 | 66 | 81 | 1 |
subtype2 | 1 | 43 | 2 | 4 |
subtype3 | 2 | 99 | 13 | 3 |
subtype4 | 4 | 126 | 3 | 26 |
Figure S68. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'
![](D5V8.png)
P value = 0.0147 (Fisher's exact test), Q value = 1
Table S74. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 13 | 463 |
subtype1 | 1 | 149 |
subtype2 | 0 | 50 |
subtype3 | 2 | 115 |
subtype4 | 10 | 149 |
Figure S69. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D5V9.png)
P value = 0.93 (Fisher's exact test), Q value = 1
Table S75. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONEXPOSURE'
nPatients | NO | YES |
---|---|---|
ALL | 402 | 17 |
subtype1 | 125 | 6 |
subtype2 | 42 | 2 |
subtype3 | 99 | 4 |
subtype4 | 136 | 5 |
Figure S70. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONEXPOSURE'
![](D5V10.png)
P value = 5.21e-08 (Chi-square test), Q value = 6.8e-06
Table S76. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'EXTRATHYROIDAL.EXTENSION'
nPatients | MINIMAL (T3) | MODERATE/ADVANCED (T4A) | NONE | VERY ADVANCED (T4B) |
---|---|---|---|---|
ALL | 124 | 16 | 320 | 1 |
subtype1 | 19 | 1 | 123 | 0 |
subtype2 | 12 | 2 | 35 | 0 |
subtype3 | 29 | 1 | 84 | 0 |
subtype4 | 64 | 12 | 78 | 1 |
Figure S71. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'EXTRATHYROIDAL.EXTENSION'
![](D5V11.png)
P value = 0.256 (Chi-square test), Q value = 1
Table S77. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'
nPatients | R0 | R1 | R2 | RX |
---|---|---|---|---|
ALL | 368 | 46 | 4 | 29 |
subtype1 | 120 | 9 | 0 | 7 |
subtype2 | 36 | 5 | 0 | 5 |
subtype3 | 91 | 9 | 2 | 8 |
subtype4 | 121 | 23 | 2 | 9 |
Figure S72. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'
![](D5V12.png)
P value = 1.19e-05 (ANOVA), Q value = 0.0014
Table S78. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 376 | 3.5 (6.1) |
subtype1 | 108 | 1.2 (3.0) |
subtype2 | 43 | 5.8 (7.8) |
subtype3 | 93 | 3.8 (6.3) |
subtype4 | 132 | 4.4 (6.7) |
Figure S73. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'
![](D5V13.png)
P value = 0.177 (Fisher's exact test), Q value = 1
Table S79. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'
nPatients | MULTIFOCAL | UNIFOCAL |
---|---|---|
ALL | 215 | 251 |
subtype1 | 70 | 77 |
subtype2 | 23 | 25 |
subtype3 | 60 | 54 |
subtype4 | 62 | 95 |
Figure S74. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'
![](D5V14.png)
P value = 0.38 (ANOVA), Q value = 1
Table S80. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 380 | 3.0 (1.6) |
subtype1 | 118 | 3.0 (1.6) |
subtype2 | 36 | 2.7 (1.8) |
subtype3 | 93 | 2.8 (1.5) |
subtype4 | 133 | 3.1 (1.6) |
Figure S75. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'
![](D5V15.png)
Table S81. Description of clustering approach #6: 'RNAseq cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 133 | 280 | 63 |
P value = 0.792 (logrank test), Q value = 1
Table S82. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 471 | 14 | 0.0 - 158.8 (14.8) |
subtype1 | 132 | 3 | 0.2 - 132.4 (14.1) |
subtype2 | 277 | 9 | 0.1 - 158.8 (16.4) |
subtype3 | 62 | 2 | 0.0 - 130.7 (12.3) |
Figure S76. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
![](D6V1.png)
P value = 0.325 (ANOVA), Q value = 1
Table S83. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 476 | 47.2 (15.6) |
subtype1 | 133 | 48.9 (15.8) |
subtype2 | 280 | 46.5 (15.7) |
subtype3 | 63 | 46.7 (14.8) |
Figure S77. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'
![](D6V2.png)
P value = 1.68e-05 (Chi-square test), Q value = 0.002
Table S84. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'
nPatients | STAGE I | STAGE II | STAGE III | STAGE IV | STAGE IVA | STAGE IVC |
---|---|---|---|---|---|---|
ALL | 268 | 51 | 105 | 2 | 42 | 6 |
subtype1 | 77 | 29 | 20 | 1 | 3 | 2 |
subtype2 | 152 | 20 | 67 | 1 | 35 | 4 |
subtype3 | 39 | 2 | 18 | 0 | 4 | 0 |
Figure S78. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'
![](D6V3.png)
P value = 2.14e-05 (Chi-square test), Q value = 0.0025
Table S85. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'
nPatients | T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 136 | 159 | 158 | 21 |
subtype1 | 45 | 54 | 32 | 2 |
subtype2 | 62 | 89 | 109 | 19 |
subtype3 | 29 | 16 | 17 | 0 |
Figure S79. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'
![](D6V4.png)
P value = 4.18e-17 (Fisher's exact test), Q value = 6e-15
Table S86. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'
nPatients | 0 | 1 |
---|---|---|
ALL | 216 | 214 |
subtype1 | 91 | 17 |
subtype2 | 94 | 166 |
subtype3 | 31 | 31 |
Figure S80. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'
![](D6V5.png)
P value = 0.000455 (Chi-square test), Q value = 0.047
Table S87. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'
nPatients | M0 | M1 | MX |
---|---|---|---|
ALL | 261 | 9 | 205 |
subtype1 | 55 | 3 | 74 |
subtype2 | 159 | 6 | 115 |
subtype3 | 47 | 0 | 16 |
Figure S81. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'
![](D6V6.png)
P value = 0.699 (Fisher's exact test), Q value = 1
Table S88. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 349 | 127 |
subtype1 | 96 | 37 |
subtype2 | 204 | 76 |
subtype3 | 49 | 14 |
Figure S82. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'
![](D6V7.png)
P value = 3.42e-35 (Chi-square test), Q value = 5.1e-33
Table S89. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'
nPatients | OTHER SPECIFY | THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL | THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED) | THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES) |
---|---|---|---|---|
ALL | 9 | 334 | 99 | 34 |
subtype1 | 2 | 52 | 79 | 0 |
subtype2 | 6 | 233 | 11 | 30 |
subtype3 | 1 | 49 | 9 | 4 |
Figure S83. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'
![](D6V8.png)
P value = 0.193 (Fisher's exact test), Q value = 1
Table S90. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 13 | 463 |
subtype1 | 2 | 131 |
subtype2 | 11 | 269 |
subtype3 | 0 | 63 |
Figure S84. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D6V9.png)
P value = 1 (Fisher's exact test), Q value = 1
Table S91. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONEXPOSURE'
nPatients | NO | YES |
---|---|---|
ALL | 402 | 17 |
subtype1 | 111 | 5 |
subtype2 | 241 | 10 |
subtype3 | 50 | 2 |
Figure S85. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONEXPOSURE'
![](D6V10.png)
P value = 4.19e-07 (Chi-square test), Q value = 5.4e-05
Table S92. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'EXTRATHYROIDAL.EXTENSION'
nPatients | MINIMAL (T3) | MODERATE/ADVANCED (T4A) | NONE | VERY ADVANCED (T4B) |
---|---|---|---|---|
ALL | 124 | 16 | 320 | 1 |
subtype1 | 13 | 1 | 112 | 0 |
subtype2 | 96 | 15 | 161 | 1 |
subtype3 | 15 | 0 | 47 | 0 |
Figure S86. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'EXTRATHYROIDAL.EXTENSION'
![](D6V11.png)
P value = 0.41 (Chi-square test), Q value = 1
Table S93. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'
nPatients | R0 | R1 | R2 | RX |
---|---|---|---|---|
ALL | 368 | 46 | 4 | 29 |
subtype1 | 104 | 9 | 0 | 7 |
subtype2 | 213 | 33 | 4 | 18 |
subtype3 | 51 | 4 | 0 | 4 |
Figure S87. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'
![](D6V12.png)
P value = 9.51e-06 (ANOVA), Q value = 0.0012
Table S94. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 376 | 3.5 (6.1) |
subtype1 | 93 | 1.0 (2.9) |
subtype2 | 233 | 4.6 (6.7) |
subtype3 | 50 | 3.3 (6.2) |
Figure S88. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'
![](D6V13.png)
P value = 0.304 (Fisher's exact test), Q value = 1
Table S95. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'
nPatients | MULTIFOCAL | UNIFOCAL |
---|---|---|
ALL | 215 | 251 |
subtype1 | 62 | 68 |
subtype2 | 120 | 155 |
subtype3 | 33 | 28 |
Figure S89. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'
![](D6V14.png)
P value = 0.0168 (ANOVA), Q value = 1
Table S96. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 380 | 3.0 (1.6) |
subtype1 | 109 | 3.1 (1.5) |
subtype2 | 228 | 3.0 (1.6) |
subtype3 | 43 | 2.3 (1.7) |
Figure S90. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'
![](D6V15.png)
Table S97. Description of clustering approach #7: 'MIRSEQ CNMF'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 148 | 170 | 165 |
P value = 0.0313 (logrank test), Q value = 1
Table S98. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 478 | 14 | 0.0 - 158.8 (14.9) |
subtype1 | 147 | 3 | 0.2 - 132.4 (14.2) |
subtype2 | 168 | 2 | 0.1 - 147.8 (16.0) |
subtype3 | 163 | 9 | 0.0 - 158.8 (14.4) |
Figure S91. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'
![](D7V1.png)
P value = 0.17 (ANOVA), Q value = 1
Table S99. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 483 | 47.2 (15.6) |
subtype1 | 148 | 47.1 (16.0) |
subtype2 | 170 | 45.7 (14.8) |
subtype3 | 165 | 48.9 (15.9) |
Figure S92. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'
![](D7V2.png)
P value = 2.51e-06 (Chi-square test), Q value = 0.00032
Table S100. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'
nPatients | STAGE I | STAGE II | STAGE III | STAGE IV | STAGE IVA | STAGE IVC |
---|---|---|---|---|---|---|
ALL | 273 | 51 | 106 | 2 | 43 | 6 |
subtype1 | 91 | 29 | 20 | 1 | 4 | 2 |
subtype2 | 99 | 17 | 35 | 0 | 17 | 2 |
subtype3 | 83 | 5 | 51 | 1 | 22 | 2 |
Figure S93. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'
![](D7V3.png)
P value = 0.000214 (Chi-square test), Q value = 0.023
Table S101. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'
nPatients | T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 140 | 161 | 159 | 21 |
subtype1 | 43 | 66 | 37 | 2 |
subtype2 | 44 | 61 | 58 | 7 |
subtype3 | 53 | 34 | 64 | 12 |
Figure S94. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'
![](D7V4.png)
P value = 3.91e-12 (Fisher's exact test), Q value = 5.4e-10
Table S102. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'
nPatients | 0 | 1 |
---|---|---|
ALL | 220 | 216 |
subtype1 | 94 | 27 |
subtype2 | 67 | 90 |
subtype3 | 59 | 99 |
Figure S95. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'
![](D7V5.png)
P value = 0.000721 (Chi-square test), Q value = 0.073
Table S103. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'
nPatients | M0 | M1 | MX |
---|---|---|---|
ALL | 264 | 9 | 209 |
subtype1 | 62 | 3 | 82 |
subtype2 | 92 | 4 | 74 |
subtype3 | 110 | 2 | 53 |
Figure S96. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'
![](D7V6.png)
P value = 0.833 (Fisher's exact test), Q value = 1
Table S104. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 354 | 129 |
subtype1 | 106 | 42 |
subtype2 | 125 | 45 |
subtype3 | 123 | 42 |
Figure S97. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'
![](D7V7.png)
P value = 2.45e-34 (Chi-square test), Q value = 3.6e-32
Table S105. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'
nPatients | OTHER SPECIFY | THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL | THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED) | THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES) |
---|---|---|---|---|
ALL | 9 | 340 | 99 | 35 |
subtype1 | 2 | 64 | 82 | 0 |
subtype2 | 3 | 146 | 9 | 12 |
subtype3 | 4 | 130 | 8 | 23 |
Figure S98. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'
![](D7V8.png)
P value = 0.457 (Fisher's exact test), Q value = 1
Table S106. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 14 | 469 |
subtype1 | 4 | 144 |
subtype2 | 7 | 163 |
subtype3 | 3 | 162 |
Figure S99. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D7V9.png)
P value = 1 (Fisher's exact test), Q value = 1
Table S107. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RADIATIONEXPOSURE'
nPatients | NO | YES |
---|---|---|
ALL | 407 | 17 |
subtype1 | 125 | 5 |
subtype2 | 142 | 6 |
subtype3 | 140 | 6 |
Figure S100. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RADIATIONEXPOSURE'
![](D7V10.png)
P value = 3.73e-08 (Chi-square test), Q value = 4.9e-06
Table S108. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'EXTRATHYROIDAL.EXTENSION'
nPatients | MINIMAL (T3) | MODERATE/ADVANCED (T4A) | NONE | VERY ADVANCED (T4B) |
---|---|---|---|---|
ALL | 125 | 16 | 324 | 1 |
subtype1 | 15 | 0 | 127 | 0 |
subtype2 | 53 | 5 | 107 | 0 |
subtype3 | 57 | 11 | 90 | 1 |
Figure S101. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'EXTRATHYROIDAL.EXTENSION'
![](D7V11.png)
P value = 0.198 (Chi-square test), Q value = 1
Table S109. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'
nPatients | R0 | R1 | R2 | RX |
---|---|---|---|---|
ALL | 372 | 47 | 4 | 29 |
subtype1 | 118 | 9 | 0 | 9 |
subtype2 | 135 | 15 | 2 | 8 |
subtype3 | 119 | 23 | 2 | 12 |
Figure S102. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'
![](D7V12.png)
P value = 5.95e-06 (ANOVA), Q value = 0.00074
Table S110. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 383 | 3.5 (6.2) |
subtype1 | 105 | 1.4 (3.1) |
subtype2 | 141 | 3.5 (6.0) |
subtype3 | 137 | 5.3 (7.6) |
Figure S103. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'
![](D7V13.png)
P value = 0.878 (Fisher's exact test), Q value = 1
Table S111. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #14: 'MULTIFOCALITY'
nPatients | MULTIFOCAL | UNIFOCAL |
---|---|---|
ALL | 218 | 255 |
subtype1 | 64 | 79 |
subtype2 | 76 | 90 |
subtype3 | 78 | 86 |
Figure S104. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #14: 'MULTIFOCALITY'
![](D7V14.png)
P value = 0.0205 (ANOVA), Q value = 1
Table S112. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #15: 'TUMOR.SIZE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 384 | 3.0 (1.6) |
subtype1 | 122 | 3.2 (1.5) |
subtype2 | 137 | 3.0 (1.6) |
subtype3 | 125 | 2.7 (1.6) |
Figure S105. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #15: 'TUMOR.SIZE'
![](D7V15.png)
Table S113. Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 152 | 129 | 202 |
P value = 0.0599 (logrank test), Q value = 1
Table S114. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 478 | 14 | 0.0 - 158.8 (14.9) |
subtype1 | 151 | 1 | 0.1 - 147.8 (15.8) |
subtype2 | 128 | 3 | 0.3 - 132.4 (14.1) |
subtype3 | 199 | 10 | 0.0 - 158.8 (14.6) |
Figure S106. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'
![](D8V1.png)
P value = 0.0318 (ANOVA), Q value = 1
Table S115. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 483 | 47.2 (15.6) |
subtype1 | 152 | 44.5 (15.3) |
subtype2 | 129 | 48.3 (15.7) |
subtype3 | 202 | 48.6 (15.4) |
Figure S107. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'
![](D8V2.png)
P value = 1.45e-07 (Chi-square test), Q value = 1.9e-05
Table S116. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'
nPatients | STAGE I | STAGE II | STAGE III | STAGE IV | STAGE IVA | STAGE IVC |
---|---|---|---|---|---|---|
ALL | 273 | 51 | 106 | 2 | 43 | 6 |
subtype1 | 92 | 17 | 26 | 0 | 15 | 2 |
subtype2 | 76 | 28 | 19 | 1 | 2 | 2 |
subtype3 | 105 | 6 | 61 | 1 | 26 | 2 |
Figure S108. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'
![](D8V3.png)
P value = 7.57e-05 (Chi-square test), Q value = 0.0086
Table S117. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'
nPatients | T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 140 | 161 | 159 | 21 |
subtype1 | 36 | 62 | 47 | 7 |
subtype2 | 42 | 55 | 31 | 1 |
subtype3 | 62 | 44 | 81 | 13 |
Figure S109. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'
![](D8V4.png)
P value = 1.43e-14 (Fisher's exact test), Q value = 2e-12
Table S118. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'
nPatients | 0 | 1 |
---|---|---|
ALL | 220 | 216 |
subtype1 | 62 | 81 |
subtype2 | 86 | 17 |
subtype3 | 72 | 118 |
Figure S110. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'
![](D8V5.png)
P value = 0.00131 (Chi-square test), Q value = 0.13
Table S119. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'
nPatients | M0 | M1 | MX |
---|---|---|---|
ALL | 264 | 9 | 209 |
subtype1 | 83 | 4 | 65 |
subtype2 | 52 | 3 | 73 |
subtype3 | 129 | 2 | 71 |
Figure S111. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'
![](D8V6.png)
P value = 0.661 (Fisher's exact test), Q value = 1
Table S120. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 354 | 129 |
subtype1 | 108 | 44 |
subtype2 | 94 | 35 |
subtype3 | 152 | 50 |
Figure S112. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'
![](D8V7.png)
P value = 7.71e-36 (Chi-square test), Q value = 1.1e-33
Table S121. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'
nPatients | OTHER SPECIFY | THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL | THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED) | THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES) |
---|---|---|---|---|
ALL | 9 | 340 | 99 | 35 |
subtype1 | 2 | 131 | 13 | 6 |
subtype2 | 2 | 51 | 76 | 0 |
subtype3 | 5 | 158 | 10 | 29 |
Figure S113. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'
![](D8V8.png)
P value = 0.0379 (Fisher's exact test), Q value = 1
Table S122. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 14 | 469 |
subtype1 | 9 | 143 |
subtype2 | 2 | 127 |
subtype3 | 3 | 199 |
Figure S114. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D8V9.png)
P value = 1 (Fisher's exact test), Q value = 1
Table S123. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'RADIATIONEXPOSURE'
nPatients | NO | YES |
---|---|---|
ALL | 407 | 17 |
subtype1 | 127 | 5 |
subtype2 | 109 | 5 |
subtype3 | 171 | 7 |
Figure S115. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'RADIATIONEXPOSURE'
![](D8V10.png)
P value = 3.4e-08 (Chi-square test), Q value = 4.5e-06
Table S124. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'EXTRATHYROIDAL.EXTENSION'
nPatients | MINIMAL (T3) | MODERATE/ADVANCED (T4A) | NONE | VERY ADVANCED (T4B) |
---|---|---|---|---|
ALL | 125 | 16 | 324 | 1 |
subtype1 | 37 | 4 | 106 | 0 |
subtype2 | 13 | 0 | 111 | 0 |
subtype3 | 75 | 12 | 107 | 1 |
Figure S116. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'EXTRATHYROIDAL.EXTENSION'
![](D8V11.png)
P value = 0.261 (Chi-square test), Q value = 1
Table S125. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'
nPatients | R0 | R1 | R2 | RX |
---|---|---|---|---|
ALL | 372 | 47 | 4 | 29 |
subtype1 | 122 | 12 | 1 | 8 |
subtype2 | 103 | 9 | 0 | 6 |
subtype3 | 147 | 26 | 3 | 15 |
Figure S117. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'
![](D8V12.png)
P value = 4.57e-06 (ANOVA), Q value = 0.00057
Table S126. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 383 | 3.5 (6.2) |
subtype1 | 125 | 3.4 (5.5) |
subtype2 | 90 | 1.1 (2.9) |
subtype3 | 168 | 5.0 (7.4) |
Figure S118. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'
![](D8V13.png)
P value = 0.916 (Fisher's exact test), Q value = 1
Table S127. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'MULTIFOCALITY'
nPatients | MULTIFOCAL | UNIFOCAL |
---|---|---|
ALL | 218 | 255 |
subtype1 | 68 | 80 |
subtype2 | 60 | 66 |
subtype3 | 90 | 109 |
Figure S119. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'MULTIFOCALITY'
![](D8V14.png)
P value = 0.135 (ANOVA), Q value = 1
Table S128. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #15: 'TUMOR.SIZE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 384 | 3.0 (1.6) |
subtype1 | 116 | 3.0 (1.6) |
subtype2 | 108 | 3.2 (1.5) |
subtype3 | 160 | 2.8 (1.6) |
Figure S120. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #15: 'TUMOR.SIZE'
![](D8V15.png)
Table S129. Description of clustering approach #9: 'MIRseq Mature CNMF subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 161 | 166 | 156 |
P value = 0.00454 (logrank test), Q value = 0.41
Table S130. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 478 | 14 | 0.0 - 158.8 (14.9) |
subtype1 | 160 | 3 | 0.3 - 138.1 (14.6) |
subtype2 | 164 | 1 | 0.1 - 155.5 (16.5) |
subtype3 | 154 | 10 | 0.0 - 158.8 (14.1) |
Figure S121. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D9V1.png)
P value = 0.0744 (ANOVA), Q value = 1
Table S131. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 483 | 47.2 (15.6) |
subtype1 | 161 | 47.7 (15.7) |
subtype2 | 166 | 45.1 (14.7) |
subtype3 | 156 | 49.0 (16.1) |
Figure S122. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'
![](D9V2.png)
P value = 0.000356 (Chi-square test), Q value = 0.037
Table S132. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'
nPatients | STAGE I | STAGE II | STAGE III | STAGE IV | STAGE IVA | STAGE IVC |
---|---|---|---|---|---|---|
ALL | 273 | 51 | 106 | 2 | 43 | 6 |
subtype1 | 95 | 28 | 27 | 1 | 7 | 2 |
subtype2 | 99 | 18 | 32 | 0 | 15 | 2 |
subtype3 | 79 | 5 | 47 | 1 | 21 | 2 |
Figure S123. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'
![](D9V3.png)
P value = 0.000589 (Chi-square test), Q value = 0.06
Table S133. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'
nPatients | T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 140 | 161 | 159 | 21 |
subtype1 | 46 | 66 | 46 | 3 |
subtype2 | 42 | 63 | 56 | 5 |
subtype3 | 52 | 32 | 57 | 13 |
Figure S124. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'
![](D9V4.png)
P value = 2.6e-10 (Fisher's exact test), Q value = 3.6e-08
Table S134. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'
nPatients | 0 | 1 |
---|---|---|
ALL | 220 | 216 |
subtype1 | 99 | 35 |
subtype2 | 65 | 89 |
subtype3 | 56 | 92 |
Figure S125. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'
![](D9V5.png)
P value = 0.000295 (Chi-square test), Q value = 0.032
Table S135. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'
nPatients | M0 | M1 | MX |
---|---|---|---|
ALL | 264 | 9 | 209 |
subtype1 | 68 | 3 | 89 |
subtype2 | 90 | 4 | 72 |
subtype3 | 106 | 2 | 48 |
Figure S126. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'
![](D9V6.png)
P value = 0.623 (Fisher's exact test), Q value = 1
Table S136. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 354 | 129 |
subtype1 | 114 | 47 |
subtype2 | 122 | 44 |
subtype3 | 118 | 38 |
Figure S127. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'
![](D9V7.png)
P value = 9.14e-30 (Chi-square test), Q value = 1.3e-27
Table S137. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'
nPatients | OTHER SPECIFY | THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL | THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED) | THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES) |
---|---|---|---|---|
ALL | 9 | 340 | 99 | 35 |
subtype1 | 2 | 77 | 82 | 0 |
subtype2 | 3 | 140 | 10 | 13 |
subtype3 | 4 | 123 | 7 | 22 |
Figure S128. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'
![](D9V8.png)
P value = 0.0793 (Fisher's exact test), Q value = 1
Table S138. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 14 | 469 |
subtype1 | 3 | 158 |
subtype2 | 9 | 157 |
subtype3 | 2 | 154 |
Figure S129. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D9V9.png)
P value = 0.808 (Fisher's exact test), Q value = 1
Table S139. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'RADIATIONEXPOSURE'
nPatients | NO | YES |
---|---|---|
ALL | 407 | 17 |
subtype1 | 135 | 7 |
subtype2 | 141 | 5 |
subtype3 | 131 | 5 |
Figure S130. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'RADIATIONEXPOSURE'
![](D9V10.png)
P value = 1.93e-06 (Chi-square test), Q value = 0.00025
Table S140. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'EXTRATHYROIDAL.EXTENSION'
nPatients | MINIMAL (T3) | MODERATE/ADVANCED (T4A) | NONE | VERY ADVANCED (T4B) |
---|---|---|---|---|
ALL | 125 | 16 | 324 | 1 |
subtype1 | 23 | 1 | 130 | 0 |
subtype2 | 49 | 3 | 108 | 0 |
subtype3 | 53 | 12 | 86 | 1 |
Figure S131. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'EXTRATHYROIDAL.EXTENSION'
![](D9V11.png)
P value = 0.112 (Chi-square test), Q value = 1
Table S141. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'
nPatients | R0 | R1 | R2 | RX |
---|---|---|---|---|
ALL | 372 | 47 | 4 | 29 |
subtype1 | 125 | 11 | 0 | 11 |
subtype2 | 136 | 14 | 2 | 6 |
subtype3 | 111 | 22 | 2 | 12 |
Figure S132. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'
![](D9V12.png)
P value = 0.000237 (ANOVA), Q value = 0.026
Table S142. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 383 | 3.5 (6.2) |
subtype1 | 119 | 1.7 (4.1) |
subtype2 | 137 | 3.9 (6.8) |
subtype3 | 127 | 4.8 (6.8) |
Figure S133. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'
![](D9V13.png)
P value = 0.417 (Fisher's exact test), Q value = 1
Table S143. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'
nPatients | MULTIFOCAL | UNIFOCAL |
---|---|---|
ALL | 218 | 255 |
subtype1 | 77 | 78 |
subtype2 | 69 | 94 |
subtype3 | 72 | 83 |
Figure S134. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'
![](D9V14.png)
P value = 0.0299 (ANOVA), Q value = 1
Table S144. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 384 | 3.0 (1.6) |
subtype1 | 129 | 3.2 (1.6) |
subtype2 | 138 | 2.9 (1.5) |
subtype3 | 117 | 2.7 (1.6) |
Figure S135. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'
![](D9V15.png)
Table S145. Description of clustering approach #10: 'MIRseq Mature cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 192 | 127 | 164 |
P value = 0.0219 (logrank test), Q value = 1
Table S146. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 478 | 14 | 0.0 - 158.8 (14.9) |
subtype1 | 189 | 10 | 0.0 - 158.8 (14.5) |
subtype2 | 126 | 3 | 0.3 - 132.4 (14.0) |
subtype3 | 163 | 1 | 0.1 - 147.8 (16.4) |
Figure S136. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
![](D10V1.png)
P value = 0.00194 (ANOVA), Q value = 0.18
Table S147. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 483 | 47.2 (15.6) |
subtype1 | 192 | 49.3 (15.1) |
subtype2 | 127 | 48.5 (15.7) |
subtype3 | 164 | 43.8 (15.5) |
Figure S137. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'
![](D10V2.png)
P value = 9.18e-09 (Chi-square test), Q value = 1.2e-06
Table S148. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'
nPatients | STAGE I | STAGE II | STAGE III | STAGE IV | STAGE IVA | STAGE IVC |
---|---|---|---|---|---|---|
ALL | 273 | 51 | 106 | 2 | 43 | 6 |
subtype1 | 96 | 5 | 61 | 1 | 26 | 2 |
subtype2 | 74 | 28 | 19 | 1 | 2 | 2 |
subtype3 | 103 | 18 | 26 | 0 | 15 | 2 |
Figure S138. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'
![](D10V3.png)
P value = 2.84e-05 (Chi-square test), Q value = 0.0033
Table S149. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'
nPatients | T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 140 | 161 | 159 | 21 |
subtype1 | 59 | 40 | 78 | 13 |
subtype2 | 42 | 54 | 30 | 1 |
subtype3 | 39 | 67 | 51 | 7 |
Figure S139. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'
![](D10V4.png)
P value = 9.2e-15 (Fisher's exact test), Q value = 1.3e-12
Table S150. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'
nPatients | 0 | 1 |
---|---|---|
ALL | 220 | 216 |
subtype1 | 69 | 112 |
subtype2 | 85 | 16 |
subtype3 | 66 | 88 |
Figure S140. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'
![](D10V5.png)
P value = 0.000476 (Chi-square test), Q value = 0.049
Table S151. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'
nPatients | M0 | M1 | MX |
---|---|---|---|
ALL | 264 | 9 | 209 |
subtype1 | 126 | 2 | 64 |
subtype2 | 51 | 3 | 72 |
subtype3 | 87 | 4 | 73 |
Figure S141. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'
![](D10V6.png)
P value = 0.65 (Fisher's exact test), Q value = 1
Table S152. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 354 | 129 |
subtype1 | 145 | 47 |
subtype2 | 92 | 35 |
subtype3 | 117 | 47 |
Figure S142. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'
![](D10V7.png)
P value = 1.57e-36 (Chi-square test), Q value = 2.3e-34
Table S153. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'
nPatients | OTHER SPECIFY | THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL | THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED) | THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES) |
---|---|---|---|---|
ALL | 9 | 340 | 99 | 35 |
subtype1 | 4 | 150 | 10 | 28 |
subtype2 | 2 | 49 | 76 | 0 |
subtype3 | 3 | 141 | 13 | 7 |
Figure S143. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'
![](D10V8.png)
P value = 0.0153 (Fisher's exact test), Q value = 1
Table S154. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 14 | 469 |
subtype1 | 2 | 190 |
subtype2 | 2 | 125 |
subtype3 | 10 | 154 |
Figure S144. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D10V9.png)
P value = 0.867 (Fisher's exact test), Q value = 1
Table S155. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONEXPOSURE'
nPatients | NO | YES |
---|---|---|
ALL | 407 | 17 |
subtype1 | 163 | 6 |
subtype2 | 107 | 5 |
subtype3 | 137 | 6 |
Figure S145. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONEXPOSURE'
![](D10V10.png)
P value = 2.14e-08 (Chi-square test), Q value = 2.9e-06
Table S156. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'EXTRATHYROIDAL.EXTENSION'
nPatients | MINIMAL (T3) | MODERATE/ADVANCED (T4A) | NONE | VERY ADVANCED (T4B) |
---|---|---|---|---|
ALL | 125 | 16 | 324 | 1 |
subtype1 | 72 | 12 | 100 | 1 |
subtype2 | 13 | 0 | 109 | 0 |
subtype3 | 40 | 4 | 115 | 0 |
Figure S146. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'EXTRATHYROIDAL.EXTENSION'
![](D10V11.png)
P value = 0.0957 (Chi-square test), Q value = 1
Table S157. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'
nPatients | R0 | R1 | R2 | RX |
---|---|---|---|---|
ALL | 372 | 47 | 4 | 29 |
subtype1 | 137 | 27 | 3 | 14 |
subtype2 | 101 | 9 | 0 | 6 |
subtype3 | 134 | 11 | 1 | 9 |
Figure S147. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'
![](D10V12.png)
P value = 1.89e-05 (ANOVA), Q value = 0.0022
Table S158. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 383 | 3.5 (6.2) |
subtype1 | 160 | 4.7 (6.7) |
subtype2 | 88 | 0.9 (2.7) |
subtype3 | 135 | 3.9 (6.8) |
Figure S148. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'
![](D10V13.png)
P value = 0.818 (Fisher's exact test), Q value = 1
Table S159. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'
nPatients | MULTIFOCAL | UNIFOCAL |
---|---|---|
ALL | 218 | 255 |
subtype1 | 88 | 102 |
subtype2 | 60 | 65 |
subtype3 | 70 | 88 |
Figure S149. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'
![](D10V14.png)
P value = 0.212 (ANOVA), Q value = 1
Table S160. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 384 | 3.0 (1.6) |
subtype1 | 149 | 2.8 (1.7) |
subtype2 | 107 | 3.2 (1.5) |
subtype3 | 128 | 2.9 (1.5) |
Figure S150. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'
![](D10V15.png)
-
Cluster data file = THCA-TP.mergedcluster.txt
-
Clinical data file = THCA-TP.merged_data.txt
-
Number of patients = 484
-
Number of clustering approaches = 10
-
Number of selected clinical features = 15
-
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 multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.test' function in R
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.