Correlation between molecular cancer subtypes and selected clinical features
Thyroid Adenocarcinoma (Primary solid tumor)
21 April 2013 | analyses__2013_04_21
Maintainer Information
Citation Information
doi:10.7908/C10R9MCF
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Thyroid Adenocarcinoma (Primary solid tumor cohort) - 21 April 2013: Correlation between molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C10R9MCF
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 8 different clustering approaches and 15 clinical features across 424 patients, 34 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', 'EXTRATHYROIDAL.EXTENSION', and 'NEOPLASM.DISEASESTAGE'.

- 3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'HISTOLOGICAL.TYPE', 'EXTRATHYROIDAL.EXTENSION', 'LYMPH.NODE.METASTASIS', 'NUMBER.OF.LYMPH.NODES', and 'NEOPLASM.DISEASESTAGE'.

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

- Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that correlate to 'AGE', 'HISTOLOGICAL.TYPE', and 'LYMPH.NODE.METASTASIS'.

- CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'HISTOLOGICAL.TYPE', 'EXTRATHYROIDAL.EXTENSION', 'LYMPH.NODE.METASTASIS', and 'NUMBER.OF.LYMPH.NODES'.

- Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'HISTOLOGICAL.TYPE', 'EXTRATHYROIDAL.EXTENSION', 'LYMPH.NODE.METASTASIS', 'NUMBER.OF.LYMPH.NODES', and 'NEOPLASM.DISEASESTAGE'.

- 3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'HISTOLOGICAL.TYPE', 'EXTRATHYROIDAL.EXTENSION', 'LYMPH.NODE.METASTASIS', 'NUMBER.OF.LYMPH.NODES', and 'NEOPLASM.DISEASESTAGE'.

- 3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'HISTOLOGICAL.TYPE', 'EXTRATHYROIDAL.EXTENSION', 'LYMPH.NODE.METASTASIS', 'NUMBER.OF.LYMPH.NODES', and 'NEOPLASM.DISEASESTAGE'.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 8 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, 34 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
Time to Death | logrank test | 0.0564 (1.00) | 0.587 (1.00) | 0.739 (1.00) | 0.0266 (1.00) | 0.205 (1.00) | 0.256 (1.00) | 0.0223 (1.00) | 0.0215 (1.00)
AGE | ANOVA | 5.8e-05 (0.00539) | 0.128 (1.00) | 0.000749 (0.0614) | 0.000266 (0.0234) | 0.0583 (1.00) | 0.0522 (1.00) | 0.0823 (1.00) | 0.0059 (0.454)
GENDER | Fisher's exact test | 0.336 (1.00) | 0.817 (1.00) | 0.836 (1.00) | 0.578 (1.00) | 0.967 (1.00) | 0.669 (1.00) | 0.763 (1.00) | 0.796 (1.00)
HISTOLOGICAL TYPE | Chi-square test | 0.182 (1.00) | 1.92e-20 (2.08e-18) | 0.000694 (0.0576) | 4.29e-07 (4.29e-05) | 2.75e-25 (3.03e-23) | 9.37e-30 (1.05e-27) | 5.72e-24 (6.24e-22) | 1e-26 (1.11e-24)
RADIATIONS RADIATION REGIMENINDICATION | Fisher's exact test | 0.301 (1.00) | 0.144 (1.00) | 0.0132 (0.942) | 0.341 (1.00) | 0.0153 (1.00) | 0.0841 (1.00) | 0.308 (1.00) | 0.0233 (1.00)
RADIATIONEXPOSURE | Fisher's exact test | 0.201 (1.00) | 0.727 (1.00) | 0.63 (1.00) | 0.0605 (1.00) | 0.908 (1.00) | 0.769 (1.00) | 1 (1.00) | 0.949 (1.00)
DISTANT METASTASIS | Chi-square test | 0.0722 (1.00) | 0.147 (1.00) | 0.393 (1.00) | 0.481 (1.00) | 0.0837 (1.00) | 0.0112 (0.819) | 0.0153 (1.00) | 0.0168 (1.00)
EXTRATHYROIDAL EXTENSION | Chi-square test | 0.00184 (0.149) | 2.61e-06 (0.000253) | 0.00211 (0.169) | 0.0267 (1.00) | 3e-07 (3.03e-05) | 1.39e-08 (1.42e-06) | 1.15e-06 (0.000113) | 5.49e-07 (5.43e-05)
LYMPH NODE METASTASIS | Chi-square test | 0.187 (1.00) | 2.84e-09 (2.92e-07) | 0.0186 (1.00) | 0.000379 (0.033) | 1.74e-09 (1.81e-07) | 5e-11 (5.35e-09) | 6.29e-10 (6.67e-08) | 7.14e-10 (7.5e-08)
COMPLETENESS OF RESECTION | Chi-square test | 0.422 (1.00) | 0.57 (1.00) | 0.39 (1.00) | 0.637 (1.00) | 0.506 (1.00) | 0.765 (1.00) | 0.503 (1.00) | 0.425 (1.00)
NUMBER OF LYMPH NODES | ANOVA | 0.229 (1.00) | 0.000118 (0.0108) | 0.463 (1.00) | 0.266 (1.00) | 4.5e-05 (0.00423) | 0.000217 (0.0195) | 0.000258 (0.0229) | 0.000396 (0.0341)
TUMOR STAGECODE | ANOVA |  |  |  |  |  |  |  |
NEOPLASM DISEASESTAGE | Chi-square test | 1.02e-05 (0.000981) | 0.000185 (0.0168) | 0.00211 (0.169) | 0.0151 (1.00) | 0.00459 (0.358) | 0.000548 (0.0466) | 0.000603 (0.0506) | 2.75e-05 (0.00261)
MULTIFOCALITY | Fisher's exact test | 0.33 (1.00) | 0.719 (1.00) | 0.00622 (0.473) | 0.642 (1.00) | 0.00703 (0.527) | 0.0579 (1.00) | 0.929 (1.00) | 0.553 (1.00)
TUMOR SIZE | ANOVA | 0.00712 (0.527) | 0.554 (1.00) | 0.297 (1.00) | 0.505 (1.00) | 0.0972 (1.00) | 0.0131 (0.942) | 0.0211 (1.00) | 0.0928 (1.00)

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

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

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 25 | 333 | 63

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

P value = 0.0564 (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 | 416 | 10 | 0.0 - 147.4 (9.3)
subtype1 | 25 | 2 | 0.4 - 83.3 (8.7)
subtype2 | 328 | 8 | 0.1 - 147.4 (9.3)
subtype3 | 63 | 0 | 0.0 - 85.1 (9.6)

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 = 5.8e-05 (ANOVA), Q value = 0.0054

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 421 | 46.6 (15.6)
subtype1 | 25 | 59.8 (13.3)
subtype2 | 333 | 45.6 (15.8)
subtype3 | 63 | 46.5 (12.8)

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 'GENDER'

P value = 0.336 (Fisher's exact test), Q value = 1

Table S4. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 314 | 107
subtype1 | 16 | 9
subtype2 | 248 | 85
subtype3 | 50 | 13

Figure S3. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.182 (Chi-square test), Q value = 1

Table S5. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: '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 | 6 | 296 | 84 | 35
subtype1 | 0 | 16 | 8 | 1
subtype2 | 5 | 241 | 57 | 30
subtype3 | 1 | 39 | 19 | 4

Figure S4. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

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

P value = 0.301 (Fisher's exact test), Q value = 1

Table S6. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 14 | 407
subtype1 | 2 | 23
subtype2 | 11 | 322
subtype3 | 1 | 62

Figure S5. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'Copy Number Ratio CNMF subtypes' versus 'RADIATIONEXPOSURE'

P value = 0.201 (Fisher's exact test), Q value = 1

Table S7. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

nPatients | NO | YES
--- | --- | ---
ALL | 363 | 16
subtype1 | 22 | 1
subtype2 | 282 | 15
subtype3 | 59 | 0

Figure S6. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

'Copy Number Ratio CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.0722 (Chi-square test), Q value = 1

Table S8. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'DISTANT.METASTASIS'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 235 | 8 | 177
subtype1 | 8 | 0 | 17
subtype2 | 194 | 6 | 132
subtype3 | 33 | 2 | 28

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

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

P value = 0.00184 (Chi-square test), Q value = 0.15

Table S9. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

nPatients | MINIMAL (T3) | MODERATE/ADVANCED (T4A) | NONE | VERY ADVANCED (T4B)
--- | --- | --- | --- | ---
ALL | 113 | 13 | 275 | 1
subtype1 | 6 | 0 | 18 | 1
subtype2 | 94 | 13 | 210 | 0
subtype3 | 13 | 0 | 47 | 0

Figure S8. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

'Copy Number Ratio CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.187 (Chi-square test), Q value = 1

Table S10. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

nPatients | N0 | N1 | N1A | N1B | NX
--- | --- | --- | --- | --- | ---
ALL | 192 | 49 | 81 | 60 | 39
subtype1 | 15 | 1 | 2 | 2 | 5
subtype2 | 144 | 41 | 69 | 48 | 31
subtype3 | 33 | 7 | 10 | 10 | 3

Figure S9. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

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

P value = 0.422 (Chi-square test), Q value = 1

Table S11. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients | R0 | R1 | R2 | RX
--- | --- | --- | --- | ---
ALL | 331 | 37 | 2 | 26
subtype1 | 19 | 2 | 0 | 3
subtype2 | 264 | 31 | 2 | 16
subtype3 | 48 | 4 | 0 | 7

Figure S10. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.229 (ANOVA), Q value = 1

Table S12. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 328 | 3.5 (6.2)
subtype1 | 17 | 1.7 (4.1)
subtype2 | 261 | 3.8 (6.6)
subtype3 | 50 | 2.7 (4.1)

Figure S11. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

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

P value = 1.02e-05 (Chi-square test), Q value = 0.00098

Table S13. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

nPatients | STAGE I | STAGE II | STAGE III | STAGE IV | STAGE IVA | STAGE IVC
--- | --- | --- | --- | --- | --- | ---
ALL | 237 | 44 | 94 | 2 | 36 | 5
subtype1 | 4 | 10 | 8 | 1 | 2 | 0
subtype2 | 196 | 28 | 74 | 0 | 28 | 4
subtype3 | 37 | 6 | 12 | 1 | 6 | 1

Figure S12. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

'Copy Number Ratio CNMF subtypes' versus 'MULTIFOCALITY'

P value = 0.33 (Fisher's exact test), Q value = 1

Table S14. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients | MULTIFOCAL | UNIFOCAL
--- | --- | ---
ALL | 181 | 231
subtype1 | 10 | 15
subtype2 | 139 | 187
subtype3 | 32 | 29

Figure S13. 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 = 0.00712 (ANOVA), Q value = 0.53

Table S15. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 331 | 2.9 (1.6)
subtype1 | 21 | 4.0 (1.5)
subtype2 | 261 | 2.9 (1.5)
subtype3 | 49 | 2.8 (1.7)

Figure S14. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

Clustering Approach #2: 'METHLYATION CNMF'

Table S16. Get Full Table Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 122 | 63 | 239

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.587 (logrank test), Q value = 1

Table S17. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 419 | 10 | 0.0 - 147.4 (9.3)
subtype1 | 121 | 3 | 0.0 - 130.7 (7.6)
subtype2 | 60 | 2 | 0.1 - 147.4 (7.1)
subtype3 | 238 | 5 | 0.1 - 138.1 (11.7)

Figure S15. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.128 (ANOVA), Q value = 1

Table S18. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 424 | 46.6 (15.6)
subtype1 | 122 | 48.5 (15.3)
subtype2 | 63 | 43.7 (15.4)
subtype3 | 239 | 46.5 (15.7)

Figure S16. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.817 (Fisher's exact test), Q value = 1

Table S19. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 316 | 108
subtype1 | 93 | 29
subtype2 | 48 | 15
subtype3 | 175 | 64

Figure S17. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 1.92e-20 (Chi-square test), Q value = 2.1e-18

Table S20. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: '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 | 7 | 296 | 86 | 35
subtype1 | 3 | 56 | 62 | 1
subtype2 | 1 | 50 | 6 | 6
subtype3 | 3 | 190 | 18 | 28

Figure S18. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

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

P value = 0.144 (Fisher's exact test), Q value = 1

Table S21. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 14 | 410
subtype1 | 1 | 121
subtype2 | 2 | 61
subtype3 | 11 | 228

Figure S19. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'RADIATIONEXPOSURE'

P value = 0.727 (Fisher's exact test), Q value = 1

Table S22. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

nPatients | NO | YES
--- | --- | ---
ALL | 365 | 16
subtype1 | 101 | 6
subtype2 | 55 | 2
subtype3 | 209 | 8

Figure S20. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

'METHLYATION CNMF' versus 'DISTANT.METASTASIS'

P value = 0.147 (Chi-square test), Q value = 1

Table S23. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'DISTANT.METASTASIS'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 237 | 8 | 178
subtype1 | 57 | 3 | 61
subtype2 | 37 | 0 | 26
subtype3 | 143 | 5 | 91

Figure S21. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'DISTANT.METASTASIS'

'METHLYATION CNMF' versus 'EXTRATHYROIDAL.EXTENSION'

P value = 2.61e-06 (Chi-square test), Q value = 0.00025

Table S24. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

nPatients | MINIMAL (T3) | MODERATE/ADVANCED (T4A) | NONE | VERY ADVANCED (T4B)
--- | --- | --- | --- | ---
ALL | 113 | 13 | 278 | 1
subtype1 | 17 | 0 | 97 | 0
subtype2 | 14 | 0 | 46 | 1
subtype3 | 82 | 13 | 135 | 0

Figure S22. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

'METHLYATION CNMF' versus 'LYMPH.NODE.METASTASIS'

P value = 2.84e-09 (Chi-square test), Q value = 2.9e-07

Table S25. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

nPatients | N0 | N1 | N1A | N1B | NX
--- | --- | --- | --- | --- | ---
ALL | 195 | 49 | 81 | 60 | 39
subtype1 | 83 | 7 | 8 | 6 | 18
subtype2 | 28 | 6 | 15 | 11 | 3
subtype3 | 84 | 36 | 58 | 43 | 18

Figure S23. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

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

P value = 0.57 (Chi-square test), Q value = 1

Table S26. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients | R0 | R1 | R2 | RX
--- | --- | --- | --- | ---
ALL | 334 | 37 | 2 | 26
subtype1 | 100 | 6 | 0 | 7
subtype2 | 46 | 7 | 0 | 4
subtype3 | 188 | 24 | 2 | 15

Figure S24. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.000118 (ANOVA), Q value = 0.011

Table S27. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 329 | 3.5 (6.2)
subtype1 | 83 | 1.1 (2.6)
subtype2 | 50 | 3.7 (6.9)
subtype3 | 196 | 4.5 (6.8)

Figure S25. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.000185 (Chi-square test), Q value = 0.017

Table S28. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

nPatients | STAGE I | STAGE II | STAGE III | STAGE IV | STAGE IVA | STAGE IVC
--- | --- | --- | --- | --- | --- | ---
ALL | 238 | 46 | 94 | 2 | 36 | 5
subtype1 | 67 | 26 | 22 | 1 | 2 | 2
subtype2 | 39 | 3 | 15 | 1 | 5 | 0
subtype3 | 132 | 17 | 57 | 0 | 29 | 3

Figure S26. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

'METHLYATION CNMF' versus 'MULTIFOCALITY'

P value = 0.719 (Fisher's exact test), Q value = 1

Table S29. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients | MULTIFOCAL | UNIFOCAL
--- | --- | ---
ALL | 182 | 233
subtype1 | 49 | 71
subtype2 | 27 | 34
subtype3 | 106 | 128

Figure S27. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #14: 'MULTIFOCALITY'

'METHLYATION CNMF' versus 'TUMOR.SIZE'

P value = 0.554 (ANOVA), Q value = 1

Table S30. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #15: 'TUMOR.SIZE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 333 | 2.9 (1.6)
subtype1 | 94 | 3.1 (1.8)
subtype2 | 48 | 2.9 (1.6)
subtype3 | 191 | 2.9 (1.5)

Figure S28. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #15: 'TUMOR.SIZE'

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S31. Get Full Table Description of clustering approach #3: 'RPPA CNMF subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 53 | 72 | 73

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.739 (logrank test), Q value = 1

Table S32. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 196 | 9 | 0.1 - 147.4 (9.7)
subtype1 | 53 | 2 | 0.3 - 145.4 (9.0)
subtype2 | 72 | 3 | 0.2 - 147.4 (12.2)
subtype3 | 71 | 4 | 0.1 - 128.6 (9.3)

Figure S29. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.000749 (ANOVA), Q value = 0.061

Table S33. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 198 | 47.5 (16.4)
subtype1 | 53 | 51.4 (14.6)
subtype2 | 72 | 50.5 (15.6)
subtype3 | 73 | 41.8 (17.1)

Figure S30. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.836 (Fisher's exact test), Q value = 1

Table S34. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 138 | 60
subtype1 | 37 | 16
subtype2 | 52 | 20
subtype3 | 49 | 24

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

'RPPA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.000694 (Chi-square test), Q value = 0.058

Table S35. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: '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 | 4 | 133 | 50 | 11
subtype1 | 0 | 29 | 23 | 1
subtype2 | 2 | 47 | 14 | 9
subtype3 | 2 | 57 | 13 | 1

Figure S32. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

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

P value = 0.0132 (Fisher's exact test), Q value = 0.94

Table S36. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 13 | 185
subtype1 | 0 | 53
subtype2 | 9 | 63
subtype3 | 4 | 69

Figure S33. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA CNMF subtypes' versus 'RADIATIONEXPOSURE'

P value = 0.63 (Fisher's exact test), Q value = 1

Table S37. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

nPatients | NO | YES
--- | --- | ---
ALL | 167 | 9
subtype1 | 47 | 1
subtype2 | 60 | 4
subtype3 | 60 | 4

Figure S34. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

'RPPA CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.393 (Chi-square test), Q value = 1

Table S38. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'DISTANT.METASTASIS'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 100 | 4 | 93
subtype1 | 32 | 1 | 19
subtype2 | 36 | 1 | 35
subtype3 | 32 | 2 | 39

Figure S35. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'DISTANT.METASTASIS'

'RPPA CNMF subtypes' versus 'EXTRATHYROIDAL.EXTENSION'

P value = 0.00211 (Chi-square test), Q value = 0.17

Table S39. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

nPatients | MINIMAL (T3) | MODERATE/ADVANCED (T4A) | NONE
--- | --- | --- | ---
ALL | 48 | 8 | 134
subtype1 | 11 | 0 | 42
subtype2 | 24 | 7 | 38
subtype3 | 13 | 1 | 54

Figure S36. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

'RPPA CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.0186 (Chi-square test), Q value = 1

Table S40. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

nPatients | N0 | N1 | N1A | N1B | NX
--- | --- | --- | --- | --- | ---
ALL | 87 | 21 | 37 | 26 | 27
subtype1 | 32 | 0 | 9 | 5 | 7
subtype2 | 26 | 14 | 11 | 12 | 9
subtype3 | 29 | 7 | 17 | 9 | 11

Figure S37. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

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

P value = 0.39 (Chi-square test), Q value = 1

Table S41. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients | R0 | R1 | R2 | RX
--- | --- | --- | --- | ---
ALL | 152 | 17 | 1 | 13
subtype1 | 41 | 5 | 1 | 5
subtype2 | 54 | 9 | 0 | 4
subtype3 | 57 | 3 | 0 | 4

Figure S38. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.463 (ANOVA), Q value = 1

Table S42. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 150 | 3.2 (5.5)
subtype1 | 37 | 2.5 (5.8)
subtype2 | 59 | 3.1 (4.5)
subtype3 | 54 | 3.9 (6.1)

Figure S39. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'RPPA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00211 (Chi-square test), Q value = 0.17

Table S43. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

nPatients | STAGE I | STAGE II | STAGE III | STAGE IVA | STAGE IVC
--- | --- | --- | --- | --- | ---
ALL | 101 | 32 | 44 | 16 | 3
subtype1 | 26 | 10 | 13 | 1 | 1
subtype2 | 26 | 13 | 21 | 12 | 0
subtype3 | 49 | 9 | 10 | 3 | 2

Figure S40. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

'RPPA CNMF subtypes' versus 'MULTIFOCALITY'

P value = 0.00622 (Fisher's exact test), Q value = 0.47

Table S44. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients | MULTIFOCAL | UNIFOCAL
--- | --- | ---
ALL | 87 | 103
subtype1 | 31 | 19
subtype2 | 23 | 47
subtype3 | 33 | 37

Figure S41. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

'RPPA CNMF subtypes' versus 'TUMOR.SIZE'

P value = 0.297 (ANOVA), Q value = 1

Table S45. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 172 | 3.2 (1.6)
subtype1 | 46 | 3.0 (1.6)
subtype2 | 63 | 3.5 (1.7)
subtype3 | 63 | 3.1 (1.5)

Figure S42. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S46. Get Full Table Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 84 | 30 | 84

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.0266 (logrank test), Q value = 1

Table S47. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 196 | 9 | 0.1 - 147.4 (9.7)
subtype1 | 84 | 5 | 0.3 - 145.4 (9.2)
subtype2 | 30 | 3 | 1.1 - 147.4 (10.1)
subtype3 | 82 | 1 | 0.1 - 128.6 (10.4)

Figure S43. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.000266 (ANOVA), Q value = 0.023

Table S48. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 198 | 47.5 (16.4)
subtype1 | 84 | 50.0 (16.3)
subtype2 | 30 | 54.9 (15.1)
subtype3 | 84 | 42.4 (15.6)

Figure S44. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.578 (Fisher's exact test), Q value = 1

Table S49. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 138 | 60
subtype1 | 55 | 29
subtype2 | 22 | 8
subtype3 | 61 | 23

Figure S45. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 4.29e-07 (Chi-square test), Q value = 4.3e-05

Table S50. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: '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 | 4 | 133 | 50 | 11
subtype1 | 1 | 49 | 32 | 2
subtype2 | 2 | 13 | 14 | 1
subtype3 | 1 | 71 | 4 | 8

Figure S46. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

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

P value = 0.341 (Fisher's exact test), Q value = 1

Table S51. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 13 | 185
subtype1 | 3 | 81
subtype2 | 2 | 28
subtype3 | 8 | 76

Figure S47. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA cHierClus subtypes' versus 'RADIATIONEXPOSURE'

P value = 0.0605 (Fisher's exact test), Q value = 1

Table S52. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

nPatients | NO | YES
--- | --- | ---
ALL | 167 | 9
subtype1 | 75 | 2
subtype2 | 24 | 4
subtype3 | 68 | 3

Figure S48. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

'RPPA cHierClus subtypes' versus 'DISTANT.METASTASIS'

P value = 0.481 (Chi-square test), Q value = 1

Table S53. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'DISTANT.METASTASIS'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 100 | 4 | 93
subtype1 | 41 | 2 | 40
subtype2 | 12 | 0 | 18
subtype3 | 47 | 2 | 35

Figure S49. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'DISTANT.METASTASIS'

'RPPA cHierClus subtypes' versus 'EXTRATHYROIDAL.EXTENSION'

P value = 0.0267 (Chi-square test), Q value = 1

Table S54. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

nPatients | MINIMAL (T3) | MODERATE/ADVANCED (T4A) | NONE
--- | --- | --- | ---
ALL | 48 | 8 | 134
subtype1 | 16 | 0 | 66
subtype2 | 7 | 2 | 21
subtype3 | 25 | 6 | 47

Figure S50. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

'RPPA cHierClus subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.000379 (Chi-square test), Q value = 0.033

Table S55. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

nPatients | N0 | N1 | N1A | N1B | NX
--- | --- | --- | --- | --- | ---
ALL | 87 | 21 | 37 | 26 | 27
subtype1 | 49 | 2 | 12 | 10 | 11
subtype2 | 13 | 2 | 3 | 5 | 7
subtype3 | 25 | 17 | 22 | 11 | 9

Figure S51. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

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

P value = 0.637 (Chi-square test), Q value = 1

Table S56. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients | R0 | R1 | R2 | RX
--- | --- | --- | --- | ---
ALL | 152 | 17 | 1 | 13
subtype1 | 65 | 8 | 1 | 6
subtype2 | 24 | 2 | 0 | 4
subtype3 | 63 | 7 | 0 | 3

Figure S52. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.266 (ANOVA), Q value = 1

Table S57. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 150 | 3.2 (5.5)
subtype1 | 61 | 2.5 (5.1)
subtype2 | 24 | 2.9 (5.5)
subtype3 | 65 | 4.0 (5.7)

Figure S53. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'RPPA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0151 (Chi-square test), Q value = 1

Table S58. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

nPatients | STAGE I | STAGE II | STAGE III | STAGE IVA | STAGE IVC
--- | --- | --- | --- | --- | ---
ALL | 101 | 32 | 44 | 16 | 3
subtype1 | 45 | 15 | 18 | 2 | 2
subtype2 | 7 | 8 | 10 | 5 | 0
subtype3 | 49 | 9 | 16 | 9 | 1

Figure S54. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

'RPPA cHierClus subtypes' versus 'MULTIFOCALITY'

P value = 0.642 (Fisher's exact test), Q value = 1

Table S59. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients | MULTIFOCAL | UNIFOCAL
--- | --- | ---
ALL | 87 | 103
subtype1 | 39 | 42
subtype2 | 11 | 18
subtype3 | 37 | 43

Figure S55. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

'RPPA cHierClus subtypes' versus 'TUMOR.SIZE'

P value = 0.505 (ANOVA), Q value = 1

Table S60. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 172 | 3.2 (1.6)
subtype1 | 74 | 3.1 (1.6)
subtype2 | 24 | 3.6 (1.9)
subtype3 | 74 | 3.2 (1.5)

Figure S56. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S61. Get Full Table Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels | 1 | 2 | 3 | 4
--- | --- | --- | --- | ---
Number of samples | 116 | 49 | 97 | 135

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.205 (logrank test), Q value = 1

Table S62. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 392 | 10 | 0.0 - 147.4 (9.3)
subtype1 | 116 | 3 | 0.0 - 130.7 (7.3)
subtype2 | 47 | 1 | 0.1 - 145.4 (6.4)
subtype3 | 95 | 0 | 0.1 - 138.1 (11.9)
subtype4 | 134 | 6 | 0.1 - 147.4 (11.1)

Figure S57. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.0583 (ANOVA), Q value = 1

Table S63. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 397 | 46.5 (15.7)
subtype1 | 116 | 48.8 (15.3)
subtype2 | 49 | 43.0 (16.0)
subtype3 | 97 | 44.2 (14.8)
subtype4 | 135 | 47.3 (16.2)

Figure S58. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.967 (Fisher's exact test), Q value = 1

Table S64. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 298 | 99
subtype1 | 88 | 28
subtype2 | 36 | 13
subtype3 | 74 | 23
subtype4 | 100 | 35

Figure S59. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 2.75e-25 (Chi-square test), Q value = 3e-23

Table S65. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: '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 | 6 | 273 | 84 | 34
subtype1 | 3 | 49 | 63 | 1
subtype2 | 1 | 41 | 3 | 4
subtype3 | 1 | 78 | 15 | 3
subtype4 | 1 | 105 | 3 | 26

Figure S60. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

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

P value = 0.0153 (Fisher's exact test), Q value = 1

Table S66. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 13 | 384
subtype1 | 1 | 115
subtype2 | 0 | 49
subtype3 | 2 | 95
subtype4 | 10 | 125

Figure S61. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'RADIATIONEXPOSURE'

P value = 0.908 (Fisher's exact test), Q value = 1

Table S67. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

nPatients | NO | YES
--- | --- | ---
ALL | 339 | 16
subtype1 | 98 | 6
subtype2 | 42 | 1
subtype3 | 83 | 4
subtype4 | 116 | 5

Figure S62. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

'RNAseq CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.0837 (Chi-square test), Q value = 1

Table S68. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'DISTANT.METASTASIS'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 218 | 7 | 171
subtype1 | 51 | 2 | 62
subtype2 | 34 | 0 | 15
subtype3 | 58 | 2 | 37
subtype4 | 75 | 3 | 57

Figure S63. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'DISTANT.METASTASIS'

'RNAseq CNMF subtypes' versus 'EXTRATHYROIDAL.EXTENSION'

P value = 3e-07 (Chi-square test), Q value = 3e-05

Table S69. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

nPatients | MINIMAL (T3) | MODERATE/ADVANCED (T4A) | NONE | VERY ADVANCED (T4B)
--- | --- | --- | --- | ---
ALL | 104 | 12 | 263 | 1
subtype1 | 14 | 0 | 96 | 0
subtype2 | 12 | 2 | 33 | 0
subtype3 | 25 | 0 | 70 | 0
subtype4 | 53 | 10 | 64 | 1

Figure S64. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

'RNAseq CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 1.74e-09 (Chi-square test), Q value = 1.8e-07

Table S70. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

nPatients | N0 | N1 | N1A | N1B | NX
--- | --- | --- | --- | --- | ---
ALL | 188 | 42 | 75 | 55 | 37
subtype1 | 81 | 5 | 8 | 5 | 17
subtype2 | 20 | 5 | 14 | 10 | 0
subtype3 | 41 | 7 | 26 | 16 | 7
subtype4 | 46 | 25 | 27 | 24 | 13

Figure S65. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

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

P value = 0.506 (Chi-square test), Q value = 1

Table S71. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients | R0 | R1 | R2 | RX
--- | --- | --- | --- | ---
ALL | 313 | 32 | 2 | 25
subtype1 | 96 | 5 | 0 | 7
subtype2 | 33 | 6 | 0 | 5
subtype3 | 80 | 7 | 1 | 5
subtype4 | 104 | 14 | 1 | 8

Figure S66. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

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

P value = 4.5e-05 (ANOVA), Q value = 0.0042

Table S72. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 303 | 3.3 (6.0)
subtype1 | 81 | 0.9 (2.3)
subtype2 | 40 | 5.6 (8.0)
subtype3 | 74 | 4.3 (6.6)
subtype4 | 108 | 3.7 (6.0)

Figure S67. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00459 (Chi-square test), Q value = 0.36

Table S73. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

nPatients | STAGE I | STAGE II | STAGE III | STAGE IV | STAGE IVA | STAGE IVC
--- | --- | --- | --- | --- | --- | ---
ALL | 225 | 45 | 86 | 1 | 32 | 5
subtype1 | 64 | 24 | 22 | 0 | 2 | 2
subtype2 | 34 | 2 | 9 | 0 | 4 | 0
subtype3 | 56 | 12 | 18 | 0 | 9 | 1
subtype4 | 71 | 7 | 37 | 1 | 17 | 2

Figure S68. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

'RNAseq CNMF subtypes' versus 'MULTIFOCALITY'

P value = 0.00703 (Fisher's exact test), Q value = 0.53

Table S74. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients | MULTIFOCAL | UNIFOCAL
--- | --- | ---
ALL | 173 | 215
subtype1 | 50 | 64
subtype2 | 23 | 24
subtype3 | 54 | 40
subtype4 | 46 | 87

Figure S69. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

'RNAseq CNMF subtypes' versus 'TUMOR.SIZE'

P value = 0.0972 (ANOVA), Q value = 1

Table S75. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 313 | 2.9 (1.6)
subtype1 | 91 | 3.1 (1.7)
subtype2 | 35 | 2.5 (1.7)
subtype3 | 75 | 2.6 (1.4)
subtype4 | 112 | 3.1 (1.6)

Figure S70. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S76. Get Full Table Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3 | 4
--- | --- | --- | --- | ---
Number of samples | 100 | 165 | 49 | 83

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.256 (logrank test), Q value = 1

Table S77. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 392 | 10 | 0.0 - 147.4 (9.3)
subtype1 | 100 | 3 | 0.3 - 112.6 (7.5)
subtype2 | 162 | 6 | 0.1 - 147.4 (11.1)
subtype3 | 48 | 1 | 0.0 - 130.7 (6.4)
subtype4 | 82 | 0 | 0.1 - 138.1 (11.9)

Figure S71. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.0522 (ANOVA), Q value = 1

Table S78. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 397 | 46.5 (15.7)
subtype1 | 100 | 49.7 (15.4)
subtype2 | 165 | 46.5 (16.2)
subtype3 | 49 | 43.7 (13.7)
subtype4 | 83 | 44.1 (15.6)

Figure S72. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.669 (Fisher's exact test), Q value = 1

Table S79. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 298 | 99
subtype1 | 75 | 25
subtype2 | 120 | 45
subtype3 | 40 | 9
subtype4 | 63 | 20

Figure S73. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 9.37e-30 (Chi-square test), Q value = 1e-27

Table S80. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: '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 | 6 | 273 | 84 | 34
subtype1 | 3 | 36 | 61 | 0
subtype2 | 3 | 129 | 4 | 29
subtype3 | 0 | 39 | 6 | 4
subtype4 | 0 | 69 | 13 | 1

Figure S74. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

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

P value = 0.0841 (Fisher's exact test), Q value = 1

Table S81. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 13 | 384
subtype1 | 1 | 99
subtype2 | 10 | 155
subtype3 | 0 | 49
subtype4 | 2 | 81

Figure S75. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'RADIATIONEXPOSURE'

P value = 0.769 (Fisher's exact test), Q value = 1

Table S82. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

nPatients | NO | YES
--- | --- | ---
ALL | 339 | 16
subtype1 | 86 | 4
subtype2 | 143 | 6
subtype3 | 37 | 3
subtype4 | 73 | 3

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

'RNAseq cHierClus subtypes' versus 'DISTANT.METASTASIS'

P value = 0.0112 (Chi-square test), Q value = 0.82

Table S83. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'DISTANT.METASTASIS'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 218 | 7 | 171
subtype1 | 41 | 2 | 56
subtype2 | 94 | 3 | 68
subtype3 | 37 | 0 | 12
subtype4 | 46 | 2 | 35

Figure S77. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'DISTANT.METASTASIS'

'RNAseq cHierClus subtypes' versus 'EXTRATHYROIDAL.EXTENSION'

P value = 1.39e-08 (Chi-square test), Q value = 1.4e-06

Table S84. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

nPatients | MINIMAL (T3) | MODERATE/ADVANCED (T4A) | NONE | VERY ADVANCED (T4B)
--- | --- | --- | --- | ---
ALL | 104 | 12 | 263 | 1
subtype1 | 9 | 0 | 85 | 0
subtype2 | 64 | 12 | 80 | 1
subtype3 | 11 | 0 | 36 | 0
subtype4 | 20 | 0 | 62 | 0

Figure S78. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

'RNAseq cHierClus subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 5e-11 (Chi-square test), Q value = 5.3e-09

Table S85. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

nPatients | N0 | N1 | N1A | N1B | NX
--- | --- | --- | --- | --- | ---
ALL | 188 | 42 | 75 | 55 | 37
subtype1 | 73 | 3 | 3 | 4 | 17
subtype2 | 53 | 28 | 40 | 31 | 13
subtype3 | 23 | 3 | 15 | 7 | 1
subtype4 | 39 | 8 | 17 | 13 | 6

Figure S79. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

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

P value = 0.765 (Chi-square test), Q value = 1

Table S86. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients | R0 | R1 | R2 | RX
--- | --- | --- | --- | ---
ALL | 313 | 32 | 2 | 25
subtype1 | 81 | 5 | 0 | 7
subtype2 | 126 | 18 | 1 | 9
subtype3 | 37 | 4 | 0 | 4
subtype4 | 69 | 5 | 1 | 5

Figure S80. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.000217 (ANOVA), Q value = 0.02

Table S87. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 303 | 3.3 (6.0)
subtype1 | 67 | 0.7 (2.1)
subtype2 | 136 | 4.3 (6.6)
subtype3 | 39 | 3.0 (4.9)
subtype4 | 61 | 4.5 (7.0)

Figure S81. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.000548 (Chi-square test), Q value = 0.047

Table S88. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

nPatients | STAGE I | STAGE II | STAGE III | STAGE IV | STAGE IVA | STAGE IVC
--- | --- | --- | --- | --- | --- | ---
ALL | 225 | 45 | 86 | 1 | 32 | 5
subtype1 | 53 | 24 | 17 | 0 | 2 | 2
subtype2 | 92 | 8 | 43 | 1 | 19 | 2
subtype3 | 34 | 1 | 11 | 0 | 3 | 0
subtype4 | 46 | 12 | 15 | 0 | 8 | 1

Figure S82. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

'RNAseq cHierClus subtypes' versus 'MULTIFOCALITY'

P value = 0.0579 (Fisher's exact test), Q value = 1

Table S89. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients | MULTIFOCAL | UNIFOCAL
--- | --- | ---
ALL | 173 | 215
subtype1 | 44 | 54
subtype2 | 61 | 101
subtype3 | 23 | 24
subtype4 | 45 | 36

Figure S83. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

'RNAseq cHierClus subtypes' versus 'TUMOR.SIZE'

P value = 0.0131 (ANOVA), Q value = 0.94

Table S90. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 313 | 2.9 (1.6)
subtype1 | 83 | 3.2 (1.7)
subtype2 | 133 | 2.9 (1.6)
subtype3 | 32 | 2.2 (1.6)
subtype4 | 65 | 2.8 (1.4)

Figure S84. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S91. Get Full Table Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 105 | 134 | 134

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.0223 (logrank test), Q value = 1

Table S92. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 368 | 10 | 0.0 - 147.4 (9.0)
subtype1 | 105 | 3 | 0.2 - 112.6 (7.7)
subtype2 | 131 | 0 | 0.1 - 147.4 (10.4)
subtype3 | 132 | 7 | 0.0 - 145.4 (8.5)

Figure S85. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.0823 (ANOVA), Q value = 1

Table S93. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 373 | 46.4 (15.8)
subtype1 | 105 | 48.0 (16.3)
subtype2 | 134 | 44.0 (14.9)
subtype3 | 134 | 47.7 (16.1)

Figure S86. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.763 (Fisher's exact test), Q value = 1

Table S94. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 279 | 94
subtype1 | 78 | 27
subtype2 | 98 | 36
subtype3 | 103 | 31

Figure S87. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 5.72e-24 (Chi-square test), Q value = 6.2e-22

Table S95. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: '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 | 6 | 254 | 82 | 31
subtype1 | 3 | 40 | 61 | 1
subtype2 | 1 | 111 | 12 | 10
subtype3 | 2 | 103 | 9 | 20

Figure S88. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

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

P value = 0.308 (Fisher's exact test), Q value = 1

Table S96. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 14 | 359
subtype1 | 3 | 102
subtype2 | 8 | 126
subtype3 | 3 | 131

Figure S89. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CNMF' versus 'RADIATIONEXPOSURE'

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

Table S97. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

nPatients | NO | YES
--- | --- | ---
ALL | 318 | 16
subtype1 | 90 | 4
subtype2 | 112 | 6
subtype3 | 116 | 6

Figure S90. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

'MIRSEQ CNMF' versus 'DISTANT.METASTASIS'

P value = 0.0153 (Chi-square test), Q value = 1

Table S98. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'DISTANT.METASTASIS'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 195 | 6 | 171
subtype1 | 42 | 2 | 60
subtype2 | 69 | 3 | 62
subtype3 | 84 | 1 | 49

Figure S91. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'DISTANT.METASTASIS'

'MIRSEQ CNMF' versus 'EXTRATHYROIDAL.EXTENSION'

P value = 1.15e-06 (Chi-square test), Q value = 0.00011

Table S99. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

nPatients | MINIMAL (T3) | MODERATE/ADVANCED (T4A) | NONE | VERY ADVANCED (T4B)
--- | --- | --- | --- | ---
ALL | 96 | 12 | 248 | 1
subtype1 | 10 | 0 | 89 | 0
subtype2 | 39 | 2 | 86 | 0
subtype3 | 47 | 10 | 73 | 1

Figure S92. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

'MIRSEQ CNMF' versus 'LYMPH.NODE.METASTASIS'

P value = 6.29e-10 (Chi-square test), Q value = 6.7e-08

Table S100. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

nPatients | N0 | N1 | N1A | N1B | NX
--- | --- | --- | --- | --- | ---
ALL | 176 | 38 | 75 | 49 | 35
subtype1 | 72 | 5 | 4 | 6 | 18
subtype2 | 53 | 17 | 39 | 15 | 10
subtype3 | 51 | 16 | 32 | 28 | 7

Figure S93. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

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

P value = 0.503 (Chi-square test), Q value = 1

Table S101. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients | R0 | R1 | R2 | RX
--- | --- | --- | --- | ---
ALL | 293 | 30 | 2 | 24
subtype1 | 83 | 7 | 0 | 8
subtype2 | 108 | 7 | 1 | 8
subtype3 | 102 | 16 | 1 | 8

Figure S94. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.000258 (ANOVA), Q value = 0.023

Table S102. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 288 | 3.2 (5.6)
subtype1 | 71 | 1.1 (2.9)
subtype2 | 108 | 3.2 (5.4)
subtype3 | 109 | 4.5 (6.6)

Figure S95. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.000603 (Chi-square test), Q value = 0.051

Table S103. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

nPatients | STAGE I | STAGE II | STAGE III | STAGE IV | STAGE IVA | STAGE IVC
--- | --- | --- | --- | --- | --- | ---
ALL | 210 | 43 | 83 | 1 | 29 | 4
subtype1 | 59 | 23 | 16 | 0 | 3 | 2
subtype2 | 81 | 14 | 28 | 0 | 10 | 1
subtype3 | 70 | 6 | 39 | 1 | 16 | 1

Figure S96. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

'MIRSEQ CNMF' versus 'MULTIFOCALITY'

P value = 0.929 (Fisher's exact test), Q value = 1

Table S104. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients | MULTIFOCAL | UNIFOCAL
--- | --- | ---
ALL | 165 | 199
subtype1 | 45 | 58
subtype2 | 59 | 69
subtype3 | 61 | 72

Figure S97. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #14: 'MULTIFOCALITY'

'MIRSEQ CNMF' versus 'TUMOR.SIZE'

P value = 0.0211 (ANOVA), Q value = 1

Table S105. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #15: 'TUMOR.SIZE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 290 | 2.9 (1.6)
subtype1 | 86 | 3.2 (1.6)
subtype2 | 107 | 2.9 (1.5)
subtype3 | 97 | 2.5 (1.5)

Figure S98. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #15: 'TUMOR.SIZE'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S106. Get Full Table Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 92 | 149 | 132

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.0215 (logrank test), Q value = 1

Table S107. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 368 | 10 | 0.0 - 147.4 (9.0)
subtype1 | 92 | 3 | 0.3 - 112.6 (7.2)
subtype2 | 146 | 7 | 0.0 - 147.4 (8.5)
subtype3 | 130 | 0 | 0.1 - 138.1 (11.9)

Figure S99. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.0059 (ANOVA), Q value = 0.45

Table S108. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 373 | 46.4 (15.8)
subtype1 | 92 | 49.2 (15.5)
subtype2 | 149 | 47.8 (15.8)
subtype3 | 132 | 43.0 (15.5)

Figure S100. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.796 (Fisher's exact test), Q value = 1

Table S109. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 279 | 94
subtype1 | 70 | 22
subtype2 | 113 | 36
subtype3 | 96 | 36

Figure S101. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 1e-26 (Chi-square test), Q value = 1.1e-24

Table S110. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: '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 | 6 | 254 | 82 | 31
subtype1 | 3 | 32 | 57 | 0
subtype2 | 1 | 114 | 9 | 25
subtype3 | 2 | 108 | 16 | 6

Figure S102. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

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

P value = 0.0233 (Fisher's exact test), Q value = 1

Table S111. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 14 | 359
subtype1 | 1 | 91
subtype2 | 3 | 146
subtype3 | 10 | 122

Figure S103. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CHIERARCHICAL' versus 'RADIATIONEXPOSURE'

P value = 0.949 (Fisher's exact test), Q value = 1

Table S112. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

nPatients | NO | YES
--- | --- | ---
ALL | 318 | 16
subtype1 | 79 | 4
subtype2 | 128 | 7
subtype3 | 111 | 5

Figure S104. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

'MIRSEQ CHIERARCHICAL' versus 'DISTANT.METASTASIS'

P value = 0.0168 (Chi-square test), Q value = 1

Table S113. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'DISTANT.METASTASIS'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 195 | 6 | 171
subtype1 | 36 | 2 | 53
subtype2 | 92 | 1 | 56
subtype3 | 67 | 3 | 62

Figure S105. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'DISTANT.METASTASIS'

'MIRSEQ CHIERARCHICAL' versus 'EXTRATHYROIDAL.EXTENSION'

P value = 5.49e-07 (Chi-square test), Q value = 5.4e-05

Table S114. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

nPatients | MINIMAL (T3) | MODERATE/ADVANCED (T4A) | NONE | VERY ADVANCED (T4B)
--- | --- | --- | --- | ---
ALL | 96 | 12 | 248 | 1
subtype1 | 8 | 0 | 79 | 0
subtype2 | 56 | 10 | 78 | 1
subtype3 | 32 | 2 | 91 | 0

Figure S106. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

'MIRSEQ CHIERARCHICAL' versus 'LYMPH.NODE.METASTASIS'

P value = 7.14e-10 (Chi-square test), Q value = 7.5e-08

Table S115. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

nPatients | N0 | N1 | N1A | N1B | NX
--- | --- | --- | --- | --- | ---
ALL | 176 | 38 | 75 | 49 | 35
subtype1 | 67 | 3 | 2 | 4 | 16
subtype2 | 54 | 18 | 40 | 26 | 11
subtype3 | 55 | 17 | 33 | 19 | 8

Figure S107. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

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

P value = 0.425 (Chi-square test), Q value = 1

Table S116. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients | R0 | R1 | R2 | RX
--- | --- | --- | --- | ---
ALL | 293 | 30 | 2 | 24
subtype1 | 77 | 3 | 0 | 6
subtype2 | 112 | 17 | 1 | 10
subtype3 | 104 | 10 | 1 | 8

Figure S108. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.000396 (ANOVA), Q value = 0.034

Table S117. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 288 | 3.2 (5.6)
subtype1 | 62 | 0.7 (2.2)
subtype2 | 120 | 3.9 (5.3)
subtype3 | 106 | 3.8 (6.7)

Figure S109. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 2.75e-05 (Chi-square test), Q value = 0.0026

Table S118. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

nPatients | STAGE I | STAGE II | STAGE III | STAGE IV | STAGE IVA | STAGE IVC
--- | --- | --- | --- | --- | --- | ---
ALL | 210 | 43 | 83 | 1 | 29 | 4
subtype1 | 49 | 21 | 16 | 0 | 2 | 2
subtype2 | 77 | 6 | 48 | 1 | 15 | 1
subtype3 | 84 | 16 | 19 | 0 | 12 | 1

Figure S110. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

'MIRSEQ CHIERARCHICAL' versus 'MULTIFOCALITY'

P value = 0.553 (Fisher's exact test), Q value = 1

Table S119. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients | MULTIFOCAL | UNIFOCAL
--- | --- | ---
ALL | 165 | 199
subtype1 | 40 | 51
subtype2 | 63 | 84
subtype3 | 62 | 64

Figure S111. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'MULTIFOCALITY'

'MIRSEQ CHIERARCHICAL' versus 'TUMOR.SIZE'

P value = 0.0928 (ANOVA), Q value = 1

Table S120. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #15: 'TUMOR.SIZE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 290 | 2.9 (1.6)
subtype1 | 76 | 3.1 (1.7)
subtype2 | 111 | 2.6 (1.5)
subtype3 | 103 | 3.0 (1.5)

Figure S112. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #15: 'TUMOR.SIZE'

Methods & Data
Input

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

- Clinical data file = THCA-TP.clin.merged.picked.txt

- Number of patients = 424

- Number of clustering approaches = 8

- 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

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

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

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

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

- 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] 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)
[6] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
[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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