Correlation between aggregated molecular cancer subtypes and selected clinical features

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 501 patients, 84 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 'YEARS_TO_BIRTH', 'NEOPLASM_DISEASESTAGE', 'MULTIFOCALITY', and 'TUMOR_SIZE'.
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'NEOPLASM_DISEASESTAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'HISTOLOGICAL_TYPE', 'EXTRATHYROIDAL_EXTENSION', and 'NUMBER_OF_LYMPH_NODES'.
CNMF clustering analysis on RPPA data identified 3 subtypes that correlate to 'YEARS_TO_BIRTH', 'NEOPLASM_DISEASESTAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'HISTOLOGICAL_TYPE', 'RADIATIONS_RADIATION_REGIMENINDICATION', 'EXTRATHYROIDAL_EXTENSION', and 'MULTIFOCALITY'.
Consensus hierarchical clustering analysis on RPPA data identified 7 subtypes that correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'NEOPLASM_DISEASESTAGE', '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 'NEOPLASM_DISEASESTAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'HISTOLOGICAL_TYPE', 'RADIATIONS_RADIATION_REGIMENINDICATION', 'EXTRATHYROIDAL_EXTENSION', 'NUMBER_OF_LYMPH_NODES', 'TUMOR_SIZE', and 'ETHNICITY'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 5 subtypes that correlate to 'YEARS_TO_BIRTH', 'NEOPLASM_DISEASESTAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'HISTOLOGICAL_TYPE', 'EXTRATHYROIDAL_EXTENSION', 'COMPLETENESS_OF_RESECTION', 'NUMBER_OF_LYMPH_NODES', 'TUMOR_SIZE', 'RACE', and 'ETHNICITY'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'Time to Death', 'NEOPLASM_DISEASESTAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'HISTOLOGICAL_TYPE', 'EXTRATHYROIDAL_EXTENSION', 'NUMBER_OF_LYMPH_NODES', and 'TUMOR_SIZE'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'NEOPLASM_DISEASESTAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'HISTOLOGICAL_TYPE', 'RADIATIONS_RADIATION_REGIMENINDICATION', 'EXTRATHYROIDAL_EXTENSION', 'NUMBER_OF_LYMPH_NODES', and 'TUMOR_SIZE'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'NEOPLASM_DISEASESTAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'HISTOLOGICAL_TYPE', 'RADIATIONS_RADIATION_REGIMENINDICATION', 'EXTRATHYROIDAL_EXTENSION', 'COMPLETENESS_OF_RESECTION', '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', 'NEOPLASM_DISEASESTAGE', '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, 84 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	0.396 (0.539)	0.247 (0.366)	0.586 (0.73)	0.00158 (0.00479)	0.526 (0.677)	0.677 (0.788)	0.0239 (0.0534)	0.0135 (0.0333)	0.0198 (0.0472)	0.0037 (0.0103)
YEARS TO BIRTH	Kruskal-Wallis (anova)	6.72e-08 (1.71e-06)	0.171 (0.285)	0.00134 (0.00423)	0.000999 (0.00333)	0.157 (0.268)	0.00242 (0.00697)	0.0512 (0.102)	0.000963 (0.00327)	0.0201 (0.0472)	0.00018 (0.000665)
NEOPLASM DISEASESTAGE	Fisher's exact test	2e-05 (8.95e-05)	3e-05 (0.000127)	0.00167 (0.00498)	0.00104 (0.0034)	1e-05 (4.59e-05)	1e-05 (4.59e-05)	1e-05 (4.59e-05)	1e-05 (4.59e-05)	1e-05 (4.59e-05)	1e-05 (4.59e-05)
PATHOLOGY T STAGE	Fisher's exact test	0.108 (0.195)	0.00188 (0.00551)	0.00356 (0.0101)	0.00013 (0.000491)	1e-05 (4.59e-05)	1e-05 (4.59e-05)	7e-05 (0.000277)	5e-05 (0.000202)	8e-05 (0.000309)	3e-05 (0.000127)
PATHOLOGY N STAGE	Fisher's exact test	0.908 (0.965)	1e-05 (4.59e-05)	0.0309 (0.0663)	0.00882 (0.0231)	1e-05 (4.59e-05)	1e-05 (4.59e-05)	1e-05 (4.59e-05)	1e-05 (4.59e-05)	0.00083 (0.00288)	1e-05 (4.59e-05)
PATHOLOGY M STAGE	Fisher's exact test	0.6 (0.739)	0.639 (0.765)	0.524 (0.677)	0.757 (0.847)	0.755 (0.847)	0.393 (0.539)	0.461 (0.603)	0.335 (0.479)	0.436 (0.575)	0.369 (0.519)
GENDER	Fisher's exact test	0.387 (0.534)	0.949 (0.978)	0.656 (0.769)	0.942 (0.978)	0.878 (0.958)	0.169 (0.284)	0.883 (0.958)	0.646 (0.765)	0.956 (0.978)	0.184 (0.304)
HISTOLOGICAL TYPE	Fisher's exact test	0.702 (0.8)	1e-05 (4.59e-05)	4e-05 (0.000166)	1e-05 (4.59e-05)	1e-05 (4.59e-05)	1e-05 (4.59e-05)	1e-05 (4.59e-05)	1e-05 (4.59e-05)	1e-05 (4.59e-05)	1e-05 (4.59e-05)
RADIATIONS RADIATION REGIMENINDICATION	Fisher's exact test	0.62 (0.753)	0.117 (0.207)	0.0104 (0.0261)	0.19 (0.307)	0.00568 (0.0153)	0.0733 (0.142)	0.193 (0.307)	0.00952 (0.0245)	0.00019 (0.000687)	0.105 (0.191)
RADIATION EXPOSURE	Fisher's exact test	0.576 (0.73)	0.648 (0.765)	1 (1.00)	0.236 (0.358)	0.984 (0.995)	0.254 (0.373)	0.951 (0.978)	0.906 (0.965)	0.431 (0.575)	0.854 (0.943)
EXTRATHYROIDAL EXTENSION	Fisher's exact test	0.0564 (0.11)	1e-05 (4.59e-05)	0.00073 (0.00259)	1e-05 (4.59e-05)	1e-05 (4.59e-05)	1e-05 (4.59e-05)	1e-05 (4.59e-05)	1e-05 (4.59e-05)	1e-05 (4.59e-05)	1e-05 (4.59e-05)
COMPLETENESS OF RESECTION	Fisher's exact test	0.0834 (0.157)	0.238 (0.358)	0.277 (0.399)	0.0938 (0.173)	0.156 (0.267)	0.0209 (0.0481)	0.19 (0.307)	0.203 (0.316)	0.032 (0.0673)	0.0516 (0.102)
NUMBER OF LYMPH NODES	Kruskal-Wallis (anova)	0.617 (0.753)	2.12e-08 (7.2e-07)	0.431 (0.575)	0.375 (0.523)	8.57e-09 (3.75e-07)	8.83e-09 (3.75e-07)	6.95e-09 (3.75e-07)	2.21e-09 (3.75e-07)	0.00995 (0.0252)	7.02e-08 (1.71e-06)
MULTIFOCALITY	Fisher's exact test	0.0289 (0.0631)	0.942 (0.978)	0.0143 (0.0348)	0.117 (0.207)	0.236 (0.358)	0.225 (0.348)	0.885 (0.958)	0.589 (0.73)	0.905 (0.965)	0.583 (0.73)
TUMOR SIZE	Kruskal-Wallis (anova)	0.00509 (0.014)	0.57 (0.729)	0.711 (0.806)	0.0203 (0.0472)	0.0384 (0.0786)	0.00154 (0.00474)	0.0233 (0.0527)	0.0352 (0.0729)	0.128 (0.224)	0.0271 (0.0599)
RACE	Fisher's exact test	0.2 (0.314)	0.645 (0.765)	0.0808 (0.154)	0.277 (0.399)	0.701 (0.8)	0.00114 (0.00366)	0.357 (0.505)	0.192 (0.307)	0.0406 (0.0822)	0.0874 (0.163)
ETHNICITY	Fisher's exact test	0.96 (0.978)	0.433 (0.575)	0.146 (0.253)	0.937 (0.978)	0.00686 (0.0182)	0.0312 (0.0663)	0.698 (0.8)	0.777 (0.864)	1 (1.00)	0.247 (0.366)

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	56	355	88

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

P value = 0.396 (logrank test), Q value = 0.54

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	497	14	0.1 - 169.3 (20.4)
subtype1	55	3	1.0 - 136.0 (22.3)
subtype2	354	10	0.1 - 169.3 (21.0)
subtype3	88	1	0.3 - 139.9 (16.7)

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 = 6.72e-08 (Kruskal-Wallis (anova)), Q value = 1.7e-06

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

	nPatients	Mean (Std.Dev)
ALL	499	47.2 (15.8)
subtype1	56	58.7 (14.7)
subtype2	355	45.3 (15.6)
subtype3	88	47.4 (14.2)

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 'NEOPLASM_DISEASESTAGE'

P value = 2e-05 (Fisher's exact test), Q value = 8.9e-05

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV	STAGE IVA	STAGE IVC
ALL	282	52	109	2	46	6
subtype1	14	14	16	1	10	1
subtype2	215	32	77	0	26	4
subtype3	53	6	16	1	10	1

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.108 (Fisher's exact test), Q value = 0.2

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

nPatients	T1	T2	T3	T4
ALL	141	166	167	23
subtype1	8	21	22	5
subtype2	103	114	121	15
subtype3	30	31	24	3

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.908 (Fisher's exact test), Q value = 0.96

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

nPatients	0	1
ALL	224	225
subtype1	24	23
subtype2	157	162
subtype3	43	40

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 = 0.6 (Fisher's exact test), Q value = 0.74

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

nPatients	0	1
ALL	277	9
subtype1	24	1
subtype2	204	6
subtype3	49	2

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

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

nPatients	FEMALE	MALE
ALL	366	133
subtype1	37	19
subtype2	265	90
subtype3	64	24

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

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.702 (Fisher's exact test), Q value = 0.8

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

nPatients	OTHER SPECIFY	THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL	THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED)	THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
ALL	9	356	98	36
subtype1	0	40	12	4
subtype2	8	255	64	28
subtype3	1	61	22	4

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

'Copy Number Ratio CNMF subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.62 (Fisher's exact test), Q value = 0.75

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

nPatients	NO	YES
ALL	14	485
subtype1	2	54
subtype2	11	344
subtype3	1	87

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

'Copy Number Ratio CNMF subtypes' versus 'RADIATION_EXPOSURE'

P value = 0.576 (Fisher's exact test), Q value = 0.73

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

nPatients	NO	YES
ALL	421	17
subtype1	49	3
subtype2	296	12
subtype3	76	2

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 = 0.0564 (Fisher's exact test), Q value = 0.11

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	15	4	34	1
subtype2	100	13	231	0
subtype3	17	1	65	0

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

'Copy Number Ratio CNMF subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.0834 (Fisher's exact test), Q value = 0.16

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

nPatients	R0	R1	R2	RX
ALL	381	52	4	30
subtype1	38	9	0	6
subtype2	279	36	3	15
subtype3	64	7	1	9

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

'Copy Number Ratio CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.617 (Kruskal-Wallis (anova)), Q value = 0.75

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

	nPatients	Mean (Std.Dev)
ALL	383	3.7 (6.2)
subtype1	42	2.8 (4.9)
subtype2	272	3.8 (6.6)
subtype3	69	3.7 (5.5)

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 = 0.0289 (Fisher's exact test), Q value = 0.063

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

nPatients	MULTIFOCAL	UNIFOCAL
ALL	225	264
subtype1	21	35
subtype2	155	194
subtype3	49	35

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 = 0.00509 (Kruskal-Wallis (anova)), Q value = 0.014

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	50	3.6 (1.6)
subtype2	281	2.9 (1.6)
subtype3	70	2.9 (1.5)

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 = 0.2 (Fisher's exact test), Q value = 0.31

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	325
subtype1	0	1	4	39
subtype2	1	40	17	232
subtype3	0	11	6	54

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 = 0.96 (Fisher's exact test), Q value = 0.98

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	358
subtype1	4	37
subtype2	27	259
subtype3	7	62

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
Number of samples	276	155	70

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.247 (logrank test), Q value = 0.37

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	499	14	0.1 - 169.3 (20.2)
subtype1	276	7	0.1 - 169.3 (23.3)
subtype2	153	3	0.2 - 132.4 (17.2)
subtype3	70	4	0.1 - 147.4 (17.9)

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 = 0.171 (Kruskal-Wallis (anova)), Q value = 0.29

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

	nPatients	Mean (Std.Dev)
ALL	501	47.2 (15.8)
subtype1	276	47.0 (15.5)
subtype2	155	48.6 (15.2)
subtype3	70	45.0 (17.9)

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

'METHLYATION CNMF' versus 'NEOPLASM_DISEASESTAGE'

P value = 3e-05 (Fisher's exact test), Q value = 0.00013

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV	STAGE IVA	STAGE IVC
ALL	283	53	109	2	46	6
subtype1	148	20	67	0	36	4
subtype2	89	31	27	1	4	2
subtype3	46	2	15	1	6	0

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

'METHLYATION CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.00188 (Fisher's exact test), Q value = 0.0055

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

nPatients	T1	T2	T3	T4
ALL	142	167	167	23
subtype1	64	89	102	20
subtype2	53	58	43	1
subtype3	25	20	22	2

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 = 4.6e-05

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

nPatients	0	1
ALL	226	225
subtype1	96	159
subtype2	100	29
subtype3	30	37

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 = 0.639 (Fisher's exact test), Q value = 0.77

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

nPatients	0	1
ALL	278	9
subtype1	164	6
subtype2	73	3
subtype3	41	0

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.949 (Fisher's exact test), Q value = 0.98

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

nPatients	FEMALE	MALE
ALL	367	134
subtype1	203	73
subtype2	112	43
subtype3	52	18

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

'METHLYATION CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4.6e-05

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

nPatients	OTHER SPECIFY	THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL	THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED)	THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
ALL	9	356	100	36
subtype1	5	225	17	29
subtype2	2	75	78	0
subtype3	2	56	5	7

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

'METHLYATION CNMF' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.117 (Fisher's exact test), Q value = 0.21

Table S28. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	14	487
subtype1	11	265
subtype2	1	154
subtype3	2	68

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

'METHLYATION CNMF' versus 'RADIATION_EXPOSURE'

P value = 0.648 (Fisher's exact test), Q value = 0.77

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

nPatients	NO	YES
ALL	422	17
subtype1	238	8
subtype2	125	6
subtype3	59	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 = 4.6e-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	132	18	332	1
subtype1	93	17	160	0
subtype2	24	0	122	0
subtype3	15	1	50	1

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

'METHLYATION CNMF' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.238 (Fisher's exact test), Q value = 0.36

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

nPatients	R0	R1	R2	RX
ALL	383	52	4	30
subtype1	207	36	4	18
subtype2	123	9	0	8
subtype3	53	7	0	4

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

'METHLYATION CNMF' versus 'NUMBER_OF_LYMPH_NODES'

P value = 2.12e-08 (Kruskal-Wallis (anova)), Q value = 7.2e-07

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

	nPatients	Mean (Std.Dev)
ALL	384	3.7 (6.2)
subtype1	227	4.5 (6.6)
subtype2	100	1.7 (4.3)
subtype3	57	4.0 (6.8)

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 = 0.942 (Fisher's exact test), Q value = 0.98

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

nPatients	MULTIFOCAL	UNIFOCAL
ALL	226	265
subtype1	125	146
subtype2	71	81
subtype3	30	38

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

'METHLYATION CNMF' versus 'TUMOR_SIZE'

P value = 0.57 (Kruskal-Wallis (anova)), Q value = 0.73

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

	nPatients	Mean (Std.Dev)
ALL	402	3.0 (1.6)
subtype1	224	3.0 (1.6)
subtype2	123	3.1 (1.6)
subtype3	55	2.8 (1.4)

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

'METHLYATION CNMF' versus 'RACE'

P value = 0.645 (Fisher's exact test), Q value = 0.77

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	326
subtype1	1	30	17	188
subtype2	0	14	9	85
subtype3	0	8	1	53

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

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.433 (Fisher's exact test), Q value = 0.57

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	38	359
subtype1	24	207
subtype2	7	99
subtype3	7	53

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
Number of samples	61	79	82

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.586 (logrank test), Q value = 0.73

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	222	13	0.1 - 158.8 (22.1)
subtype1	61	4	1.2 - 158.8 (16.8)
subtype2	79	4	0.2 - 147.4 (27.2)
subtype3	82	5	0.1 - 147.8 (23.2)

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 = 0.00134 (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	61	51.6 (15.3)
subtype2	79	50.7 (16.0)
subtype3	82	42.9 (17.4)

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

'RPPA CNMF subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.00167 (Fisher's exact test), Q value = 0.005

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IVA	STAGE IVC
ALL	117	33	46	20	4
subtype1	31	11	14	2	1
subtype2	31	13	21	14	0
subtype3	55	9	11	4	3

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

'RPPA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00356 (Fisher's exact test), Q value = 0.01

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

nPatients	T1	T2	T3	T4
ALL	51	84	75	11
subtype1	22	23	15	0
subtype2	12	25	34	8
subtype3	17	36	26	3

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 = 0.0309 (Fisher's exact test), Q value = 0.066

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

nPatients	0	1
ALL	98	95
subtype1	34	18
subtype2	29	41
subtype3	35	36

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 = 0.524 (Fisher's exact test), Q value = 0.68

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

nPatients	0	1
ALL	118	5
subtype1	38	1
subtype2	43	1
subtype3	37	3

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 = 0.656 (Fisher's exact test), Q value = 0.77

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

nPatients	FEMALE	MALE
ALL	154	68
subtype1	43	18
subtype2	57	22
subtype3	54	28

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

'RPPA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 4e-05 (Fisher's exact test), Q value = 0.00017

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

nPatients	OTHER SPECIFY	THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL	THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED)	THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
ALL	2	158	52	10
subtype1	0	35	25	1
subtype2	1	55	14	9
subtype3	1	68	13	0

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

'RPPA CNMF subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.0104 (Fisher's exact test), Q value = 0.026

Table S46. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	13	209
subtype1	0	61
subtype2	9	70
subtype3	4	78

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

'RPPA CNMF subtypes' versus 'RADIATION_EXPOSURE'

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

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

nPatients	NO	YES
ALL	184	11
subtype1	52	3
subtype2	67	4
subtype3	65	4

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 = 0.00073 (Fisher's exact test), Q value = 0.0026

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	0	48
subtype2	28	8	41
subtype3	15	2	60

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

'RPPA CNMF subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.277 (Fisher's exact test), Q value = 0.4

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

nPatients	R0	R1	R2	RX
ALL	168	23	2	14
subtype1	48	6	1	5
subtype2	58	13	0	4
subtype3	62	4	1	5

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

'RPPA CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.431 (Kruskal-Wallis (anova)), Q value = 0.57

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	43	3.8 (7.5)
subtype2	63	3.2 (4.6)
subtype3	62	3.5 (5.8)

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 = 0.0143 (Fisher's exact test), Q value = 0.035

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

nPatients	MULTIFOCAL	UNIFOCAL
ALL	101	114
subtype1	35	23
subtype2	27	50
subtype3	39	41

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 = 0.711 (Kruskal-Wallis (anova)), Q value = 0.81

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	52	3.2 (1.6)
subtype2	66	3.4 (1.5)
subtype3	73	3.2 (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 = 0.0808 (Fisher's exact test), Q value = 0.15

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

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	16	13	147
subtype1	3	7	34
subtype2	4	4	59
subtype3	9	2	54

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

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.146 (Fisher's exact test), Q value = 0.25

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	1	46
subtype2	8	59
subtype3	7	63

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 = 0.00158 (logrank test), Q value = 0.0048

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	222	13	0.1 - 158.8 (22.1)
subtype1	47	4	1.2 - 158.8 (17.9)
subtype2	28	1	1.4 - 66.8 (17.3)
subtype3	33	0	0.2 - 139.0 (30.2)
subtype4	29	2	0.3 - 155.5 (21.0)
subtype5	49	1	0.1 - 147.8 (27.6)
subtype6	27	1	3.4 - 81.3 (24.7)
subtype7	9	4	14.0 - 147.4 (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 = 0.000999 (Kruskal-Wallis (anova)), Q value = 0.0033

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	52.5 (16.1)
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 'NEOPLASM_DISEASESTAGE'

P value = 0.00104 (Fisher's exact test), Q value = 0.0034

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

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: 'NEOPLASM_DISEASESTAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00013 (Fisher's exact test), Q value = 0.00049

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

nPatients	T1	T2	T3	T4
ALL	51	84	75	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	10	10	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 = 0.00882 (Fisher's exact test), Q value = 0.023

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 = 0.757 (Fisher's exact test), Q value = 0.85

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 = 0.98

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 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4.6e-05

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

nPatients	OTHER SPECIFY	THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL	THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED)	THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
ALL	2	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 S59. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'RPPA cHierClus subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.19 (Fisher's exact test), Q value = 0.31

Table S64. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	13	209
subtype1	0	47
subtype2	2	26
subtype3	4	29
subtype4	1	28
subtype5	3	46
subtype6	3	24
subtype7	0	9

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

'RPPA cHierClus subtypes' versus 'RADIATION_EXPOSURE'

P value = 0.236 (Fisher's exact test), Q value = 0.36

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 = 4.6e-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 'COMPLETENESS_OF_RESECTION'

P value = 0.0938 (Fisher's exact test), Q value = 0.17

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

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: 'COMPLETENESS_OF_RESECTION'

'RPPA cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.375 (Kruskal-Wallis (anova)), Q value = 0.52

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	31	2.9 (6.6)
subtype2	25	3.9 (7.0)
subtype3	26	3.3 (3.9)
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 = 0.117 (Fisher's exact test), Q value = 0.21

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

nPatients	MULTIFOCAL	UNIFOCAL
ALL	101	114
subtype1	28	16
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 = 0.047

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 = 0.277 (Fisher's exact test), Q value = 0.4

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

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	16	13	147
subtype1	1	6	24
subtype2	2	1	19
subtype3	3	0	28
subtype4	2	1	20
subtype5	7	2	32
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 = 0.98

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	167	156	59	117

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.526 (logrank test), Q value = 0.68

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	497	14	0.1 - 169.3 (20.2)
subtype1	167	7	0.1 - 155.5 (22.3)
subtype2	155	3	0.2 - 132.4 (17.2)
subtype3	59	2	0.1 - 157.2 (16.3)
subtype4	116	2	0.2 - 169.3 (27.3)

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 = 0.157 (Kruskal-Wallis (anova)), Q value = 0.27

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

	nPatients	Mean (Std.Dev)
ALL	499	47.2 (15.8)
subtype1	167	48.3 (15.9)
subtype2	156	48.6 (15.3)
subtype3	59	45.3 (18.9)
subtype4	117	44.8 (14.5)

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

'RNAseq CNMF subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 4.6e-05

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV	STAGE IVA	STAGE IVC
ALL	282	53	108	2	46	6
subtype1	84	7	45	1	28	2
subtype2	90	30	27	1	5	2
subtype3	41	1	13	0	4	0
subtype4	67	15	23	0	9	2

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

'RNAseq CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 4.6e-05

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

nPatients	T1	T2	T3	T4
ALL	141	166	167	23
subtype1	27	47	77	15
subtype2	53	57	44	2
subtype3	27	14	15	2
subtype4	34	48	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 = 4.6e-05

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

nPatients	0	1
ALL	225	224
subtype1	51	101
subtype2	101	29
subtype3	23	36
subtype4	50	58

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 = 0.755 (Fisher's exact test), Q value = 0.85

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

nPatients	0	1
ALL	277	9
subtype1	94	3
subtype2	70	3
subtype3	41	0
subtype4	72	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 = 0.878 (Fisher's exact test), Q value = 0.96

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

nPatients	FEMALE	MALE
ALL	365	134
subtype1	120	47
subtype2	113	43
subtype3	43	16
subtype4	89	28

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

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4.6e-05

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

nPatients	OTHER SPECIFY	THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL	THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED)	THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
ALL	9	355	100	35
subtype1	4	134	3	26
subtype2	2	71	82	1
subtype3	1	50	3	5
subtype4	2	100	12	3

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

'RNAseq CNMF subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.00568 (Fisher's exact test), Q value = 0.015

Table S82. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	14	485
subtype1	11	156
subtype2	1	155
subtype3	0	59
subtype4	2	115

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

'RNAseq CNMF subtypes' versus 'RADIATION_EXPOSURE'

P value = 0.984 (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	420	17
subtype1	143	5
subtype2	129	6
subtype3	49	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 = 4.6e-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	132	18	330	1
subtype1	67	14	81	1
subtype2	23	1	123	0
subtype3	13	2	41	0
subtype4	29	1	85	0

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

'RNAseq CNMF subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.156 (Fisher's exact test), Q value = 0.27

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

nPatients	R0	R1	R2	RX
ALL	381	52	4	30
subtype1	126	26	2	9
subtype2	124	9	0	8
subtype3	41	7	0	5
subtype4	90	10	2	8

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

'RNAseq CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 8.57e-09 (Kruskal-Wallis (anova)), Q value = 3.8e-07

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

	nPatients	Mean (Std.Dev)
ALL	382	3.7 (6.2)
subtype1	136	4.4 (6.6)
subtype2	104	1.7 (4.4)
subtype3	52	5.2 (7.4)
subtype4	90	4.0 (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 = 0.236 (Fisher's exact test), Q value = 0.36

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

nPatients	MULTIFOCAL	UNIFOCAL
ALL	225	264
subtype1	66	99
subtype2	74	79
subtype3	26	31
subtype4	59	55

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 = 0.0384 (Kruskal-Wallis (anova)), Q value = 0.079

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

	nPatients	Mean (Std.Dev)
ALL	401	3.0 (1.6)
subtype1	142	3.2 (1.6)
subtype2	123	3.1 (1.6)
subtype3	43	2.5 (1.7)
subtype4	93	2.8 (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 = 0.701 (Fisher's exact test), Q value = 0.8

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	326
subtype1	1	17	11	118
subtype2	0	11	9	88
subtype3	0	7	1	45
subtype4	0	16	6	75

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

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.00686 (Fisher's exact test), Q value = 0.018

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	38	357
subtype1	23	120
subtype2	9	98
subtype3	3	49
subtype4	3	90

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	135	86	96	77

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.677 (logrank test), Q value = 0.79

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	497	14	0.1 - 169.3 (20.2)
subtype1	105	5	1.4 - 157.2 (22.9)
subtype2	134	4	0.2 - 132.4 (16.6)
subtype3	86	1	0.1 - 130.7 (17.8)
subtype4	95	2	0.2 - 169.3 (27.9)
subtype5	77	2	0.1 - 147.8 (23.5)

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 = 0.00242 (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	499	47.2 (15.8)
subtype1	105	51.0 (16.1)
subtype2	135	49.6 (16.0)
subtype3	86	44.5 (15.1)
subtype4	96	45.3 (14.6)
subtype5	77	43.4 (15.9)

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

'RNAseq cHierClus subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 4.6e-05

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV	STAGE IVA	STAGE IVC
ALL	282	53	108	2	46	6
subtype1	46	2	39	1	16	1
subtype2	76	30	21	1	4	2
subtype3	57	3	18	0	8	0
subtype4	54	12	19	0	8	2
subtype5	49	6	11	0	10	1

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

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 4.6e-05

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

nPatients	T1	T2	T3	T4
ALL	141	166	167	23
subtype1	17	22	56	10
subtype2	43	55	35	2
subtype3	38	22	23	1
subtype4	26	41	25	4
subtype5	17	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 = 4.6e-05

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

nPatients	0	1
ALL	225	224
subtype1	29	68
subtype2	90	19
subtype3	38	45
subtype4	43	45
subtype5	25	47

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 = 0.393 (Fisher's exact test), Q value = 0.54

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

nPatients	0	1
ALL	277	9
subtype1	56	1
subtype2	56	3
subtype3	60	0
subtype4	58	3
subtype5	47	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 = 0.169 (Fisher's exact test), Q value = 0.28

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

nPatients	FEMALE	MALE
ALL	365	134
subtype1	84	21
subtype2	96	39
subtype3	61	25
subtype4	74	22
subtype5	50	27

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

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4.6e-05

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

nPatients	OTHER SPECIFY	THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL	THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED)	THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
ALL	9	355	100	35
subtype1	1	79	2	23
subtype2	2	57	76	0
subtype3	2	68	10	6
subtype4	0	83	11	2
subtype5	4	68	1	4

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

'RNAseq cHierClus subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.0733 (Fisher's exact test), Q value = 0.14

Table S100. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	14	485
subtype1	5	100
subtype2	1	134
subtype3	1	85
subtype4	2	94
subtype5	5	72

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

'RNAseq cHierClus subtypes' versus 'RADIATION_EXPOSURE'

P value = 0.254 (Fisher's exact test), Q value = 0.37

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

nPatients	NO	YES
ALL	420	17
subtype1	95	3
subtype2	111	5
subtype3	66	6
subtype4	85	1
subtype5	63	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 = 4.6e-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	132	18	330	1
subtype1	53	10	38	1
subtype2	15	1	110	0
subtype3	20	1	62	0
subtype4	22	1	71	0
subtype5	22	5	49	0

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

'RNAseq cHierClus subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.0209 (Fisher's exact test), Q value = 0.048

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

nPatients	R0	R1	R2	RX
ALL	381	52	4	30
subtype1	70	23	2	6
subtype2	103	10	0	8
subtype3	70	4	0	6
subtype4	76	8	2	6
subtype5	62	7	0	4

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

'RNAseq cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 8.83e-09 (Kruskal-Wallis (anova)), Q value = 3.8e-07

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

	nPatients	Mean (Std.Dev)
ALL	382	3.7 (6.2)
subtype1	92	4.7 (6.6)
subtype2	86	1.4 (3.5)
subtype3	71	4.8 (8.5)
subtype4	72	3.8 (6.3)
subtype5	61	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 = 0.225 (Fisher's exact test), Q value = 0.35

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

nPatients	MULTIFOCAL	UNIFOCAL
ALL	225	264
subtype1	39	66
subtype2	64	68
subtype3	43	41
subtype4	47	46
subtype5	32	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 = 0.00154 (Kruskal-Wallis (anova)), Q value = 0.0047

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

	nPatients	Mean (Std.Dev)
ALL	401	3.0 (1.6)
subtype1	93	3.1 (1.5)
subtype2	111	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 = 0.00114 (Fisher's exact test), Q value = 0.0037

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	326
subtype1	1	4	11	77
subtype2	0	7	8	74
subtype3	0	17	1	60
subtype4	0	12	6	59
subtype5	0	11	1	56

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

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.0312 (Fisher's exact test), Q value = 0.066

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	38	357
subtype1	15	77
subtype2	9	81
subtype3	3	71
subtype4	3	71
subtype5	8	57

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
Number of samples	182	152	166

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.0239 (logrank test), Q value = 0.053

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	498	14	0.1 - 169.3 (20.3)
subtype1	182	10	0.1 - 158.8 (20.1)
subtype2	151	3	0.2 - 132.4 (17.1)
subtype3	165	1	0.1 - 169.3 (24.3)

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 = 0.0512 (Kruskal-Wallis (anova)), Q value = 0.1

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

	nPatients	Mean (Std.Dev)
ALL	500	47.3 (15.8)
subtype1	182	49.4 (16.0)
subtype2	152	47.0 (16.2)
subtype3	166	45.1 (14.9)

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

'MIRSEQ CNMF' versus 'NEOPLASM_DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 4.6e-05

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV	STAGE IVA	STAGE IVC
ALL	282	53	109	2	46	6
subtype1	90	6	56	1	26	2
subtype2	93	29	21	1	5	2
subtype3	99	18	32	0	15	2

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

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 7e-05 (Fisher's exact test), Q value = 0.00028

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

nPatients	T1	T2	T3	T4
ALL	142	167	166	23
subtype1	56	39	70	15
subtype2	43	68	39	2
subtype3	43	60	57	6

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 = 4.6e-05

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

nPatients	0	1
ALL	226	224
subtype1	65	108
subtype2	96	28
subtype3	65	88

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 = 0.461 (Fisher's exact test), Q value = 0.6

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

nPatients	0	1
ALL	277	9
subtype1	122	2
subtype2	65	3
subtype3	90	4

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

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.883 (Fisher's exact test), Q value = 0.96

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

nPatients	FEMALE	MALE
ALL	366	134
subtype1	135	47
subtype2	109	43
subtype3	122	44

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

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4.6e-05

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

nPatients	OTHER SPECIFY	THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL	THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED)	THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
ALL	9	355	100	36
subtype1	4	144	8	26
subtype2	2	67	83	0
subtype3	3	144	9	10

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

'MIRSEQ CNMF' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.193 (Fisher's exact test), Q value = 0.31

Table S118. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	14	486
subtype1	3	179
subtype2	3	149
subtype3	8	158

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

'MIRSEQ CNMF' versus 'RADIATION_EXPOSURE'

P value = 0.951 (Fisher's exact test), Q value = 0.98

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

nPatients	NO	YES
ALL	421	17
subtype1	154	7
subtype2	127	5
subtype3	140	5

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

'MIRSEQ CNMF' versus 'EXTRATHYROIDAL_EXTENSION'

P value = 1e-05 (Fisher's exact test), Q value = 4.6e-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	131	18	332	1
subtype1	64	14	97	1
subtype2	16	0	128	0
subtype3	51	4	107	0

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

'MIRSEQ CNMF' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.19 (Fisher's exact test), Q value = 0.31

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

nPatients	R0	R1	R2	RX
ALL	382	52	4	30
subtype1	132	26	3	12
subtype2	119	10	0	10
subtype3	131	16	1	8

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

'MIRSEQ CNMF' versus 'NUMBER_OF_LYMPH_NODES'

P value = 6.95e-09 (Kruskal-Wallis (anova)), Q value = 3.8e-07

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

	nPatients	Mean (Std.Dev)
ALL	383	3.7 (6.2)
subtype1	149	5.1 (7.3)
subtype2	100	1.6 (3.5)
subtype3	134	3.6 (6.1)

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 = 0.885 (Fisher's exact test), Q value = 0.96

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

nPatients	MULTIFOCAL	UNIFOCAL
ALL	225	265
subtype1	82	99
subtype2	66	81
subtype3	77	85

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

'MIRSEQ CNMF' versus 'TUMOR_SIZE'

P value = 0.0233 (Kruskal-Wallis (anova)), Q value = 0.053

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

	nPatients	Mean (Std.Dev)
ALL	401	3.0 (1.6)
subtype1	142	2.8 (1.7)
subtype2	126	3.2 (1.5)
subtype3	133	2.9 (1.5)

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

'MIRSEQ CNMF' versus 'RACE'

P value = 0.357 (Fisher's exact test), Q value = 0.51

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	326
subtype1	0	25	8	132
subtype2	0	9	10	86
subtype3	1	18	9	108

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

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.698 (Fisher's exact test), Q value = 0.8

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	38	358
subtype1	13	144
subtype2	10	95
subtype3	15	119

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	191

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.0135 (logrank test), Q value = 0.033

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	498	14	0.1 - 169.3 (20.3)
subtype1	179	10	0.1 - 158.8 (19.2)
subtype2	129	3	0.3 - 132.4 (16.6)
subtype3	190	1	0.1 - 169.3 (25.0)

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 = 0.000963 (Kruskal-Wallis (anova)), Q value = 0.0033

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

	nPatients	Mean (Std.Dev)
ALL	500	47.3 (15.8)
subtype1	179	49.7 (16.2)
subtype2	130	49.1 (15.5)
subtype3	191	43.7 (15.0)

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

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM_DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 4.6e-05

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV	STAGE IVA	STAGE IVC
ALL	282	53	109	2	46	6
subtype1	88	5	57	1	25	2
subtype2	74	28	20	1	4	2
subtype3	120	20	32	0	17	2

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

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 5e-05 (Fisher's exact test), Q value = 2e-04

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

nPatients	T1	T2	T3	T4
ALL	142	167	166	23
subtype1	55	37	70	15
subtype2	41	53	35	1
subtype3	46	77	61	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 = 4.6e-05

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

nPatients	0	1
ALL	226	224
subtype1	66	103
subtype2	86	19
subtype3	74	102

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 = 0.335 (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	277	9
subtype1	124	2
subtype2	55	3
subtype3	98	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 = 0.646 (Fisher's exact test), Q value = 0.77

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

nPatients	FEMALE	MALE
ALL	366	134
subtype1	135	44
subtype2	92	38
subtype3	139	52

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

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4.6e-05

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

nPatients	OTHER SPECIFY	THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL	THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED)	THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
ALL	9	355	100	36
subtype1	3	141	9	26
subtype2	2	51	77	0
subtype3	4	163	14	10

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

'MIRSEQ CHIERARCHICAL' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.00952 (Fisher's exact test), Q value = 0.025

Table S136. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	14	486
subtype1	2	177
subtype2	1	129
subtype3	11	180

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

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_EXPOSURE'

P value = 0.906 (Fisher's exact test), Q value = 0.96

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

nPatients	NO	YES
ALL	421	17
subtype1	153	6
subtype2	107	5
subtype3	161	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 = 4.6e-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	131	18	332	1
subtype1	67	14	91	1
subtype2	15	0	107	0
subtype3	49	4	134	0

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

'MIRSEQ CHIERARCHICAL' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.203 (Fisher's exact test), Q value = 0.32

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

nPatients	R0	R1	R2	RX
ALL	382	52	4	30
subtype1	129	26	3	13
subtype2	101	9	0	7
subtype3	152	17	1	10

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

'MIRSEQ CHIERARCHICAL' versus 'NUMBER_OF_LYMPH_NODES'

P value = 2.21e-09 (Kruskal-Wallis (anova)), Q value = 3.8e-07

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

	nPatients	Mean (Std.Dev)
ALL	383	3.7 (6.2)
subtype1	143	4.7 (6.5)
subtype2	85	1.7 (4.6)
subtype3	155	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 = 0.589 (Fisher's exact test), Q value = 0.73

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

nPatients	MULTIFOCAL	UNIFOCAL
ALL	225	265
subtype1	76	101
subtype2	62	66
subtype3	87	98

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

'MIRSEQ CHIERARCHICAL' versus 'TUMOR_SIZE'

P value = 0.0352 (Kruskal-Wallis (anova)), Q value = 0.073

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

	nPatients	Mean (Std.Dev)
ALL	401	3.0 (1.6)
subtype1	139	2.8 (1.6)
subtype2	109	3.2 (1.6)
subtype3	153	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 = 0.192 (Fisher's exact test), Q value = 0.31

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	326
subtype1	0	26	7	130
subtype2	0	6	8	71
subtype3	1	20	12	125

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

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.777 (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	358
subtype1	13	142
subtype2	8	76
subtype3	17	140

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
Number of samples	161	164	145

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

P value = 0.0198 (logrank test), Q value = 0.047

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	468	14	0.1 - 169.3 (20.2)
subtype1	161	10	0.1 - 157.2 (19.2)
subtype2	164	2	0.1 - 169.3 (23.1)
subtype3	143	2	0.1 - 132.1 (19.1)

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 = 0.0201 (Kruskal-Wallis (anova)), Q value = 0.047

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

	nPatients	Mean (Std.Dev)
ALL	470	47.3 (15.8)
subtype1	161	49.7 (16.5)
subtype2	164	44.8 (15.8)
subtype3	145	47.4 (14.6)

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 'NEOPLASM_DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 4.6e-05

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV	STAGE IVA	STAGE IVC
ALL	266	51	102	2	41	6
subtype1	78	6	52	1	22	2
subtype2	105	28	20	0	8	2
subtype3	83	17	30	1	11	2

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

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 8e-05 (Fisher's exact test), Q value = 0.00031

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

nPatients	T1	T2	T3	T4
ALL	135	157	154	22
subtype1	42	35	68	15
subtype2	47	67	45	5
subtype3	46	55	41	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 = 0.00083 (Fisher's exact test), Q value = 0.0029

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

nPatients	0	1
ALL	212	211
subtype1	59	95
subtype2	87	58
subtype3	66	58

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 = 0.436 (Fisher's exact test), Q value = 0.57

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

nPatients	0	1
ALL	261	9
subtype1	108	2
subtype2	71	4
subtype3	82	3

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 = 0.956 (Fisher's exact test), Q value = 0.98

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

nPatients	FEMALE	MALE
ALL	344	126
subtype1	119	42
subtype2	119	45
subtype3	106	39

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

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4.6e-05

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

nPatients	OTHER SPECIFY	THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL	THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED)	THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
ALL	9	339	90	32
subtype1	3	126	7	25
subtype2	2	113	46	3
subtype3	4	100	37	4

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

'MIRseq Mature CNMF subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.00019 (Fisher's exact test), Q value = 0.00069

Table S154. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	14	456
subtype1	2	159
subtype2	12	152
subtype3	0	145

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

'MIRseq Mature CNMF subtypes' versus 'RADIATION_EXPOSURE'

P value = 0.431 (Fisher's exact test), Q value = 0.57

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

nPatients	NO	YES
ALL	396	16
subtype1	135	5
subtype2	136	8
subtype3	125	3

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 = 1e-05 (Fisher's exact test), Q value = 4.6e-05

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	124	17	311	1
subtype1	60	14	79	1
subtype2	30	2	127	0
subtype3	34	1	105	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 'COMPLETENESS_OF_RESECTION'

P value = 0.032 (Fisher's exact test), Q value = 0.067

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

nPatients	R0	R1	R2	RX
ALL	358	49	4	29
subtype1	112	25	3	9
subtype2	137	9	1	11
subtype3	109	15	0	9

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

'MIRseq Mature CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.00995 (Kruskal-Wallis (anova)), Q value = 0.025

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

	nPatients	Mean (Std.Dev)
ALL	358	3.7 (6.2)
subtype1	135	4.6 (6.9)
subtype2	116	3.0 (4.7)
subtype3	107	3.4 (6.5)

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 = 0.905 (Fisher's exact test), Q value = 0.96

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

nPatients	MULTIFOCAL	UNIFOCAL
ALL	214	248
subtype1	75	86
subtype2	71	87
subtype3	68	75

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 = 0.128 (Kruskal-Wallis (anova)), Q value = 0.22

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

	nPatients	Mean (Std.Dev)
ALL	377	3.0 (1.6)
subtype1	126	2.9 (1.7)
subtype2	132	3.1 (1.5)
subtype3	119	2.9 (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 = 0.0406 (Fisher's exact test), Q value = 0.082

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	308
subtype1	0	18	6	122
subtype2	0	12	7	107
subtype3	1	19	13	79

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

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	35	340
subtype1	14	130
subtype2	12	117
subtype3	9	93

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	165	127	178

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

P value = 0.0037 (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	468	14	0.1 - 169.3 (20.2)
subtype1	165	10	0.1 - 157.2 (19.0)
subtype2	126	3	0.3 - 132.4 (16.4)
subtype3	177	1	0.1 - 169.3 (27.1)

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 = 0.00018 (Kruskal-Wallis (anova)), Q value = 0.00066

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

	nPatients	Mean (Std.Dev)
ALL	470	47.3 (15.8)
subtype1	165	50.1 (16.3)
subtype2	127	49.1 (15.7)
subtype3	178	43.3 (14.5)

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 'NEOPLASM_DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 4.6e-05

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV	STAGE IVA	STAGE IVC
ALL	266	51	102	2	41	6
subtype1	77	4	58	1	23	2
subtype2	70	27	23	1	3	2
subtype3	119	20	21	0	15	2

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

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 3e-05 (Fisher's exact test), Q value = 0.00013

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

nPatients	T1	T2	T3	T4
ALL	135	157	154	22
subtype1	47	34	67	15
subtype2	38	54	34	1
subtype3	50	69	53	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 = 4.6e-05

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

nPatients	0	1
ALL	212	211
subtype1	59	98
subtype2	80	22
subtype3	73	91

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 = 0.369 (Fisher's exact test), Q value = 0.52

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

nPatients	0	1
ALL	261	9
subtype1	114	2
subtype2	51	3
subtype3	96	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 = 0.184 (Fisher's exact test), Q value = 0.3

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

nPatients	FEMALE	MALE
ALL	344	126
subtype1	125	40
subtype2	85	42
subtype3	134	44

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

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4.6e-05

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

nPatients	OTHER SPECIFY	THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL	THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED)	THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
ALL	9	339	90	32
subtype1	3	130	7	25
subtype2	2	56	69	0
subtype3	4	153	14	7

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

'MIRseq Mature cHierClus subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.105 (Fisher's exact test), Q value = 0.19

Table S172. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	14	456
subtype1	2	163
subtype2	3	124
subtype3	9	169

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

'MIRseq Mature cHierClus subtypes' versus 'RADIATION_EXPOSURE'

P value = 0.854 (Fisher's exact test), Q value = 0.94

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

nPatients	NO	YES
ALL	396	16
subtype1	139	6
subtype2	106	5
subtype3	151	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 = 4.6e-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	124	17	311	1
subtype1	63	14	81	1
subtype2	17	0	103	0
subtype3	44	3	127	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 'COMPLETENESS_OF_RESECTION'

P value = 0.0516 (Fisher's exact test), Q value = 0.1

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

nPatients	R0	R1	R2	RX
ALL	358	49	4	29
subtype1	115	27	2	11
subtype2	97	9	0	9
subtype3	146	13	2	9

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

'MIRseq Mature cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 7.02e-08 (Kruskal-Wallis (anova)), Q value = 1.7e-06

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

	nPatients	Mean (Std.Dev)
ALL	358	3.7 (6.2)
subtype1	135	4.7 (6.2)
subtype2	80	1.4 (3.3)
subtype3	143	4.1 (7.1)

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 = 0.583 (Fisher's exact test), Q value = 0.73

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

nPatients	MULTIFOCAL	UNIFOCAL
ALL	214	248
subtype1	72	92
subtype2	62	62
subtype3	80	94

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 = 0.0271 (Kruskal-Wallis (anova)), Q value = 0.06

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

	nPatients	Mean (Std.Dev)
ALL	377	3.0 (1.6)
subtype1	126	2.7 (1.6)
subtype2	107	3.2 (1.6)
subtype3	144	3.0 (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 = 0.0874 (Fisher's exact test), Q value = 0.16

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	308
subtype1	0	25	5	122
subtype2	0	7	9	69
subtype3	1	17	12	117

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 = 0.247 (Fisher's exact test), Q value = 0.37

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	35	340
subtype1	10	136
subtype2	7	79
subtype3	18	125

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/15125146/THCA-TP.mergedcluster.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/THCA-TP/15092795/THCA-TP.merged_data.txt
Number of patients = 501
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.

References

[1] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)

[2] Monti et al., Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data, Machine Learning 52(1):91-118 (2003)

[3] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)

[4] 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)

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