Correlation between aggregated molecular cancer subtypes and selected clinical features
Thyroid Adenocarcinoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C18W3CSH
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 17 clinical features across 503 patients, 78 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 'RACE'.

  • 5 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'HISTOLOGICAL_TYPE',  'EXTRATHYROIDAL_EXTENSION', and 'NUMBER_OF_LYMPH_NODES'.

  • CNMF clustering analysis on RPPA data identified 4 subtypes that correlate to 'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'HISTOLOGICAL_TYPE', and 'EXTRATHYROIDAL_EXTENSION'.

  • Consensus hierarchical clustering analysis on RPPA data identified 7 subtypes that correlate to 'Time to Death',  'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'HISTOLOGICAL_TYPE',  'EXTRATHYROIDAL_EXTENSION', and 'TUMOR_SIZE'.

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'HISTOLOGICAL_TYPE',  'EXTRATHYROIDAL_EXTENSION',  'NUMBER_OF_LYMPH_NODES', and 'ETHNICITY'.

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 5 subtypes that correlate to 'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'HISTOLOGICAL_TYPE',  'EXTRATHYROIDAL_EXTENSION',  'RESIDUAL_TUMOR',  'NUMBER_OF_LYMPH_NODES',  'TUMOR_SIZE',  'RACE', and 'ETHNICITY'.

  • 4 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'HISTOLOGICAL_TYPE',  'EXTRATHYROIDAL_EXTENSION',  'NUMBER_OF_LYMPH_NODES',  'MULTIFOCALITY',  'TUMOR_SIZE', and 'RACE'.

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'Time to Death',  'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'HISTOLOGICAL_TYPE',  'EXTRATHYROIDAL_EXTENSION',  'NUMBER_OF_LYMPH_NODES', and 'TUMOR_SIZE'.

  • 4 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'Time to Death',  'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'HISTOLOGICAL_TYPE',  'RADIATION_EXPOSURE',  'EXTRATHYROIDAL_EXTENSION',  'NUMBER_OF_LYMPH_NODES', and 'RACE'.

  • 3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'Time to Death',  'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'HISTOLOGICAL_TYPE',  'EXTRATHYROIDAL_EXTENSION',  'NUMBER_OF_LYMPH_NODES', and 'TUMOR_SIZE'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 17 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 78 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 0.776
(0.849)
0.218
(0.357)
0.272
(0.428)
0.000176
(0.000635)
0.181
(0.311)
0.411
(0.537)
0.152
(0.277)
0.00998
(0.0261)
0.00375
(0.0105)
0.00358
(0.0102)
YEARS TO BIRTH Kruskal-Wallis (anova) 0.207
(0.341)
0.0375
(0.0846)
0.00137
(0.0042)
0.000996
(0.00321)
0.0419
(0.0924)
0.00239
(0.007)
0.111
(0.215)
0.00138
(0.0042)
0.000587
(0.002)
0.000305
(0.00108)
PATHOLOGIC STAGE Fisher's exact test 0.357
(0.495)
4e-05
(0.000158)
0.00036
(0.00125)
0.00112
(0.00353)
2e-05
(8.5e-05)
1e-05
(4.72e-05)
1e-05
(4.72e-05)
1e-05
(4.72e-05)
2e-05
(8.5e-05)
1e-05
(4.72e-05)
PATHOLOGY T STAGE Fisher's exact test 0.301
(0.454)
4e-05
(0.000158)
0.0121
(0.0311)
0.00013
(0.00048)
1e-05
(4.72e-05)
1e-05
(4.72e-05)
6e-05
(0.000227)
2e-05
(8.5e-05)
0.00076
(0.00253)
6e-05
(0.000227)
PATHOLOGY N STAGE Fisher's exact test 0.544
(0.685)
1e-05
(4.72e-05)
1e-05
(4.72e-05)
0.00833
(0.0225)
1e-05
(4.72e-05)
1e-05
(4.72e-05)
1e-05
(4.72e-05)
1e-05
(4.72e-05)
1e-05
(4.72e-05)
1e-05
(4.72e-05)
PATHOLOGY M STAGE Fisher's exact test 0.519
(0.663)
0.779
(0.849)
0.669
(0.79)
0.756
(0.841)
0.754
(0.841)
0.402
(0.534)
0.663
(0.79)
0.334
(0.477)
0.406
(0.535)
0.333
(0.477)
GENDER Fisher's exact test 0.974
(0.986)
0.892
(0.937)
0.434
(0.559)
0.942
(0.965)
0.717
(0.824)
0.174
(0.305)
0.986
(0.992)
0.638
(0.774)
0.701
(0.817)
0.328
(0.477)
RADIATION THERAPY Fisher's exact test 0.376
(0.515)
0.0845
(0.171)
0.131
(0.248)
0.786
(0.851)
0.352
(0.494)
0.157
(0.283)
0.808
(0.864)
0.744
(0.841)
0.906
(0.939)
0.747
(0.841)
HISTOLOGICAL TYPE Fisher's exact test 0.387
(0.526)
1e-05
(4.72e-05)
1e-05
(4.72e-05)
1e-05
(4.72e-05)
1e-05
(4.72e-05)
1e-05
(4.72e-05)
1e-05
(4.72e-05)
1e-05
(4.72e-05)
1e-05
(4.72e-05)
1e-05
(4.72e-05)
RADIATION EXPOSURE Fisher's exact test 0.664
(0.79)
0.358
(0.495)
0.974
(0.986)
0.235
(0.38)
1
(1.00)
0.252
(0.401)
0.858
(0.906)
0.905
(0.939)
0.0293
(0.0691)
0.729
(0.831)
EXTRATHYROIDAL EXTENSION Fisher's exact test 0.328
(0.477)
1e-05
(4.72e-05)
0.00571
(0.0157)
1e-05
(4.72e-05)
1e-05
(4.72e-05)
1e-05
(4.72e-05)
2e-05
(8.5e-05)
1e-05
(4.72e-05)
3e-05
(0.000124)
1e-05
(4.72e-05)
RESIDUAL TUMOR Fisher's exact test 0.0836
(0.171)
0.0872
(0.174)
0.134
(0.248)
0.0952
(0.188)
0.0618
(0.133)
0.0194
(0.0491)
0.306
(0.454)
0.192
(0.326)
0.426
(0.553)
0.202
(0.337)
NUMBER OF LYMPH NODES Kruskal-Wallis (anova) 0.16
(0.283)
3.83e-08
(1.63e-06)
0.0625
(0.133)
0.4
(0.534)
7.41e-08
(2.52e-06)
8.8e-09
(4.99e-07)
3.59e-09
(3.62e-07)
4.26e-09
(3.62e-07)
2.8e-07
(6.81e-06)
9.07e-08
(2.57e-06)
MULTIFOCALITY Fisher's exact test 0.0671
(0.141)
0.286
(0.446)
0.159
(0.283)
0.18
(0.311)
0.121
(0.231)
0.242
(0.387)
0.0205
(0.0505)
0.608
(0.746)
0.348
(0.494)
0.648
(0.781)
TUMOR SIZE Kruskal-Wallis (anova) 0.606
(0.746)
0.685
(0.804)
0.768
(0.848)
0.0203
(0.0505)
0.0784
(0.163)
0.00195
(0.00583)
0.0298
(0.0693)
0.0378
(0.0846)
0.109
(0.212)
0.0221
(0.0537)
RACE Fisher's exact test 0.00984
(0.0261)
0.821
(0.872)
0.305
(0.454)
0.297
(0.454)
0.578
(0.723)
0.001
(0.00321)
0.0278
(0.0667)
0.2
(0.337)
0.0445
(0.0969)
0.134
(0.248)
ETHNICITY Fisher's exact test 0.305
(0.454)
0.39
(0.526)
0.61
(0.746)
0.937
(0.965)
0.00355
(0.0102)
0.0307
(0.0706)
0.307
(0.454)
0.798
(0.859)
0.715
(0.824)
0.523
(0.664)
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 132 138 229
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.776 (logrank test), Q value = 0.85

Table S2.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 498 16 0.2 - 178.3 (31.2)
subtype1 132 5 0.9 - 158.8 (31.5)
subtype2 138 5 2.6 - 178.3 (31.5)
subtype3 228 6 0.2 - 169.3 (31.0)

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 'YEARS_TO_BIRTH'

P value = 0.207 (Kruskal-Wallis (anova)), Q value = 0.34

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

nPatients Mean (Std.Dev)
ALL 499 47.3 (15.8)
subtype1 132 49.3 (16.1)
subtype2 138 46.5 (16.7)
subtype3 229 46.5 (14.9)

Figure S2.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.357 (Fisher's exact test), Q value = 0.5

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

nPatients STAGE I STAGE II STAGE III STAGE IV STAGE IVA STAGE IVC
ALL 282 50 111 2 46 6
subtype1 71 9 29 2 17 2
subtype2 80 17 27 0 13 1
subtype3 131 24 55 0 16 3

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.301 (Fisher's exact test), Q value = 0.45

Table S5.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 142 165 167 23
subtype1 39 37 46 10
subtype2 34 50 46 7
subtype3 69 78 75 6

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 = 0.544 (Fisher's exact test), Q value = 0.68

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

nPatients 0 1
ALL 225 224
subtype1 58 63
subtype2 60 66
subtype3 107 95

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 = 0.519 (Fisher's exact test), Q value = 0.66

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

nPatients 0 1
ALL 278 9
subtype1 72 4
subtype2 87 2
subtype3 119 3

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 = 0.974 (Fisher's exact test), Q value = 0.99

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

nPatients FEMALE MALE
ALL 365 134
subtype1 96 36
subtype2 102 36
subtype3 167 62

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 'RADIATION_THERAPY'

P value = 0.376 (Fisher's exact test), Q value = 0.52

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

nPatients NO YES
ALL 180 303
subtype1 40 85
subtype2 53 83
subtype3 87 135

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

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.387 (Fisher's exact test), Q value = 0.53

Table S10.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: '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 7 356 100 36
subtype1 4 90 30 8
subtype2 1 99 24 14
subtype3 2 167 46 14

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

'Copy Number Ratio CNMF subtypes' versus 'RADIATION_EXPOSURE'

P value = 0.664 (Fisher's exact test), Q value = 0.79

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

nPatients NO YES
ALL 420 17
subtype1 114 3
subtype2 113 6
subtype3 193 8

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

'Copy Number Ratio CNMF subtypes' versus 'EXTRATHYROIDAL_EXTENSION'

P value = 0.328 (Fisher's exact test), Q value = 0.48

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 132 18 330 1
subtype1 35 6 84 1
subtype2 42 6 84 0
subtype3 55 6 162 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 'RESIDUAL_TUMOR'

P value = 0.0836 (Fisher's exact test), Q value = 0.17

Table S13.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 383 51 4 30
subtype1 94 16 2 13
subtype2 110 10 2 6
subtype3 179 25 0 11

Figure S12.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'RESIDUAL_TUMOR'

'Copy Number Ratio CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.16 (Kruskal-Wallis (anova)), Q value = 0.28

Table S14.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 386 3.6 (6.0)
subtype1 102 3.8 (6.3)
subtype2 109 3.9 (5.6)
subtype3 175 3.2 (6.1)

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 = 0.0671 (Fisher's exact test), Q value = 0.14

Table S15.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients MULTIFOCAL UNIFOCAL
ALL 225 264
subtype1 71 59
subtype2 56 79
subtype3 98 126

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 = 0.606 (Kruskal-Wallis (anova)), Q value = 0.75

Table S16.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #15: 'TUMOR_SIZE'

nPatients Mean (Std.Dev)
ALL 401 3.0 (1.6)
subtype1 103 3.0 (1.6)
subtype2 115 2.8 (1.5)
subtype3 183 3.0 (1.6)

Figure S15.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #15: 'TUMOR_SIZE'

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.00984 (Fisher's exact test), Q value = 0.026

Table S17.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #16: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 52 27 329
subtype1 0 13 9 82
subtype2 1 6 6 106
subtype3 0 33 12 141

Figure S16.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #16: 'RACE'

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.305 (Fisher's exact test), Q value = 0.45

Table S18.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #17: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 38 360
subtype1 11 92
subtype2 14 100
subtype3 13 168

Figure S17.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #17: 'ETHNICITY'

Clustering Approach #2: 'METHLYATION CNMF'

Table S19.  Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels 1 2 3 4 5
Number of samples 46 149 85 42 181
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.218 (logrank test), Q value = 0.36

Table S20.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 502 16 0.2 - 178.3 (31.1)
subtype1 46 2 0.2 - 155.5 (33.2)
subtype2 149 5 1.1 - 166.6 (23.9)
subtype3 85 5 2.3 - 178.3 (31.0)
subtype4 41 0 8.4 - 169.3 (29.6)
subtype5 181 4 0.9 - 158.8 (34.0)

Figure S18.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.0375 (Kruskal-Wallis (anova)), Q value = 0.085

Table S21.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 503 47.3 (15.8)
subtype1 46 51.7 (13.8)
subtype2 149 49.0 (15.2)
subtype3 85 46.8 (17.9)
subtype4 42 43.0 (14.8)
subtype5 181 45.9 (15.6)

Figure S19.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'METHLYATION CNMF' versus 'PATHOLOGIC_STAGE'

P value = 4e-05 (Fisher's exact test), Q value = 0.00016

Table S22.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV STAGE IVA STAGE IVC
ALL 284 51 111 2 47 6
subtype1 19 1 16 0 10 0
subtype2 86 29 26 1 4 2
subtype3 51 2 22 1 8 1
subtype4 25 4 11 0 2 0
subtype5 103 15 36 0 23 3

Figure S20.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 4e-05 (Fisher's exact test), Q value = 0.00016

Table S23.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 143 166 169 23
subtype1 4 10 26 6
subtype2 52 57 39 1
subtype3 29 24 27 4
subtype4 10 15 17 0
subtype5 48 60 60 12

Figure S21.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 4.7e-05

Table S24.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 227 226
subtype1 15 30
subtype2 98 25
subtype3 34 47
subtype4 16 21
subtype5 64 103

Figure S22.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.779 (Fisher's exact test), Q value = 0.85

Table S25.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 280 9
subtype1 28 0
subtype2 69 3
subtype3 55 1
subtype4 25 0
subtype5 103 5

Figure S23.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.892 (Fisher's exact test), Q value = 0.94

Table S26.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 368 135
subtype1 32 14
subtype2 108 41
subtype3 61 24
subtype4 33 9
subtype5 134 47

Figure S24.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

'METHLYATION CNMF' versus 'RADIATION_THERAPY'

P value = 0.0845 (Fisher's exact test), Q value = 0.17

Table S27.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 181 306
subtype1 9 37
subtype2 54 88
subtype3 32 49
subtype4 19 22
subtype5 67 110

Figure S25.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

'METHLYATION CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4.7e-05

Table S28.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: '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 7 358 102 36
subtype1 0 34 0 12
subtype2 2 70 77 0
subtype3 1 68 7 9
subtype4 0 37 3 2
subtype5 4 149 15 13

Figure S26.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'METHLYATION CNMF' versus 'RADIATION_EXPOSURE'

P value = 0.358 (Fisher's exact test), Q value = 0.5

Table S29.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'RADIATION_EXPOSURE'

nPatients NO YES
ALL 423 17
subtype1 40 2
subtype2 120 6
subtype3 72 4
subtype4 31 2
subtype5 160 3

Figure S27.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'RADIATION_EXPOSURE'

'METHLYATION CNMF' versus 'EXTRATHYROIDAL_EXTENSION'

P value = 1e-05 (Fisher's exact test), Q value = 4.7e-05

Table S30.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'EXTRATHYROIDAL_EXTENSION'

nPatients MINIMAL (T3) MODERATE/ADVANCED (T4A) NONE VERY ADVANCED (T4B)
ALL 133 18 333 1
subtype1 22 6 17 0
subtype2 21 0 119 0
subtype3 22 3 55 1
subtype4 16 0 24 0
subtype5 52 9 118 0

Figure S28.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'EXTRATHYROIDAL_EXTENSION'

'METHLYATION CNMF' versus 'RESIDUAL_TUMOR'

P value = 0.0872 (Fisher's exact test), Q value = 0.17

Table S31.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 385 52 4 30
subtype1 33 9 1 2
subtype2 118 8 0 8
subtype3 62 10 1 5
subtype4 36 1 0 1
subtype5 136 24 2 14

Figure S29.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'RESIDUAL_TUMOR'

'METHLYATION CNMF' versus 'NUMBER_OF_LYMPH_NODES'

P value = 3.83e-08 (Kruskal-Wallis (anova)), Q value = 1.6e-06

Table S32.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #13: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 389 3.7 (6.2)
subtype1 42 3.6 (4.6)
subtype2 97 1.6 (4.3)
subtype3 73 4.1 (6.9)
subtype4 32 4.0 (7.7)
subtype5 145 4.7 (6.6)

Figure S30.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #13: 'NUMBER_OF_LYMPH_NODES'

'METHLYATION CNMF' versus 'MULTIFOCALITY'

P value = 0.286 (Fisher's exact test), Q value = 0.45

Table S33.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients MULTIFOCAL UNIFOCAL
ALL 227 266
subtype1 18 28
subtype2 69 77
subtype3 38 45
subtype4 13 27
subtype5 89 89

Figure S31.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #14: 'MULTIFOCALITY'

'METHLYATION CNMF' versus 'TUMOR_SIZE'

P value = 0.685 (Kruskal-Wallis (anova)), Q value = 0.8

Table S34.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #15: 'TUMOR_SIZE'

nPatients Mean (Std.Dev)
ALL 403 3.0 (1.6)
subtype1 37 3.2 (1.6)
subtype2 118 3.1 (1.6)
subtype3 68 2.9 (1.6)
subtype4 32 3.0 (1.7)
subtype5 148 2.9 (1.5)

Figure S32.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #15: 'TUMOR_SIZE'

'METHLYATION CNMF' versus 'RACE'

P value = 0.821 (Fisher's exact test), Q value = 0.87

Table S35.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #16: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 52 27 331
subtype1 1 5 3 33
subtype2 0 11 9 84
subtype3 0 10 4 63
subtype4 0 4 1 33
subtype5 0 22 10 118

Figure S33.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #16: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.39 (Fisher's exact test), Q value = 0.53

Table S36.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #17: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 38 362
subtype1 7 33
subtype2 7 97
subtype3 7 68
subtype4 2 31
subtype5 15 133

Figure S34.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #17: 'ETHNICITY'

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S37.  Description of clustering approach #3: 'RPPA CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 64 64 43 51
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.272 (logrank test), Q value = 0.43

Table S38.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 222 14 1.2 - 178.3 (36.9)
subtype1 64 6 1.2 - 158.8 (35.5)
subtype2 64 2 4.0 - 178.3 (41.7)
subtype3 43 2 3.0 - 166.6 (33.5)
subtype4 51 4 2.7 - 147.8 (37.5)

Figure S35.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.00137 (Kruskal-Wallis (anova)), Q value = 0.0042

Table S39.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 222 48.1 (16.8)
subtype1 64 51.8 (14.9)
subtype2 64 51.5 (15.1)
subtype3 43 43.6 (18.0)
subtype4 51 42.8 (18.1)

Figure S36.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.00036 (Fisher's exact test), Q value = 0.0012

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

nPatients STAGE I STAGE II STAGE III STAGE IVA STAGE IVC
ALL 117 33 46 20 4
subtype1 32 12 13 3 2
subtype2 22 11 19 12 0
subtype3 30 8 3 2 0
subtype4 33 2 11 3 2

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

'RPPA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.0121 (Fisher's exact test), Q value = 0.031

Table S41.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 51 83 76 11
subtype1 23 23 16 1
subtype2 12 19 27 6
subtype3 9 22 12 0
subtype4 7 19 21 4

Figure S38.  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 = 1e-05 (Fisher's exact test), Q value = 4.7e-05

Table S42.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 98 95
subtype1 37 19
subtype2 25 31
subtype3 25 11
subtype4 11 34

Figure S39.  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 = 0.669 (Fisher's exact test), Q value = 0.79

Table S43.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 118 5
subtype1 38 2
subtype2 35 1
subtype3 21 0
subtype4 24 2

Figure S40.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.434 (Fisher's exact test), Q value = 0.56

Table S44.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 154 68
subtype1 44 20
subtype2 47 17
subtype3 32 11
subtype4 31 20

Figure S41.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.131 (Fisher's exact test), Q value = 0.25

Table S45.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 90 128
subtype1 31 31
subtype2 24 40
subtype3 20 22
subtype4 15 35

Figure S42.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4.7e-05

Table S46.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: '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 2 158 52 10
subtype1 0 36 27 1
subtype2 0 44 13 7
subtype3 1 32 10 0
subtype4 1 46 2 2

Figure S43.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'RPPA CNMF subtypes' versus 'RADIATION_EXPOSURE'

P value = 0.974 (Fisher's exact test), Q value = 0.99

Table S47.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RADIATION_EXPOSURE'

nPatients NO YES
ALL 184 11
subtype1 55 4
subtype2 55 3
subtype3 29 2
subtype4 45 2

Figure S44.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RADIATION_EXPOSURE'

'RPPA CNMF subtypes' versus 'EXTRATHYROIDAL_EXTENSION'

P value = 0.00571 (Fisher's exact test), Q value = 0.016

Table S48.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'EXTRATHYROIDAL_EXTENSION'

nPatients MINIMAL (T3) MODERATE/ADVANCED (T4A) NONE
ALL 55 10 149
subtype1 12 1 51
subtype2 22 6 34
subtype3 5 0 32
subtype4 16 3 32

Figure S45.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'EXTRATHYROIDAL_EXTENSION'

'RPPA CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.134 (Fisher's exact test), Q value = 0.25

Table S49.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 168 23 2 14
subtype1 50 6 1 6
subtype2 45 12 0 4
subtype3 34 2 0 0
subtype4 39 3 1 4

Figure S46.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'RESIDUAL_TUMOR'

'RPPA CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0625 (Kruskal-Wallis (anova)), Q value = 0.13

Table S50.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 168 3.5 (5.8)
subtype1 47 3.7 (7.2)
subtype2 50 3.0 (4.5)
subtype3 26 2.7 (4.4)
subtype4 45 4.4 (6.3)

Figure S47.  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 = 0.159 (Fisher's exact test), Q value = 0.28

Table S51.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients MULTIFOCAL UNIFOCAL
ALL 100 115
subtype1 34 27
subtype2 22 40
subtype3 20 22
subtype4 24 26

Figure S48.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

'RPPA CNMF subtypes' versus 'TUMOR_SIZE'

P value = 0.768 (Kruskal-Wallis (anova)), Q value = 0.85

Table S52.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #15: 'TUMOR_SIZE'

nPatients Mean (Std.Dev)
ALL 191 3.3 (1.6)
subtype1 55 3.2 (1.6)
subtype2 52 3.4 (1.6)
subtype3 36 3.2 (1.3)
subtype4 48 3.4 (1.5)

Figure S49.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #15: 'TUMOR_SIZE'

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.305 (Fisher's exact test), Q value = 0.45

Table S53.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #16: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 16 13 149
subtype1 3 7 36
subtype2 3 3 48
subtype3 4 1 28
subtype4 6 2 37

Figure S50.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #16: 'RACE'

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.61 (Fisher's exact test), Q value = 0.75

Table S54.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #17: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 16 168
subtype1 3 45
subtype2 5 49
subtype3 5 31
subtype4 3 43

Figure S51.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #17: 'ETHNICITY'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S55.  Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels 1 2 3 4 5 6 7
Number of samples 47 28 33 29 49 27 9
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.000176 (logrank test), Q value = 0.00064

Table S56.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 222 14 1.2 - 178.3 (36.9)
subtype1 47 5 1.2 - 166.6 (37.6)
subtype2 28 1 6.0 - 95.3 (34.3)
subtype3 33 0 4.0 - 139.0 (48.1)
subtype4 29 2 3.0 - 155.5 (22.5)
subtype5 49 1 2.7 - 150.5 (41.5)
subtype6 27 1 6.0 - 112.8 (34.4)
subtype7 9 4 17.5 - 178.3 (42.1)

Figure S52.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.000996 (Kruskal-Wallis (anova)), Q value = 0.0032

Table S57.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 222 48.1 (16.8)
subtype1 47 52.6 (16.2)
subtype2 28 49.6 (15.4)
subtype3 33 47.6 (15.3)
subtype4 29 45.3 (19.2)
subtype5 49 40.1 (14.7)
subtype6 27 52.2 (16.7)
subtype7 9 61.8 (14.9)

Figure S53.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.00112 (Fisher's exact test), Q value = 0.0035

Table S58.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE III STAGE IVA STAGE IVC
ALL 117 33 46 20 4
subtype1 26 11 6 1 1
subtype2 16 3 7 2 0
subtype3 13 4 11 5 0
subtype4 17 4 7 0 1
subtype5 35 3 6 4 1
subtype6 9 7 7 4 0
subtype7 1 1 2 4 1

Figure S54.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00013 (Fisher's exact test), Q value = 0.00048

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

nPatients T1 T2 T3 T4
ALL 51 83 76 11
subtype1 16 22 9 0
subtype2 11 9 8 0
subtype3 3 11 17 2
subtype4 4 11 12 1
subtype5 11 21 16 1
subtype6 5 9 11 2
subtype7 1 0 3 5

Figure S55.  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 = 0.00833 (Fisher's exact test), Q value = 0.022

Table S60.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 98 95
subtype1 28 11
subtype2 13 13
subtype3 10 22
subtype4 17 9
subtype5 16 26
subtype6 11 10
subtype7 3 4

Figure S56.  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 = 0.756 (Fisher's exact test), Q value = 0.84

Table S61.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 118 5
subtype1 21 1
subtype2 19 0
subtype3 21 1
subtype4 13 1
subtype5 26 1
subtype6 11 0
subtype7 7 1

Figure S57.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.942 (Fisher's exact test), Q value = 0.97

Table S62.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 154 68
subtype1 31 16
subtype2 18 10
subtype3 25 8
subtype4 21 8
subtype5 33 16
subtype6 20 7
subtype7 6 3

Figure S58.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.786 (Fisher's exact test), Q value = 0.85

Table S63.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 90 128
subtype1 21 24
subtype2 14 14
subtype3 13 20
subtype4 11 17
subtype5 20 28
subtype6 8 19
subtype7 3 6

Figure S59.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4.7e-05

Table S64.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: '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 2 158 52 10
subtype1 0 22 25 0
subtype2 0 22 5 1
subtype3 0 25 0 8
subtype4 0 22 7 0
subtype5 1 46 2 0
subtype6 1 12 13 1
subtype7 0 9 0 0

Figure S60.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'RPPA cHierClus subtypes' versus 'RADIATION_EXPOSURE'

P value = 0.235 (Fisher's exact test), Q value = 0.38

Table S65.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RADIATION_EXPOSURE'

nPatients NO YES
ALL 184 11
subtype1 39 3
subtype2 26 0
subtype3 29 0
subtype4 22 2
subtype5 39 2
subtype6 23 3
subtype7 6 1

Figure S61.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RADIATION_EXPOSURE'

'RPPA cHierClus subtypes' versus 'EXTRATHYROIDAL_EXTENSION'

P value = 1e-05 (Fisher's exact test), Q value = 4.7e-05

Table S66.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'EXTRATHYROIDAL_EXTENSION'

nPatients MINIMAL (T3) MODERATE/ADVANCED (T4A) NONE
ALL 55 10 149
subtype1 4 0 42
subtype2 9 0 19
subtype3 15 3 14
subtype4 6 1 18
subtype5 12 0 36
subtype6 6 2 19
subtype7 3 4 1

Figure S62.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'EXTRATHYROIDAL_EXTENSION'

'RPPA cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.0952 (Fisher's exact test), Q value = 0.19

Table S67.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 168 23 2 14
subtype1 37 5 0 3
subtype2 22 3 0 2
subtype3 24 7 0 0
subtype4 19 2 2 1
subtype5 39 3 0 3
subtype6 23 1 0 3
subtype7 4 2 0 2

Figure S63.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'RESIDUAL_TUMOR'

'RPPA cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.4 (Kruskal-Wallis (anova)), Q value = 0.53

Table S68.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 168 3.5 (5.8)
subtype1 30 3.0 (6.7)
subtype2 25 3.9 (7.0)
subtype3 27 3.4 (3.8)
subtype4 22 2.8 (4.2)
subtype5 38 4.0 (6.6)
subtype6 19 3.7 (5.9)
subtype7 7 4.0 (5.3)

Figure S64.  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 = 0.18 (Fisher's exact test), Q value = 0.31

Table S69.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients MULTIFOCAL UNIFOCAL
ALL 100 115
subtype1 27 17
subtype2 12 16
subtype3 12 20
subtype4 12 17
subtype5 25 22
subtype6 10 16
subtype7 2 7

Figure S65.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

'RPPA cHierClus subtypes' versus 'TUMOR_SIZE'

P value = 0.0203 (Kruskal-Wallis (anova)), Q value = 0.051

Table S70.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #15: 'TUMOR_SIZE'

nPatients Mean (Std.Dev)
ALL 191 3.3 (1.6)
subtype1 38 3.4 (1.5)
subtype2 27 2.6 (1.6)
subtype3 29 3.3 (1.3)
subtype4 24 3.6 (1.5)
subtype5 45 3.1 (1.5)
subtype6 22 3.3 (1.6)
subtype7 6 5.2 (1.8)

Figure S66.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #15: 'TUMOR_SIZE'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.297 (Fisher's exact test), Q value = 0.45

Table S71.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #16: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 16 13 149
subtype1 1 6 25
subtype2 2 1 19
subtype3 3 0 28
subtype4 2 1 20
subtype5 7 2 33
subtype6 1 2 16
subtype7 0 1 8

Figure S67.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #16: 'RACE'

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.937 (Fisher's exact test), Q value = 0.96

Table S72.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #17: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 16 168
subtype1 2 32
subtype2 1 23
subtype3 3 28
subtype4 2 22
subtype5 5 38
subtype6 2 17
subtype7 1 8

Figure S68.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #17: 'ETHNICITY'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S73.  Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 158 169 59 115
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.181 (logrank test), Q value = 0.31

Table S74.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 500 16 0.2 - 178.3 (31.0)
subtype1 158 7 2.6 - 178.3 (31.2)
subtype2 169 7 0.2 - 166.6 (25.5)
subtype3 59 0 2.3 - 157.2 (30.1)
subtype4 114 2 0.9 - 169.3 (35.0)

Figure S69.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0419 (Kruskal-Wallis (anova)), Q value = 0.092

Table S75.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 501 47.3 (15.8)
subtype1 158 48.4 (16.0)
subtype2 169 49.1 (15.5)
subtype3 59 43.1 (17.2)
subtype4 115 45.2 (14.7)

Figure S70.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 2e-05 (Fisher's exact test), Q value = 8.5e-05

Table S76.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV STAGE IVA STAGE IVC
ALL 283 51 110 2 47 6
subtype1 78 6 44 1 27 2
subtype2 99 29 31 1 6 2
subtype3 41 1 12 0 5 0
subtype4 65 15 23 0 9 2

Figure S71.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 4.7e-05

Table S77.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 142 165 169 23
subtype1 26 42 75 15
subtype2 57 61 48 2
subtype3 26 15 15 2
subtype4 33 47 31 4

Figure S72.  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 = 1e-05 (Fisher's exact test), Q value = 4.7e-05

Table S78.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 226 225
subtype1 47 97
subtype2 108 34
subtype3 22 37
subtype4 49 57

Figure S73.  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 = 0.754 (Fisher's exact test), Q value = 0.84

Table S79.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 279 9
subtype1 86 3
subtype2 82 3
subtype3 41 0
subtype4 70 3

Figure S74.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.717 (Fisher's exact test), Q value = 0.82

Table S80.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 366 135
subtype1 115 43
subtype2 119 50
subtype3 44 15
subtype4 88 27

Figure S75.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.352 (Fisher's exact test), Q value = 0.49

Table S81.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 180 305
subtype1 49 106
subtype2 64 96
subtype3 24 33
subtype4 43 70

Figure S76.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4.7e-05

Table S82.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: '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 7 357 102 35
subtype1 3 128 3 24
subtype2 3 80 84 2
subtype3 0 50 2 7
subtype4 1 99 13 2

Figure S77.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'RNAseq CNMF subtypes' versus 'RADIATION_EXPOSURE'

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

Table S83.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RADIATION_EXPOSURE'

nPatients NO YES
ALL 421 17
subtype1 137 5
subtype2 138 6
subtype3 47 2
subtype4 99 4

Figure S78.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RADIATION_EXPOSURE'

'RNAseq CNMF subtypes' versus 'EXTRATHYROIDAL_EXTENSION'

P value = 1e-05 (Fisher's exact test), Q value = 4.7e-05

Table S84.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'EXTRATHYROIDAL_EXTENSION'

nPatients MINIMAL (T3) MODERATE/ADVANCED (T4A) NONE VERY ADVANCED (T4B)
ALL 133 18 331 1
subtype1 65 14 74 1
subtype2 27 1 132 0
subtype3 13 2 41 0
subtype4 28 1 84 0

Figure S79.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'EXTRATHYROIDAL_EXTENSION'

'RNAseq CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.0618 (Fisher's exact test), Q value = 0.13

Table S85.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 383 52 4 30
subtype1 119 26 1 8
subtype2 134 10 0 9
subtype3 40 7 1 5
subtype4 90 9 2 8

Figure S80.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'RESIDUAL_TUMOR'

'RNAseq CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 7.41e-08 (Kruskal-Wallis (anova)), Q value = 2.5e-06

Table S86.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 387 3.7 (6.2)
subtype1 132 4.3 (6.2)
subtype2 114 2.1 (5.2)
subtype3 51 5.2 (7.4)
subtype4 90 3.8 (6.3)

Figure S81.  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 = 0.121 (Fisher's exact test), Q value = 0.23

Table S87.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients MULTIFOCAL UNIFOCAL
ALL 226 265
subtype1 60 96
subtype2 79 87
subtype3 29 28
subtype4 58 54

Figure S82.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

'RNAseq CNMF subtypes' versus 'TUMOR_SIZE'

P value = 0.0784 (Kruskal-Wallis (anova)), Q value = 0.16

Table S88.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #15: 'TUMOR_SIZE'

nPatients Mean (Std.Dev)
ALL 402 3.0 (1.6)
subtype1 134 3.2 (1.6)
subtype2 135 3.0 (1.6)
subtype3 41 2.6 (1.6)
subtype4 92 2.9 (1.5)

Figure S83.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #15: 'TUMOR_SIZE'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.578 (Fisher's exact test), Q value = 0.72

Table S89.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #16: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 51 27 331
subtype1 1 14 11 114
subtype2 0 13 9 98
subtype3 0 10 1 43
subtype4 0 14 6 76

Figure S84.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #16: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.00355 (Fisher's exact test), Q value = 0.01

Table S90.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #17: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 38 360
subtype1 23 113
subtype2 9 108
subtype3 3 51
subtype4 3 88

Figure S85.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #17: 'ETHNICITY'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S91.  Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3 4 5
Number of samples 105 136 86 96 78
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.411 (logrank test), Q value = 0.54

Table S92.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 500 16 0.2 - 178.3 (31.0)
subtype1 105 5 2.8 - 178.3 (34.4)
subtype2 136 6 0.2 - 166.6 (23.5)
subtype3 86 1 2.3 - 142.5 (31.4)
subtype4 95 2 0.9 - 169.3 (34.9)
subtype5 78 2 2.6 - 150.5 (28.0)

Figure S86.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.00239 (Kruskal-Wallis (anova)), Q value = 0.007

Table S93.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 501 47.3 (15.8)
subtype1 105 51.0 (16.1)
subtype2 136 49.7 (16.0)
subtype3 86 44.5 (15.1)
subtype4 96 45.3 (14.6)
subtype5 78 43.5 (15.8)

Figure S87.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 4.7e-05

Table S94.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV STAGE IVA STAGE IVC
ALL 283 51 110 2 47 6
subtype1 46 2 39 1 16 1
subtype2 77 29 22 1 4 2
subtype3 57 3 18 0 8 0
subtype4 54 12 19 0 8 2
subtype5 49 5 12 0 11 1

Figure S88.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 4.7e-05

Table S95.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 142 165 169 23
subtype1 17 21 57 10
subtype2 43 55 36 2
subtype3 38 22 23 1
subtype4 26 41 25 4
subtype5 18 26 28 6

Figure S89.  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 = 1e-05 (Fisher's exact test), Q value = 4.7e-05

Table S96.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 226 225
subtype1 29 68
subtype2 91 19
subtype3 38 45
subtype4 43 45
subtype5 25 48

Figure S90.  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 = 0.402 (Fisher's exact test), Q value = 0.53

Table S97.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 279 9
subtype1 56 1
subtype2 57 3
subtype3 60 0
subtype4 58 3
subtype5 48 2

Figure S91.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.174 (Fisher's exact test), Q value = 0.3

Table S98.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 366 135
subtype1 84 21
subtype2 96 40
subtype3 61 25
subtype4 74 22
subtype5 51 27

Figure S92.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.157 (Fisher's exact test), Q value = 0.28

Table S99.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 180 305
subtype1 30 74
subtype2 47 83
subtype3 38 43
subtype4 37 57
subtype5 28 48

Figure S93.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4.7e-05

Table S100.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: '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 7 357 102 35
subtype1 1 79 2 23
subtype2 2 57 77 0
subtype3 1 69 10 6
subtype4 0 83 11 2
subtype5 3 69 2 4

Figure S94.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'RNAseq cHierClus subtypes' versus 'RADIATION_EXPOSURE'

P value = 0.252 (Fisher's exact test), Q value = 0.4

Table S101.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATION_EXPOSURE'

nPatients NO YES
ALL 421 17
subtype1 95 3
subtype2 111 5
subtype3 66 6
subtype4 85 1
subtype5 64 2

Figure S95.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATION_EXPOSURE'

'RNAseq cHierClus subtypes' versus 'EXTRATHYROIDAL_EXTENSION'

P value = 1e-05 (Fisher's exact test), Q value = 4.7e-05

Table S102.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'EXTRATHYROIDAL_EXTENSION'

nPatients MINIMAL (T3) MODERATE/ADVANCED (T4A) NONE VERY ADVANCED (T4B)
ALL 133 18 331 1
subtype1 53 10 38 1
subtype2 16 1 110 0
subtype3 20 1 62 0
subtype4 22 1 71 0
subtype5 22 5 50 0

Figure S96.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'EXTRATHYROIDAL_EXTENSION'

'RNAseq cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.0194 (Fisher's exact test), Q value = 0.049

Table S103.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 383 52 4 30
subtype1 70 23 2 6
subtype2 104 10 0 8
subtype3 70 4 0 6
subtype4 76 8 2 6
subtype5 63 7 0 4

Figure S97.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'RESIDUAL_TUMOR'

'RNAseq cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 8.8e-09 (Kruskal-Wallis (anova)), Q value = 5e-07

Table S104.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 387 3.7 (6.2)
subtype1 92 4.7 (6.6)
subtype2 87 1.4 (3.5)
subtype3 72 4.8 (8.5)
subtype4 73 3.8 (6.2)
subtype5 63 3.8 (4.6)

Figure S98.  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 = 0.242 (Fisher's exact test), Q value = 0.39

Table S105.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients MULTIFOCAL UNIFOCAL
ALL 226 265
subtype1 39 66
subtype2 64 69
subtype3 43 41
subtype4 47 46
subtype5 33 43

Figure S99.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

'RNAseq cHierClus subtypes' versus 'TUMOR_SIZE'

P value = 0.00195 (Kruskal-Wallis (anova)), Q value = 0.0058

Table S106.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #15: 'TUMOR_SIZE'

nPatients Mean (Std.Dev)
ALL 402 3.0 (1.6)
subtype1 93 3.1 (1.5)
subtype2 112 3.2 (1.5)
subtype3 62 2.3 (1.5)
subtype4 76 2.9 (1.5)
subtype5 59 3.2 (1.8)

Figure S100.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #15: 'TUMOR_SIZE'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.001 (Fisher's exact test), Q value = 0.0032

Table S107.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #16: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 51 27 331
subtype1 1 4 11 77
subtype2 0 7 8 75
subtype3 0 17 1 61
subtype4 0 12 6 60
subtype5 0 11 1 58

Figure S101.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #16: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.0307 (Fisher's exact test), Q value = 0.071

Table S108.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #17: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 38 360
subtype1 15 77
subtype2 9 82
subtype3 3 72
subtype4 3 71
subtype5 8 58

Figure S102.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #17: 'ETHNICITY'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S109.  Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels 1 2 3 4
Number of samples 166 147 120 69
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.152 (logrank test), Q value = 0.28

Table S110.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 501 16 0.2 - 178.3 (31.0)
subtype1 166 9 2.3 - 158.8 (31.2)
subtype2 147 4 1.1 - 166.6 (29.1)
subtype3 119 1 0.2 - 169.3 (30.0)
subtype4 69 2 2.7 - 178.3 (42.1)

Figure S103.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.111 (Kruskal-Wallis (anova)), Q value = 0.21

Table S111.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 502 47.3 (15.8)
subtype1 166 49.6 (15.9)
subtype2 147 46.8 (16.3)
subtype3 120 46.3 (14.6)
subtype4 69 44.6 (15.8)

Figure S104.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'PATHOLOGIC_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 4.7e-05

Table S112.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV STAGE IVA STAGE IVC
ALL 283 51 111 2 47 6
subtype1 80 4 53 1 25 2
subtype2 92 23 24 1 4 2
subtype3 72 15 21 0 10 2
subtype4 39 9 13 0 8 0

Figure S105.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 6e-05 (Fisher's exact test), Q value = 0.00023

Table S113.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 143 166 168 23
subtype1 51 34 65 14
subtype2 45 62 39 1
subtype3 35 45 35 5
subtype4 12 25 29 3

Figure S106.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 4.7e-05

Table S114.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 227 225
subtype1 57 103
subtype2 93 26
subtype3 47 63
subtype4 30 33

Figure S107.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.663 (Fisher's exact test), Q value = 0.79

Table S115.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 279 9
subtype1 113 2
subtype2 65 3
subtype3 69 3
subtype4 32 1

Figure S108.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.986 (Fisher's exact test), Q value = 0.99

Table S116.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 367 135
subtype1 121 45
subtype2 106 41
subtype3 89 31
subtype4 51 18

Figure S109.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CNMF' versus 'RADIATION_THERAPY'

P value = 0.808 (Fisher's exact test), Q value = 0.86

Table S117.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 181 305
subtype1 55 103
subtype2 52 90
subtype3 46 71
subtype4 28 41

Figure S110.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4.7e-05

Table S118.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: '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 7 357 102 36
subtype1 3 130 8 25
subtype2 2 66 79 0
subtype3 1 107 6 6
subtype4 1 54 9 5

Figure S111.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'MIRSEQ CNMF' versus 'RADIATION_EXPOSURE'

P value = 0.858 (Fisher's exact test), Q value = 0.91

Table S119.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RADIATION_EXPOSURE'

nPatients NO YES
ALL 422 17
subtype1 140 5
subtype2 124 4
subtype3 99 5
subtype4 59 3

Figure S112.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RADIATION_EXPOSURE'

'MIRSEQ CNMF' versus 'EXTRATHYROIDAL_EXTENSION'

P value = 2e-05 (Fisher's exact test), Q value = 8.5e-05

Table S120.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'EXTRATHYROIDAL_EXTENSION'

nPatients MINIMAL (T3) MODERATE/ADVANCED (T4A) NONE VERY ADVANCED (T4B)
ALL 132 18 333 1
subtype1 57 13 89 1
subtype2 22 0 118 0
subtype3 33 3 81 0
subtype4 20 2 45 0

Figure S113.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'EXTRATHYROIDAL_EXTENSION'

'MIRSEQ CNMF' versus 'RESIDUAL_TUMOR'

P value = 0.306 (Fisher's exact test), Q value = 0.45

Table S121.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 384 52 4 30
subtype1 119 24 2 12
subtype2 116 9 0 10
subtype3 94 13 1 5
subtype4 55 6 1 3

Figure S114.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'RESIDUAL_TUMOR'

'MIRSEQ CNMF' versus 'NUMBER_OF_LYMPH_NODES'

P value = 3.59e-09 (Kruskal-Wallis (anova)), Q value = 3.6e-07

Table S122.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #13: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 388 3.6 (6.2)
subtype1 138 5.3 (7.5)
subtype2 97 1.5 (3.2)
subtype3 96 3.8 (6.5)
subtype4 57 2.9 (4.7)

Figure S115.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #13: 'NUMBER_OF_LYMPH_NODES'

'MIRSEQ CNMF' versus 'MULTIFOCALITY'

P value = 0.0205 (Fisher's exact test), Q value = 0.051

Table S123.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients MULTIFOCAL UNIFOCAL
ALL 226 266
subtype1 77 88
subtype2 62 80
subtype3 65 52
subtype4 22 46

Figure S116.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #14: 'MULTIFOCALITY'

'MIRSEQ CNMF' versus 'TUMOR_SIZE'

P value = 0.0298 (Kruskal-Wallis (anova)), Q value = 0.069

Table S124.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #15: 'TUMOR_SIZE'

nPatients Mean (Std.Dev)
ALL 402 3.0 (1.6)
subtype1 130 2.8 (1.7)
subtype2 121 3.1 (1.5)
subtype3 97 2.8 (1.5)
subtype4 54 3.4 (1.6)

Figure S117.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #15: 'TUMOR_SIZE'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.0278 (Fisher's exact test), Q value = 0.067

Table S125.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #16: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 52 27 331
subtype1 0 24 8 119
subtype2 0 9 10 84
subtype3 1 16 8 70
subtype4 0 3 1 58

Figure S118.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #16: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.307 (Fisher's exact test), Q value = 0.45

Table S126.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #17: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 38 361
subtype1 12 131
subtype2 9 94
subtype3 7 85
subtype4 10 51

Figure S119.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #17: 'ETHNICITY'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S127.  Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3
Number of samples 179 130 193
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.00998 (logrank test), Q value = 0.026

Table S128.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 501 16 0.2 - 178.3 (31.0)
subtype1 179 10 2.3 - 178.3 (31.5)
subtype2 130 5 1.1 - 166.6 (23.3)
subtype3 192 1 0.2 - 169.3 (33.2)

Figure S120.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 0.00138 (Kruskal-Wallis (anova)), Q value = 0.0042

Table S129.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 502 47.3 (15.8)
subtype1 179 49.7 (16.2)
subtype2 130 49.1 (15.5)
subtype3 193 43.9 (15.0)

Figure S121.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGIC_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 4.7e-05

Table S130.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV STAGE IVA STAGE IVC
ALL 283 51 111 2 47 6
subtype1 88 5 57 1 25 2
subtype2 75 27 20 1 4 2
subtype3 120 19 34 0 18 2

Figure S122.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 2e-05 (Fisher's exact test), Q value = 8.5e-05

Table S131.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 143 166 168 23
subtype1 55 36 71 15
subtype2 41 53 35 1
subtype3 47 77 62 7

Figure S123.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_N_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 4.7e-05

Table S132.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 227 225
subtype1 66 103
subtype2 86 19
subtype3 75 103

Figure S124.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_M_STAGE'

P value = 0.334 (Fisher's exact test), Q value = 0.48

Table S133.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 279 9
subtype1 124 2
subtype2 55 3
subtype3 100 4

Figure S125.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.638 (Fisher's exact test), Q value = 0.77

Table S134.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 367 135
subtype1 135 44
subtype2 92 38
subtype3 140 53

Figure S126.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY'

P value = 0.744 (Fisher's exact test), Q value = 0.84

Table S135.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 181 305
subtype1 68 104
subtype2 44 80
subtype3 69 121

Figure S127.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4.7e-05

Table S136.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: '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 7 357 102 36
subtype1 3 141 9 26
subtype2 2 51 77 0
subtype3 2 165 16 10

Figure S128.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_EXPOSURE'

P value = 0.905 (Fisher's exact test), Q value = 0.94

Table S137.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'RADIATION_EXPOSURE'

nPatients NO YES
ALL 422 17
subtype1 153 6
subtype2 107 5
subtype3 162 6

Figure S129.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'RADIATION_EXPOSURE'

'MIRSEQ CHIERARCHICAL' versus 'EXTRATHYROIDAL_EXTENSION'

P value = 1e-05 (Fisher's exact test), Q value = 4.7e-05

Table S138.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'EXTRATHYROIDAL_EXTENSION'

nPatients MINIMAL (T3) MODERATE/ADVANCED (T4A) NONE VERY ADVANCED (T4B)
ALL 132 18 333 1
subtype1 67 14 91 1
subtype2 15 0 107 0
subtype3 50 4 135 0

Figure S130.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'EXTRATHYROIDAL_EXTENSION'

'MIRSEQ CHIERARCHICAL' versus 'RESIDUAL_TUMOR'

P value = 0.192 (Fisher's exact test), Q value = 0.33

Table S139.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 384 52 4 30
subtype1 129 26 3 13
subtype2 101 9 0 7
subtype3 154 17 1 10

Figure S131.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'RESIDUAL_TUMOR'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER_OF_LYMPH_NODES'

P value = 4.26e-09 (Kruskal-Wallis (anova)), Q value = 3.6e-07

Table S140.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 388 3.6 (6.2)
subtype1 144 4.7 (6.4)
subtype2 85 1.7 (4.6)
subtype3 159 3.8 (6.5)

Figure S132.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'NUMBER_OF_LYMPH_NODES'

'MIRSEQ CHIERARCHICAL' versus 'MULTIFOCALITY'

P value = 0.608 (Fisher's exact test), Q value = 0.75

Table S141.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients MULTIFOCAL UNIFOCAL
ALL 226 266
subtype1 76 101
subtype2 61 67
subtype3 89 98

Figure S133.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'MULTIFOCALITY'

'MIRSEQ CHIERARCHICAL' versus 'TUMOR_SIZE'

P value = 0.0378 (Kruskal-Wallis (anova)), Q value = 0.085

Table S142.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #15: 'TUMOR_SIZE'

nPatients Mean (Std.Dev)
ALL 402 3.0 (1.6)
subtype1 139 2.8 (1.6)
subtype2 109 3.2 (1.6)
subtype3 154 3.0 (1.5)

Figure S134.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #15: 'TUMOR_SIZE'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.2 (Fisher's exact test), Q value = 0.34

Table S143.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #16: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 52 27 331
subtype1 0 26 7 131
subtype2 0 6 8 71
subtype3 1 20 12 129

Figure S135.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #16: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.798 (Fisher's exact test), Q value = 0.86

Table S144.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #17: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 38 361
subtype1 13 143
subtype2 8 76
subtype3 17 142

Figure S136.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #17: 'ETHNICITY'

Clustering Approach #9: 'MIRseq Mature CNMF subtypes'

Table S145.  Description of clustering approach #9: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 133 126 108 105
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.00375 (logrank test), Q value = 0.01

Table S146.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 471 16 0.9 - 178.3 (31.0)
subtype1 133 10 2.3 - 178.3 (31.3)
subtype2 126 5 1.1 - 169.3 (24.3)
subtype3 108 0 3.0 - 158.8 (42.5)
subtype4 104 1 0.9 - 157.2 (30.1)

Figure S137.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.000587 (Kruskal-Wallis (anova)), Q value = 0.002

Table S147.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 472 47.3 (15.8)
subtype1 133 49.3 (17.3)
subtype2 126 49.2 (15.9)
subtype3 108 41.9 (14.8)
subtype4 105 48.1 (13.2)

Figure S138.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 2e-05 (Fisher's exact test), Q value = 8.5e-05

Table S148.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV STAGE IVA STAGE IVC
ALL 267 49 104 2 42 6
subtype1 70 4 38 1 18 2
subtype2 71 26 22 1 3 2
subtype3 72 11 13 0 10 1
subtype4 54 8 31 0 11 1

Figure S139.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00076 (Fisher's exact test), Q value = 0.0025

Table S149.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 136 156 156 22
subtype1 39 28 51 14
subtype2 38 51 36 1
subtype3 26 44 33 5
subtype4 33 33 36 2

Figure S140.  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 = 1e-05 (Fisher's exact test), Q value = 4.7e-05

Table S150.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 213 212
subtype1 49 79
subtype2 83 19
subtype3 43 54
subtype4 38 60

Figure S141.  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 = 0.406 (Fisher's exact test), Q value = 0.53

Table S151.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 263 9
subtype1 91 2
subtype2 55 3
subtype3 48 3
subtype4 69 1

Figure S142.  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 = 0.701 (Fisher's exact test), Q value = 0.82

Table S152.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 345 127
subtype1 99 34
subtype2 87 39
subtype3 80 28
subtype4 79 26

Figure S143.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.906 (Fisher's exact test), Q value = 0.94

Table S153.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 172 284
subtype1 50 77
subtype2 46 75
subtype3 41 66
subtype4 35 66

Figure S144.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4.7e-05

Table S154.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: '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 7 341 92 32
subtype1 2 105 6 20
subtype2 1 54 71 0
subtype3 1 93 8 6
subtype4 3 89 7 6

Figure S145.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'MIRseq Mature CNMF subtypes' versus 'RADIATION_EXPOSURE'

P value = 0.0293 (Fisher's exact test), Q value = 0.069

Table S155.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'RADIATION_EXPOSURE'

nPatients NO YES
ALL 397 16
subtype1 113 3
subtype2 101 7
subtype3 89 6
subtype4 94 0

Figure S146.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'RADIATION_EXPOSURE'

'MIRseq Mature CNMF subtypes' versus 'EXTRATHYROIDAL_EXTENSION'

P value = 3e-05 (Fisher's exact test), Q value = 0.00012

Table S156.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'EXTRATHYROIDAL_EXTENSION'

nPatients MINIMAL (T3) MODERATE/ADVANCED (T4A) NONE VERY ADVANCED (T4B)
ALL 125 17 312 1
subtype1 46 12 68 1
subtype2 21 0 98 0
subtype3 26 3 77 0
subtype4 32 2 69 0

Figure S147.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'EXTRATHYROIDAL_EXTENSION'

'MIRseq Mature CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.426 (Fisher's exact test), Q value = 0.55

Table S157.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 360 49 4 29
subtype1 96 20 1 7
subtype2 96 9 0 8
subtype3 85 8 2 9
subtype4 83 12 1 5

Figure S148.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'RESIDUAL_TUMOR'

'MIRseq Mature CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 2.8e-07 (Kruskal-Wallis (anova)), Q value = 6.8e-06

Table S158.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 363 3.7 (6.2)
subtype1 110 4.6 (6.3)
subtype2 79 1.4 (3.2)
subtype3 84 3.6 (5.0)
subtype4 90 4.7 (8.1)

Figure S149.  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 = 0.348 (Fisher's exact test), Q value = 0.49

Table S159.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients MULTIFOCAL UNIFOCAL
ALL 215 249
subtype1 62 70
subtype2 59 64
subtype3 41 64
subtype4 53 51

Figure S150.  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 = 0.109 (Kruskal-Wallis (anova)), Q value = 0.21

Table S160.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #15: 'TUMOR_SIZE'

nPatients Mean (Std.Dev)
ALL 378 3.0 (1.6)
subtype1 98 2.8 (1.7)
subtype2 103 3.2 (1.6)
subtype3 91 3.1 (1.5)
subtype4 86 2.8 (1.6)

Figure S151.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #15: 'TUMOR_SIZE'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.0445 (Fisher's exact test), Q value = 0.097

Table S161.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #16: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 49 26 313
subtype1 0 19 4 99
subtype2 0 7 10 69
subtype3 0 8 4 81
subtype4 1 15 8 64

Figure S152.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #16: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.715 (Fisher's exact test), Q value = 0.82

Table S162.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #17: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 35 343
subtype1 9 111
subtype2 7 79
subtype3 11 82
subtype4 8 71

Figure S153.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #17: 'ETHNICITY'

Clustering Approach #10: 'MIRseq Mature cHierClus subtypes'

Table S163.  Description of clustering approach #10: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 164 122 186
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.00358 (logrank test), Q value = 0.01

Table S164.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 471 16 0.9 - 178.3 (31.0)
subtype1 164 10 2.3 - 178.3 (31.4)
subtype2 122 5 1.1 - 166.6 (22.9)
subtype3 185 1 0.9 - 169.3 (33.2)

Figure S154.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.000305 (Kruskal-Wallis (anova)), Q value = 0.0011

Table S165.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 472 47.3 (15.8)
subtype1 164 50.0 (16.5)
subtype2 122 49.4 (15.9)
subtype3 186 43.5 (14.2)

Figure S155.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 4.7e-05

Table S166.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV STAGE IVA STAGE IVC
ALL 267 49 104 2 42 6
subtype1 77 4 58 1 22 2
subtype2 67 27 21 1 3 2
subtype3 123 18 25 0 17 2

Figure S156.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 6e-05 (Fisher's exact test), Q value = 0.00023

Table S167.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 136 156 156 22
subtype1 47 33 67 15
subtype2 34 53 34 1
subtype3 55 70 55 6

Figure S157.  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 = 1e-05 (Fisher's exact test), Q value = 4.7e-05

Table S168.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 213 212
subtype1 58 97
subtype2 79 18
subtype3 76 97

Figure S158.  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 = 0.333 (Fisher's exact test), Q value = 0.48

Table S169.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 263 9
subtype1 113 2
subtype2 49 3
subtype3 101 4

Figure S159.  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 = 0.328 (Fisher's exact test), Q value = 0.48

Table S170.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 345 127
subtype1 124 40
subtype2 83 39
subtype3 138 48

Figure S160.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.747 (Fisher's exact test), Q value = 0.84

Table S171.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 172 284
subtype1 60 97
subtype2 40 75
subtype3 72 112

Figure S161.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4.7e-05

Table S172.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: '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 7 341 92 32
subtype1 3 129 7 25
subtype2 2 51 69 0
subtype3 2 161 16 7

Figure S162.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'MIRseq Mature cHierClus subtypes' versus 'RADIATION_EXPOSURE'

P value = 0.729 (Fisher's exact test), Q value = 0.83

Table S173.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'RADIATION_EXPOSURE'

nPatients NO YES
ALL 397 16
subtype1 137 6
subtype2 100 5
subtype3 160 5

Figure S163.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'RADIATION_EXPOSURE'

'MIRseq Mature cHierClus subtypes' versus 'EXTRATHYROIDAL_EXTENSION'

P value = 1e-05 (Fisher's exact test), Q value = 4.7e-05

Table S174.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'EXTRATHYROIDAL_EXTENSION'

nPatients MINIMAL (T3) MODERATE/ADVANCED (T4A) NONE VERY ADVANCED (T4B)
ALL 125 17 312 1
subtype1 63 14 80 1
subtype2 16 0 99 0
subtype3 46 3 133 0

Figure S164.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'EXTRATHYROIDAL_EXTENSION'

'MIRseq Mature cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.202 (Fisher's exact test), Q value = 0.34

Table S175.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 360 49 4 29
subtype1 116 25 2 11
subtype2 93 9 0 8
subtype3 151 15 2 10

Figure S165.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'RESIDUAL_TUMOR'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 9.07e-08 (Kruskal-Wallis (anova)), Q value = 2.6e-06

Table S176.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 363 3.7 (6.2)
subtype1 134 4.4 (5.9)
subtype2 76 1.4 (3.3)
subtype3 153 4.2 (7.2)

Figure S166.  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 = 0.648 (Fisher's exact test), Q value = 0.78

Table S177.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients MULTIFOCAL UNIFOCAL
ALL 215 249
subtype1 71 92
subtype2 58 61
subtype3 86 96

Figure S167.  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 = 0.0221 (Kruskal-Wallis (anova)), Q value = 0.054

Table S178.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #15: 'TUMOR_SIZE'

nPatients Mean (Std.Dev)
ALL 378 3.0 (1.6)
subtype1 125 2.8 (1.6)
subtype2 102 3.2 (1.5)
subtype3 151 2.9 (1.6)

Figure S168.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #15: 'TUMOR_SIZE'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.134 (Fisher's exact test), Q value = 0.25

Table S179.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #16: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 49 26 313
subtype1 0 24 5 123
subtype2 0 8 9 65
subtype3 1 17 12 125

Figure S169.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #16: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.523 (Fisher's exact test), Q value = 0.66

Table S180.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #17: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 35 343
subtype1 11 135
subtype2 7 76
subtype3 17 132

Figure S170.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #17: 'ETHNICITY'

Methods & Data
Input
  • Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/THCA-TP/22542946/THCA-TP.mergedcluster.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/THCA-TP/22507188/THCA-TP.merged_data.txt

  • Number of patients = 503

  • Number of clustering approaches = 10

  • Number of selected clinical features = 17

  • 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

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.

References
[1] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)
[3] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)
[4] 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)
[5] 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)