Correlation between aggregated molecular cancer subtypes and selected clinical features
Thyroid Adenocarcinoma (Primary solid tumor)
16 April 2014 | analyses__2014_04_16
Maintainer Information
Citation Information
doi:10.7908/C1348J2X
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1348J2X
Overview
Introduction

This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.

Summary

Testing the association between subtypes identified by 10 different clustering approaches and 15 clinical features across 486 patients, 56 significant findings detected with P value < 0.05 and Q value < 0.25.

- 3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'AGE', 'NEOPLASM.DISEASESTAGE', and 'EXTRATHYROIDAL.EXTENSION'.

- 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'.

- CNMF clustering analysis on RPPA data identified 3 subtypes that correlate to 'AGE', 'HISTOLOGICAL.TYPE', and 'EXTRATHYROIDAL.EXTENSION'.

- Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that correlate to 'AGE', 'PATHOLOGY.N.STAGE', 'HISTOLOGICAL.TYPE', and 'EXTRATHYROIDAL.EXTENSION'.

- 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'.

- Consensus hierarchical 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'.

- 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'.

- 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'.

- 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'.

- 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'.

Results
Overview of the results

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, 56 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 | 0.0613 (1.00) | 0.602 (1.00) | 0.565 (1.00) | 0.0619 (1.00) | 0.672 (1.00) | 0.825 (1.00) | 0.031 (1.00) | 0.0614 (1.00) | 0.00462 (0.425) | 0.0227 (1.00)
AGE | ANOVA | 4.95e-05 (0.00579) | 0.279 (1.00) | 0.00249 (0.239) | 0.00123 (0.123) | 0.0729 (1.00) | 0.199 (1.00) | 0.158 (1.00) | 0.0308 (1.00) | 0.07 (1.00) | 0.00183 (0.179)
NEOPLASM DISEASESTAGE | Chi-square test | 0.000594 (0.0618) | 1.31e-05 (0.00159) | 0.00359 (0.333) | 0.00838 (0.738) | 5.56e-05 (0.00639) | 2.7e-05 (0.00318) | 1.75e-06 (0.000218) | 1.11e-07 (1.44e-05) | 0.00025 (0.027) | 6.69e-09 (9.1e-07)
PATHOLOGY T STAGE | Chi-square test | 0.466 (1.00) | 0.00272 (0.255) | 0.00518 (0.471) | 0.0198 (1.00) | 3.04e-06 (0.000377) | 5.8e-07 (7.42e-05) | 0.000146 (0.0162) | 5.22e-05 (0.00606) | 0.000459 (0.0487) | 1.94e-05 (0.00231)
PATHOLOGY N STAGE | Fisher's exact test | 0.0574 (1.00) | 1.63e-13 (2.27e-11) | 0.0405 (1.00) | 6.14e-05 (0.00699) | 1.42e-13 (1.99e-11) | 5.77e-17 (8.25e-15) | 2.01e-12 (2.77e-10) | 6.88e-15 (9.7e-13) | 1.41e-10 (1.93e-08) | 4.42e-15 (6.27e-13)
PATHOLOGY M STAGE | Chi-square test | 0.0681 (1.00) | 0.175 (1.00) | 0.219 (1.00) | 0.448 (1.00) | 0.122 (1.00) | 0.00594 (0.535) | 0.00117 (0.119) | 0.00205 (0.199) | 0.000496 (0.0521) | 0.000781 (0.0797)
GENDER | Fisher's exact test | 0.343 (1.00) | 0.861 (1.00) | 0.608 (1.00) | 0.518 (1.00) | 0.879 (1.00) | 0.93 (1.00) | 0.896 (1.00) | 0.716 (1.00) | 0.706 (1.00) | 0.709 (1.00)
HISTOLOGICAL TYPE | Chi-square test | 0.233 (1.00) | 7.01e-27 (1.01e-24) | 9.21e-05 (0.0104) | 9.73e-09 (1.3e-06) | 7.1e-32 (1.04e-29) | 1.29e-34 (1.9e-32) | 3.83e-34 (5.64e-32) | 1.37e-35 (2.04e-33) | 1.27e-29 (1.85e-27) | 2.87e-36 (4.31e-34)
RADIATIONS RADIATION REGIMENINDICATION | Fisher's exact test | 0.376 (1.00) | 0.0878 (1.00) | 0.0123 (1.00) | 0.243 (1.00) | 0.00755 (0.672) | 0.16 (1.00) | 0.456 (1.00) | 0.0376 (1.00) | 0.0787 (1.00) | 0.014 (1.00)
RADIATIONEXPOSURE | Fisher's exact test | 0.155 (1.00) | 0.501 (1.00) | 1 (1.00) | 0.109 (1.00) | 0.912 (1.00) | 0.821 (1.00) | 1 (1.00) | 1 (1.00) | 0.812 (1.00) | 0.871 (1.00)
EXTRATHYROIDAL EXTENSION | Chi-square test | 0.00259 (0.246) | 1.71e-06 (0.000215) | 0.00147 (0.146) | 0.000701 (0.0722) | 1.11e-07 (1.44e-05) | 7.15e-09 (9.65e-07) | 2.41e-08 (3.15e-06) | 2.03e-08 (2.68e-06) | 1.32e-06 (0.000168) | 1.26e-08 (1.68e-06)
COMPLETENESS OF RESECTION | Chi-square test | 0.425 (1.00) | 0.205 (1.00) | 0.377 (1.00) | 0.831 (1.00) | 0.216 (1.00) | 0.207 (1.00) | 0.202 (1.00) | 0.262 (1.00) | 0.106 (1.00) | 0.0911 (1.00)
NUMBER OF LYMPH NODES | ANOVA | 0.166 (1.00) | 0.000161 (0.0177) | 0.849 (1.00) | 0.507 (1.00) | 0.000307 (0.0329) | 0.000144 (0.0161) | 5.21e-06 (0.000636) | 3.89e-06 (0.000479) | 0.000209 (0.0228) | 1.59e-05 (0.00191)
MULTIFOCALITY | Fisher's exact test | 0.276 (1.00) | 0.916 (1.00) | 0.00914 (0.795) | 0.258 (1.00) | 0.359 (1.00) | 0.0947 (1.00) | 0.826 (1.00) | 0.961 (1.00) | 0.441 (1.00) | 0.847 (1.00)
TUMOR SIZE | ANOVA | 0.0184 (1.00) | 0.633 (1.00) | 0.643 (1.00) | 0.583 (1.00) | 0.253 (1.00) | 0.011 (0.942) | 0.0236 (1.00) | 0.147 (1.00) | 0.0341 (1.00) | 0.23 (1.00)

Clustering Approach #1: 'Copy Number Ratio CNMF subtypes'

Table S1. Description of clustering approach #1: 'Copy Number Ratio CNMF subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 32 | 377 | 75

'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.0613 (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 | 480 | 14 | 0.0 - 158.8 (15.5)
subtype1 | 31 | 3 | 0.0 - 98.6 (14.4)
subtype2 | 374 | 10 | 0.1 - 158.8 (16.2)
subtype3 | 75 | 1 | 0.0 - 85.2 (14.5)

Figure S1. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 4.95e-05 (ANOVA), Q value = 0.0058

Table S3. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 484 | 47.1 (15.6)
subtype1 | 32 | 58.8 (14.5)
subtype2 | 377 | 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'

'Copy Number Ratio CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.000594 (Chi-square test), Q value = 0.062

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 | 274 | 50 | 106 | 2 | 44 | 6
subtype1 | 9 | 10 | 9 | 1 | 2 | 1
subtype2 | 220 | 33 | 84 | 0 | 34 | 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'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.466 (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 | 161 | 161 | 21
subtype1 | 5 | 13 | 12 | 2
subtype2 | 109 | 120 | 130 | 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'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.0574 (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 | 219 | 218
subtype1 | 18 | 7
subtype2 | 164 | 178
subtype3 | 37 | 33

Figure S5. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.0681 (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 | 265 | 9 | 209
subtype1 | 10 | 1 | 21
subtype2 | 216 | 6 | 154
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'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.343 (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 | 355 | 129
subtype1 | 20 | 12
subtype2 | 280 | 97
subtype3 | 55 | 20

Figure S7. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.233 (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 | 343 | 97 | 35
subtype1 | 0 | 21 | 10 | 1
subtype2 | 8 | 271 | 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'

'Copy Number Ratio CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.376 (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 | 470
subtype1 | 2 | 30
subtype2 | 11 | 366
subtype3 | 1 | 74

Figure S9. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'Copy Number Ratio CNMF subtypes' versus 'RADIATIONEXPOSURE'

P value = 0.155 (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 | 408 | 17
subtype1 | 28 | 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'

'Copy Number Ratio CNMF subtypes' versus 'EXTRATHYROIDAL.EXTENSION'

P value = 0.00259 (Chi-square test), Q value = 0.25

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 | 127 | 16 | 323 | 1
subtype1 | 6 | 0 | 24 | 1
subtype2 | 106 | 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'

'Copy Number Ratio CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.425 (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 | 49 | 4 | 29
subtype1 | 22 | 4 | 0 | 4
subtype2 | 293 | 39 | 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'

'Copy Number Ratio CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.166 (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 | 385 | 3.6 (6.2)
subtype1 | 23 | 1.4 (3.6)
subtype2 | 302 | 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'

'Copy Number Ratio CNMF subtypes' versus 'MULTIFOCALITY'

P value = 0.276 (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 | 219 | 255
subtype1 | 12 | 20
subtype2 | 168 | 201
subtype3 | 39 | 34

Figure S14. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

'Copy Number Ratio CNMF subtypes' versus 'TUMOR.SIZE'

P value = 0.0184 (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 | 386 | 3.0 (1.6)
subtype1 | 27 | 3.8 (1.7)
subtype2 | 301 | 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'

Clustering Approach #2: 'METHLYATION CNMF'

Table S17. Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 150 | 79 | 257

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.602 (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 | 482 | 14 | 0.0 - 158.8 (15.5)
subtype1 | 148 | 3 | 0.0 - 132.4 (14.1)
subtype2 | 77 | 4 | 0.0 - 157.2 (14.5)
subtype3 | 257 | 7 | 0.1 - 158.8 (16.9)

Figure S16. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.279 (ANOVA), Q value = 1

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 486 | 47.2 (15.6)
subtype1 | 150 | 48.6 (15.1)
subtype2 | 79 | 45.2 (16.4)
subtype3 | 257 | 46.9 (15.6)

Figure S17. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 1.31e-05 (Chi-square test), Q value = 0.0016

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 | 275 | 51 | 106 | 2 | 44 | 6
subtype1 | 87 | 30 | 26 | 1 | 3 | 2
subtype2 | 51 | 2 | 18 | 1 | 7 | 0
subtype3 | 137 | 19 | 62 | 0 | 34 | 4

Figure S18. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.T.STAGE'

P value = 0.00272 (Chi-square test), Q value = 0.26

Table S21. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 140 | 162 | 161 | 21
subtype1 | 54 | 56 | 39 | 1
subtype2 | 26 | 24 | 26 | 2
subtype3 | 60 | 82 | 96 | 18

Figure S19. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.N.STAGE'

P value = 1.63e-13 (Fisher's exact test), Q value = 2.3e-11

Table S22. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients | 0 | 1
--- | --- | ---
ALL | 221 | 218
subtype1 | 99 | 27
subtype2 | 31 | 45
subtype3 | 91 | 146

Figure S20. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.M.STAGE'

P value = 0.175 (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 | 266 | 9 | 210
subtype1 | 71 | 3 | 75
subtype2 | 47 | 0 | 32
subtype3 | 148 | 6 | 103

Figure S21. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.861 (Fisher's exact test), Q value = 1

Table S24. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 356 | 130
subtype1 | 109 | 41
subtype2 | 60 | 19
subtype3 | 187 | 70

Figure S22. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 7.01e-27 (Chi-square test), Q value = 1e-24

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 | 343 | 99 | 35
subtype1 | 2 | 71 | 77 | 0
subtype2 | 2 | 65 | 6 | 6
subtype3 | 5 | 207 | 16 | 29

Figure S23. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

P value = 0.0878 (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 | 472
subtype1 | 1 | 149
subtype2 | 2 | 77
subtype3 | 11 | 246

Figure S24. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'RADIATIONEXPOSURE'

P value = 0.501 (Fisher's exact test), Q value = 1

Table S27. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'RADIATIONEXPOSURE'

nPatients | NO | YES
--- | --- | ---
ALL | 409 | 17
subtype1 | 122 | 6
subtype2 | 67 | 4
subtype3 | 220 | 7

Figure S25. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'RADIATIONEXPOSURE'

'METHLYATION CNMF' versus 'EXTRATHYROIDAL.EXTENSION'

P value = 1.71e-06 (Chi-square test), Q value = 0.00021

Table S28. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'EXTRATHYROIDAL.EXTENSION'

nPatients | MINIMAL (T3) | MODERATE/ADVANCED (T4A) | NONE | VERY ADVANCED (T4B)
--- | --- | --- | --- | ---
ALL | 127 | 16 | 325 | 1
subtype1 | 21 | 0 | 121 | 0
subtype2 | 20 | 1 | 54 | 1
subtype3 | 86 | 15 | 150 | 0

Figure S26. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'EXTRATHYROIDAL.EXTENSION'

'METHLYATION CNMF' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.205 (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 | 49 | 4 | 29
subtype1 | 120 | 9 | 0 | 7
subtype2 | 60 | 8 | 0 | 4
subtype3 | 193 | 32 | 4 | 18

Figure S27. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'

'METHLYATION CNMF' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.000161 (ANOVA), Q value = 0.018

Table S30. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 386 | 3.5 (6.2)
subtype1 | 105 | 1.4 (4.0)
subtype2 | 67 | 4.2 (7.1)
subtype3 | 214 | 4.4 (6.5)

Figure S28. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'

'METHLYATION CNMF' versus 'MULTIFOCALITY'

P value = 0.916 (Fisher's exact test), Q value = 1

Table S31. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients | MULTIFOCAL | UNIFOCAL
--- | --- | ---
ALL | 220 | 256
subtype1 | 68 | 79
subtype2 | 34 | 43
subtype3 | 118 | 134

Figure S29. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #14: 'MULTIFOCALITY'

'METHLYATION CNMF' versus 'TUMOR.SIZE'

P value = 0.633 (ANOVA), Q value = 1

Table S32. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #15: 'TUMOR.SIZE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 387 | 3.0 (1.6)
subtype1 | 118 | 3.1 (1.6)
subtype2 | 63 | 3.0 (1.6)
subtype3 | 206 | 2.9 (1.5)

Figure S30. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #15: 'TUMOR.SIZE'

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S33. Description of clustering approach #3: 'RPPA CNMF subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 60 | 79 | 80

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.565 (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 | 218 | 13 | 0.1 - 158.8 (17.6)
subtype1 | 60 | 4 | 1.2 - 158.8 (16.4)
subtype2 | 79 | 4 | 0.2 - 147.4 (20.6)
subtype3 | 79 | 5 | 0.1 - 147.8 (15.0)

Figure S31. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.00249 (ANOVA), Q value = 0.24

Table S35. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 219 | 48.4 (16.7)
subtype1 | 60 | 52.0 (15.2)
subtype2 | 79 | 50.7 (16.0)
subtype3 | 80 | 43.3 (17.4)

Figure S32. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00359 (Chi-square test), Q value = 0.33

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 | 114 | 33 | 45 | 21 | 4
subtype1 | 30 | 11 | 14 | 2 | 1
subtype2 | 31 | 13 | 21 | 14 | 0
subtype3 | 53 | 9 | 10 | 5 | 3

Figure S33. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.00518 (Chi-square test), Q value = 0.47

Table S37. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 51 | 82 | 74 | 11
subtype1 | 22 | 22 | 15 | 0
subtype2 | 13 | 25 | 33 | 8
subtype3 | 16 | 35 | 26 | 3

Figure S34. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.0405 (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 | 96 | 94
subtype1 | 33 | 18
subtype2 | 29 | 41
subtype3 | 34 | 35

Figure S35. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.219 (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 | 115 | 5 | 98
subtype1 | 37 | 1 | 21
subtype2 | 43 | 1 | 35
subtype3 | 35 | 3 | 42

Figure S36. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.608 (Fisher's exact test), Q value = 1

Table S40. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 151 | 68
subtype1 | 42 | 18
subtype2 | 57 | 22
subtype3 | 52 | 28

Figure S37. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 9.21e-05 (Chi-square test), Q value = 0.01

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 | 155 | 52 | 10
subtype1 | 0 | 34 | 25 | 1
subtype2 | 1 | 55 | 14 | 9
subtype3 | 1 | 66 | 13 | 0

Figure S38. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

'RPPA CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.0123 (Fisher's exact test), Q value = 1

Table S42. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 13 | 206
subtype1 | 0 | 60
subtype2 | 9 | 70
subtype3 | 4 | 76

Figure S39. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA CNMF subtypes' versus 'RADIATIONEXPOSURE'

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 | 182 | 11
subtype1 | 52 | 3
subtype2 | 67 | 4
subtype3 | 63 | 4

Figure S40. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONEXPOSURE'

'RPPA CNMF subtypes' versus 'EXTRATHYROIDAL.EXTENSION'

P value = 0.00147 (Chi-square test), Q value = 0.15

Table S44. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'EXTRATHYROIDAL.EXTENSION'

nPatients | MINIMAL (T3) | MODERATE/ADVANCED (T4A) | NONE
--- | --- | --- | ---
ALL | 56 | 10 | 146
subtype1 | 13 | 0 | 47
subtype2 | 28 | 8 | 41
subtype3 | 15 | 2 | 58

Figure S41. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'EXTRATHYROIDAL.EXTENSION'

'RPPA CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.377 (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 | 23 | 2 | 14
subtype1 | 47 | 6 | 1 | 5
subtype2 | 58 | 13 | 0 | 4
subtype3 | 60 | 4 | 1 | 5

Figure S42. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'

'RPPA CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.849 (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 | 169 | 3.4 (5.8)
subtype1 | 43 | 3.8 (7.5)
subtype2 | 65 | 3.1 (4.5)
subtype3 | 61 | 3.5 (5.8)

Figure S43. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'

'RPPA CNMF subtypes' versus 'MULTIFOCALITY'

P value = 0.00914 (Fisher's exact test), Q value = 0.79

Table S47. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients | MULTIFOCAL | UNIFOCAL
--- | --- | ---
ALL | 101 | 111
subtype1 | 35 | 22
subtype2 | 27 | 50
subtype3 | 39 | 39

Figure S44. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

'RPPA CNMF subtypes' versus 'TUMOR.SIZE'

P value = 0.643 (ANOVA), Q value = 1

Table S48. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 188 | 3.3 (1.6)
subtype1 | 51 | 3.2 (1.6)
subtype2 | 66 | 3.4 (1.5)
subtype3 | 71 | 3.2 (1.5)

Figure S45. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S49. Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 31 | 93 | 95

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.0619 (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 | 218 | 13 | 0.1 - 158.8 (17.6)
subtype1 | 31 | 4 | 1.1 - 147.4 (19.8)
subtype2 | 92 | 3 | 0.1 - 147.8 (17.5)
subtype3 | 95 | 6 | 0.3 - 158.8 (16.8)

Figure S46. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.00123 (ANOVA), Q value = 0.12

Table S51. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 219 | 48.4 (16.7)
subtype1 | 31 | 54.7 (16.1)
subtype2 | 93 | 43.9 (16.2)
subtype3 | 95 | 50.7 (16.4)

Figure S47. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00838 (Chi-square test), Q value = 0.74

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 | 114 | 33 | 45 | 21 | 4
subtype1 | 8 | 9 | 8 | 6 | 0
subtype2 | 53 | 7 | 20 | 11 | 2
subtype3 | 53 | 17 | 17 | 4 | 2

Figure S48. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.0198 (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 | 82 | 74 | 11
subtype1 | 6 | 11 | 10 | 4
subtype2 | 15 | 33 | 38 | 6
subtype3 | 30 | 38 | 26 | 1

Figure S49. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 6.14e-05 (Fisher's exact test), Q value = 0.007

Table S54. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients | 0 | 1
--- | --- | ---
ALL | 96 | 94
subtype1 | 13 | 11
subtype2 | 28 | 56
subtype3 | 55 | 27

Figure S50. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.448 (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 | 115 | 5 | 98
subtype1 | 14 | 0 | 17
subtype2 | 54 | 3 | 36
subtype3 | 47 | 2 | 45

Figure S51. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.518 (Fisher's exact test), Q value = 1

Table S56. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 151 | 68
subtype1 | 24 | 7
subtype2 | 64 | 29
subtype3 | 63 | 32

Figure S52. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 9.73e-09 (Chi-square test), Q value = 1.3e-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 | 155 | 52 | 10
subtype1 | 1 | 15 | 14 | 1
subtype2 | 1 | 82 | 2 | 8
subtype3 | 0 | 58 | 36 | 1

Figure S53. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

'RPPA cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.243 (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 | 206
subtype1 | 3 | 28
subtype2 | 7 | 86
subtype3 | 3 | 92

Figure S54. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA cHierClus subtypes' versus 'RADIATIONEXPOSURE'

P value = 0.109 (Fisher's exact test), Q value = 1

Table S59. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONEXPOSURE'

nPatients | NO | YES
--- | --- | ---
ALL | 182 | 11
subtype1 | 25 | 4
subtype2 | 75 | 4
subtype3 | 82 | 3

Figure S55. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONEXPOSURE'

'RPPA cHierClus subtypes' versus 'EXTRATHYROIDAL.EXTENSION'

P value = 0.000701 (Chi-square test), Q value = 0.072

Table S60. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'EXTRATHYROIDAL.EXTENSION'

nPatients | MINIMAL (T3) | MODERATE/ADVANCED (T4A) | NONE
--- | --- | --- | ---
ALL | 56 | 10 | 146
subtype1 | 8 | 4 | 19
subtype2 | 32 | 6 | 52
subtype3 | 16 | 0 | 75

Figure S56. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'EXTRATHYROIDAL.EXTENSION'

'RPPA cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.831 (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 | 23 | 2 | 14
subtype1 | 26 | 2 | 0 | 3
subtype2 | 68 | 12 | 1 | 4
subtype3 | 71 | 9 | 1 | 7

Figure S57. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'

'RPPA cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.507 (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 | 169 | 3.4 (5.8)
subtype1 | 24 | 2.6 (5.1)
subtype2 | 75 | 4.0 (5.6)
subtype3 | 70 | 3.1 (6.3)

Figure S58. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'

'RPPA cHierClus subtypes' versus 'MULTIFOCALITY'

P value = 0.258 (Fisher's exact test), Q value = 1

Table S63. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients | MULTIFOCAL | UNIFOCAL
--- | --- | ---
ALL | 101 | 111
subtype1 | 11 | 19
subtype2 | 41 | 49
subtype3 | 49 | 43

Figure S59. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

'RPPA cHierClus subtypes' versus 'TUMOR.SIZE'

P value = 0.583 (ANOVA), Q value = 1

Table S64. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 188 | 3.3 (1.6)
subtype1 | 25 | 3.5 (1.6)
subtype2 | 82 | 3.3 (1.5)
subtype3 | 81 | 3.2 (1.6)

Figure S60. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S65. Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels | 1 | 2 | 3 | 4
--- | --- | --- | --- | ---
Number of samples | 153 | 52 | 115 | 164

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.672 (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 | 480 | 14 | 0.0 - 158.8 (15.5)
subtype1 | 152 | 4 | 0.0 - 132.4 (14.4)
subtype2 | 51 | 1 | 0.0 - 157.2 (13.6)
subtype3 | 114 | 2 | 0.1 - 158.8 (15.4)
subtype4 | 163 | 7 | 0.1 - 155.5 (17.5)

Figure S61. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.0729 (ANOVA), Q value = 1

Table S67. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 484 | 47.2 (15.6)
subtype1 | 153 | 49.0 (15.5)
subtype2 | 52 | 44.4 (17.4)
subtype3 | 115 | 44.8 (14.6)
subtype4 | 164 | 48.0 (15.6)

Figure S62. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 5.56e-05 (Chi-square test), Q value = 0.0064

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 | 274 | 51 | 105 | 2 | 44 | 6
subtype1 | 89 | 29 | 27 | 1 | 4 | 2
subtype2 | 36 | 1 | 12 | 0 | 3 | 0
subtype3 | 66 | 14 | 22 | 0 | 10 | 2
subtype4 | 83 | 7 | 44 | 1 | 27 | 2

Figure S63. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 3.04e-06 (Chi-square test), Q value = 0.00038

Table S69. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 139 | 161 | 161 | 21
subtype1 | 55 | 55 | 41 | 2
subtype2 | 22 | 14 | 14 | 1
subtype3 | 33 | 48 | 30 | 4
subtype4 | 29 | 44 | 76 | 14

Figure S64. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 1.42e-13 (Fisher's exact test), Q value = 2e-11

Table S70. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients | 0 | 1
--- | --- | ---
ALL | 220 | 217
subtype1 | 101 | 28
subtype2 | 20 | 32
subtype3 | 48 | 58
subtype4 | 51 | 99

Figure S65. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.122 (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 | 265 | 9 | 209
subtype1 | 70 | 3 | 79
subtype2 | 34 | 0 | 18
subtype3 | 70 | 3 | 42
subtype4 | 91 | 3 | 70

Figure S66. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.879 (Fisher's exact test), Q value = 1

Table S72. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 354 | 130
subtype1 | 110 | 43
subtype2 | 39 | 13
subtype3 | 87 | 28
subtype4 | 118 | 46

Figure S67. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 7.1e-32 (Chi-square test), Q value = 1e-29

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 | 342 | 99 | 34
subtype1 | 2 | 69 | 81 | 1
subtype2 | 1 | 44 | 3 | 4
subtype3 | 2 | 98 | 12 | 3
subtype4 | 4 | 131 | 3 | 26

Figure S68. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

'RNAseq CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.00755 (Fisher's exact test), Q value = 0.67

Table S74. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 14 | 470
subtype1 | 1 | 152
subtype2 | 0 | 52
subtype3 | 2 | 113
subtype4 | 11 | 153

Figure S69. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'RADIATIONEXPOSURE'

P value = 0.912 (Fisher's exact test), Q value = 1

Table S75. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONEXPOSURE'

nPatients | NO | YES
--- | --- | ---
ALL | 407 | 17
subtype1 | 128 | 6
subtype2 | 41 | 2
subtype3 | 97 | 4
subtype4 | 141 | 5

Figure S70. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONEXPOSURE'

'RNAseq CNMF subtypes' versus 'EXTRATHYROIDAL.EXTENSION'

P value = 1.11e-07 (Chi-square test), Q value = 1.4e-05

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 | 127 | 16 | 323 | 1
subtype1 | 22 | 1 | 123 | 0
subtype2 | 12 | 1 | 36 | 0
subtype3 | 28 | 1 | 83 | 0
subtype4 | 65 | 13 | 81 | 1

Figure S71. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'EXTRATHYROIDAL.EXTENSION'

'RNAseq CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.216 (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 | 371 | 49 | 4 | 29
subtype1 | 122 | 10 | 0 | 7
subtype2 | 36 | 5 | 0 | 5
subtype3 | 89 | 9 | 2 | 8
subtype4 | 124 | 25 | 2 | 9

Figure S72. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'

'RNAseq CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.000307 (ANOVA), Q value = 0.033

Table S78. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 384 | 3.5 (6.2)
subtype1 | 111 | 1.5 (4.1)
subtype2 | 47 | 5.0 (7.7)
subtype3 | 91 | 3.9 (6.3)
subtype4 | 135 | 4.5 (6.6)

Figure S73. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'

'RNAseq CNMF subtypes' versus 'MULTIFOCALITY'

P value = 0.359 (Fisher's exact test), Q value = 1

Table S79. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients | MULTIFOCAL | UNIFOCAL
--- | --- | ---
ALL | 219 | 255
subtype1 | 72 | 78
subtype2 | 22 | 28
subtype3 | 58 | 54
subtype4 | 67 | 95

Figure S74. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

'RNAseq CNMF subtypes' versus 'TUMOR.SIZE'

P value = 0.253 (ANOVA), Q value = 1

Table S80. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 386 | 3.0 (1.6)
subtype1 | 120 | 3.0 (1.5)
subtype2 | 36 | 2.6 (1.8)
subtype3 | 91 | 2.8 (1.5)
subtype4 | 139 | 3.1 (1.6)

Figure S75. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S81. Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3 | 4
--- | --- | --- | --- | ---
Number of samples | 192 | 128 | 94 | 70

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.825 (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 | 480 | 14 | 0.0 - 158.8 (15.5)
subtype1 | 189 | 7 | 0.1 - 157.2 (17.5)
subtype2 | 127 | 3 | 0.2 - 132.4 (14.1)
subtype3 | 94 | 2 | 0.1 - 158.8 (15.8)
subtype4 | 70 | 2 | 0.0 - 130.7 (13.2)

Figure S76. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.199 (ANOVA), Q value = 1

Table S83. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 484 | 47.2 (15.6)
subtype1 | 192 | 47.4 (15.9)
subtype2 | 128 | 49.2 (15.7)
subtype3 | 94 | 44.7 (15.2)
subtype4 | 70 | 46.2 (14.8)

Figure S77. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 2.7e-05 (Chi-square test), Q value = 0.0032

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 | 274 | 51 | 105 | 2 | 44 | 6
subtype1 | 101 | 9 | 52 | 1 | 27 | 2
subtype2 | 74 | 28 | 19 | 1 | 3 | 2
subtype3 | 53 | 11 | 17 | 0 | 10 | 2
subtype4 | 46 | 3 | 17 | 0 | 4 | 0

Figure S78. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 5.8e-07 (Chi-square test), Q value = 7.4e-05

Table S85. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 139 | 161 | 161 | 21
subtype1 | 40 | 49 | 87 | 15
subtype2 | 43 | 53 | 30 | 2
subtype3 | 24 | 39 | 27 | 4
subtype4 | 32 | 20 | 17 | 0

Figure S79. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 5.77e-17 (Fisher's exact test), Q value = 8.3e-15

Table S86. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients | 0 | 1
--- | --- | ---
ALL | 220 | 217
subtype1 | 55 | 125
subtype2 | 86 | 17
subtype3 | 42 | 45
subtype4 | 37 | 30

Figure S80. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.00594 (Chi-square test), Q value = 0.53

Table S87. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 265 | 9 | 209
subtype1 | 107 | 3 | 82
subtype2 | 53 | 3 | 71
subtype3 | 56 | 3 | 35
subtype4 | 49 | 0 | 21

Figure S81. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.93 (Fisher's exact test), Q value = 1

Table S88. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 354 | 130
subtype1 | 138 | 54
subtype2 | 93 | 35
subtype3 | 70 | 24
subtype4 | 53 | 17

Figure S82. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1.29e-34 (Chi-square test), Q value = 1.9e-32

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 | 342 | 99 | 34
subtype1 | 6 | 154 | 3 | 29
subtype2 | 2 | 52 | 74 | 0
subtype3 | 0 | 82 | 10 | 2
subtype4 | 1 | 54 | 12 | 3

Figure S83. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

'RNAseq cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.16 (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 | 14 | 470
subtype1 | 10 | 182
subtype2 | 2 | 126
subtype3 | 1 | 93
subtype4 | 1 | 69

Figure S84. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'RADIATIONEXPOSURE'

P value = 0.821 (Fisher's exact test), Q value = 1

Table S91. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONEXPOSURE'

nPatients | NO | YES
--- | --- | ---
ALL | 407 | 17
subtype1 | 163 | 7
subtype2 | 106 | 5
subtype3 | 83 | 2
subtype4 | 55 | 3

Figure S85. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONEXPOSURE'

'RNAseq cHierClus subtypes' versus 'EXTRATHYROIDAL.EXTENSION'

P value = 7.15e-09 (Chi-square test), Q value = 9.7e-07

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 | 127 | 16 | 323 | 1
subtype1 | 75 | 14 | 97 | 1
subtype2 | 13 | 1 | 107 | 0
subtype3 | 24 | 1 | 67 | 0
subtype4 | 15 | 0 | 52 | 0

Figure S86. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'EXTRATHYROIDAL.EXTENSION'

'RNAseq cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.207 (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 | 371 | 49 | 4 | 29
subtype1 | 140 | 29 | 2 | 12
subtype2 | 98 | 10 | 0 | 7
subtype3 | 77 | 6 | 2 | 6
subtype4 | 56 | 4 | 0 | 4

Figure S87. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'

'RNAseq cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.000144 (ANOVA), Q value = 0.016

Table S94. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 384 | 3.5 (6.2)
subtype1 | 166 | 4.7 (6.7)
subtype2 | 88 | 1.1 (3.0)
subtype3 | 75 | 4.1 (6.9)
subtype4 | 55 | 3.3 (6.4)

Figure S88. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'

'RNAseq cHierClus subtypes' versus 'MULTIFOCALITY'

P value = 0.0947 (Fisher's exact test), Q value = 1

Table S95. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients | MULTIFOCAL | UNIFOCAL
--- | --- | ---
ALL | 219 | 255
subtype1 | 75 | 114
subtype2 | 59 | 66
subtype3 | 49 | 43
subtype4 | 36 | 32

Figure S89. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

'RNAseq cHierClus subtypes' versus 'TUMOR.SIZE'

P value = 0.011 (ANOVA), Q value = 0.94

Table S96. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 386 | 3.0 (1.6)
subtype1 | 157 | 3.0 (1.6)
subtype2 | 105 | 3.2 (1.5)
subtype3 | 74 | 2.9 (1.5)
subtype4 | 50 | 2.3 (1.5)

Figure S90. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S97. Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 149 | 170 | 166

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.031 (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 | 481 | 14 | 0.0 - 158.8 (15.5)
subtype1 | 148 | 3 | 0.2 - 132.4 (14.8)
subtype2 | 168 | 2 | 0.1 - 147.8 (16.7)
subtype3 | 165 | 9 | 0.0 - 158.8 (14.6)

Figure S91. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.158 (ANOVA), Q value = 1

Table S99. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 485 | 47.2 (15.5)
subtype1 | 149 | 47.1 (16.0)
subtype2 | 170 | 45.7 (14.8)
subtype3 | 166 | 49.0 (15.8)

Figure S92. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 1.75e-06 (Chi-square test), Q value = 0.00022

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 | 274 | 51 | 106 | 2 | 44 | 6
subtype1 | 92 | 29 | 20 | 1 | 4 | 2
subtype2 | 99 | 17 | 35 | 0 | 17 | 2
subtype3 | 83 | 5 | 51 | 1 | 23 | 2

Figure S93. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.T.STAGE'

P value = 0.000146 (Chi-square test), Q value = 0.016

Table S101. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 140 | 162 | 160 | 21
subtype1 | 43 | 67 | 37 | 2
subtype2 | 44 | 61 | 58 | 7
subtype3 | 53 | 34 | 65 | 12

Figure S94. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.N.STAGE'

P value = 2.01e-12 (Fisher's exact test), Q value = 2.8e-10

Table S102. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients | 0 | 1
--- | --- | ---
ALL | 221 | 217
subtype1 | 95 | 27
subtype2 | 67 | 90
subtype3 | 59 | 100

Figure S95. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.M.STAGE'

P value = 0.00117 (Chi-square test), Q value = 0.12

Table S103. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 265 | 9 | 210
subtype1 | 63 | 3 | 82
subtype2 | 92 | 4 | 74
subtype3 | 110 | 2 | 54

Figure S96. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.896 (Fisher's exact test), Q value = 1

Table S104. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 355 | 130
subtype1 | 107 | 42
subtype2 | 125 | 45
subtype3 | 123 | 43

Figure S97. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 3.83e-34 (Chi-square test), Q value = 5.6e-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 | 342 | 99 | 35
subtype1 | 2 | 65 | 82 | 0
subtype2 | 3 | 146 | 9 | 12
subtype3 | 4 | 131 | 8 | 23

Figure S98. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

'MIRSEQ CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.456 (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 | 471
subtype1 | 4 | 145
subtype2 | 7 | 163
subtype3 | 3 | 163

Figure S99. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CNMF' versus 'RADIATIONEXPOSURE'

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 | 408 | 17
subtype1 | 126 | 5
subtype2 | 142 | 6
subtype3 | 140 | 6

Figure S100. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RADIATIONEXPOSURE'

'MIRSEQ CNMF' versus 'EXTRATHYROIDAL.EXTENSION'

P value = 2.41e-08 (Chi-square test), Q value = 3.2e-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 | 126 | 16 | 325 | 1
subtype1 | 15 | 0 | 128 | 0
subtype2 | 53 | 5 | 107 | 0
subtype3 | 58 | 11 | 90 | 1

Figure S101. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'EXTRATHYROIDAL.EXTENSION'

'MIRSEQ CNMF' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.202 (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 | 49 | 4 | 29
subtype1 | 118 | 10 | 0 | 9
subtype2 | 135 | 15 | 2 | 8
subtype3 | 119 | 24 | 2 | 12

Figure S102. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'

'MIRSEQ CNMF' versus 'NUMBER.OF.LYMPH.NODES'

P value = 5.21e-06 (ANOVA), Q value = 0.00064

Table S110. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 385 | 3.5 (6.2)
subtype1 | 106 | 1.4 (3.1)
subtype2 | 141 | 3.5 (6.0)
subtype3 | 138 | 5.3 (7.6)

Figure S103. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'

'MIRSEQ CNMF' versus 'MULTIFOCALITY'

P value = 0.826 (Fisher's exact test), Q value = 1

Table S111. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients | MULTIFOCAL | UNIFOCAL
--- | --- | ---
ALL | 219 | 256
subtype1 | 64 | 80
subtype2 | 76 | 90
subtype3 | 79 | 86

Figure S104. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #14: 'MULTIFOCALITY'

'MIRSEQ CNMF' versus 'TUMOR.SIZE'

P value = 0.0236 (ANOVA), Q value = 1

Table S112. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #15: 'TUMOR.SIZE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 386 | 3.0 (1.6)
subtype1 | 123 | 3.2 (1.5)
subtype2 | 137 | 3.0 (1.6)
subtype3 | 126 | 2.7 (1.6)

Figure S105. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #15: 'TUMOR.SIZE'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S113. Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 152 | 130 | 203

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.0614 (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 | 481 | 14 | 0.0 - 158.8 (15.5)
subtype1 | 151 | 1 | 0.1 - 147.8 (16.3)
subtype2 | 129 | 3 | 0.3 - 132.4 (14.5)
subtype3 | 201 | 10 | 0.0 - 158.8 (15.1)

Figure S106. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.0308 (ANOVA), Q value = 1

Table S115. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 485 | 47.2 (15.5)
subtype1 | 152 | 44.5 (15.3)
subtype2 | 130 | 48.2 (15.7)
subtype3 | 203 | 48.7 (15.4)

Figure S107. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 1.11e-07 (Chi-square test), Q value = 1.4e-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 | 274 | 51 | 106 | 2 | 44 | 6
subtype1 | 92 | 17 | 26 | 0 | 15 | 2
subtype2 | 77 | 28 | 19 | 1 | 2 | 2
subtype3 | 105 | 6 | 61 | 1 | 27 | 2

Figure S108. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.T.STAGE'

P value = 5.22e-05 (Chi-square test), Q value = 0.0061

Table S117. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 140 | 162 | 160 | 21
subtype1 | 36 | 62 | 47 | 7
subtype2 | 42 | 56 | 31 | 1
subtype3 | 62 | 44 | 82 | 13

Figure S109. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.N.STAGE'

P value = 6.88e-15 (Fisher's exact test), Q value = 9.7e-13

Table S118. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients | 0 | 1
--- | --- | ---
ALL | 221 | 217
subtype1 | 62 | 81
subtype2 | 87 | 17
subtype3 | 72 | 119

Figure S110. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.M.STAGE'

P value = 0.00205 (Chi-square test), Q value = 0.2

Table S119. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 265 | 9 | 210
subtype1 | 83 | 4 | 65
subtype2 | 53 | 3 | 73
subtype3 | 129 | 2 | 72

Figure S111. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.716 (Fisher's exact test), Q value = 1

Table S120. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 355 | 130
subtype1 | 108 | 44
subtype2 | 95 | 35
subtype3 | 152 | 51

Figure S112. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 1.37e-35 (Chi-square test), Q value = 2e-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 | 342 | 99 | 35
subtype1 | 2 | 131 | 13 | 6
subtype2 | 2 | 52 | 76 | 0
subtype3 | 5 | 159 | 10 | 29

Figure S113. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

'MIRSEQ CHIERARCHICAL' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.0376 (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 | 471
subtype1 | 9 | 143
subtype2 | 2 | 128
subtype3 | 3 | 200

Figure S114. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CHIERARCHICAL' versus 'RADIATIONEXPOSURE'

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 | 408 | 17
subtype1 | 127 | 5
subtype2 | 110 | 5
subtype3 | 171 | 7

Figure S115. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'RADIATIONEXPOSURE'

'MIRSEQ CHIERARCHICAL' versus 'EXTRATHYROIDAL.EXTENSION'

P value = 2.03e-08 (Chi-square test), Q value = 2.7e-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 | 126 | 16 | 325 | 1
subtype1 | 37 | 4 | 106 | 0
subtype2 | 13 | 0 | 112 | 0
subtype3 | 76 | 12 | 107 | 1

Figure S116. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'EXTRATHYROIDAL.EXTENSION'

'MIRSEQ CHIERARCHICAL' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.262 (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 | 49 | 4 | 29
subtype1 | 122 | 12 | 1 | 8
subtype2 | 103 | 10 | 0 | 6
subtype3 | 147 | 27 | 3 | 15

Figure S117. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER.OF.LYMPH.NODES'

P value = 3.89e-06 (ANOVA), Q value = 0.00048

Table S126. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 385 | 3.5 (6.2)
subtype1 | 125 | 3.4 (5.5)
subtype2 | 91 | 1.0 (2.9)
subtype3 | 169 | 5.0 (7.4)

Figure S118. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'

'MIRSEQ CHIERARCHICAL' versus 'MULTIFOCALITY'

P value = 0.961 (Fisher's exact test), Q value = 1

Table S127. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients | MULTIFOCAL | UNIFOCAL
--- | --- | ---
ALL | 219 | 256
subtype1 | 68 | 80
subtype2 | 60 | 67
subtype3 | 91 | 109

Figure S119. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'MULTIFOCALITY'

'MIRSEQ CHIERARCHICAL' versus 'TUMOR.SIZE'

P value = 0.147 (ANOVA), Q value = 1

Table S128. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #15: 'TUMOR.SIZE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 386 | 3.0 (1.6)
subtype1 | 116 | 3.0 (1.6)
subtype2 | 109 | 3.1 (1.5)
subtype3 | 161 | 2.8 (1.6)

Figure S120. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #15: 'TUMOR.SIZE'

Clustering Approach #9: 'MIRseq Mature CNMF subtypes'

Table S129. Description of clustering approach #9: 'MIRseq Mature CNMF subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 162 | 166 | 157

'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.00462 (logrank test), Q value = 0.42

Table S130. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 481 | 14 | 0.0 - 158.8 (15.5)
subtype1 | 161 | 3 | 0.3 - 137.9 (15.0)
subtype2 | 164 | 1 | 0.1 - 155.5 (17.4)
subtype3 | 156 | 10 | 0.0 - 158.8 (14.5)

Figure S121. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.07 (ANOVA), Q value = 1

Table S131. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 485 | 47.2 (15.5)
subtype1 | 162 | 47.7 (15.6)
subtype2 | 166 | 45.1 (14.7)
subtype3 | 157 | 49.0 (16.1)

Figure S122. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00025 (Chi-square test), Q value = 0.027

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 | 274 | 51 | 106 | 2 | 44 | 6
subtype1 | 96 | 28 | 27 | 1 | 7 | 2
subtype2 | 99 | 18 | 32 | 0 | 15 | 2
subtype3 | 79 | 5 | 47 | 1 | 22 | 2

Figure S123. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.000459 (Chi-square test), Q value = 0.049

Table S133. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 140 | 162 | 160 | 21
subtype1 | 46 | 67 | 46 | 3
subtype2 | 42 | 63 | 56 | 5
subtype3 | 52 | 32 | 58 | 13

Figure S124. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 1.41e-10 (Fisher's exact test), Q value = 1.9e-08

Table S134. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients | 0 | 1
--- | --- | ---
ALL | 221 | 217
subtype1 | 100 | 35
subtype2 | 65 | 89
subtype3 | 56 | 93

Figure S125. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.000496 (Chi-square test), Q value = 0.052

Table S135. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 265 | 9 | 210
subtype1 | 69 | 3 | 89
subtype2 | 90 | 4 | 72
subtype3 | 106 | 2 | 49

Figure S126. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.706 (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 | 355 | 130
subtype1 | 115 | 47
subtype2 | 122 | 44
subtype3 | 118 | 39

Figure S127. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1.27e-29 (Chi-square test), Q value = 1.8e-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 | 342 | 99 | 35
subtype1 | 2 | 78 | 82 | 0
subtype2 | 3 | 140 | 10 | 13
subtype3 | 4 | 124 | 7 | 22

Figure S128. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

'MIRseq Mature CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.0787 (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 | 471
subtype1 | 3 | 159
subtype2 | 9 | 157
subtype3 | 2 | 155

Figure S129. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq Mature CNMF subtypes' versus 'RADIATIONEXPOSURE'

P value = 0.812 (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 | 408 | 17
subtype1 | 136 | 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'

'MIRseq Mature CNMF subtypes' versus 'EXTRATHYROIDAL.EXTENSION'

P value = 1.32e-06 (Chi-square test), Q value = 0.00017

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 | 126 | 16 | 325 | 1
subtype1 | 23 | 1 | 131 | 0
subtype2 | 49 | 3 | 108 | 0
subtype3 | 54 | 12 | 86 | 1

Figure S131. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'EXTRATHYROIDAL.EXTENSION'

'MIRseq Mature CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.106 (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 | 49 | 4 | 29
subtype1 | 125 | 12 | 0 | 11
subtype2 | 136 | 14 | 2 | 6
subtype3 | 111 | 23 | 2 | 12

Figure S132. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'

'MIRseq Mature CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.000209 (ANOVA), Q value = 0.023

Table S142. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 385 | 3.5 (6.2)
subtype1 | 120 | 1.7 (4.1)
subtype2 | 137 | 3.9 (6.8)
subtype3 | 128 | 4.8 (6.7)

Figure S133. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'

'MIRseq Mature CNMF subtypes' versus 'MULTIFOCALITY'

P value = 0.441 (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 | 219 | 256
subtype1 | 77 | 79
subtype2 | 69 | 94
subtype3 | 73 | 83

Figure S134. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

'MIRseq Mature CNMF subtypes' versus 'TUMOR.SIZE'

P value = 0.0341 (ANOVA), Q value = 1

Table S144. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 386 | 3.0 (1.6)
subtype1 | 130 | 3.2 (1.6)
subtype2 | 138 | 2.9 (1.5)
subtype3 | 118 | 2.7 (1.6)

Figure S135. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

Clustering Approach #10: 'MIRseq Mature cHierClus subtypes'

Table S145. Description of clustering approach #10: 'MIRseq Mature cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 193 | 128 | 164

'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.0227 (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 | 481 | 14 | 0.0 - 158.8 (15.5)
subtype1 | 191 | 10 | 0.0 - 158.8 (14.7)
subtype2 | 127 | 3 | 0.3 - 132.4 (14.2)
subtype3 | 163 | 1 | 0.1 - 147.8 (17.2)

Figure S136. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.00183 (ANOVA), Q value = 0.18

Table S147. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 485 | 47.2 (15.5)
subtype1 | 193 | 49.4 (15.0)
subtype2 | 128 | 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'

'MIRseq Mature cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 6.69e-09 (Chi-square test), Q value = 9.1e-07

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 | 274 | 51 | 106 | 2 | 44 | 6
subtype1 | 96 | 5 | 61 | 1 | 27 | 2
subtype2 | 75 | 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'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 1.94e-05 (Chi-square test), Q value = 0.0023

Table S149. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 140 | 162 | 160 | 21
subtype1 | 59 | 40 | 79 | 13
subtype2 | 42 | 55 | 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'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 4.42e-15 (Fisher's exact test), Q value = 6.3e-13

Table S150. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients | 0 | 1
--- | --- | ---
ALL | 221 | 217
subtype1 | 69 | 113
subtype2 | 86 | 16
subtype3 | 66 | 88

Figure S140. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.000781 (Chi-square test), Q value = 0.08

Table S151. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 265 | 9 | 210
subtype1 | 126 | 2 | 65
subtype2 | 52 | 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'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.709 (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 | 355 | 130
subtype1 | 145 | 48
subtype2 | 93 | 35
subtype3 | 117 | 47

Figure S142. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 2.87e-36 (Chi-square test), Q value = 4.3e-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 | 342 | 99 | 35
subtype1 | 4 | 151 | 10 | 28
subtype2 | 2 | 50 | 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'

'MIRseq Mature cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.014 (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 | 471
subtype1 | 2 | 191
subtype2 | 2 | 126
subtype3 | 10 | 154

Figure S144. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq Mature cHierClus subtypes' versus 'RADIATIONEXPOSURE'

P value = 0.871 (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 | 408 | 17
subtype1 | 163 | 6
subtype2 | 108 | 5
subtype3 | 137 | 6

Figure S145. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONEXPOSURE'

'MIRseq Mature cHierClus subtypes' versus 'EXTRATHYROIDAL.EXTENSION'

P value = 1.26e-08 (Chi-square test), Q value = 1.7e-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 | 126 | 16 | 325 | 1
subtype1 | 73 | 12 | 100 | 1
subtype2 | 13 | 0 | 110 | 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'

'MIRseq Mature cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.0911 (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 | 49 | 4 | 29
subtype1 | 137 | 28 | 3 | 14
subtype2 | 101 | 10 | 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'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 1.59e-05 (ANOVA), Q value = 0.0019

Table S158. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 385 | 3.5 (6.2)
subtype1 | 161 | 4.7 (6.7)
subtype2 | 89 | 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'

'MIRseq Mature cHierClus subtypes' versus 'MULTIFOCALITY'

P value = 0.847 (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 | 219 | 256
subtype1 | 89 | 102
subtype2 | 60 | 66
subtype3 | 70 | 88

Figure S149. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

'MIRseq Mature cHierClus subtypes' versus 'TUMOR.SIZE'

P value = 0.23 (ANOVA), Q value = 1

Table S160. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 386 | 3.0 (1.6)
subtype1 | 150 | 2.8 (1.6)
subtype2 | 108 | 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'

Methods & Data
Input

- Cluster data file = THCA-TP.mergedcluster.txt

- Clinical data file = THCA-TP.merged_data.txt

- Number of patients = 486

- Number of clustering approaches = 10

- Number of selected clinical features = 15

- Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches
CNMF clustering

consensus non-negative matrix factorization clustering approach (Brunet et al. 2004)

Consensus hierarchical clustering

Resampling-based clustering method (Monti et al. 2003)

Survival analysis

For survival clinical features, the Kaplan-Meier survival curves of tumors with and without gene mutations were plotted and the statistical significance P values were estimated by logrank test (Bland and Altman 2004) using the 'survdiff' function in R

ANOVA analysis

For continuous numerical clinical features, one-way analysis of variance (Howell 2002) was applied to compare the clinical values between tumor subtypes using 'anova' function in R

Chi-square test

For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.test' function in R

Fisher's exact test

For binary clinical features, two-tailed Fisher's exact tests (Fisher 1922) were used to estimate the P values using the 'fisher.test' function in R

Q value calculation

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.

Download Results

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.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

References
[1] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)
[2] Monti et al., Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data, Machine Learning 52(1):91-118 (2003)
[3] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)
[4] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[5] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
[6] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)
[7] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)
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