Correlation between aggregated molecular cancer subtypes and selected clinical features

Thyroid Adenocarcinoma (Primary solid tumor)

15 July 2014 | analyses__2014_07_15

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/C1QJ7G3W

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 490 patients, 57 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.T.STAGE', 'PATHOLOGY.N.STAGE', 'HISTOLOGICAL.TYPE', 'EXTRATHYROIDAL.EXTENSION', and 'NUMBER.OF.LYMPH.NODES'.
CNMF clustering analysis on RPPA data identified 3 subtypes that correlate to 'HISTOLOGICAL.TYPE' and 'EXTRATHYROIDAL.EXTENSION'.
Consensus hierarchical clustering analysis on RPPA data identified 7 subtypes that correlate to 'Time to Death', 'AGE', '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', 'HISTOLOGICAL.TYPE', 'EXTRATHYROIDAL.EXTENSION', and 'NUMBER.OF.LYMPH.NODES'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.STAGE', 'PATHOLOGY.N.STAGE', 'PATHOLOGY.M.STAGE', 'HISTOLOGICAL.TYPE', 'EXTRATHYROIDAL.EXTENSION', and 'NUMBER.OF.LYMPH.NODES'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to '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', 'EXTRATHYROIDAL.EXTENSION', and 'NUMBER.OF.LYMPH.NODES'.
4 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, 57 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	0.0778 (1.00)	0.269 (1.00)	0.559 (1.00)	0.00217 (0.247)	0.664 (1.00)	0.738 (1.00)	0.0322 (1.00)	0.00442 (0.482)	0.00487 (0.516)	0.0133 (1.00)
AGE	Kruskal-Wallis (anova)	4.11e-05 (0.00534)	0.174 (1.00)	0.00271 (0.306)	0.00157 (0.182)	0.103 (1.00)	0.00545 (0.572)	0.14 (1.00)	0.000361 (0.0437)	0.0966 (1.00)	0.000104 (0.0132)
NEOPLASM DISEASESTAGE	Fisher's exact test	0.00093 (0.11)	1e-05 (0.00163)	0.00278 (0.311)	0.00325 (0.361)	1e-05 (0.00163)	1e-05 (0.00163)	1e-05 (0.00163)	1e-05 (0.00163)	5e-05 (0.00645)	1e-05 (0.00163)
PATHOLOGY T STAGE	Fisher's exact test	0.346 (1.00)	0.0018 (0.207)	0.00644 (0.67)	0.00014 (0.0174)	3e-05 (0.00393)	1e-05 (0.00163)	0.00013 (0.0164)	1e-05 (0.00163)	0.00047 (0.0564)	1e-05 (0.00163)
PATHOLOGY N STAGE	Fisher's exact test	0.0935 (1.00)	1e-05 (0.00163)	0.0311 (1.00)	0.0048 (0.514)	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 M STAGE	Fisher's exact test	0.0578 (1.00)	0.198 (1.00)	0.169 (1.00)	0.11 (1.00)	0.127 (1.00)	0.0165 (1.00)	0.00053 (0.0631)	0.00013 (0.0164)	0.00021 (0.0258)	0.00033 (0.0403)
GENDER	Fisher's exact test	0.404 (1.00)	0.958 (1.00)	0.611 (1.00)	0.944 (1.00)	0.899 (1.00)	0.543 (1.00)	0.902 (1.00)	0.567 (1.00)	0.7 (1.00)	0.0809 (1.00)
HISTOLOGICAL TYPE	Fisher's exact test	0.322 (1.00)	1e-05 (0.00163)	6e-05 (0.00768)	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	0.381 (1.00)	0.114 (1.00)	0.0111 (1.00)	0.192 (1.00)	0.00765 (0.788)	0.0938 (1.00)	0.437 (1.00)	0.0222 (1.00)	0.0901 (1.00)	0.0153 (1.00)
RADIATIONEXPOSURE	Fisher's exact test	0.143 (1.00)	0.648 (1.00)	1 (1.00)	0.231 (1.00)	0.938 (1.00)	0.748 (1.00)	1 (1.00)	0.859 (1.00)	0.823 (1.00)	0.691 (1.00)
EXTRATHYROIDAL EXTENSION	Fisher's exact test	0.0834 (1.00)	1e-05 (0.00163)	0.0014 (0.164)	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	0.328 (1.00)	0.282 (1.00)	0.303 (1.00)	0.0949 (1.00)	0.193 (1.00)	0.0599 (1.00)	0.186 (1.00)	0.0868 (1.00)	0.0852 (1.00)	0.0145 (1.00)
NUMBER OF LYMPH NODES	Kruskal-Wallis (anova)	0.0226 (1.00)	6.76e-10 (1.11e-07)	0.512 (1.00)	0.314 (1.00)	4.3e-10 (7.14e-08)	3.64e-11 (6.15e-09)	1.88e-10 (3.14e-08)	1.26e-10 (2.11e-08)	2.58e-09 (4.24e-07)	1.17e-12 (1.99e-10)
MULTIFOCALITY	Fisher's exact test	0.275 (1.00)	0.91 (1.00)	0.0126 (1.00)	0.115 (1.00)	0.343 (1.00)	0.196 (1.00)	0.839 (1.00)	0.796 (1.00)	0.467 (1.00)	0.935 (1.00)
TUMOR SIZE	Kruskal-Wallis (anova)	0.0107 (1.00)	0.727 (1.00)	0.688 (1.00)	0.0181 (1.00)	0.122 (1.00)	0.038 (1.00)	0.00465 (0.503)	0.077 (1.00)	0.00973 (0.992)	0.0486 (1.00)
RACE	Fisher's exact test	0.814 (1.00)	0.844 (1.00)	0.0619 (1.00)	0.169 (1.00)	0.825 (1.00)	0.0155 (1.00)	0.184 (1.00)	0.232 (1.00)	0.0648 (1.00)	0.00337 (0.371)
ETHNICITY	Fisher's exact test	0.893 (1.00)	0.5 (1.00)	0.146 (1.00)	0.932 (1.00)	0.0102 (1.00)	0.0166 (1.00)	0.388 (1.00)	0.702 (1.00)	0.369 (1.00)	0.0815 (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	33	377	76

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

P value = 0.0778 (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	482	14	0.0 - 158.8 (15.8)
subtype1	32	3	0.0 - 98.6 (14.5)
subtype2	374	10	0.1 - 158.8 (16.4)
subtype3	76	1	0.0 - 85.2 (14.6)

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

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 4.11e-05 (Kruskal-Wallis (anova)), Q value = 0.0053

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

	nPatients	Mean (Std.Dev)
ALL	486	47.2 (15.6)
subtype1	33	59.5 (14.7)
subtype2	377	46.2 (15.7)
subtype3	76	46.7 (13.4)

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

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

P value = 0.00093 (Fisher's exact test), Q value = 0.11

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	275	50	107	2	44	6
subtype1	9	10	10	1	2	1
subtype2	220	33	84	0	34	4
subtype3	46	7	13	1	8	1

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

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

P value = 0.346 (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	139	163	161	21
subtype1	5	13	13	2
subtype2	109	121	129	16
subtype3	25	29	19	3

Figure S4. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.0935 (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	220	219
subtype1	18	8
subtype2	164	178
subtype3	38	33

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

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

P value = 0.0578 (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	267	9	209
subtype1	11	1	21
subtype2	216	6	154
subtype3	40	2	34

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

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.404 (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	357	129
subtype1	21	12
subtype2	280	97
subtype3	56	20

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

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

P value = 0.322 (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	345	97	35
subtype1	0	22	10	1
subtype2	8	271	67	31
subtype3	1	52	20	3

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

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

P value = 0.381 (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	472
subtype1	2	31
subtype2	11	366
subtype3	1	75

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 = 0.143 (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	409	17
subtype1	29	1
subtype2	311	16
subtype3	69	0

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

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

P value = 0.0834 (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	128	16	323	1
subtype1	7	0	24	1
subtype2	106	15	243	0
subtype3	15	1	56	0

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

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

P value = 0.328 (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	373	49	4	29
subtype1	23	4	0	4
subtype2	293	39	3	18
subtype3	57	6	1	7

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

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

P value = 0.0226 (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	386	3.5 (6.2)
subtype1	24	1.4 (3.5)
subtype2	302	3.8 (6.5)
subtype3	60	3.1 (5.4)

Figure S13. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'

'Copy Number Ratio CNMF subtypes' versus 'MULTIFOCALITY'

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

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

nPatients	MULTIFOCAL	UNIFOCAL
ALL	219	257
subtype1	12	21
subtype2	168	201
subtype3	39	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 = 0.0107 (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	388	3.0 (1.6)
subtype1	28	3.8 (1.6)
subtype2	301	2.9 (1.6)
subtype3	59	2.8 (1.4)

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 = 0.814 (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	20	318
subtype1	0	1	1	21
subtype2	1	39	16	251
subtype3	0	9	3	46

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 = 0.893 (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	341
subtype1	1	21
subtype2	31	269
subtype3	6	51

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	271	151	68

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.269 (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	486	14	0.0 - 158.8 (15.7)
subtype1	271	7	0.1 - 158.8 (17.4)
subtype2	149	3	0.0 - 132.4 (14.1)
subtype3	66	4	0.0 - 147.4 (13.9)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.174 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	490	47.2 (15.6)
subtype1	271	47.1 (15.5)
subtype2	151	48.5 (15.1)
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	277	51	108	2	44	6
subtype1	145	19	67	0	35	4
subtype2	88	30	26	1	3	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 = 0.0018 (Fisher's exact test), Q value = 0.21

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

nPatients	T1	T2	T3	T4
ALL	140	165	162	21
subtype1	63	88	101	18
subtype2	54	57	39	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	223	219
subtype1	94	156
subtype2	100	27
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 = 0.198 (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	270	9	210
subtype1	159	6	106
subtype2	72	3	75
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 = 0.958 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	360	130
subtype1	199	72
subtype2	110	41
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	347	99	35
subtype1	5	220	17	29
subtype2	2	72	77	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	476
subtype1	11	260
subtype2	1	150
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	412	17
subtype1	233	8
subtype2	122	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	129	16	326	1
subtype1	93	15	157	0
subtype2	21	0	121	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 = 0.282 (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	376	50	4	29
subtype1	204	34	4	18
subtype2	121	9	0	7
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 = 6.76e-10 (Kruskal-Wallis (anova)), Q value = 1.1e-07

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

	nPatients	Mean (Std.Dev)
ALL	388	3.5 (6.2)
subtype1	227	4.4 (6.6)
subtype2	105	1.4 (4.0)
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 = 0.91 (Fisher's exact test), Q value = 1

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

nPatients	MULTIFOCAL	UNIFOCAL
ALL	222	258
subtype1	125	141
subtype2	68	80
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 = 0.727 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	391	3.0 (1.6)
subtype1	219	2.9 (1.6)
subtype2	119	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 = 0.844 (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	22	319
subtype1	1	29	15	186
subtype2	0	13	6	82
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 = 0.5 (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	344
subtype1	24	201
subtype2	7	93
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	80

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.559 (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	219	13	0.1 - 158.8 (17.6)
subtype1	61	4	1.2 - 158.8 (16.3)
subtype2	79	4	0.2 - 147.4 (20.6)
subtype3	79	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 = 0.00271 (Kruskal-Wallis (anova)), Q value = 0.31

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

	nPatients	Mean (Std.Dev)
ALL	220	48.3 (16.7)
subtype1	61	51.6 (15.3)
subtype2	79	50.7 (16.0)
subtype3	80	43.3 (17.4)

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 = 0.00278 (Fisher's exact test), Q value = 0.31

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	115	33	45	21	4
subtype1	31	11	14	2	1
subtype2	31	13	21	14	0
subtype3	53	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 = 0.00644 (Fisher's exact test), Q value = 0.67

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

nPatients	T1	T2	T3	T4
ALL	51	83	74	11
subtype1	22	23	15	0
subtype2	13	25	33	8
subtype3	16	35	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 = 0.0311 (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	97	94
subtype1	34	18
subtype2	29	41
subtype3	34	35

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 = 0.169 (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	116	5	98
subtype1	38	1	21
subtype2	43	1	35
subtype3	35	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 = 0.611 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	152	68
subtype1	43	18
subtype2	57	22
subtype3	52	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 = 6e-05 (Fisher's exact test), Q value = 0.0077

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	156	52	10
subtype1	0	35	25	1
subtype2	1	55	14	9
subtype3	1	66	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 = 0.0111 (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	207
subtype1	0	61
subtype2	9	70
subtype3	4	76

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	182	11
subtype1	52	3
subtype2	67	4
subtype3	63	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 = 0.0014 (Fisher's exact test), Q value = 0.16

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

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

Figure 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 = 0.303 (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	166	23	2	14
subtype1	48	6	1	5
subtype2	58	13	0	4
subtype3	60	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 = 0.512 (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	169	3.4 (5.8)
subtype1	43	3.8 (7.5)
subtype2	65	3.1 (4.5)
subtype3	61	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 = 0.0126 (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	112
subtype1	35	23
subtype2	27	50
subtype3	39	39

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 = 0.688 (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	189	3.3 (1.6)
subtype1	52	3.2 (1.6)
subtype2	66	3.4 (1.5)
subtype3	71	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 = 0.0619 (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	143
subtype1	2	7	34
subtype2	3	4	59
subtype3	8	2	50

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

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.146 (Fisher's exact test), Q value = 1

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	16	162
subtype1	1	45
subtype2	8	58
subtype3	7	59

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	28	48	27	9

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.00217 (logrank test), Q value = 0.25

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	219	13	0.1 - 158.8 (17.6)
subtype1	47	4	1.2 - 158.8 (17.9)
subtype2	28	1	1.4 - 66.8 (12.8)
subtype3	33	0	0.2 - 127.0 (21.5)
subtype4	28	2	0.3 - 155.5 (17.4)
subtype5	47	1	0.1 - 147.8 (15.2)
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 = 0.00157 (Kruskal-Wallis (anova)), Q value = 0.18

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

	nPatients	Mean (Std.Dev)
ALL	220	48.3 (16.7)
subtype1	47	52.5 (16.1)
subtype2	28	49.6 (15.4)
subtype3	33	47.6 (15.3)
subtype4	28	46.3 (19.0)
subtype5	48	40.2 (14.9)
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 = 0.00325 (Fisher's exact test), Q value = 0.36

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	115	33	45	21	4
subtype1	26	11	6	1	1
subtype2	16	3	7	2	0
subtype3	13	4	11	5	0
subtype4	16	4	6	1	1
subtype5	34	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 = 0.00014 (Fisher's exact test), Q value = 0.017

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

nPatients	T1	T2	T3	T4
ALL	51	83	74	11
subtype1	16	22	9	0
subtype2	11	9	8	0
subtype3	3	11	17	2
subtype4	4	10	12	1
subtype5	10	21	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 = 0.0048 (Fisher's exact test), Q value = 0.51

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

nPatients	0	1
ALL	97	94
subtype1	28	11
subtype2	13	13
subtype3	10	22
subtype4	17	8
subtype5	15	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 = 0.11 (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	116	5	98
subtype1	21	1	24
subtype2	19	0	9
subtype3	21	1	11
subtype4	12	1	15
subtype5	25	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 = 0.944 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	152	68
subtype1	31	16
subtype2	18	10
subtype3	25	8
subtype4	20	8
subtype5	32	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	156	52	10
subtype1	0	22	25	0
subtype2	0	22	5	1
subtype3	0	25	0	8
subtype4	0	21	7	0
subtype5	1	45	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 = 0.192 (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	207
subtype1	0	47
subtype2	2	26
subtype3	4	29
subtype4	1	27
subtype5	3	45
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 = 0.231 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	182	11
subtype1	39	3
subtype2	26	0
subtype3	29	0
subtype4	21	2
subtype5	38	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	56	10	146
subtype1	4	0	42
subtype2	9	0	19
subtype3	15	3	14
subtype4	6	1	17
subtype5	13	0	34
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 = 0.0949 (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	166	23	2	14
subtype1	37	5	0	3
subtype2	22	3	0	2
subtype3	24	7	0	0
subtype4	18	2	2	1
subtype5	38	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 = 0.314 (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	169	3.4 (5.8)
subtype1	31	2.9 (6.6)
subtype2	25	3.9 (7.0)
subtype3	26	3.3 (3.9)
subtype4	21	2.7 (4.3)
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 = 0.115 (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	112
subtype1	28	16
subtype2	12	16
subtype3	12	20
subtype4	12	16
subtype5	25	21
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 = 0.0181 (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	189	3.3 (1.6)
subtype1	38	3.4 (1.5)
subtype2	27	2.6 (1.6)
subtype3	29	3.3 (1.3)
subtype4	23	3.6 (1.5)
subtype5	44	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 = 0.169 (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	143
subtype1	0	6	24
subtype2	2	1	18
subtype3	2	0	28
subtype4	2	1	18
subtype5	6	2	31
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 = 0.932 (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	162
subtype1	2	31
subtype2	1	23
subtype3	3	27
subtype4	2	20
subtype5	5	36
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	154	52	115	165

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.664 (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	482	14	0.0 - 158.8 (15.8)
subtype1	153	4	0.0 - 132.4 (14.5)
subtype2	51	1	0.0 - 157.2 (13.6)
subtype3	114	2	0.1 - 158.8 (15.6)
subtype4	164	7	0.1 - 155.5 (17.8)

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 = 0.103 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	486	47.2 (15.7)
subtype1	154	48.9 (15.5)
subtype2	52	44.4 (17.4)
subtype3	115	44.8 (14.6)
subtype4	165	48.2 (15.8)

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	275	51	106	2	44	6
subtype1	90	29	27	1	4	2
subtype2	36	1	12	0	3	0
subtype3	66	14	22	0	10	2
subtype4	83	7	45	1	27	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 = 3e-05 (Fisher's exact test), Q value = 0.0039

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

nPatients	T1	T2	T3	T4
ALL	139	163	161	21
subtype1	55	56	41	2
subtype2	22	14	14	1
subtype3	33	48	30	4
subtype4	29	45	76	14

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	221	218
subtype1	102	28
subtype2	20	32
subtype3	48	58
subtype4	51	100

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 = 0.127 (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	267	9	209
subtype1	71	3	79
subtype2	34	0	18
subtype3	70	3	42
subtype4	92	3	70

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

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

nPatients	FEMALE	MALE
ALL	356	130
subtype1	111	43
subtype2	39	13
subtype3	87	28
subtype4	119	46

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	344	99	34
subtype1	2	70	81	1
subtype2	1	44	3	4
subtype3	2	98	12	3
subtype4	4	132	3	26

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 = 0.00765 (Fisher's exact test), Q value = 0.79

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

nPatients	NO	YES
ALL	14	472
subtype1	1	153
subtype2	0	52
subtype3	2	113
subtype4	11	154

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 = 0.938 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	408	17
subtype1	128	6
subtype2	41	2
subtype3	97	4
subtype4	142	5

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	128	16	323	1
subtype1	22	1	123	0
subtype2	12	1	36	0
subtype3	28	1	83	0
subtype4	66	13	81	1

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 = 0.193 (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	373	49	4	29
subtype1	123	10	0	7
subtype2	36	5	0	5
subtype3	89	9	2	8
subtype4	125	25	2	9

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 = 4.3e-10 (Kruskal-Wallis (anova)), Q value = 7.1e-08

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

	nPatients	Mean (Std.Dev)
ALL	385	3.5 (6.2)
subtype1	111	1.5 (4.1)
subtype2	47	5.0 (7.7)
subtype3	91	3.9 (6.3)
subtype4	136	4.4 (6.6)

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 = 0.343 (Fisher's exact test), Q value = 1

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

nPatients	MULTIFOCAL	UNIFOCAL
ALL	219	257
subtype1	72	79
subtype2	22	28
subtype3	58	54
subtype4	67	96

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 = 0.122 (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	388	3.0 (1.6)
subtype1	121	3.0 (1.5)
subtype2	36	2.6 (1.8)
subtype3	91	2.8 (1.5)
subtype4	140	3.1 (1.6)

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 = 0.825 (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	20	319
subtype1	0	10	6	88
subtype2	0	6	1	38
subtype3	0	16	5	73
subtype4	1	16	8	120

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

'RNAseq CNMF subtypes' versus 'ETHNICITY'

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

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	38	340
subtype1	9	94
subtype2	3	41
subtype3	3	87
subtype4	23	118

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	127	97	72	113	77

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.738 (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	482	14	0.0 - 158.8 (15.8)
subtype1	126	3	0.2 - 132.4 (14.1)
subtype2	97	2	0.0 - 130.7 (13.6)
subtype3	71	2	0.6 - 158.8 (17.5)
subtype4	111	6	0.7 - 157.2 (17.5)
subtype5	77	1	0.1 - 147.8 (18.1)

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 = 0.00545 (Kruskal-Wallis (anova)), Q value = 0.57

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

	nPatients	Mean (Std.Dev)
ALL	486	47.2 (15.7)
subtype1	127	49.1 (15.8)
subtype2	97	45.4 (15.5)
subtype3	72	46.2 (13.8)
subtype4	113	50.4 (16.5)
subtype5	77	42.6 (14.9)

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	275	51	106	2	44	6
subtype1	73	28	19	1	3	2
subtype2	62	7	21	0	7	0
subtype3	40	8	14	0	7	2
subtype4	50	2	41	1	18	1
subtype5	50	6	11	0	9	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	139	163	161	21
subtype1	43	51	31	2
subtype2	38	32	25	1
subtype3	19	32	18	3
subtype4	21	22	59	10
subtype5	18	26	28	5

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	221	218
subtype1	86	17
subtype2	50	44
subtype3	31	33
subtype4	29	77
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 = 0.0165 (Fisher's exact test), Q value = 1

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

nPatients	M0	M1	MX
ALL	267	9	209
subtype1	53	3	70
subtype2	63	1	33
subtype3	44	2	26
subtype4	60	1	52
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 = 0.543 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	356	130
subtype1	92	35
subtype2	71	26
subtype3	55	17
subtype4	87	26
subtype5	51	26

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	344	99	34
subtype1	2	53	72	0
subtype2	1	74	19	3
subtype3	0	65	5	2
subtype4	2	84	2	25
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 = 0.0938 (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	472
subtype1	1	126
subtype2	1	96
subtype3	2	70
subtype4	5	108
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 = 0.748 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	408	17
subtype1	104	5
subtype2	77	5
subtype3	63	1
subtype4	102	4
subtype5	62	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	128	16	323	1
subtype1	13	1	105	0
subtype2	24	0	70	0
subtype3	14	1	54	0
subtype4	56	10	44	1
subtype5	21	4	50	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 = 0.0599 (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	373	49	4	29
subtype1	99	8	0	7
subtype2	78	7	1	5
subtype3	56	5	1	6
subtype4	77	23	2	7
subtype5	63	6	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 = 3.64e-11 (Kruskal-Wallis (anova)), Q value = 6.1e-09

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

	nPatients	Mean (Std.Dev)
ALL	385	3.5 (6.2)
subtype1	87	1.1 (3.0)
subtype2	78	3.8 (7.0)
subtype3	55	3.3 (5.8)
subtype4	103	5.4 (7.8)
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 = 0.196 (Fisher's exact test), Q value = 1

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

nPatients	MULTIFOCAL	UNIFOCAL
ALL	219	257
subtype1	59	65
subtype2	46	48
subtype3	39	31
subtype4	44	69
subtype5	31	44

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 = 0.038 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	388	3.0 (1.6)
subtype1	103	3.2 (1.5)
subtype2	71	2.5 (1.5)
subtype3	57	2.8 (1.5)
subtype4	98	3.0 (1.5)
subtype5	59	3.1 (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 = 0.0155 (Fisher's exact test), Q value = 1

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	20	319
subtype1	0	6	5	69
subtype2	0	20	2	62
subtype3	0	7	4	47
subtype4	1	5	7	86
subtype5	0	10	2	55

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

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.0166 (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	340
subtype1	9	72
subtype2	2	80
subtype3	3	50
subtype4	16	83
subtype5	8	55

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	150	170	167

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.0322 (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	483	14	0.0 - 158.8 (15.8)
subtype1	149	3	0.2 - 132.4 (15.0)
subtype2	168	2	0.1 - 147.8 (17.1)
subtype3	166	9	0.0 - 158.8 (14.7)

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

'MIRSEQ CNMF' versus 'AGE'

P value = 0.14 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	487	47.3 (15.6)
subtype1	150	46.9 (16.0)
subtype2	170	45.7 (14.8)
subtype3	167	49.1 (16.0)

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	275	51	107	2	44	6
subtype1	93	29	20	1	4	2
subtype2	99	17	35	0	17	2
subtype3	83	5	52	1	23	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 = 0.00013 (Fisher's exact test), Q value = 0.016

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

nPatients	T1	T2	T3	T4
ALL	140	164	160	21
subtype1	43	68	37	2
subtype2	44	61	58	7
subtype3	53	35	65	12

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	222	218
subtype1	96	27
subtype2	67	90
subtype3	59	101

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

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

P value = 0.00053 (Fisher's exact test), Q value = 0.063

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

nPatients	M0	M1	MX
ALL	267	9	210
subtype1	64	3	82
subtype2	92	4	74
subtype3	111	2	54

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

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.902 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	357	130
subtype1	108	42
subtype2	125	45
subtype3	124	43

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	344	99	35
subtype1	2	66	82	0
subtype2	3	146	9	12
subtype3	4	132	8	23

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 = 0.437 (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	473
subtype1	4	146
subtype2	7	163
subtype3	3	164

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

'MIRSEQ CNMF' versus 'RADIATIONEXPOSURE'

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

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

nPatients	NO	YES
ALL	409	17
subtype1	126	5
subtype2	142	6
subtype3	141	6

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	127	16	325	1
subtype1	15	0	128	0
subtype2	53	5	107	0
subtype3	59	11	90	1

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 = 0.186 (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	374	49	4	29
subtype1	119	10	0	9
subtype2	135	15	2	8
subtype3	120	24	2	12

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 = 1.88e-10 (Kruskal-Wallis (anova)), Q value = 3.1e-08

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

	nPatients	Mean (Std.Dev)
ALL	386	3.5 (6.2)
subtype1	106	1.4 (3.1)
subtype2	141	3.5 (6.0)
subtype3	139	5.2 (7.5)

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 = 0.839 (Fisher's exact test), Q value = 1

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

nPatients	MULTIFOCAL	UNIFOCAL
ALL	219	258
subtype1	64	81
subtype2	76	90
subtype3	79	87

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

'MIRSEQ CNMF' versus 'TUMOR.SIZE'

P value = 0.00465 (Kruskal-Wallis (anova)), Q value = 0.5

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

	nPatients	Mean (Std.Dev)
ALL	388	3.0 (1.6)
subtype1	124	3.2 (1.5)
subtype2	137	3.0 (1.6)
subtype3	127	2.7 (1.6)

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

'MIRSEQ CNMF' versus 'RACE'

P value = 0.184 (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	20	319
subtype1	0	8	8	85
subtype2	1	16	7	114
subtype3	0	25	5	120

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

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.388 (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	341
subtype1	10	91
subtype2	17	118
subtype3	11	132

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	127	201	159

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.00442 (logrank test), Q value = 0.48

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	483	14	0.0 - 158.8 (15.8)
subtype1	126	3	0.3 - 132.4 (14.6)
subtype2	199	1	0.1 - 147.8 (17.5)
subtype3	158	10	0.0 - 158.8 (14.6)

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

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.000361 (Kruskal-Wallis (anova)), Q value = 0.044

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

	nPatients	Mean (Std.Dev)
ALL	487	47.3 (15.6)
subtype1	127	48.8 (15.5)
subtype2	201	43.8 (14.9)
subtype3	159	50.4 (15.8)

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	275	51	107	2	44	6
subtype1	74	28	18	1	3	2
subtype2	125	19	36	0	19	2
subtype3	76	4	53	1	22	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 = 1e-05 (Fisher's exact test), Q value = 0.0016

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

nPatients	T1	T2	T3	T4
ALL	140	164	160	21
subtype1	42	53	31	1
subtype2	50	80	64	7
subtype3	48	31	65	13

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	222	218
subtype1	86	18
subtype2	77	109
subtype3	59	91

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

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

P value = 0.00013 (Fisher's exact test), Q value = 0.016

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

nPatients	M0	M1	MX
ALL	267	9	210
subtype1	54	3	69
subtype2	103	4	94
subtype3	110	2	47

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

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.567 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	357	130
subtype1	90	37
subtype2	146	55
subtype3	121	38

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	344	99	35
subtype1	2	49	76	0
subtype2	4	172	15	10
subtype3	3	123	8	25

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 = 0.0222 (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	473
subtype1	1	126
subtype2	11	190
subtype3	2	157

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

'MIRSEQ CHIERARCHICAL' versus 'RADIATIONEXPOSURE'

P value = 0.859 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	409	17
subtype1	105	5
subtype2	171	6
subtype3	133	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	127	16	325	1
subtype1	13	0	107	0
subtype2	51	4	140	0
subtype3	63	12	78	1

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 = 0.0868 (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	374	49	4	29
subtype1	101	9	0	5
subtype2	161	17	1	11
subtype3	112	23	3	13

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 = 1.26e-10 (Kruskal-Wallis (anova)), Q value = 2.1e-08

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

	nPatients	Mean (Std.Dev)
ALL	386	3.5 (6.2)
subtype1	91	1.4 (4.3)
subtype2	165	3.8 (6.4)
subtype3	130	4.7 (6.7)

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

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

nPatients	MULTIFOCAL	UNIFOCAL
ALL	219	258
subtype1	60	65
subtype2	90	105
subtype3	69	88

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

'MIRSEQ CHIERARCHICAL' versus 'TUMOR.SIZE'

P value = 0.077 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	388	3.0 (1.6)
subtype1	106	3.1 (1.5)
subtype2	159	3.0 (1.5)
subtype3	123	2.8 (1.6)

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

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.232 (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	20	319
subtype1	0	5	6	69
subtype2	1	21	9	132
subtype3	0	23	5	118

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

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.702 (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	341
subtype1	8	72
subtype2	18	139
subtype3	12	130

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	163	166	158

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

P value = 0.00487 (logrank test), Q value = 0.52

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	483	14	0.0 - 158.8 (15.8)
subtype1	162	3	0.3 - 137.9 (15.2)
subtype2	164	1	0.1 - 155.5 (17.5)
subtype3	157	10	0.0 - 158.8 (14.6)

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 = 0.0966 (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	487	47.3 (15.6)
subtype1	163	47.6 (15.6)
subtype2	166	45.1 (14.7)
subtype3	158	49.2 (16.2)

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 = 5e-05 (Fisher's exact test), Q value = 0.0064

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	275	51	107	2	44	6
subtype1	97	28	27	1	7	2
subtype2	99	18	32	0	15	2
subtype3	79	5	48	1	22	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 = 0.00047 (Fisher's exact test), Q value = 0.056

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

nPatients	T1	T2	T3	T4
ALL	140	164	160	21
subtype1	46	68	46	3
subtype2	42	63	56	5
subtype3	52	33	58	13

Figure S140. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

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

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

nPatients	0	1
ALL	222	218
subtype1	101	35
subtype2	65	89
subtype3	56	94

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 = 0.00021 (Fisher's exact test), Q value = 0.026

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

nPatients	M0	M1	MX
ALL	267	9	210
subtype1	70	3	89
subtype2	90	4	72
subtype3	107	2	49

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 = 0.7 (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	357	130
subtype1	116	47
subtype2	122	44
subtype3	119	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	344	99	35
subtype1	2	79	82	0
subtype2	3	140	10	13
subtype3	4	125	7	22

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

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

P value = 0.0901 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	14	473
subtype1	3	160
subtype2	9	157
subtype3	2	156

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 = 0.823 (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	409	17
subtype1	136	7
subtype2	141	5
subtype3	132	5

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	127	16	325	1
subtype1	23	1	131	0
subtype2	49	3	108	0
subtype3	55	12	86	1

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 = 0.0852 (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	374	49	4	29
subtype1	126	12	0	11
subtype2	136	14	2	6
subtype3	112	23	2	12

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 = 2.58e-09 (Kruskal-Wallis (anova)), Q value = 4.2e-07

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

	nPatients	Mean (Std.Dev)
ALL	386	3.5 (6.2)
subtype1	120	1.7 (4.1)
subtype2	137	3.9 (6.8)
subtype3	129	4.8 (6.7)

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 = 0.467 (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	219	258
subtype1	77	80
subtype2	69	94
subtype3	73	84

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 = 0.00973 (Kruskal-Wallis (anova)), Q value = 0.99

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

	nPatients	Mean (Std.Dev)
ALL	388	3.0 (1.6)
subtype1	131	3.2 (1.5)
subtype2	138	2.9 (1.5)
subtype3	119	2.7 (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 = 0.0648 (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	49	20	319
subtype1	0	8	9	91
subtype2	1	16	7	115
subtype3	0	25	4	113

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

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.369 (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	38	341
subtype1	11	97
subtype2	17	118
subtype3	10	126

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	4
Number of samples	124	185	102	76

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

P value = 0.0133 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	483	14	0.0 - 158.8 (15.8)
subtype1	123	3	0.3 - 132.4 (14.1)
subtype2	184	1	0.1 - 147.8 (17.6)
subtype3	102	3	0.0 - 158.8 (14.1)
subtype4	74	7	0.7 - 157.2 (18.2)

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

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.000104 (Kruskal-Wallis (anova)), Q value = 0.013

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

	nPatients	Mean (Std.Dev)
ALL	487	47.3 (15.6)
subtype1	124	48.8 (15.6)
subtype2	185	43.9 (15.0)
subtype3	102	46.8 (14.1)
subtype4	76	53.6 (16.9)

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	275	51	107	2	44	6
subtype1	71	28	19	1	2	2
subtype2	118	18	30	0	17	2
subtype3	62	3	24	0	11	1
subtype4	24	2	34	1	14	1

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

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

nPatients	T1	T2	T3	T4
ALL	140	164	160	21
subtype1	41	53	29	1
subtype2	45	73	60	7
subtype3	43	25	29	3
subtype4	11	13	42	10

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	222	218
subtype1	86	14
subtype2	73	99
subtype3	45	52
subtype4	18	53

Figure S158. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.00033 (Fisher's exact test), Q value = 0.04

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

nPatients	M0	M1	MX
ALL	267	9	210
subtype1	52	3	68
subtype2	95	4	86
subtype3	74	1	27
subtype4	46	1	29

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 = 0.0809 (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	357	130
subtype1	91	33
subtype2	133	52
subtype3	69	33
subtype4	64	12

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	344	99	35
subtype1	2	48	74	0
subtype2	4	156	15	10
subtype3	2	85	9	6
subtype4	1	55	1	19

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 = 0.0153 (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	473
subtype1	1	123
subtype2	10	175
subtype3	0	102
subtype4	3	73

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 = 0.691 (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	409	17
subtype1	103	5
subtype2	156	6
subtype3	84	2
subtype4	66	4

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	127	16	325	1
subtype1	13	0	106	0
subtype2	48	4	128	0
subtype3	23	3	68	0
subtype4	43	9	23	1

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 = 0.0145 (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	374	49	4	29
subtype1	100	8	0	6
subtype2	151	13	1	10
subtype3	72	12	1	9
subtype4	51	16	2	4

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 = 1.17e-12 (Kruskal-Wallis (anova)), Q value = 2e-10

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

	nPatients	Mean (Std.Dev)
ALL	386	3.5 (6.2)
subtype1	87	0.8 (2.4)
subtype2	153	3.9 (6.6)
subtype3	79	5.2 (7.8)
subtype4	67	4.4 (5.4)

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 = 0.935 (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	219	258
subtype1	57	65
subtype2	81	98
subtype3	48	52
subtype4	33	43

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 = 0.0486 (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	388	3.0 (1.6)
subtype1	106	3.1 (1.5)
subtype2	148	3.0 (1.5)
subtype3	69	2.6 (1.7)
subtype4	65	3.0 (1.5)

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 = 0.00337 (Fisher's exact test), Q value = 0.37

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

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	49	20	319
subtype1	0	5	5	68
subtype2	1	19	10	120
subtype3	0	22	3	65
subtype4	0	3	2	66

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 = 0.0815 (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	38	341
subtype1	8	69
subtype2	17	130
subtype3	3	83
subtype4	10	59

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 = 490
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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