Correlation between aggregated molecular cancer subtypes and selected clinical features

Thyroid Adenocarcinoma (Primary solid tumor)

23 September 2013 | analyses__2013_09_23

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 (2013): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1NP22T6

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 15 clinical features across 477 patients, 58 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', 'NEOPLASM.DISEASESTAGE', and 'EXTRATHYROIDAL.EXTENSION'.
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.N.STAGE', 'HISTOLOGICAL.TYPE', 'EXTRATHYROIDAL.EXTENSION', and 'NUMBER.OF.LYMPH.NODES'.
CNMF clustering analysis on RPPA data identified 3 subtypes that correlate to 'AGE', 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.STAGE', 'HISTOLOGICAL.TYPE', and 'EXTRATHYROIDAL.EXTENSION'.
Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that correlate to 'AGE', 'PATHOLOGY.N.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 4 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 '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'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'AGE', 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.STAGE', 'PATHOLOGY.N.STAGE', 'PATHOLOGY.M.STAGE', 'HISTOLOGICAL.TYPE', 'EXTRATHYROIDAL.EXTENSION', and 'NUMBER.OF.LYMPH.NODES'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 15 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 58 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	0.304 (1.00)	0.474 (1.00)	0.6 (1.00)	0.0299 (1.00)	0.397 (1.00)	0.363 (1.00)	0.0585 (1.00)	0.0845 (1.00)	0.00893 (0.777)	0.0336 (1.00)
AGE	ANOVA	4.53e-05 (0.00544)	0.271 (1.00)	0.000216 (0.0235)	0.000372 (0.0383)	0.165 (1.00)	0.125 (1.00)	0.2 (1.00)	0.037 (1.00)	0.0899 (1.00)	0.00267 (0.248)
NEOPLASM DISEASESTAGE	Chi-square test	0.000293 (0.0311)	5.17e-05 (0.00614)	0.00194 (0.183)	0.00561 (0.511)	0.000325 (0.0338)	0.000177 (0.0198)	5.44e-06 (0.00068)	2.46e-07 (3.17e-05)	0.000628 (0.0628)	2.28e-08 (3.07e-06)
PATHOLOGY T STAGE	Chi-square test	0.494 (1.00)	0.00828 (0.729)	0.00145 (0.139)	0.0251 (1.00)	2.79e-06 (0.000357)	5.16e-05 (0.00614)	0.000473 (0.0482)	0.00013 (0.0147)	0.00182 (0.173)	7.2e-05 (0.00842)
PATHOLOGY N STAGE	Fisher's exact test	0.0427 (1.00)	3.25e-12 (4.52e-10)	0.0312 (1.00)	0.000492 (0.0497)	2.19e-13 (3.06e-11)	4.91e-16 (7.02e-14)	3.74e-11 (5.16e-09)	1.73e-13 (2.44e-11)	2e-09 (2.74e-07)	1.28e-13 (1.82e-11)
PATHOLOGY M STAGE	Chi-square test	0.0385 (1.00)	0.109 (1.00)	0.235 (1.00)	0.423 (1.00)	0.0228 (1.00)	0.00503 (0.463)	0.00019 (0.0209)	0.000257 (0.0275)	8.35e-05 (0.00969)	0.000113 (0.0129)
GENDER	Fisher's exact test	0.225 (1.00)	0.864 (1.00)	0.744 (1.00)	0.554 (1.00)	0.846 (1.00)	0.876 (1.00)	0.808 (1.00)	0.694 (1.00)	0.566 (1.00)	0.544 (1.00)
HISTOLOGICAL TYPE	Chi-square test	0.206 (1.00)	1.06e-27 (1.53e-25)	0.000184 (0.0204)	1e-08 (1.36e-06)	1.33e-33 (1.95e-31)	7.84e-37 (1.16e-34)	3.63e-35 (5.33e-33)	6.86e-37 (1.02e-34)	2.22e-30 (3.22e-28)	1.05e-37 (1.57e-35)
RADIATIONS RADIATION REGIMENINDICATION	Fisher's exact test	0.319 (1.00)	0.0863 (1.00)	0.0127 (1.00)	0.24 (1.00)	0.0154 (1.00)	0.124 (1.00)	0.465 (1.00)	0.0466 (1.00)	0.0889 (1.00)	0.018 (1.00)
RADIATIONEXPOSURE	Fisher's exact test	0.164 (1.00)	0.501 (1.00)	1 (1.00)	0.104 (1.00)	0.984 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)	0.821 (1.00)	0.951 (1.00)
EXTRATHYROIDAL EXTENSION	Chi-square test	0.00119 (0.115)	2.92e-06 (0.000367)	0.000778 (0.0771)	0.00116 (0.114)	3.3e-08 (4.35e-06)	1.51e-07 (1.96e-05)	4.02e-08 (5.27e-06)	2.38e-08 (3.19e-06)	2.8e-06 (0.000357)	2.44e-08 (3.25e-06)
COMPLETENESS OF RESECTION	Chi-square test	0.628 (1.00)	0.292 (1.00)	0.299 (1.00)	0.678 (1.00)	0.295 (1.00)	0.761 (1.00)	0.207 (1.00)	0.347 (1.00)	0.118 (1.00)	0.207 (1.00)
NUMBER OF LYMPH NODES	ANOVA	0.204 (1.00)	0.000246 (0.0266)	0.857 (1.00)	0.556 (1.00)	6.28e-06 (0.000772)	0.000105 (0.0121)	7.45e-06 (0.000909)	5.79e-06 (0.000718)	0.000297 (0.0312)	2.58e-05 (0.00312)
MULTIFOCALITY	Fisher's exact test	0.285 (1.00)	0.928 (1.00)	0.00567 (0.511)	0.17 (1.00)	0.199 (1.00)	0.216 (1.00)	0.751 (1.00)	0.985 (1.00)	0.435 (1.00)	0.89 (1.00)
TUMOR SIZE	ANOVA	0.028 (1.00)	0.474 (1.00)	0.508 (1.00)	0.6 (1.00)	0.177 (1.00)	0.0473 (1.00)	0.00815 (0.726)	0.0753 (1.00)	0.0125 (1.00)	0.128 (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	30	371	74

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

P value = 0.304 (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	470	13	0.0 - 158.8 (14.2)
subtype1	29	2	0.0 - 98.6 (12.3)
subtype2	367	10	0.1 - 158.8 (14.5)
subtype3	74	1	0.0 - 85.1 (13.4)

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 = 4.53e-05 (ANOVA), Q value = 0.0054

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

	nPatients	Mean (Std.Dev)
ALL	475	47.0 (15.5)
subtype1	30	59.2 (14.6)
subtype2	371	46.1 (15.7)
subtype3	74	46.7 (13.3)

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 = 0.000293 (Chi-square test), Q value = 0.031

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	270	49	106	2	41	5
subtype1	8	10	9	1	2	0
subtype2	217	32	84	0	32	4
subtype3	45	7	13	1	7	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 = 0.494 (Chi-square 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	137	159	158	19
subtype1	5	12	12	1
subtype2	107	119	127	16
subtype3	25	28	19	2

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 = 0.0427 (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	214	214
subtype1	17	6
subtype2	160	176
subtype3	37	32

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 = 0.0385 (Chi-square 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	258	8	208
subtype1	9	0	21
subtype2	211	6	153
subtype3	38	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 = 0.225 (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	350	125
subtype1	18	12
subtype2	277	94
subtype3	55	19

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 = 0.206 (Chi-square 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	334	97	35
subtype1	0	19	10	1
subtype2	8	265	67	31
subtype3	1	50	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 = 0.319 (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	461
subtype1	2	28
subtype2	11	360
subtype3	1	73

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 = 0.164 (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	401	17
subtype1	26	1
subtype2	307	16
subtype3	68	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 = 0.00119 (Chi-square test), Q value = 0.12

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	124	15	320	1
subtype1	6	0	22	1
subtype2	103	15	242	0
subtype3	15	0	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 = 0.628 (Chi-square 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	369	46	3	28
subtype1	22	3	0	3
subtype2	291	37	3	18
subtype3	56	6	0	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 = 0.204 (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	379	3.6 (6.2)
subtype1	21	1.5 (3.8)
subtype2	299	3.8 (6.5)
subtype3	59	3.0 (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 = 0.285 (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	214	251
subtype1	12	18
subtype2	163	200
subtype3	39	33

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 = 0.028 (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	380	2.9 (1.6)
subtype1	25	3.7 (1.7)
subtype2	298	2.9 (1.6)
subtype3	57	2.7 (1.4)

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

Clustering Approach #2: 'METHLYATION CNMF'

Table S17. Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	145	79	253

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.474 (logrank test), Q value = 1

Table S18. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	472	13	0.0 - 158.8 (14.2)
subtype1	143	3	0.0 - 132.4 (13.6)
subtype2	76	4	0.0 - 157.2 (11.3)
subtype3	253	6	0.1 - 158.8 (15.0)

Figure S16. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.271 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	477	47.1 (15.5)
subtype1	145	48.6 (14.9)
subtype2	79	45.2 (16.4)
subtype3	253	46.7 (15.6)

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

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 5.17e-05 (Chi-square test), Q value = 0.0061

Table S20. 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	271	50	106	2	41	5
subtype1	83	29	26	1	3	2
subtype2	51	2	18	1	7	0
subtype3	137	19	62	0	31	3

Figure S18. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.T.STAGE'

P value = 0.00828 (Chi-square test), Q value = 0.73

Table S21. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	138	160	158	19
subtype1	52	54	38	1
subtype2	26	24	26	2
subtype3	60	82	94	16

Figure S19. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.N.STAGE'

P value = 3.25e-12 (Fisher's exact test), Q value = 4.5e-10

Table S22. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	216	214
subtype1	94	27
subtype2	31	45
subtype3	91	142

Figure S20. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.M.STAGE'

P value = 0.109 (Chi-square test), Q value = 1

Table S23. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	259	8	209
subtype1	66	3	75
subtype2	47	0	32
subtype3	146	5	102

Figure S21. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.864 (Fisher's exact test), Q value = 1

Table S24. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	351	126
subtype1	105	40
subtype2	60	19
subtype3	186	67

Figure S22. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 1.06e-27 (Chi-square test), Q value = 1.5e-25

Table S25. 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	334	99	35
subtype1	2	66	77	0
subtype2	2	65	6	6
subtype3	5	203	16	29

Figure S23. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

P value = 0.0863 (Fisher's exact test), Q value = 1

Table S26. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	14	463
subtype1	1	144
subtype2	2	77
subtype3	11	242

Figure S24. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'RADIATIONEXPOSURE'

P value = 0.501 (Fisher's exact test), Q value = 1

Table S27. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'RADIATIONEXPOSURE'

nPatients	NO	YES
ALL	402	17
subtype1	118	6
subtype2	67	4
subtype3	217	7

Figure S25. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'RADIATIONEXPOSURE'

'METHLYATION CNMF' versus 'EXTRATHYROIDAL.EXTENSION'

P value = 2.92e-06 (Chi-square test), Q value = 0.00037

Table S28. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'EXTRATHYROIDAL.EXTENSION'

nPatients	MINIMAL (T3)	MODERATE/ADVANCED (T4A)	NONE	VERY ADVANCED (T4B)
ALL	124	15	322	1
subtype1	20	0	119	0
subtype2	20	1	54	1
subtype3	84	14	149	0

Figure S26. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'EXTRATHYROIDAL.EXTENSION'

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

P value = 0.292 (Chi-square test), Q value = 1

Table S29. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	371	46	3	28
subtype1	118	8	0	7
subtype2	60	8	0	4
subtype3	193	30	3	17

Figure S27. 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 = 0.000246 (ANOVA), Q value = 0.027

Table S30. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	380	3.6 (6.2)
subtype1	103	1.4 (4.0)
subtype2	67	4.2 (7.1)
subtype3	210	4.4 (6.6)

Figure S28. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'

'METHLYATION CNMF' versus 'MULTIFOCALITY'

P value = 0.928 (Fisher's exact test), Q value = 1

Table S31. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients	MULTIFOCAL	UNIFOCAL
ALL	215	252
subtype1	65	77
subtype2	34	43
subtype3	116	132

Figure S29. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #14: 'MULTIFOCALITY'

'METHLYATION CNMF' versus 'TUMOR.SIZE'

P value = 0.474 (ANOVA), Q value = 1

Table S32. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #15: 'TUMOR.SIZE'

	nPatients	Mean (Std.Dev)
ALL	381	2.9 (1.6)
subtype1	115	3.1 (1.7)
subtype2	63	3.0 (1.6)
subtype3	203	2.9 (1.5)

Figure S30. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #15: 'TUMOR.SIZE'

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S33. Description of clustering approach #3: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	60	76	75

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.6 (logrank test), Q value = 1

Table S34. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	210	12	0.1 - 158.8 (17.5)
subtype1	60	4	1.0 - 158.8 (16.4)
subtype2	76	4	0.2 - 147.4 (21.8)
subtype3	74	4	0.1 - 147.8 (14.5)

Figure S31. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.000216 (ANOVA), Q value = 0.024

Table S35. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	211	48.1 (16.7)
subtype1	60	52.0 (15.2)
subtype2	76	51.1 (16.1)
subtype3	75	41.8 (16.9)

Figure S32. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00194 (Chi-square test), Q value = 0.18

Table S36. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IVA	STAGE IVC
ALL	111	32	45	18	3
subtype1	30	11	14	2	1
subtype2	29	13	21	13	0
subtype3	52	8	10	3	2

Figure S33. 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 = 0.00145 (Chi-square test), Q value = 0.14

Table S37. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	50	80	71	9
subtype1	22	22	15	0
subtype2	13	24	31	8
subtype3	15	34	25	1

Figure S34. 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 = 0.0312 (Fisher's exact test), Q value = 1

Table S38. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	92	90
subtype1	33	18
subtype2	27	40
subtype3	32	32

Figure S35. 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 = 0.235 (Chi-square test), Q value = 1

Table S39. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	109	4	97
subtype1	37	1	21
subtype2	40	1	35
subtype3	32	2	41

Figure S36. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.744 (Fisher's exact test), Q value = 1

Table S40. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	147	64
subtype1	42	18
subtype2	55	21
subtype3	50	25

Figure S37. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.000184 (Chi-square test), Q value = 0.02

Table S41. 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	147	52	10
subtype1	0	34	25	1
subtype2	1	52	14	9
subtype3	1	61	13	0

Figure S38. 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 = 0.0127 (Fisher's exact test), Q value = 1

Table S42. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	13	198
subtype1	0	60
subtype2	9	67
subtype3	4	71

Figure S39. 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 S43. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONEXPOSURE'

nPatients	NO	YES
ALL	176	11
subtype1	52	3
subtype2	64	4
subtype3	60	4

Figure S40. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONEXPOSURE'

'RPPA CNMF subtypes' versus 'EXTRATHYROIDAL.EXTENSION'

P value = 0.000778 (Chi-square test), Q value = 0.077

Table S44. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'EXTRATHYROIDAL.EXTENSION'

nPatients	MINIMAL (T3)	MODERATE/ADVANCED (T4A)	NONE
ALL	53	9	143
subtype1	13	0	47
subtype2	26	8	40
subtype3	14	1	56

Figure S41. 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 = 0.299 (Chi-square test), Q value = 1

Table S45. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	163	20	1	13
subtype1	47	6	1	5
subtype2	57	11	0	4
subtype3	59	3	0	4

Figure S42. 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 = 0.857 (ANOVA), Q value = 1

Table S46. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	164	3.4 (5.9)
subtype1	43	3.8 (7.5)
subtype2	63	3.1 (4.6)
subtype3	58	3.4 (5.9)

Figure S43. 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 = 0.00567 (Fisher's exact test), Q value = 0.51

Table S47. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients	MULTIFOCAL	UNIFOCAL
ALL	97	107
subtype1	35	22
subtype2	25	49
subtype3	37	36

Figure S44. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

'RPPA CNMF subtypes' versus 'TUMOR.SIZE'

P value = 0.508 (ANOVA), Q value = 1

Table S48. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

	nPatients	Mean (Std.Dev)
ALL	183	3.3 (1.6)
subtype1	51	3.2 (1.6)
subtype2	65	3.4 (1.6)
subtype3	67	3.1 (1.5)

Figure S45. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S49. Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	30	89	92

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.0299 (logrank test), Q value = 1

Table S50. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	210	12	0.1 - 158.8 (17.5)
subtype1	30	4	1.1 - 147.4 (20.9)
subtype2	88	2	0.1 - 147.8 (17.5)
subtype3	92	6	0.3 - 158.8 (16.6)

Figure S46. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.000372 (ANOVA), Q value = 0.038

Table S51. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	211	48.1 (16.7)
subtype1	30	55.6 (15.6)
subtype2	89	43.2 (16.2)
subtype3	92	50.3 (16.3)

Figure S47. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00561 (Chi-square test), Q value = 0.51

Table S52. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IVA	STAGE IVC
ALL	111	32	45	18	3
subtype1	7	9	8	6	0
subtype2	53	7	20	8	1
subtype3	51	16	17	4	2

Figure S48. 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 = 0.0251 (Chi-square test), Q value = 1

Table S53. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	50	80	71	9
subtype1	6	11	9	4
subtype2	15	33	36	4
subtype3	29	36	26	1

Figure S49. 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 = 0.000492 (Fisher's exact test), Q value = 0.05

Table S54. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	92	90
subtype1	12	11
subtype2	28	52
subtype3	52	27

Figure S50. 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 = 0.423 (Chi-square test), Q value = 1

Table S55. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	109	4	97
subtype1	13	0	17
subtype2	52	2	35
subtype3	44	2	45

Figure S51. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.554 (Fisher's exact test), Q value = 1

Table S56. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	147	64
subtype1	23	7
subtype2	63	26
subtype3	61	31

Figure S52. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1e-08 (Chi-square test), Q value = 1.4e-06

Table S57. 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	147	52	10
subtype1	1	14	14	1
subtype2	1	78	2	8
subtype3	0	55	36	1

Figure S53. 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 = 0.24 (Fisher's exact test), Q value = 1

Table S58. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	13	198
subtype1	3	27
subtype2	7	82
subtype3	3	89

Figure S54. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA cHierClus subtypes' versus 'RADIATIONEXPOSURE'

P value = 0.104 (Fisher's exact test), Q value = 1

Table S59. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONEXPOSURE'

nPatients	NO	YES
ALL	176	11
subtype1	24	4
subtype2	72	4
subtype3	80	3

Figure S55. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONEXPOSURE'

'RPPA cHierClus subtypes' versus 'EXTRATHYROIDAL.EXTENSION'

P value = 0.00116 (Chi-square test), Q value = 0.11

Table S60. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'EXTRATHYROIDAL.EXTENSION'

nPatients	MINIMAL (T3)	MODERATE/ADVANCED (T4A)	NONE
ALL	53	9	143
subtype1	7	4	19
subtype2	30	5	51
subtype3	16	0	73

Figure S56. 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 = 0.678 (Chi-square test), Q value = 1

Table S61. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	163	20	1	13
subtype1	25	2	0	3
subtype2	68	10	0	3
subtype3	70	8	1	7

Figure S57. 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 = 0.556 (ANOVA), Q value = 1

Table S62. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	164	3.4 (5.9)
subtype1	24	2.6 (5.1)
subtype2	71	4.0 (5.7)
subtype3	69	3.1 (6.4)

Figure S58. 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 = 0.17 (Fisher's exact test), Q value = 1

Table S63. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients	MULTIFOCAL	UNIFOCAL
ALL	97	107
subtype1	10	19
subtype2	39	47
subtype3	48	41

Figure S59. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

'RPPA cHierClus subtypes' versus 'TUMOR.SIZE'

P value = 0.6 (ANOVA), Q value = 1

Table S64. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

	nPatients	Mean (Std.Dev)
ALL	183	3.3 (1.6)
subtype1	25	3.5 (1.6)
subtype2	79	3.2 (1.5)
subtype3	79	3.2 (1.6)

Figure S60. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S65. Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	144	52	111	156

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.397 (logrank test), Q value = 1

Table S66. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	458	13	0.0 - 158.8 (14.1)
subtype1	143	3	0.0 - 132.4 (13.8)
subtype2	50	2	0.1 - 157.2 (10.7)
subtype3	110	1	0.1 - 158.8 (14.6)
subtype4	155	7	0.1 - 155.5 (15.7)

Figure S61. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.165 (ANOVA), Q value = 1

Table S67. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	463	47.0 (15.6)
subtype1	144	48.7 (15.0)
subtype2	52	45.2 (18.1)
subtype3	111	44.7 (14.6)
subtype4	156	47.7 (15.8)

Figure S62. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.000325 (Chi-square test), Q value = 0.034

Table S68. 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	263	50	102	2	39	5
subtype1	84	28	25	1	3	2
subtype2	35	1	12	0	4	0
subtype3	63	14	23	0	9	1
subtype4	81	7	42	1	23	2

Figure S63. 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 = 2.79e-06 (Chi-square test), Q value = 0.00036

Table S69. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	134	154	154	19
subtype1	53	53	36	2
subtype2	22	12	15	2
subtype3	32	46	31	2
subtype4	27	43	72	13

Figure S64. 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 = 2.19e-13 (Fisher's exact test), Q value = 3.1e-11

Table S70. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	210	207
subtype1	95	25
subtype2	18	34
subtype3	47	55
subtype4	50	93

Figure S65. 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 = 0.0228 (Chi-square test), Q value = 1

Table S71. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	250	8	204
subtype1	61	3	79
subtype2	36	0	16
subtype3	67	2	42
subtype4	86	3	67

Figure S66. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.846 (Fisher's exact test), Q value = 1

Table S72. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	340	123
subtype1	105	39
subtype2	37	15
subtype3	85	26
subtype4	113	43

Figure S67. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1.33e-33 (Chi-square test), Q value = 1.9e-31

Table S73. 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	321	99	34
subtype1	2	60	81	1
subtype2	1	44	2	5
subtype3	2	94	13	2
subtype4	4	123	3	26

Figure S68. 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 = 0.0154 (Fisher's exact test), Q value = 1

Table S74. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	13	450
subtype1	1	143
subtype2	0	52
subtype3	2	109
subtype4	10	146

Figure S69. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'RADIATIONEXPOSURE'

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

Table S75. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONEXPOSURE'

nPatients	NO	YES
ALL	390	17
subtype1	120	6
subtype2	44	2
subtype3	93	4
subtype4	133	5

Figure S70. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONEXPOSURE'

'RNAseq CNMF subtypes' versus 'EXTRATHYROIDAL.EXTENSION'

P value = 3.3e-08 (Chi-square test), Q value = 4.4e-06

Table S76. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'EXTRATHYROIDAL.EXTENSION'

nPatients	MINIMAL (T3)	MODERATE/ADVANCED (T4A)	NONE	VERY ADVANCED (T4B)
ALL	120	15	314	1
subtype1	17	1	121	0
subtype2	13	2	36	0
subtype3	29	0	79	0
subtype4	61	12	78	1

Figure S71. 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 = 0.295 (Chi-square test), Q value = 1

Table S77. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	361	44	3	28
subtype1	117	8	0	7
subtype2	36	7	0	5
subtype3	88	8	1	7
subtype4	120	21	2	9

Figure S72. 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 = 6.28e-06 (ANOVA), Q value = 0.00077

Table S78. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	366	3.5 (6.2)
subtype1	105	1.2 (2.9)
subtype2	45	6.1 (7.8)
subtype3	87	3.9 (6.3)
subtype4	129	4.4 (6.8)

Figure S73. 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 = 0.199 (Fisher's exact test), Q value = 1

Table S79. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients	MULTIFOCAL	UNIFOCAL
ALL	208	245
subtype1	66	75
subtype2	24	26
subtype3	57	51
subtype4	61	93

Figure S74. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

'RNAseq CNMF subtypes' versus 'TUMOR.SIZE'

P value = 0.177 (ANOVA), Q value = 1

Table S80. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

	nPatients	Mean (Std.Dev)
ALL	370	2.9 (1.6)
subtype1	114	3.1 (1.6)
subtype2	37	2.7 (1.8)
subtype3	88	2.7 (1.4)
subtype4	131	3.1 (1.6)

Figure S75. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S81. Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	127	205	90	41

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.363 (logrank test), Q value = 1

Table S82. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	458	13	0.0 - 158.8 (14.1)
subtype1	126	3	0.2 - 132.4 (13.8)
subtype2	202	7	0.1 - 157.2 (15.0)
subtype3	90	1	0.1 - 158.8 (15.6)
subtype4	40	2	0.0 - 130.7 (8.8)

Figure S76. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.125 (ANOVA), Q value = 1

Table S83. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	463	47.0 (15.6)
subtype1	127	49.3 (15.4)
subtype2	205	46.6 (15.8)
subtype3	90	44.4 (15.8)
subtype4	41	48.1 (14.6)

Figure S77. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.000177 (Chi-square test), Q value = 0.02

Table S84. 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	263	50	102	2	39	5
subtype1	72	28	20	1	3	2
subtype2	114	10	53	1	25	2
subtype3	51	12	17	0	8	1
subtype4	26	0	12	0	3	0

Figure S78. 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 = 5.16e-05 (Chi-square test), Q value = 0.0061

Table S85. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	134	154	154	19
subtype1	43	52	30	2
subtype2	50	53	86	15
subtype3	23	40	25	2
subtype4	18	9	13	0

Figure S79. 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 = 4.91e-16 (Fisher's exact test), Q value = 7e-14

Table S86. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	210	207
subtype1	86	17
subtype2	63	130
subtype3	40	41
subtype4	21	19

Figure S80. 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 = 0.00503 (Chi-square test), Q value = 0.46

Table S87. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	250	8	204
subtype1	51	3	72
subtype2	116	3	86
subtype3	52	2	36
subtype4	31	0	10

Figure S81. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.876 (Fisher's exact test), Q value = 1

Table S88. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	340	123
subtype1	91	36
subtype2	150	55
subtype3	69	21
subtype4	30	11

Figure S82. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 7.84e-37 (Chi-square test), Q value = 1.2e-34

Table S89. 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	321	99	34
subtype1	2	47	78	0
subtype2	6	164	5	30
subtype3	0	78	11	1
subtype4	1	32	5	3

Figure S83. 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 = 0.124 (Fisher's exact test), Q value = 1

Table S90. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	13	450
subtype1	1	126
subtype2	10	195
subtype3	2	88
subtype4	0	41

Figure S84. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'RADIATIONEXPOSURE'

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

Table S91. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONEXPOSURE'

nPatients	NO	YES
ALL	390	17
subtype1	106	5
subtype2	172	8
subtype3	79	3
subtype4	33	1

Figure S85. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONEXPOSURE'

'RNAseq cHierClus subtypes' versus 'EXTRATHYROIDAL.EXTENSION'

P value = 1.51e-07 (Chi-square test), Q value = 2e-05

Table S92. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'EXTRATHYROIDAL.EXTENSION'

nPatients	MINIMAL (T3)	MODERATE/ADVANCED (T4A)	NONE	VERY ADVANCED (T4B)
ALL	120	15	314	1
subtype1	12	1	109	0
subtype2	73	14	111	1
subtype3	23	0	66	0
subtype4	12	0	28	0

Figure S86. 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 = 0.761 (Chi-square test), Q value = 1

Table S93. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	361	44	3	28
subtype1	101	8	0	7
subtype2	153	25	2	14
subtype3	75	7	1	5
subtype4	32	4	0	2

Figure S87. 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 = 0.000105 (ANOVA), Q value = 0.012

Table S94. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	366	3.5 (6.2)
subtype1	91	1.1 (2.9)
subtype2	177	4.6 (6.7)
subtype3	68	3.9 (6.4)
subtype4	30	4.2 (7.5)

Figure S88. 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 = 0.216 (Fisher's exact test), Q value = 1

Table S95. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients	MULTIFOCAL	UNIFOCAL
ALL	208	245
subtype1	59	65
subtype2	82	119
subtype3	46	43
subtype4	21	18

Figure S89. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

'RNAseq cHierClus subtypes' versus 'TUMOR.SIZE'

P value = 0.0473 (ANOVA), Q value = 1

Table S96. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

	nPatients	Mean (Std.Dev)
ALL	370	2.9 (1.6)
subtype1	106	3.2 (1.5)
subtype2	168	2.9 (1.6)
subtype3	70	2.9 (1.4)
subtype4	26	2.2 (1.7)

Figure S90. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S97. Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels	1	2	3
Number of samples	144	169	163

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.0585 (logrank test), Q value = 1

Table S98. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	471	13	0.0 - 158.8 (14.2)
subtype1	143	3	0.2 - 132.4 (14.1)
subtype2	167	2	0.1 - 147.8 (15.6)
subtype3	161	8	0.0 - 158.8 (12.0)

Figure S91. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.2 (ANOVA), Q value = 1

Table S99. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	476	47.1 (15.5)
subtype1	144	47.0 (15.8)
subtype2	169	45.7 (14.8)
subtype3	163	48.7 (15.9)

Figure S92. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 5.44e-06 (Chi-square test), Q value = 0.00068

Table S100. 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	270	50	106	2	41	5
subtype1	88	28	20	1	4	2
subtype2	99	17	35	0	16	2
subtype3	83	5	51	1	21	1

Figure S93. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.T.STAGE'

P value = 0.000473 (Chi-square test), Q value = 0.048

Table S101. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	138	160	157	19
subtype1	41	65	36	2
subtype2	44	61	57	7
subtype3	53	34	64	10

Figure S94. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.N.STAGE'

P value = 3.74e-11 (Fisher's exact test), Q value = 5.2e-09

Table S102. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	216	213
subtype1	90	27
subtype2	67	89
subtype3	59	97

Figure S95. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.00019 (Chi-square test), Q value = 0.021

Table S103. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	258	8	209
subtype1	58	3	82
subtype2	91	4	74
subtype3	109	1	53

Figure S96. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.808 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	350	126
subtype1	103	41
subtype2	125	44
subtype3	122	41

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

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 3.63e-35 (Chi-square test), Q value = 5.3e-33

Table S105. 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	333	99	35
subtype1	2	60	82	0
subtype2	3	145	9	12
subtype3	4	128	8	23

Figure S98. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

P value = 0.465 (Fisher's exact test), Q value = 1

Table S106. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	14	462
subtype1	4	140
subtype2	7	162
subtype3	3	160

Figure S99. 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 S107. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RADIATIONEXPOSURE'

nPatients	NO	YES
ALL	401	17
subtype1	122	5
subtype2	141	6
subtype3	138	6

Figure S100. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RADIATIONEXPOSURE'

'MIRSEQ CNMF' versus 'EXTRATHYROIDAL.EXTENSION'

P value = 4.02e-08 (Chi-square test), Q value = 5.3e-06

Table S108. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'EXTRATHYROIDAL.EXTENSION'

nPatients	MINIMAL (T3)	MODERATE/ADVANCED (T4A)	NONE	VERY ADVANCED (T4B)
ALL	123	15	322	1
subtype1	14	0	126	0
subtype2	52	5	107	0
subtype3	57	10	89	1

Figure S101. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'EXTRATHYROIDAL.EXTENSION'

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

P value = 0.207 (Chi-square test), Q value = 1

Table S109. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	370	46	3	28
subtype1	116	9	0	9
subtype2	135	14	2	8
subtype3	119	23	1	11

Figure S102. 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 = 7.45e-06 (ANOVA), Q value = 0.00091

Table S110. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	379	3.5 (6.2)
subtype1	104	1.4 (3.1)
subtype2	140	3.5 (6.0)
subtype3	135	5.3 (7.6)

Figure S103. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'

'MIRSEQ CNMF' versus 'MULTIFOCALITY'

P value = 0.751 (Fisher's exact test), Q value = 1

Table S111. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients	MULTIFOCAL	UNIFOCAL
ALL	214	252
subtype1	61	78
subtype2	75	90
subtype3	78	84

Figure S104. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #14: 'MULTIFOCALITY'

'MIRSEQ CNMF' versus 'TUMOR.SIZE'

P value = 0.00815 (ANOVA), Q value = 0.73

Table S112. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #15: 'TUMOR.SIZE'

	nPatients	Mean (Std.Dev)
ALL	380	2.9 (1.6)
subtype1	120	3.2 (1.5)
subtype2	137	3.0 (1.6)
subtype3	123	2.6 (1.6)

Figure S105. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #15: 'TUMOR.SIZE'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S113. Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3
Number of samples	151	125	200

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.0845 (logrank test), Q value = 1

Table S114. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	471	13	0.0 - 158.8 (14.2)
subtype1	150	1	0.1 - 147.8 (15.6)
subtype2	124	3	0.3 - 132.4 (13.9)
subtype3	197	9	0.0 - 158.8 (12.8)

Figure S106. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.037 (ANOVA), Q value = 1

Table S115. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	476	47.1 (15.5)
subtype1	151	44.4 (15.4)
subtype2	125	48.2 (15.5)
subtype3	200	48.5 (15.4)

Figure S107. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 2.46e-07 (Chi-square test), Q value = 3.2e-05

Table S116. 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	270	50	106	2	41	5
subtype1	92	17	26	0	14	2
subtype2	73	27	19	1	2	2
subtype3	105	6	61	1	25	1

Figure S108. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.T.STAGE'

P value = 0.00013 (Chi-square test), Q value = 0.015

Table S117. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	138	160	157	19
subtype1	36	62	46	7
subtype2	40	54	30	1
subtype3	62	44	81	11

Figure S109. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.N.STAGE'

P value = 1.73e-13 (Fisher's exact test), Q value = 2.4e-11

Table S118. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	216	213
subtype1	62	80
subtype2	82	17
subtype3	72	116

Figure S110. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.000257 (Chi-square test), Q value = 0.027

Table S119. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	258	8	209
subtype1	82	4	65
subtype2	48	3	73
subtype3	128	1	71

Figure S111. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.694 (Fisher's exact test), Q value = 1

Table S120. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	350	126
subtype1	108	43
subtype2	91	34
subtype3	151	49

Figure S112. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 6.86e-37 (Chi-square test), Q value = 1e-34

Table S121. 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	333	99	35
subtype1	2	130	13	6
subtype2	2	47	76	0
subtype3	5	156	10	29

Figure S113. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

P value = 0.0466 (Fisher's exact test), Q value = 1

Table S122. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	14	462
subtype1	9	142
subtype2	2	123
subtype3	3	197

Figure S114. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CHIERARCHICAL' versus 'RADIATIONEXPOSURE'

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

Table S123. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'RADIATIONEXPOSURE'

nPatients	NO	YES
ALL	401	17
subtype1	126	5
subtype2	106	5
subtype3	169	7

Figure S115. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'RADIATIONEXPOSURE'

'MIRSEQ CHIERARCHICAL' versus 'EXTRATHYROIDAL.EXTENSION'

P value = 2.38e-08 (Chi-square test), Q value = 3.2e-06

Table S124. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'EXTRATHYROIDAL.EXTENSION'

nPatients	MINIMAL (T3)	MODERATE/ADVANCED (T4A)	NONE	VERY ADVANCED (T4B)
ALL	123	15	322	1
subtype1	36	4	106	0
subtype2	12	0	110	0
subtype3	75	11	106	1

Figure S116. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'EXTRATHYROIDAL.EXTENSION'

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

P value = 0.347 (Chi-square test), Q value = 1

Table S125. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	370	46	3	28
subtype1	122	11	1	8
subtype2	101	9	0	6
subtype3	147	26	2	14

Figure S117. 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 = 5.79e-06 (ANOVA), Q value = 0.00072

Table S126. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	379	3.5 (6.2)
subtype1	124	3.3 (5.6)
subtype2	89	1.1 (2.9)
subtype3	166	5.0 (7.5)

Figure S118. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'

'MIRSEQ CHIERARCHICAL' versus 'MULTIFOCALITY'

P value = 0.985 (Fisher's exact test), Q value = 1

Table S127. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients	MULTIFOCAL	UNIFOCAL
ALL	214	252
subtype1	67	80
subtype2	57	65
subtype3	90	107

Figure S119. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'MULTIFOCALITY'

'MIRSEQ CHIERARCHICAL' versus 'TUMOR.SIZE'

P value = 0.0753 (ANOVA), Q value = 1

Table S128. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #15: 'TUMOR.SIZE'

	nPatients	Mean (Std.Dev)
ALL	380	2.9 (1.6)
subtype1	116	3.0 (1.6)
subtype2	106	3.2 (1.5)
subtype3	158	2.7 (1.6)

Figure S120. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #15: 'TUMOR.SIZE'

Clustering Approach #9: 'MIRseq Mature CNMF subtypes'

Table S129. Description of clustering approach #9: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	157	165	154

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

P value = 0.00893 (logrank test), Q value = 0.78

Table S130. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	471	13	0.0 - 158.8 (14.2)
subtype1	156	3	0.3 - 138.1 (14.3)
subtype2	163	1	0.1 - 155.5 (15.7)
subtype3	152	9	0.0 - 158.8 (11.1)

Figure S121. 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 = 0.0899 (ANOVA), Q value = 1

Table S131. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	476	47.1 (15.5)
subtype1	157	47.6 (15.5)
subtype2	165	45.1 (14.8)
subtype3	154	48.8 (16.2)

Figure S122. 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 = 0.000628 (Chi-square test), Q value = 0.063

Table S132. 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	270	50	106	2	41	5
subtype1	92	27	27	1	7	2
subtype2	99	18	32	0	14	2
subtype3	79	5	47	1	20	1

Figure S123. 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 = 0.00182 (Chi-square test), Q value = 0.17

Table S133. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	138	160	157	19
subtype1	44	65	45	3
subtype2	42	63	55	5
subtype3	52	32	57	11

Figure S124. 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 = 2e-09 (Fisher's exact test), Q value = 2.7e-07

Table S134. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	216	213
subtype1	95	35
subtype2	65	88
subtype3	56	90

Figure S125. 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 = 8.35e-05 (Chi-square test), Q value = 0.0097

Table S135. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	258	8	209
subtype1	64	3	89
subtype2	89	4	72
subtype3	105	1	48

Figure S126. 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 = 0.566 (Fisher's exact test), Q value = 1

Table S136. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	350	126
subtype1	111	46
subtype2	122	43
subtype3	117	37

Figure S127. 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 = 2.22e-30 (Chi-square test), Q value = 3.2e-28

Table S137. 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	333	99	35
subtype1	2	73	82	0
subtype2	3	139	10	13
subtype3	4	121	7	22

Figure S128. 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 = 0.0889 (Fisher's exact test), Q value = 1

Table S138. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	14	462
subtype1	3	154
subtype2	9	156
subtype3	2	152

Figure S129. 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 = 0.821 (Fisher's exact test), Q value = 1

Table S139. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'RADIATIONEXPOSURE'

nPatients	NO	YES
ALL	401	17
subtype1	132	7
subtype2	140	5
subtype3	129	5

Figure S130. 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 = 2.8e-06 (Chi-square test), Q value = 0.00036

Table S140. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'EXTRATHYROIDAL.EXTENSION'

nPatients	MINIMAL (T3)	MODERATE/ADVANCED (T4A)	NONE	VERY ADVANCED (T4B)
ALL	123	15	322	1
subtype1	22	1	129	0
subtype2	48	3	108	0
subtype3	53	11	85	1

Figure S131. 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 = 0.118 (Chi-square test), Q value = 1

Table S141. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	370	46	3	28
subtype1	123	11	0	11
subtype2	136	13	2	6
subtype3	111	22	1	11

Figure S132. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'

'MIRseq Mature CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.000297 (ANOVA), Q value = 0.031

Table S142. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	379	3.5 (6.2)
subtype1	118	1.7 (4.1)
subtype2	136	3.9 (6.8)
subtype3	125	4.8 (6.8)

Figure S133. 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 = 0.435 (Fisher's exact test), Q value = 1

Table S143. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients	MULTIFOCAL	UNIFOCAL
ALL	214	252
subtype1	74	77
subtype2	68	94
subtype3	72	81

Figure S134. 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 = 0.0125 (ANOVA), Q value = 1

Table S144. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

	nPatients	Mean (Std.Dev)
ALL	380	2.9 (1.6)
subtype1	127	3.2 (1.6)
subtype2	138	2.9 (1.5)
subtype3	115	2.6 (1.6)

Figure S135. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

Clustering Approach #10: 'MIRseq Mature cHierClus subtypes'

Table S145. Description of clustering approach #10: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	189	123	164

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

P value = 0.0336 (logrank test), Q value = 1

Table S146. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	471	13	0.0 - 158.8 (14.2)
subtype1	186	9	0.0 - 158.8 (12.3)
subtype2	122	3	0.3 - 132.4 (13.8)
subtype3	163	1	0.1 - 147.8 (15.8)

Figure S136. 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 = 0.00267 (ANOVA), Q value = 0.25

Table S147. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	476	47.1 (15.5)
subtype1	189	49.2 (15.1)
subtype2	123	48.4 (15.5)
subtype3	164	43.8 (15.5)

Figure S137. 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 = 2.28e-08 (Chi-square test), Q value = 3.1e-06

Table S148. 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	270	50	106	2	41	5
subtype1	96	5	61	1	24	1
subtype2	71	27	19	1	2	2
subtype3	103	18	26	0	15	2

Figure S138. 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 = 7.2e-05 (Chi-square test), Q value = 0.0084

Table S149. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	138	160	157	19
subtype1	59	40	77	11
subtype2	40	53	29	1
subtype3	39	67	51	7

Figure S139. 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 = 1.28e-13 (Fisher's exact test), Q value = 1.8e-11

Table S150. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	216	213
subtype1	69	109
subtype2	81	16
subtype3	66	88

Figure S140. 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 = 0.000113 (Chi-square test), Q value = 0.013

Table S151. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	258	8	209
subtype1	124	1	64
subtype2	47	3	72
subtype3	87	4	73

Figure S141. 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 = 0.544 (Fisher's exact test), Q value = 1

Table S152. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	350	126
subtype1	144	45
subtype2	89	34
subtype3	117	47

Figure S142. 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 = 1.05e-37 (Chi-square test), Q value = 1.6e-35

Table S153. 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	333	99	35
subtype1	4	147	10	28
subtype2	2	45	76	0
subtype3	3	141	13	7

Figure S143. 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 = 0.018 (Fisher's exact test), Q value = 1

Table S154. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	14	462
subtype1	2	187
subtype2	2	121
subtype3	10	154

Figure S144. 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 = 0.951 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	401	17
subtype1	160	6
subtype2	104	5
subtype3	137	6

Figure S145. 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 = 2.44e-08 (Chi-square test), Q value = 3.2e-06

Table S156. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'EXTRATHYROIDAL.EXTENSION'

nPatients	MINIMAL (T3)	MODERATE/ADVANCED (T4A)	NONE	VERY ADVANCED (T4B)
ALL	123	15	322	1
subtype1	71	11	99	1
subtype2	12	0	108	0
subtype3	40	4	115	0

Figure S146. 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 = 0.207 (Chi-square test), Q value = 1

Table S157. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	370	46	3	28
subtype1	137	26	2	13
subtype2	99	9	0	6
subtype3	134	11	1	9

Figure S147. 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 = 2.58e-05 (ANOVA), Q value = 0.0031

Table S158. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	379	3.5 (6.2)
subtype1	157	4.7 (6.7)
subtype2	87	1.0 (2.7)
subtype3	135	3.9 (6.8)

Figure S148. 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 = 0.89 (Fisher's exact test), Q value = 1

Table S159. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients	MULTIFOCAL	UNIFOCAL
ALL	214	252
subtype1	87	100
subtype2	57	64
subtype3	70	88

Figure S149. 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 = 0.128 (ANOVA), Q value = 1

Table S160. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

	nPatients	Mean (Std.Dev)
ALL	380	2.9 (1.6)
subtype1	147	2.8 (1.6)
subtype2	105	3.2 (1.5)
subtype3	128	2.9 (1.5)

Figure S150. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

Methods & Data

Input

Cluster data file = THCA-TP.mergedcluster.txt
Clinical data file = THCA-TP.clin.merged.picked.txt
Number of patients = 477
Number of clustering approaches = 10
Number of selected clinical features = 15
Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches

CNMF clustering

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

Consensus hierarchical clustering

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

Survival analysis

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

ANOVA analysis

For continuous numerical clinical features, one-way analysis of variance (Howell 2002) was applied to compare the clinical values between tumor subtypes using 'anova' function in R

Chi-square test

For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.test' function in R

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] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)

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

[6] 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)

[7] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)

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