Correlation between aggregated molecular cancer subtypes and selected clinical features

Thyroid Adenocarcinoma (Primary solid tumor)

17 October 2014 | analyses__2014_10_17

Maintainer Information

Citation Information

Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)

Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1V98719

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 497 patients, 61 significant findings detected with P value < 0.05 and Q value < 0.25.

3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'AGE' and 'NEOPLASM.DISEASESTAGE'.
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.N.STAGE', 'HISTOLOGICAL.TYPE', 'EXTRATHYROIDAL.EXTENSION', and 'NUMBER.OF.LYMPH.NODES'.
CNMF clustering analysis on RPPA data identified 3 subtypes that correlate to 'AGE', 'NEOPLASM.DISEASESTAGE', 'HISTOLOGICAL.TYPE', and 'EXTRATHYROIDAL.EXTENSION'.
Consensus hierarchical clustering analysis on RPPA data identified 7 subtypes that correlate to 'Time to Death', 'AGE', 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.STAGE', 'HISTOLOGICAL.TYPE', and 'EXTRATHYROIDAL.EXTENSION'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.STAGE', 'PATHOLOGY.N.STAGE', 'HISTOLOGICAL.TYPE', 'EXTRATHYROIDAL.EXTENSION', and 'NUMBER.OF.LYMPH.NODES'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 5 subtypes that correlate to 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.STAGE', 'PATHOLOGY.N.STAGE', 'PATHOLOGY.M.STAGE', 'HISTOLOGICAL.TYPE', 'EXTRATHYROIDAL.EXTENSION', 'NUMBER.OF.LYMPH.NODES', and 'RACE'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.STAGE', 'PATHOLOGY.N.STAGE', 'PATHOLOGY.M.STAGE', 'HISTOLOGICAL.TYPE', 'EXTRATHYROIDAL.EXTENSION', and 'NUMBER.OF.LYMPH.NODES'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'AGE', 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.STAGE', 'PATHOLOGY.N.STAGE', 'PATHOLOGY.M.STAGE', 'HISTOLOGICAL.TYPE', 'EXTRATHYROIDAL.EXTENSION', and 'NUMBER.OF.LYMPH.NODES'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.STAGE', 'PATHOLOGY.N.STAGE', 'PATHOLOGY.M.STAGE', 'HISTOLOGICAL.TYPE', 'RADIATIONS.RADIATION.REGIMENINDICATION', and 'EXTRATHYROIDAL.EXTENSION'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'AGE', 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.STAGE', 'PATHOLOGY.N.STAGE', 'PATHOLOGY.M.STAGE', 'HISTOLOGICAL.TYPE', 'EXTRATHYROIDAL.EXTENSION', and 'NUMBER.OF.LYMPH.NODES'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 17 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 61 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	0.464 (1.00)	0.238 (1.00)	0.59 (1.00)	0.00156 (0.177)	0.574 (1.00)	0.689 (1.00)	0.0185 (1.00)	0.00835 (0.835)	0.0167 (1.00)	0.00244 (0.264)
AGE	Kruskal-Wallis (anova)	2.53e-07 (4.15e-05)	0.159 (1.00)	0.0014 (0.161)	0.00104 (0.121)	0.172 (1.00)	0.00591 (0.615)	0.0858 (1.00)	0.00124 (0.144)	0.0306 (1.00)	0.000242 (0.0298)
NEOPLASM DISEASESTAGE	Fisher's exact test	1e-05 (0.00163)	1e-05 (0.00163)	0.00178 (0.199)	0.00211 (0.232)	1e-05 (0.00163)	1e-05 (0.00163)	1e-05 (0.00163)	1e-05 (0.00163)	1e-05 (0.00163)	1e-05 (0.00163)
PATHOLOGY T STAGE	Fisher's exact test	0.193 (1.00)	0.00387 (0.414)	0.00616 (0.634)	0.00016 (0.0202)	1e-05 (0.00163)	1e-05 (0.00163)	0.00011 (0.014)	0.00017 (0.0212)	0.0003 (0.0366)	6e-05 (0.0078)
PATHOLOGY N STAGE	Fisher's exact test	0.854 (1.00)	1e-05 (0.00163)	0.0309 (1.00)	0.00813 (0.821)	1e-05 (0.00163)	1e-05 (0.00163)	1e-05 (0.00163)	1e-05 (0.00163)	0.00067 (0.0804)	1e-05 (0.00163)
PATHOLOGY M STAGE	Fisher's exact test	0.173 (1.00)	0.127 (1.00)	0.225 (1.00)	0.124 (1.00)	0.0271 (1.00)	0.00145 (0.165)	0.0004 (0.0484)	7e-05 (0.00896)	0.00077 (0.0916)	6e-05 (0.0078)
GENDER	Fisher's exact test	0.456 (1.00)	0.899 (1.00)	0.656 (1.00)	0.941 (1.00)	0.891 (1.00)	0.109 (1.00)	0.802 (1.00)	0.581 (1.00)	0.904 (1.00)	0.156 (1.00)
HISTOLOGICAL TYPE	Fisher's exact test	0.543 (1.00)	1e-05 (0.00163)	3e-05 (0.00393)	1e-05 (0.00163)	1e-05 (0.00163)	1e-05 (0.00163)	1e-05 (0.00163)	1e-05 (0.00163)	1e-05 (0.00163)	1e-05 (0.00163)
RADIATIONS RADIATION REGIMENINDICATION	Fisher's exact test	0.613 (1.00)	0.114 (1.00)	0.0102 (1.00)	0.188 (1.00)	0.00548 (0.581)	0.0689 (1.00)	0.218 (1.00)	0.0113 (1.00)	0.0002 (0.0248)	0.119 (1.00)
RADIATIONEXPOSURE	Fisher's exact test	0.566 (1.00)	0.648 (1.00)	1 (1.00)	0.238 (1.00)	0.984 (1.00)	0.248 (1.00)	0.952 (1.00)	0.952 (1.00)	0.429 (1.00)	0.853 (1.00)
EXTRATHYROIDAL EXTENSION	Fisher's exact test	0.153 (1.00)	1e-05 (0.00163)	0.00084 (0.0991)	1e-05 (0.00163)	1e-05 (0.00163)	1e-05 (0.00163)	1e-05 (0.00163)	1e-05 (0.00163)	1e-05 (0.00163)	1e-05 (0.00163)
COMPLETENESS OF RESECTION	Fisher's exact test	0.099 (1.00)	0.261 (1.00)	0.275 (1.00)	0.0943 (1.00)	0.171 (1.00)	0.0288 (1.00)	0.221 (1.00)	0.211 (1.00)	0.0384 (1.00)	0.0585 (1.00)
NUMBER OF LYMPH NODES	Kruskal-Wallis (anova)	0.429 (1.00)	4.39e-09 (7.28e-07)	0.496 (1.00)	0.36 (1.00)	1.66e-09 (2.76e-07)	1.14e-09 (1.92e-07)	5.19e-10 (8.77e-08)	2.86e-10 (4.87e-08)	0.00582 (0.611)	1.29e-08 (2.12e-06)
MULTIFOCALITY	Fisher's exact test	0.0485 (1.00)	0.941 (1.00)	0.0131 (1.00)	0.117 (1.00)	0.274 (1.00)	0.222 (1.00)	0.908 (1.00)	0.644 (1.00)	0.878 (1.00)	0.633 (1.00)
TUMOR SIZE	Kruskal-Wallis (anova)	0.0159 (1.00)	0.668 (1.00)	0.711 (1.00)	0.0203 (1.00)	0.0822 (1.00)	0.00235 (0.257)	0.013 (1.00)	0.0277 (1.00)	0.11 (1.00)	0.0208 (1.00)
RACE	Fisher's exact test	0.293 (1.00)	0.681 (1.00)	0.0633 (1.00)	0.173 (1.00)	0.659 (1.00)	0.00194 (0.215)	0.209 (1.00)	0.0771 (1.00)	0.027 (1.00)	0.0253 (1.00)
ETHNICITY	Fisher's exact test	0.885 (1.00)	0.406 (1.00)	0.147 (1.00)	0.935 (1.00)	0.00735 (0.75)	0.0297 (1.00)	0.678 (1.00)	0.782 (1.00)	1 (1.00)	0.247 (1.00)

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

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

Cluster Labels	1	2	3
Number of samples	54	354	87

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

P value = 0.464 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	491	14	0.0 - 158.8 (16.6)
subtype1	53	3	0.0 - 124.0 (17.1)
subtype2	351	10	0.1 - 158.8 (17.3)
subtype3	87	1	0.0 - 85.2 (14.7)

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 'AGE'

P value = 2.53e-07 (Kruskal-Wallis (anova)), Q value = 4.1e-05

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

	nPatients	Mean (Std.Dev)
ALL	495	47.1 (15.6)
subtype1	54	58.4 (14.9)
subtype2	354	45.4 (15.5)
subtype3	87	46.9 (13.7)

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

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

P value = 1e-05 (Fisher's exact test), Q value = 0.0016

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	280	51	109	2	45	6
subtype1	14	14	16	1	8	1
subtype2	214	31	78	0	26	4
subtype3	52	6	15	1	11	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 = 0.193 (Fisher's exact test), Q value = 1

Table S5. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	140	166	165	21
subtype1	8	21	22	3
subtype2	102	114	120	15
subtype3	30	31	23	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 = 0.854 (Fisher's exact test), Q value = 1

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

nPatients	0	1
ALL	223	222
subtype1	24	21
subtype2	157	161
subtype3	42	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 = 0.173 (Fisher's exact test), Q value = 1

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

nPatients	M0	M1	MX
ALL	273	9	212
subtype1	22	1	31
subtype2	203	6	145
subtype3	48	2	36

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 = 0.456 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	364	131
subtype1	36	18
subtype2	264	90
subtype3	64	23

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 = 0.543 (Fisher's exact test), Q value = 1

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

nPatients	OTHER SPECIFY	THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL	THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED)	THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
ALL	9	353	98	35
subtype1	0	38	12	4
subtype2	8	254	64	28
subtype3	1	61	22	3

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

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

P value = 0.613 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	14	481
subtype1	2	52
subtype2	11	343
subtype3	1	86

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

'Copy Number Ratio CNMF subtypes' versus 'RADIATIONEXPOSURE'

P value = 0.566 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	417	17
subtype1	47	3
subtype2	295	12
subtype3	75	2

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

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

P value = 0.153 (Fisher's exact test), Q value = 1

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	130	16	328	1
subtype1	15	2	34	1
subtype2	98	13	230	0
subtype3	17	1	64	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 = 0.099 (Fisher's exact test), Q value = 1

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

nPatients	R0	R1	R2	RX
ALL	378	51	4	30
subtype1	37	8	0	6
subtype2	278	36	3	15
subtype3	63	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 = 0.429 (Kruskal-Wallis (anova)), Q value = 1

Table S14. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	391	3.6 (6.2)
subtype1	42	2.6 (4.9)
subtype2	279	3.7 (6.5)
subtype3	70	3.6 (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 = 0.0485 (Fisher's exact test), Q value = 1

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

nPatients	MULTIFOCAL	UNIFOCAL
ALL	224	261
subtype1	21	33
subtype2	155	193
subtype3	48	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 = 0.0159 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	397	3.0 (1.6)
subtype1	48	3.5 (1.6)
subtype2	280	2.9 (1.6)
subtype3	69	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 = 0.293 (Fisher's exact test), Q value = 1

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	49	26	321
subtype1	0	1	3	38
subtype2	1	38	17	231
subtype3	0	10	6	52

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 = 0.885 (Fisher's exact test), Q value = 1

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	350
subtype1	4	35
subtype2	27	255
subtype3	7	60

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	274	155	68

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.238 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	493	14	0.0 - 158.8 (16.6)
subtype1	274	7	0.1 - 158.8 (18.4)
subtype2	153	3	0.0 - 132.4 (14.6)
subtype3	66	4	0.0 - 147.4 (14.5)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.159 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	497	47.1 (15.6)
subtype1	274	46.9 (15.5)
subtype2	155	48.6 (15.2)
subtype3	68	44.8 (17.2)

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

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0016

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	281	52	109	2	45	6
subtype1	148	19	67	0	35	4
subtype2	89	31	27	1	4	2
subtype3	44	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 = 0.00387 (Fisher's exact test), Q value = 0.41

Table S23. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	141	167	165	21
subtype1	64	89	101	18
subtype2	54	58	42	1
subtype3	23	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 = 0.0016

Table S24. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	225	222
subtype1	96	157
subtype2	100	29
subtype3	29	36

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 = 0.127 (Fisher's exact test), Q value = 1

Table S25. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	274	9	213
subtype1	162	6	106
subtype2	73	3	78
subtype3	39	0	29

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.899 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	365	132
subtype1	202	72
subtype2	112	43
subtype3	51	17

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 = 0.0016

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	353	100	35
subtype1	5	223	17	29
subtype2	2	75	78	0
subtype3	2	55	5	6

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 = 0.114 (Fisher's exact test), Q value = 1

Table S28. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	14	483
subtype1	11	263
subtype2	1	154
subtype3	2	66

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

'METHLYATION CNMF' versus 'RADIATIONEXPOSURE'

P value = 0.648 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	418	17
subtype1	236	8
subtype2	125	6
subtype3	57	3

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

'METHLYATION CNMF' versus 'EXTRATHYROIDAL.EXTENSION'

P value = 1e-05 (Fisher's exact test), Q value = 0.0016

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

nPatients	MINIMAL (T3)	MODERATE/ADVANCED (T4A)	NONE	VERY ADVANCED (T4B)
ALL	130	16	330	1
subtype1	92	15	160	0
subtype2	23	0	122	0
subtype3	15	1	48	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 = 0.261 (Fisher's exact test), Q value = 1

Table S31. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	380	51	4	30
subtype1	206	35	4	18
subtype2	123	9	0	8
subtype3	51	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 = 4.39e-09 (Kruskal-Wallis (anova)), Q value = 7.3e-07

Table S32. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	392	3.6 (6.2)
subtype1	229	4.4 (6.6)
subtype2	107	1.6 (4.2)
subtype3	56	4.0 (6.9)

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 = 0.941 (Fisher's exact test), Q value = 1

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

nPatients	MULTIFOCAL	UNIFOCAL
ALL	225	262
subtype1	125	144
subtype2	71	81
subtype3	29	37

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

'METHLYATION CNMF' versus 'TUMOR.SIZE'

P value = 0.668 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	398	3.0 (1.6)
subtype1	222	2.9 (1.6)
subtype2	123	3.1 (1.6)
subtype3	53	2.8 (1.5)

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

'METHLYATION CNMF' versus 'RACE'

P value = 0.681 (Fisher's exact test), Q value = 1

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	49	26	322
subtype1	1	29	16	188
subtype2	0	13	9	83
subtype3	0	7	1	51

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

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.406 (Fisher's exact test), Q value = 1

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	38	351
subtype1	24	204
subtype2	7	97
subtype3	7	50

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 = 0.59 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	221	13	0.1 - 158.8 (17.6)
subtype1	61	4	1.2 - 158.8 (16.4)
subtype2	79	4	0.2 - 147.4 (21.5)
subtype3	81	5	0.1 - 147.8 (15.2)

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

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.0014 (Kruskal-Wallis (anova)), Q value = 0.16

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

	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 (17.5)

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

'RPPA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00178 (Fisher's exact test), Q value = 0.2

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	45	21	4
subtype1	31	11	14	2	1
subtype2	31	13	21	14	0
subtype3	55	9	10	5	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 = 0.00616 (Fisher's exact test), Q value = 0.63

Table S41. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	52	83	74	11
subtype1	22	22	15	0
subtype2	13	25	33	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 = 1

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 = 0.225 (Fisher's exact test), Q value = 1

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

nPatients	M0	M1	MX
ALL	118	5	98
subtype1	38	1	21
subtype2	43	1	35
subtype3	37	3	42

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 = 1

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 = 3e-05 (Fisher's exact test), Q value = 0.0039

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 = 0.0102 (Fisher's exact test), Q value = 1

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 'RADIATIONEXPOSURE'

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

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

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: 'RADIATIONEXPOSURE'

'RPPA CNMF subtypes' versus 'EXTRATHYROIDAL.EXTENSION'

P value = 0.00084 (Fisher's exact test), Q value = 0.099

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 = 0.275 (Fisher's exact test), Q value = 1

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 = 0.496 (Kruskal-Wallis (anova)), Q value = 1

Table S50. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	170	3.4 (5.8)
subtype1	43	3.8 (7.5)
subtype2	65	3.1 (4.5)
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 = 0.0131 (Fisher's exact test), Q value = 1

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 = 1

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 = 0.0633 (Fisher's exact test), Q value = 1

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

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	13	13	145
subtype1	2	7	34
subtype2	3	4	59
subtype3	8	2	52

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

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.147 (Fisher's exact test), Q value = 1

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	16	164
subtype1	1	45
subtype2	8	58
subtype3	7	61

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 = 0.00156 (logrank test), Q value = 0.18

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	221	13	0.1 - 158.8 (17.6)
subtype1	47	4	1.2 - 158.8 (17.9)
subtype2	28	1	1.4 - 66.8 (13.6)
subtype3	33	0	0.2 - 139.0 (24.7)
subtype4	29	2	0.3 - 155.5 (17.5)
subtype5	48	1	0.1 - 147.8 (16.3)
subtype6	27	1	1.1 - 81.3 (18.4)
subtype7	9	4	4.9 - 147.4 (26.7)

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

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.00104 (Kruskal-Wallis (anova)), Q value = 0.12

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

	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 (19.3)
subtype5	49	40.1 (14.8)
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: 'AGE'

'RPPA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00211 (Fisher's exact test), Q value = 0.23

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	45	21	4
subtype1	26	11	6	1	1
subtype2	16	3	7	2	0
subtype3	13	4	11	5	0
subtype4	17	4	6	1	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 = 0.00016 (Fisher's exact test), Q value = 0.02

Table S59. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	52	83	74	11
subtype1	16	22	9	0
subtype2	11	9	8	0
subtype3	3	11	17	2
subtype4	4	11	12	1
subtype5	11	20	16	1
subtype6	6	10	9	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 = 0.00813 (Fisher's exact test), Q value = 0.82

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 = 0.124 (Fisher's exact test), Q value = 1

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

nPatients	M0	M1	MX
ALL	118	5	98
subtype1	21	1	24
subtype2	19	0	9
subtype3	21	1	11
subtype4	13	1	15
subtype5	26	1	22
subtype6	11	0	16
subtype7	7	1	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 = 0.941 (Fisher's exact test), Q value = 1

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 = 0.0016

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 = 0.188 (Fisher's exact test), Q value = 1

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 'RADIATIONEXPOSURE'

P value = 0.238 (Fisher's exact test), Q value = 1

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

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: 'RADIATIONEXPOSURE'

'RPPA cHierClus subtypes' versus 'EXTRATHYROIDAL.EXTENSION'

P value = 1e-05 (Fisher's exact test), Q value = 0.0016

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 = 0.0943 (Fisher's exact test), Q value = 1

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 = 0.36 (Kruskal-Wallis (anova)), Q value = 1

Table S68. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	170	3.4 (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	21	3.3 (5.7)
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 = 1

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 = 1

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 = 0.173 (Fisher's exact test), Q value = 1

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

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	13	13	145
subtype1	0	6	24
subtype2	2	1	18
subtype3	2	0	28
subtype4	2	1	19
subtype5	6	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 = 0.935 (Fisher's exact test), Q value = 1

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	16	164
subtype1	2	31
subtype2	1	23
subtype3	3	27
subtype4	2	21
subtype5	5	37
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	165	156	57	117

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.574 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	491	14	0.0 - 158.8 (16.6)
subtype1	164	7	0.1 - 155.5 (18.4)
subtype2	155	3	0.0 - 132.4 (15.0)
subtype3	56	2	0.0 - 157.2 (14.2)
subtype4	116	2	0.2 - 158.8 (18.4)

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

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.172 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	495	47.1 (15.7)
subtype1	165	48.0 (15.8)
subtype2	156	48.6 (15.3)
subtype3	57	45.1 (18.0)
subtype4	117	44.8 (14.5)

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

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0016

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	280	52	108	2	45	6
subtype1	84	7	45	1	26	2
subtype2	90	30	27	1	5	2
subtype3	39	1	13	0	4	0
subtype4	67	14	23	0	10	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 = 0.0016

Table S77. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	140	166	165	21
subtype1	27	48	76	13
subtype2	54	57	43	2
subtype3	25	14	15	2
subtype4	34	47	31	4

Figure S72. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 1e-05 (Fisher's exact test), Q value = 0.0016

Table S78. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	224	221
subtype1	51	99
subtype2	101	29
subtype3	22	35
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 = 0.0271 (Fisher's exact test), Q value = 1

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

nPatients	M0	M1	MX
ALL	273	9	212
subtype1	92	3	70
subtype2	70	3	82
subtype3	39	0	18
subtype4	72	3	42

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 = 0.891 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	363	132
subtype1	119	46
subtype2	113	43
subtype3	42	15
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 = 0.0016

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	352	100	34
subtype1	4	132	3	26
subtype2	2	71	82	1
subtype3	1	49	3	4
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 = 0.00548 (Fisher's exact test), Q value = 0.58

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

nPatients	NO	YES
ALL	14	481
subtype1	11	154
subtype2	1	155
subtype3	0	57
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 'RADIATIONEXPOSURE'

P value = 0.984 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	416	17
subtype1	141	5
subtype2	129	6
subtype3	47	2
subtype4	99	4

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

'RNAseq CNMF subtypes' versus 'EXTRATHYROIDAL.EXTENSION'

P value = 1e-05 (Fisher's exact test), Q value = 0.0016

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	130	16	328	1
subtype1	66	12	81	1
subtype2	23	1	123	0
subtype3	13	2	39	0
subtype4	28	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 = 0.171 (Fisher's exact test), Q value = 1

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

nPatients	R0	R1	R2	RX
ALL	378	51	4	30
subtype1	125	25	2	9
subtype2	124	9	0	8
subtype3	39	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 = 1.66e-09 (Kruskal-Wallis (anova)), Q value = 2.8e-07

Table S86. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	390	3.6 (6.2)
subtype1	136	4.3 (6.6)
subtype2	111	1.6 (4.2)
subtype3	51	5.3 (7.5)
subtype4	92	3.9 (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 = 0.274 (Fisher's exact test), Q value = 1

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

nPatients	MULTIFOCAL	UNIFOCAL
ALL	224	261
subtype1	66	97
subtype2	74	79
subtype3	25	30
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 = 0.0822 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	397	3.0 (1.6)
subtype1	140	3.1 (1.6)
subtype2	123	3.1 (1.6)
subtype3	41	2.6 (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 = 0.659 (Fisher's exact test), Q value = 1

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	48	26	322
subtype1	1	16	10	118
subtype2	0	10	9	87
subtype3	0	6	1	43
subtype4	0	16	6	74

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

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.00735 (Fisher's exact test), Q value = 0.75

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	38	349
subtype1	23	118
subtype2	9	96
subtype3	3	46
subtype4	3	89

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	102	135	85	96	77

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.689 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	491	14	0.0 - 158.8 (16.6)
subtype1	100	5	0.7 - 157.2 (18.9)
subtype2	134	4	0.2 - 132.4 (14.8)
subtype3	85	1	0.0 - 130.7 (14.1)
subtype4	95	2	0.2 - 158.8 (19.0)
subtype5	77	2	0.1 - 147.8 (19.0)

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

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.00591 (Kruskal-Wallis (anova)), Q value = 0.61

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

	nPatients	Mean (Std.Dev)
ALL	495	47.1 (15.7)
subtype1	102	50.3 (15.8)
subtype2	135	49.6 (16.0)
subtype3	85	44.9 (14.9)
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: 'AGE'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0016

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	280	52	108	2	45	6
subtype1	45	2	39	1	14	1
subtype2	76	30	21	1	4	2
subtype3	56	3	18	0	8	0
subtype4	54	11	19	0	9	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 = 0.0016

Table S95. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	140	166	165	21
subtype1	16	23	55	8
subtype2	44	55	34	2
subtype3	37	22	23	1
subtype4	26	40	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 = 0.0016

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

nPatients	0	1
ALL	224	221
subtype1	28	66
subtype2	90	19
subtype3	38	44
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 = 0.00145 (Fisher's exact test), Q value = 0.17

Table S97. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	273	9	212
subtype1	53	1	48
subtype2	56	3	75
subtype3	59	0	26
subtype4	58	3	35
subtype5	47	2	28

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

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

nPatients	FEMALE	MALE
ALL	363	132
subtype1	83	19
subtype2	96	39
subtype3	60	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 = 0.0016

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	352	100	34
subtype1	1	77	2	22
subtype2	2	57	76	0
subtype3	2	67	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 = 0.0689 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	14	481
subtype1	5	97
subtype2	1	134
subtype3	1	84
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 'RADIATIONEXPOSURE'

P value = 0.248 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	416	17
subtype1	92	3
subtype2	111	5
subtype3	65	6
subtype4	85	1
subtype5	63	2

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

'RNAseq cHierClus subtypes' versus 'EXTRATHYROIDAL.EXTENSION'

P value = 1e-05 (Fisher's exact test), Q value = 0.0016

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	130	16	328	1
subtype1	53	8	37	1
subtype2	15	1	110	0
subtype3	20	1	61	0
subtype4	21	1	71	0
subtype5	21	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 = 0.0288 (Fisher's exact test), Q value = 1

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

nPatients	R0	R1	R2	RX
ALL	378	51	4	30
subtype1	68	22	2	6
subtype2	103	10	0	8
subtype3	69	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 = 1.14e-09 (Kruskal-Wallis (anova)), Q value = 1.9e-07

Table S104. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	390	3.6 (6.2)
subtype1	91	4.7 (6.7)
subtype2	93	1.3 (3.4)
subtype3	70	4.8 (8.6)
subtype4	74	3.7 (6.2)
subtype5	62	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 = 0.222 (Fisher's exact test), Q value = 1

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

nPatients	MULTIFOCAL	UNIFOCAL
ALL	224	261
subtype1	38	64
subtype2	64	68
subtype3	43	40
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 = 0.00235 (Kruskal-Wallis (anova)), Q value = 0.26

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

	nPatients	Mean (Std.Dev)
ALL	397	3.0 (1.6)
subtype1	90	3.1 (1.5)
subtype2	111	3.2 (1.5)
subtype3	61	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 = 0.00194 (Fisher's exact test), Q value = 0.22

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	48	26	322
subtype1	1	4	10	76
subtype2	0	6	8	73
subtype3	0	16	1	58
subtype4	0	12	6	59
subtype5	0	10	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 = 0.0297 (Fisher's exact test), Q value = 1

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	38	349
subtype1	15	75
subtype2	9	79
subtype3	3	69
subtype4	3	70
subtype5	8	56

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	179	151	166

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.0185 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	492	14	0.0 - 158.8 (16.6)
subtype1	178	10	0.0 - 158.8 (16.1)
subtype2	150	3	0.2 - 132.4 (16.1)
subtype3	164	1	0.1 - 147.8 (18.2)

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

'MIRSEQ CNMF' versus 'AGE'

P value = 0.0858 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	496	47.2 (15.6)
subtype1	179	49.0 (15.8)
subtype2	151	47.3 (16.0)
subtype3	166	45.1 (14.9)

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

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0016

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	280	52	109	2	45	6
subtype1	89	6	55	1	25	2
subtype2	92	29	21	1	5	2
subtype3	99	17	33	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 = 0.00011 (Fisher's exact test), Q value = 0.014

Table S113. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	141	167	164	21
subtype1	55	38	70	13
subtype2	43	68	38	2
subtype3	43	61	56	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 = 0.0016

Table S114. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	225	221
subtype1	64	106
subtype2	96	27
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 = 4e-04 (Fisher's exact test), Q value = 0.048

Table S115. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	273	9	213
subtype1	119	2	58
subtype2	64	3	83
subtype3	90	4	72

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

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.802 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	364	132
subtype1	134	45
subtype2	108	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 = 0.0016

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	352	100	35
subtype1	4	142	8	25
subtype2	2	66	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 = 0.218 (Fisher's exact test), Q value = 1

Table S118. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	14	482
subtype1	3	176
subtype2	3	148
subtype3	8	158

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

'MIRSEQ CNMF' versus 'RADIATIONEXPOSURE'

P value = 0.952 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	417	17
subtype1	151	7
subtype2	126	5
subtype3	140	5

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

'MIRSEQ CNMF' versus 'EXTRATHYROIDAL.EXTENSION'

P value = 1e-05 (Fisher's exact test), Q value = 0.0016

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

nPatients	MINIMAL (T3)	MODERATE/ADVANCED (T4A)	NONE	VERY ADVANCED (T4B)
ALL	129	16	330	1
subtype1	63	12	96	1
subtype2	16	0	127	0
subtype3	50	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 = 0.221 (Fisher's exact test), Q value = 1

Table S121. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	379	51	4	30
subtype1	130	25	3	12
subtype2	118	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 = 5.19e-10 (Kruskal-Wallis (anova)), Q value = 8.8e-08

Table S122. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	391	3.5 (6.2)
subtype1	148	5.1 (7.4)
subtype2	106	1.5 (3.4)
subtype3	137	3.5 (6.0)

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

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

nPatients	MULTIFOCAL	UNIFOCAL
ALL	224	262
subtype1	81	97
subtype2	66	80
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 = 0.013 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	397	3.0 (1.6)
subtype1	139	2.8 (1.6)
subtype2	125	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 = 0.209 (Fisher's exact test), Q value = 1

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	49	26	322
subtype1	0	25	7	130
subtype2	0	8	10	84
subtype3	1	16	9	108

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

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.678 (Fisher's exact test), Q value = 1

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	38	350
subtype1	13	142
subtype2	10	92
subtype3	15	116

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	175	130	191

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.00835 (logrank test), Q value = 0.83

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	492	14	0.0 - 158.8 (16.6)
subtype1	174	10	0.0 - 158.8 (14.9)
subtype2	129	3	0.3 - 132.4 (15.0)
subtype3	189	1	0.1 - 147.8 (18.6)

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

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.00124 (Kruskal-Wallis (anova)), Q value = 0.14

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

	nPatients	Mean (Std.Dev)
ALL	496	47.2 (15.6)
subtype1	175	49.5 (15.8)
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: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0016

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	280	52	109	2	45	6
subtype1	86	5	56	1	24	2
subtype2	74	28	20	1	4	2
subtype3	120	19	33	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 = 0.00017 (Fisher's exact test), Q value = 0.021

Table S131. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	141	167	164	21
subtype1	53	38	69	13
subtype2	42	53	34	1
subtype3	46	76	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 = 0.0016

Table S132. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	225	221
subtype1	65	100
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 = 7e-05 (Fisher's exact test), Q value = 0.009

Table S133. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	273	9	213
subtype1	120	2	53
subtype2	55	3	71
subtype3	98	4	89

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

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.581 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	364	132
subtype1	133	42
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 = 0.0016

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	352	100	35
subtype1	3	138	9	25
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 = 0.0113 (Fisher's exact test), Q value = 1

Table S136. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	14	482
subtype1	2	173
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 'RADIATIONEXPOSURE'

P value = 0.952 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	417	17
subtype1	149	6
subtype2	107	5
subtype3	161	6

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

'MIRSEQ CHIERARCHICAL' versus 'EXTRATHYROIDAL.EXTENSION'

P value = 1e-05 (Fisher's exact test), Q value = 0.0016

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

nPatients	MINIMAL (T3)	MODERATE/ADVANCED (T4A)	NONE	VERY ADVANCED (T4B)
ALL	129	16	330	1
subtype1	66	12	89	1
subtype2	15	0	107	0
subtype3	48	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 = 0.211 (Fisher's exact test), Q value = 1

Table S139. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	379	51	4	30
subtype1	126	25	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 = 2.86e-10 (Kruskal-Wallis (anova)), Q value = 4.9e-08

Table S140. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	391	3.5 (6.2)
subtype1	141	4.7 (6.5)
subtype2	92	1.5 (4.5)
subtype3	158	3.7 (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 = 0.644 (Fisher's exact test), Q value = 1

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

nPatients	MULTIFOCAL	UNIFOCAL
ALL	224	262
subtype1	75	98
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 = 0.0277 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	397	3.0 (1.6)
subtype1	135	2.7 (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 = 0.0771 (Fisher's exact test), Q value = 1

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	49	26	322
subtype1	0	26	6	127
subtype2	0	5	8	70
subtype3	1	18	12	125

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

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.782 (Fisher's exact test), Q value = 1

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	38	350
subtype1	13	139
subtype2	8	74
subtype3	17	137

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	158	164	144

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

P value = 0.0167 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	462	14	0.0 - 158.8 (16.4)
subtype1	156	10	0.0 - 157.2 (15.2)
subtype2	164	2	0.1 - 158.8 (18.6)
subtype3	142	2	0.1 - 131.2 (14.0)

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 'AGE'

P value = 0.0306 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	466	47.2 (15.6)
subtype1	158	49.2 (16.2)
subtype2	164	44.8 (15.8)
subtype3	144	47.6 (14.4)

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

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

P value = 1e-05 (Fisher's exact test), Q value = 0.0016

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	264	50	102	2	40	6
subtype1	77	6	51	1	21	2
subtype2	105	27	21	0	8	2
subtype3	82	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 = 3e-04 (Fisher's exact test), Q value = 0.037

Table S149. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	134	157	152	20
subtype1	41	36	67	13
subtype2	47	66	45	5
subtype3	46	55	40	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 = 0.00067 (Fisher's exact test), Q value = 0.08

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

nPatients	0	1
ALL	211	208
subtype1	58	93
subtype2	87	58
subtype3	66	57

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 = 0.00077 (Fisher's exact test), Q value = 0.092

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

nPatients	M0	M1	MX
ALL	257	9	199
subtype1	105	2	51
subtype2	71	4	89
subtype3	81	3	59

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 = 0.904 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	342	124
subtype1	118	40
subtype2	119	45
subtype3	105	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 = 0.0016

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	336	90	31
subtype1	3	124	7	24
subtype2	2	113	46	3
subtype3	4	99	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 = 2e-04 (Fisher's exact test), Q value = 0.025

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

nPatients	NO	YES
ALL	14	452
subtype1	2	156
subtype2	12	152
subtype3	0	144

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 'RADIATIONEXPOSURE'

P value = 0.429 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	392	16
subtype1	132	5
subtype2	136	8
subtype3	124	3

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

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

P value = 1e-05 (Fisher's exact test), Q value = 0.0016

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	122	15	309	1
subtype1	60	12	78	1
subtype2	30	2	127	0
subtype3	32	1	104	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 = 0.0384 (Fisher's exact test), Q value = 1

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

nPatients	R0	R1	R2	RX
ALL	355	48	4	29
subtype1	110	24	3	9
subtype2	137	9	1	11
subtype3	108	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 = 0.00582 (Kruskal-Wallis (anova)), Q value = 0.61

Table S158. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	363	3.6 (6.2)
subtype1	134	4.6 (6.9)
subtype2	121	2.9 (4.7)
subtype3	108	3.3 (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 = 0.878 (Fisher's exact test), Q value = 1

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

nPatients	MULTIFOCAL	UNIFOCAL
ALL	213	245
subtype1	74	84
subtype2	71	87
subtype3	68	74

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 = 0.11 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	373	3.0 (1.6)
subtype1	123	2.9 (1.7)
subtype2	132	3.1 (1.5)
subtype3	118	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 = 0.027 (Fisher's exact test), Q value = 1

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	46	25	304
subtype1	0	18	5	121
subtype2	0	11	7	106
subtype3	1	17	13	77

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	332
subtype1	14	128
subtype2	12	114
subtype3	9	90

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	161	127	178

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

P value = 0.00244 (logrank test), Q value = 0.26

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	462	14	0.0 - 158.8 (16.4)
subtype1	160	10	0.0 - 157.2 (14.7)
subtype2	126	3	0.3 - 132.4 (14.2)
subtype3	176	1	0.1 - 158.8 (19.9)

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 'AGE'

P value = 0.000242 (Kruskal-Wallis (anova)), Q value = 0.03

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

	nPatients	Mean (Std.Dev)
ALL	466	47.2 (15.6)
subtype1	161	49.9 (15.9)
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: 'AGE'

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

P value = 1e-05 (Fisher's exact test), Q value = 0.0016

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	264	50	102	2	40	6
subtype1	75	4	57	1	22	2
subtype2	70	27	23	1	3	2
subtype3	119	19	22	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 = 6e-05 (Fisher's exact test), Q value = 0.0078

Table S167. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	134	157	152	20
subtype1	45	34	67	13
subtype2	39	54	33	1
subtype3	50	69	52	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 = 0.0016

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

nPatients	0	1
ALL	211	208
subtype1	58	95
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 = 6e-05 (Fisher's exact test), Q value = 0.0078

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

nPatients	M0	M1	MX
ALL	257	9	199
subtype1	110	2	49
subtype2	51	3	72
subtype3	96	4	78

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 = 0.156 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	342	124
subtype1	123	38
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 = 0.0016

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	336	90	31
subtype1	3	127	7	24
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 = 0.119 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	14	452
subtype1	2	159
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 'RADIATIONEXPOSURE'

P value = 0.853 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	392	16
subtype1	135	6
subtype2	106	5
subtype3	151	5

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

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

P value = 1e-05 (Fisher's exact test), Q value = 0.0016

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	122	15	309	1
subtype1	62	12	79	1
subtype2	17	0	103	0
subtype3	43	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 = 0.0585 (Fisher's exact test), Q value = 1

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

nPatients	R0	R1	R2	RX
ALL	355	48	4	29
subtype1	112	26	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 = 1.29e-08 (Kruskal-Wallis (anova)), Q value = 2.1e-06

Table S176. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	363	3.6 (6.2)
subtype1	133	4.6 (6.3)
subtype2	86	1.3 (3.2)
subtype3	144	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 = 0.633 (Fisher's exact test), Q value = 1

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

nPatients	MULTIFOCAL	UNIFOCAL
ALL	213	245
subtype1	71	89
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 = 0.0208 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	373	3.0 (1.6)
subtype1	122	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 = 0.0253 (Fisher's exact test), Q value = 1

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	46	25	304
subtype1	0	25	4	119
subtype2	0	6	9	68
subtype3	1	15	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 = 1

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	35	332
subtype1	10	133
subtype2	7	77
subtype3	18	122

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

Methods & Data

Input

Cluster data file = THCA-TP.mergedcluster.txt
Clinical data file = THCA-TP.merged_data.txt
Number of patients = 497
Number of clustering approaches = 10
Number of selected clinical features = 17
Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches

CNMF clustering

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

Consensus hierarchical clustering

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

Survival analysis

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

Fisher's exact test

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

Q value calculation

For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.

Download Results

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

References

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

[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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