Correlation between molecular cancer subtypes and selected clinical features

Thyroid Adenocarcinoma (Primary solid tumor)

21 April 2013 | analyses__2013_04_21

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): Thyroid Adenocarcinoma (Primary solid tumor cohort) - 21 April 2013: Correlation between molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C10R9MCF

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 8 different clustering approaches and 15 clinical features across 424 patients, 34 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', 'EXTRATHYROIDAL.EXTENSION', and 'NEOPLASM.DISEASESTAGE'.
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'HISTOLOGICAL.TYPE', 'EXTRATHYROIDAL.EXTENSION', 'LYMPH.NODE.METASTASIS', 'NUMBER.OF.LYMPH.NODES', and 'NEOPLASM.DISEASESTAGE'.
CNMF clustering analysis on RPPA data identified 3 subtypes that correlate to 'AGE', 'HISTOLOGICAL.TYPE', 'EXTRATHYROIDAL.EXTENSION', and 'NEOPLASM.DISEASESTAGE'.
Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that correlate to 'AGE', 'HISTOLOGICAL.TYPE', and 'LYMPH.NODE.METASTASIS'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'HISTOLOGICAL.TYPE', 'EXTRATHYROIDAL.EXTENSION', 'LYMPH.NODE.METASTASIS', and 'NUMBER.OF.LYMPH.NODES'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'HISTOLOGICAL.TYPE', 'EXTRATHYROIDAL.EXTENSION', 'LYMPH.NODE.METASTASIS', 'NUMBER.OF.LYMPH.NODES', and 'NEOPLASM.DISEASESTAGE'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'HISTOLOGICAL.TYPE', 'EXTRATHYROIDAL.EXTENSION', 'LYMPH.NODE.METASTASIS', 'NUMBER.OF.LYMPH.NODES', and 'NEOPLASM.DISEASESTAGE'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'HISTOLOGICAL.TYPE', 'EXTRATHYROIDAL.EXTENSION', 'LYMPH.NODE.METASTASIS', 'NUMBER.OF.LYMPH.NODES', and 'NEOPLASM.DISEASESTAGE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 8 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, 34 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
Time to Death	logrank test	0.0564 (1.00)	0.587 (1.00)	0.739 (1.00)	0.0266 (1.00)	0.205 (1.00)	0.256 (1.00)	0.0223 (1.00)	0.0215 (1.00)
AGE	ANOVA	5.8e-05 (0.00539)	0.128 (1.00)	0.000749 (0.0614)	0.000266 (0.0234)	0.0583 (1.00)	0.0522 (1.00)	0.0823 (1.00)	0.0059 (0.454)
GENDER	Fisher's exact test	0.336 (1.00)	0.817 (1.00)	0.836 (1.00)	0.578 (1.00)	0.967 (1.00)	0.669 (1.00)	0.763 (1.00)	0.796 (1.00)
HISTOLOGICAL TYPE	Chi-square test	0.182 (1.00)	1.92e-20 (2.08e-18)	0.000694 (0.0576)	4.29e-07 (4.29e-05)	2.75e-25 (3.03e-23)	9.37e-30 (1.05e-27)	5.72e-24 (6.24e-22)	1e-26 (1.11e-24)
RADIATIONS RADIATION REGIMENINDICATION	Fisher's exact test	0.301 (1.00)	0.144 (1.00)	0.0132 (0.942)	0.341 (1.00)	0.0153 (1.00)	0.0841 (1.00)	0.308 (1.00)	0.0233 (1.00)
RADIATIONEXPOSURE	Fisher's exact test	0.201 (1.00)	0.727 (1.00)	0.63 (1.00)	0.0605 (1.00)	0.908 (1.00)	0.769 (1.00)	1 (1.00)	0.949 (1.00)
DISTANT METASTASIS	Chi-square test	0.0722 (1.00)	0.147 (1.00)	0.393 (1.00)	0.481 (1.00)	0.0837 (1.00)	0.0112 (0.819)	0.0153 (1.00)	0.0168 (1.00)
EXTRATHYROIDAL EXTENSION	Chi-square test	0.00184 (0.149)	2.61e-06 (0.000253)	0.00211 (0.169)	0.0267 (1.00)	3e-07 (3.03e-05)	1.39e-08 (1.42e-06)	1.15e-06 (0.000113)	5.49e-07 (5.43e-05)
LYMPH NODE METASTASIS	Chi-square test	0.187 (1.00)	2.84e-09 (2.92e-07)	0.0186 (1.00)	0.000379 (0.033)	1.74e-09 (1.81e-07)	5e-11 (5.35e-09)	6.29e-10 (6.67e-08)	7.14e-10 (7.5e-08)
COMPLETENESS OF RESECTION	Chi-square test	0.422 (1.00)	0.57 (1.00)	0.39 (1.00)	0.637 (1.00)	0.506 (1.00)	0.765 (1.00)	0.503 (1.00)	0.425 (1.00)
NUMBER OF LYMPH NODES	ANOVA	0.229 (1.00)	0.000118 (0.0108)	0.463 (1.00)	0.266 (1.00)	4.5e-05 (0.00423)	0.000217 (0.0195)	0.000258 (0.0229)	0.000396 (0.0341)
TUMOR STAGECODE	ANOVA
NEOPLASM DISEASESTAGE	Chi-square test	1.02e-05 (0.000981)	0.000185 (0.0168)	0.00211 (0.169)	0.0151 (1.00)	0.00459 (0.358)	0.000548 (0.0466)	0.000603 (0.0506)	2.75e-05 (0.00261)
MULTIFOCALITY	Fisher's exact test	0.33 (1.00)	0.719 (1.00)	0.00622 (0.473)	0.642 (1.00)	0.00703 (0.527)	0.0579 (1.00)	0.929 (1.00)	0.553 (1.00)
TUMOR SIZE	ANOVA	0.00712 (0.527)	0.554 (1.00)	0.297 (1.00)	0.505 (1.00)	0.0972 (1.00)	0.0131 (0.942)	0.0211 (1.00)	0.0928 (1.00)

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

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

Cluster Labels	1	2	3
Number of samples	25	333	63

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

P value = 0.0564 (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	416	10	0.0 - 147.4 (9.3)
subtype1	25	2	0.4 - 83.3 (8.7)
subtype2	328	8	0.1 - 147.4 (9.3)
subtype3	63	0	0.0 - 85.1 (9.6)

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 = 5.8e-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	421	46.6 (15.6)
subtype1	25	59.8 (13.3)
subtype2	333	45.6 (15.8)
subtype3	63	46.5 (12.8)

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 'GENDER'

P value = 0.336 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	314	107
subtype1	16	9
subtype2	248	85
subtype3	50	13

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

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

P value = 0.182 (Chi-square test), Q value = 1

Table S5. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: '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	6	296	84	35
subtype1	0	16	8	1
subtype2	5	241	57	30
subtype3	1	39	19	4

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

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

P value = 0.301 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	14	407
subtype1	2	23
subtype2	11	322
subtype3	1	62

Figure S5. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'Copy Number Ratio CNMF subtypes' versus 'RADIATIONEXPOSURE'

P value = 0.201 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	363	16
subtype1	22	1
subtype2	282	15
subtype3	59	0

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

'Copy Number Ratio CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.0722 (Chi-square test), Q value = 1

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

nPatients	M0	M1	MX
ALL	235	8	177
subtype1	8	0	17
subtype2	194	6	132
subtype3	33	2	28

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

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

P value = 0.00184 (Chi-square test), Q value = 0.15

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

nPatients	MINIMAL (T3)	MODERATE/ADVANCED (T4A)	NONE	VERY ADVANCED (T4B)
ALL	113	13	275	1
subtype1	6	0	18	1
subtype2	94	13	210	0
subtype3	13	0	47	0

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

'Copy Number Ratio CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.187 (Chi-square test), Q value = 1

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

nPatients	N0	N1	N1A	N1B	NX
ALL	192	49	81	60	39
subtype1	15	1	2	2	5
subtype2	144	41	69	48	31
subtype3	33	7	10	10	3

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

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

P value = 0.422 (Chi-square test), Q value = 1

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

nPatients	R0	R1	R2	RX
ALL	331	37	2	26
subtype1	19	2	0	3
subtype2	264	31	2	16
subtype3	48	4	0	7

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

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

P value = 0.229 (ANOVA), Q value = 1

Table S12. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	328	3.5 (6.2)
subtype1	17	1.7 (4.1)
subtype2	261	3.8 (6.6)
subtype3	50	2.7 (4.1)

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

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

P value = 1.02e-05 (Chi-square test), Q value = 0.00098

Table S13. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV	STAGE IVA	STAGE IVC
ALL	237	44	94	2	36	5
subtype1	4	10	8	1	2	0
subtype2	196	28	74	0	28	4
subtype3	37	6	12	1	6	1

Figure S12. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

'Copy Number Ratio CNMF subtypes' versus 'MULTIFOCALITY'

P value = 0.33 (Fisher's exact test), Q value = 1

Table S14. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients	MULTIFOCAL	UNIFOCAL
ALL	181	231
subtype1	10	15
subtype2	139	187
subtype3	32	29

Figure S13. 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 = 0.00712 (ANOVA), Q value = 0.53

Table S15. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

	nPatients	Mean (Std.Dev)
ALL	331	2.9 (1.6)
subtype1	21	4.0 (1.5)
subtype2	261	2.9 (1.5)
subtype3	49	2.8 (1.7)

Figure S14. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

Clustering Approach #2: 'METHLYATION CNMF'

Table S16. Get Full Table Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	122	63	239

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.587 (logrank test), Q value = 1

Table S17. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	419	10	0.0 - 147.4 (9.3)
subtype1	121	3	0.0 - 130.7 (7.6)
subtype2	60	2	0.1 - 147.4 (7.1)
subtype3	238	5	0.1 - 138.1 (11.7)

Figure S15. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.128 (ANOVA), Q value = 1

Table S18. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	424	46.6 (15.6)
subtype1	122	48.5 (15.3)
subtype2	63	43.7 (15.4)
subtype3	239	46.5 (15.7)

Figure S16. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.817 (Fisher's exact test), Q value = 1

Table S19. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	316	108
subtype1	93	29
subtype2	48	15
subtype3	175	64

Figure S17. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 1.92e-20 (Chi-square test), Q value = 2.1e-18

Table S20. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	OTHER SPECIFY	THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL	THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED)	THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
ALL	7	296	86	35
subtype1	3	56	62	1
subtype2	1	50	6	6
subtype3	3	190	18	28

Figure S18. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

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

P value = 0.144 (Fisher's exact test), Q value = 1

Table S21. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	14	410
subtype1	1	121
subtype2	2	61
subtype3	11	228

Figure S19. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'RADIATIONEXPOSURE'

P value = 0.727 (Fisher's exact test), Q value = 1

Table S22. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

nPatients	NO	YES
ALL	365	16
subtype1	101	6
subtype2	55	2
subtype3	209	8

Figure S20. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

'METHLYATION CNMF' versus 'DISTANT.METASTASIS'

P value = 0.147 (Chi-square test), Q value = 1

Table S23. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'DISTANT.METASTASIS'

nPatients	M0	M1	MX
ALL	237	8	178
subtype1	57	3	61
subtype2	37	0	26
subtype3	143	5	91

Figure S21. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'DISTANT.METASTASIS'

'METHLYATION CNMF' versus 'EXTRATHYROIDAL.EXTENSION'

P value = 2.61e-06 (Chi-square test), Q value = 0.00025

Table S24. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

nPatients	MINIMAL (T3)	MODERATE/ADVANCED (T4A)	NONE	VERY ADVANCED (T4B)
ALL	113	13	278	1
subtype1	17	0	97	0
subtype2	14	0	46	1
subtype3	82	13	135	0

Figure S22. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

'METHLYATION CNMF' versus 'LYMPH.NODE.METASTASIS'

P value = 2.84e-09 (Chi-square test), Q value = 2.9e-07

Table S25. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	NX
ALL	195	49	81	60	39
subtype1	83	7	8	6	18
subtype2	28	6	15	11	3
subtype3	84	36	58	43	18

Figure S23. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

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

P value = 0.57 (Chi-square test), Q value = 1

Table S26. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	334	37	2	26
subtype1	100	6	0	7
subtype2	46	7	0	4
subtype3	188	24	2	15

Figure S24. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

'METHLYATION CNMF' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.000118 (ANOVA), Q value = 0.011

Table S27. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	329	3.5 (6.2)
subtype1	83	1.1 (2.6)
subtype2	50	3.7 (6.9)
subtype3	196	4.5 (6.8)

Figure S25. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.000185 (Chi-square test), Q value = 0.017

Table S28. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV	STAGE IVA	STAGE IVC
ALL	238	46	94	2	36	5
subtype1	67	26	22	1	2	2
subtype2	39	3	15	1	5	0
subtype3	132	17	57	0	29	3

Figure S26. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

'METHLYATION CNMF' versus 'MULTIFOCALITY'

P value = 0.719 (Fisher's exact test), Q value = 1

Table S29. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients	MULTIFOCAL	UNIFOCAL
ALL	182	233
subtype1	49	71
subtype2	27	34
subtype3	106	128

Figure S27. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #14: 'MULTIFOCALITY'

'METHLYATION CNMF' versus 'TUMOR.SIZE'

P value = 0.554 (ANOVA), Q value = 1

Table S30. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #15: 'TUMOR.SIZE'

	nPatients	Mean (Std.Dev)
ALL	333	2.9 (1.6)
subtype1	94	3.1 (1.8)
subtype2	48	2.9 (1.6)
subtype3	191	2.9 (1.5)

Figure S28. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #15: 'TUMOR.SIZE'

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S31. Get Full Table Description of clustering approach #3: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	53	72	73

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.739 (logrank test), Q value = 1

Table S32. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	196	9	0.1 - 147.4 (9.7)
subtype1	53	2	0.3 - 145.4 (9.0)
subtype2	72	3	0.2 - 147.4 (12.2)
subtype3	71	4	0.1 - 128.6 (9.3)

Figure S29. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.000749 (ANOVA), Q value = 0.061

Table S33. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	198	47.5 (16.4)
subtype1	53	51.4 (14.6)
subtype2	72	50.5 (15.6)
subtype3	73	41.8 (17.1)

Figure S30. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.836 (Fisher's exact test), Q value = 1

Table S34. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	138	60
subtype1	37	16
subtype2	52	20
subtype3	49	24

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

'RPPA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.000694 (Chi-square test), Q value = 0.058

Table S35. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: '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	4	133	50	11
subtype1	0	29	23	1
subtype2	2	47	14	9
subtype3	2	57	13	1

Figure S32. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'RPPA CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.0132 (Fisher's exact test), Q value = 0.94

Table S36. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	13	185
subtype1	0	53
subtype2	9	63
subtype3	4	69

Figure S33. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA CNMF subtypes' versus 'RADIATIONEXPOSURE'

P value = 0.63 (Fisher's exact test), Q value = 1

Table S37. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

nPatients	NO	YES
ALL	167	9
subtype1	47	1
subtype2	60	4
subtype3	60	4

Figure S34. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

'RPPA CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.393 (Chi-square test), Q value = 1

Table S38. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'DISTANT.METASTASIS'

nPatients	M0	M1	MX
ALL	100	4	93
subtype1	32	1	19
subtype2	36	1	35
subtype3	32	2	39

Figure S35. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'DISTANT.METASTASIS'

'RPPA CNMF subtypes' versus 'EXTRATHYROIDAL.EXTENSION'

P value = 0.00211 (Chi-square test), Q value = 0.17

Table S39. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

nPatients	MINIMAL (T3)	MODERATE/ADVANCED (T4A)	NONE
ALL	48	8	134
subtype1	11	0	42
subtype2	24	7	38
subtype3	13	1	54

Figure S36. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

'RPPA CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.0186 (Chi-square test), Q value = 1

Table S40. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	NX
ALL	87	21	37	26	27
subtype1	32	0	9	5	7
subtype2	26	14	11	12	9
subtype3	29	7	17	9	11

Figure S37. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

'RPPA CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.39 (Chi-square test), Q value = 1

Table S41. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	152	17	1	13
subtype1	41	5	1	5
subtype2	54	9	0	4
subtype3	57	3	0	4

Figure S38. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

'RPPA CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.463 (ANOVA), Q value = 1

Table S42. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	150	3.2 (5.5)
subtype1	37	2.5 (5.8)
subtype2	59	3.1 (4.5)
subtype3	54	3.9 (6.1)

Figure S39. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'RPPA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00211 (Chi-square test), Q value = 0.17

Table S43. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IVA	STAGE IVC
ALL	101	32	44	16	3
subtype1	26	10	13	1	1
subtype2	26	13	21	12	0
subtype3	49	9	10	3	2

Figure S40. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

'RPPA CNMF subtypes' versus 'MULTIFOCALITY'

P value = 0.00622 (Fisher's exact test), Q value = 0.47

Table S44. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients	MULTIFOCAL	UNIFOCAL
ALL	87	103
subtype1	31	19
subtype2	23	47
subtype3	33	37

Figure S41. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

'RPPA CNMF subtypes' versus 'TUMOR.SIZE'

P value = 0.297 (ANOVA), Q value = 1

Table S45. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

	nPatients	Mean (Std.Dev)
ALL	172	3.2 (1.6)
subtype1	46	3.0 (1.6)
subtype2	63	3.5 (1.7)
subtype3	63	3.1 (1.5)

Figure S42. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S46. Get Full Table Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	84	30	84

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.0266 (logrank test), Q value = 1

Table S47. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	196	9	0.1 - 147.4 (9.7)
subtype1	84	5	0.3 - 145.4 (9.2)
subtype2	30	3	1.1 - 147.4 (10.1)
subtype3	82	1	0.1 - 128.6 (10.4)

Figure S43. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.000266 (ANOVA), Q value = 0.023

Table S48. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	198	47.5 (16.4)
subtype1	84	50.0 (16.3)
subtype2	30	54.9 (15.1)
subtype3	84	42.4 (15.6)

Figure S44. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.578 (Fisher's exact test), Q value = 1

Table S49. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	138	60
subtype1	55	29
subtype2	22	8
subtype3	61	23

Figure S45. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 4.29e-07 (Chi-square test), Q value = 4.3e-05

Table S50. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: '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	4	133	50	11
subtype1	1	49	32	2
subtype2	2	13	14	1
subtype3	1	71	4	8

Figure S46. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'RPPA cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.341 (Fisher's exact test), Q value = 1

Table S51. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	13	185
subtype1	3	81
subtype2	2	28
subtype3	8	76

Figure S47. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA cHierClus subtypes' versus 'RADIATIONEXPOSURE'

P value = 0.0605 (Fisher's exact test), Q value = 1

Table S52. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

nPatients	NO	YES
ALL	167	9
subtype1	75	2
subtype2	24	4
subtype3	68	3

Figure S48. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

'RPPA cHierClus subtypes' versus 'DISTANT.METASTASIS'

P value = 0.481 (Chi-square test), Q value = 1

Table S53. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'DISTANT.METASTASIS'

nPatients	M0	M1	MX
ALL	100	4	93
subtype1	41	2	40
subtype2	12	0	18
subtype3	47	2	35

Figure S49. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'DISTANT.METASTASIS'

'RPPA cHierClus subtypes' versus 'EXTRATHYROIDAL.EXTENSION'

P value = 0.0267 (Chi-square test), Q value = 1

Table S54. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

nPatients	MINIMAL (T3)	MODERATE/ADVANCED (T4A)	NONE
ALL	48	8	134
subtype1	16	0	66
subtype2	7	2	21
subtype3	25	6	47

Figure S50. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

'RPPA cHierClus subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.000379 (Chi-square test), Q value = 0.033

Table S55. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	NX
ALL	87	21	37	26	27
subtype1	49	2	12	10	11
subtype2	13	2	3	5	7
subtype3	25	17	22	11	9

Figure S51. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

'RPPA cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.637 (Chi-square test), Q value = 1

Table S56. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	152	17	1	13
subtype1	65	8	1	6
subtype2	24	2	0	4
subtype3	63	7	0	3

Figure S52. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

'RPPA cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.266 (ANOVA), Q value = 1

Table S57. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	150	3.2 (5.5)
subtype1	61	2.5 (5.1)
subtype2	24	2.9 (5.5)
subtype3	65	4.0 (5.7)

Figure S53. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'RPPA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0151 (Chi-square test), Q value = 1

Table S58. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IVA	STAGE IVC
ALL	101	32	44	16	3
subtype1	45	15	18	2	2
subtype2	7	8	10	5	0
subtype3	49	9	16	9	1

Figure S54. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

'RPPA cHierClus subtypes' versus 'MULTIFOCALITY'

P value = 0.642 (Fisher's exact test), Q value = 1

Table S59. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients	MULTIFOCAL	UNIFOCAL
ALL	87	103
subtype1	39	42
subtype2	11	18
subtype3	37	43

Figure S55. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

'RPPA cHierClus subtypes' versus 'TUMOR.SIZE'

P value = 0.505 (ANOVA), Q value = 1

Table S60. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

	nPatients	Mean (Std.Dev)
ALL	172	3.2 (1.6)
subtype1	74	3.1 (1.6)
subtype2	24	3.6 (1.9)
subtype3	74	3.2 (1.5)

Figure S56. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S61. Get Full Table Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	116	49	97	135

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.205 (logrank test), Q value = 1

Table S62. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	392	10	0.0 - 147.4 (9.3)
subtype1	116	3	0.0 - 130.7 (7.3)
subtype2	47	1	0.1 - 145.4 (6.4)
subtype3	95	0	0.1 - 138.1 (11.9)
subtype4	134	6	0.1 - 147.4 (11.1)

Figure S57. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.0583 (ANOVA), Q value = 1

Table S63. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	397	46.5 (15.7)
subtype1	116	48.8 (15.3)
subtype2	49	43.0 (16.0)
subtype3	97	44.2 (14.8)
subtype4	135	47.3 (16.2)

Figure S58. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.967 (Fisher's exact test), Q value = 1

Table S64. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	298	99
subtype1	88	28
subtype2	36	13
subtype3	74	23
subtype4	100	35

Figure S59. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 2.75e-25 (Chi-square test), Q value = 3e-23

Table S65. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: '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	6	273	84	34
subtype1	3	49	63	1
subtype2	1	41	3	4
subtype3	1	78	15	3
subtype4	1	105	3	26

Figure S60. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'RNAseq CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.0153 (Fisher's exact test), Q value = 1

Table S66. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	13	384
subtype1	1	115
subtype2	0	49
subtype3	2	95
subtype4	10	125

Figure S61. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'RADIATIONEXPOSURE'

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

Table S67. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

nPatients	NO	YES
ALL	339	16
subtype1	98	6
subtype2	42	1
subtype3	83	4
subtype4	116	5

Figure S62. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

'RNAseq CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.0837 (Chi-square test), Q value = 1

Table S68. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'DISTANT.METASTASIS'

nPatients	M0	M1	MX
ALL	218	7	171
subtype1	51	2	62
subtype2	34	0	15
subtype3	58	2	37
subtype4	75	3	57

Figure S63. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'DISTANT.METASTASIS'

'RNAseq CNMF subtypes' versus 'EXTRATHYROIDAL.EXTENSION'

P value = 3e-07 (Chi-square test), Q value = 3e-05

Table S69. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

nPatients	MINIMAL (T3)	MODERATE/ADVANCED (T4A)	NONE	VERY ADVANCED (T4B)
ALL	104	12	263	1
subtype1	14	0	96	0
subtype2	12	2	33	0
subtype3	25	0	70	0
subtype4	53	10	64	1

Figure S64. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

'RNAseq CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 1.74e-09 (Chi-square test), Q value = 1.8e-07

Table S70. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	NX
ALL	188	42	75	55	37
subtype1	81	5	8	5	17
subtype2	20	5	14	10	0
subtype3	41	7	26	16	7
subtype4	46	25	27	24	13

Figure S65. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

'RNAseq CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.506 (Chi-square test), Q value = 1

Table S71. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	313	32	2	25
subtype1	96	5	0	7
subtype2	33	6	0	5
subtype3	80	7	1	5
subtype4	104	14	1	8

Figure S66. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

'RNAseq CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 4.5e-05 (ANOVA), Q value = 0.0042

Table S72. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	303	3.3 (6.0)
subtype1	81	0.9 (2.3)
subtype2	40	5.6 (8.0)
subtype3	74	4.3 (6.6)
subtype4	108	3.7 (6.0)

Figure S67. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00459 (Chi-square test), Q value = 0.36

Table S73. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV	STAGE IVA	STAGE IVC
ALL	225	45	86	1	32	5
subtype1	64	24	22	0	2	2
subtype2	34	2	9	0	4	0
subtype3	56	12	18	0	9	1
subtype4	71	7	37	1	17	2

Figure S68. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

'RNAseq CNMF subtypes' versus 'MULTIFOCALITY'

P value = 0.00703 (Fisher's exact test), Q value = 0.53

Table S74. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients	MULTIFOCAL	UNIFOCAL
ALL	173	215
subtype1	50	64
subtype2	23	24
subtype3	54	40
subtype4	46	87

Figure S69. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

'RNAseq CNMF subtypes' versus 'TUMOR.SIZE'

P value = 0.0972 (ANOVA), Q value = 1

Table S75. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

	nPatients	Mean (Std.Dev)
ALL	313	2.9 (1.6)
subtype1	91	3.1 (1.7)
subtype2	35	2.5 (1.7)
subtype3	75	2.6 (1.4)
subtype4	112	3.1 (1.6)

Figure S70. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S76. Get Full Table Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	100	165	49	83

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.256 (logrank test), Q value = 1

Table S77. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	392	10	0.0 - 147.4 (9.3)
subtype1	100	3	0.3 - 112.6 (7.5)
subtype2	162	6	0.1 - 147.4 (11.1)
subtype3	48	1	0.0 - 130.7 (6.4)
subtype4	82	0	0.1 - 138.1 (11.9)

Figure S71. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.0522 (ANOVA), Q value = 1

Table S78. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	397	46.5 (15.7)
subtype1	100	49.7 (15.4)
subtype2	165	46.5 (16.2)
subtype3	49	43.7 (13.7)
subtype4	83	44.1 (15.6)

Figure S72. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.669 (Fisher's exact test), Q value = 1

Table S79. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	298	99
subtype1	75	25
subtype2	120	45
subtype3	40	9
subtype4	63	20

Figure S73. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 9.37e-30 (Chi-square test), Q value = 1e-27

Table S80. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: '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	6	273	84	34
subtype1	3	36	61	0
subtype2	3	129	4	29
subtype3	0	39	6	4
subtype4	0	69	13	1

Figure S74. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'RNAseq cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.0841 (Fisher's exact test), Q value = 1

Table S81. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	13	384
subtype1	1	99
subtype2	10	155
subtype3	0	49
subtype4	2	81

Figure S75. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'RADIATIONEXPOSURE'

P value = 0.769 (Fisher's exact test), Q value = 1

Table S82. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

nPatients	NO	YES
ALL	339	16
subtype1	86	4
subtype2	143	6
subtype3	37	3
subtype4	73	3

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

'RNAseq cHierClus subtypes' versus 'DISTANT.METASTASIS'

P value = 0.0112 (Chi-square test), Q value = 0.82

Table S83. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'DISTANT.METASTASIS'

nPatients	M0	M1	MX
ALL	218	7	171
subtype1	41	2	56
subtype2	94	3	68
subtype3	37	0	12
subtype4	46	2	35

Figure S77. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'DISTANT.METASTASIS'

'RNAseq cHierClus subtypes' versus 'EXTRATHYROIDAL.EXTENSION'

P value = 1.39e-08 (Chi-square test), Q value = 1.4e-06

Table S84. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

nPatients	MINIMAL (T3)	MODERATE/ADVANCED (T4A)	NONE	VERY ADVANCED (T4B)
ALL	104	12	263	1
subtype1	9	0	85	0
subtype2	64	12	80	1
subtype3	11	0	36	0
subtype4	20	0	62	0

Figure S78. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

'RNAseq cHierClus subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 5e-11 (Chi-square test), Q value = 5.3e-09

Table S85. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	NX
ALL	188	42	75	55	37
subtype1	73	3	3	4	17
subtype2	53	28	40	31	13
subtype3	23	3	15	7	1
subtype4	39	8	17	13	6

Figure S79. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

'RNAseq cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.765 (Chi-square test), Q value = 1

Table S86. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	313	32	2	25
subtype1	81	5	0	7
subtype2	126	18	1	9
subtype3	37	4	0	4
subtype4	69	5	1	5

Figure S80. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

'RNAseq cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.000217 (ANOVA), Q value = 0.02

Table S87. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	303	3.3 (6.0)
subtype1	67	0.7 (2.1)
subtype2	136	4.3 (6.6)
subtype3	39	3.0 (4.9)
subtype4	61	4.5 (7.0)

Figure S81. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.000548 (Chi-square test), Q value = 0.047

Table S88. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV	STAGE IVA	STAGE IVC
ALL	225	45	86	1	32	5
subtype1	53	24	17	0	2	2
subtype2	92	8	43	1	19	2
subtype3	34	1	11	0	3	0
subtype4	46	12	15	0	8	1

Figure S82. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

'RNAseq cHierClus subtypes' versus 'MULTIFOCALITY'

P value = 0.0579 (Fisher's exact test), Q value = 1

Table S89. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients	MULTIFOCAL	UNIFOCAL
ALL	173	215
subtype1	44	54
subtype2	61	101
subtype3	23	24
subtype4	45	36

Figure S83. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

'RNAseq cHierClus subtypes' versus 'TUMOR.SIZE'

P value = 0.0131 (ANOVA), Q value = 0.94

Table S90. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

	nPatients	Mean (Std.Dev)
ALL	313	2.9 (1.6)
subtype1	83	3.2 (1.7)
subtype2	133	2.9 (1.6)
subtype3	32	2.2 (1.6)
subtype4	65	2.8 (1.4)

Figure S84. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S91. Get Full Table Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels	1	2	3
Number of samples	105	134	134

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.0223 (logrank test), Q value = 1

Table S92. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	368	10	0.0 - 147.4 (9.0)
subtype1	105	3	0.2 - 112.6 (7.7)
subtype2	131	0	0.1 - 147.4 (10.4)
subtype3	132	7	0.0 - 145.4 (8.5)

Figure S85. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.0823 (ANOVA), Q value = 1

Table S93. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	373	46.4 (15.8)
subtype1	105	48.0 (16.3)
subtype2	134	44.0 (14.9)
subtype3	134	47.7 (16.1)

Figure S86. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.763 (Fisher's exact test), Q value = 1

Table S94. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	279	94
subtype1	78	27
subtype2	98	36
subtype3	103	31

Figure S87. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 5.72e-24 (Chi-square test), Q value = 6.2e-22

Table S95. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: '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	6	254	82	31
subtype1	3	40	61	1
subtype2	1	111	12	10
subtype3	2	103	9	20

Figure S88. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

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

P value = 0.308 (Fisher's exact test), Q value = 1

Table S96. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	14	359
subtype1	3	102
subtype2	8	126
subtype3	3	131

Figure S89. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CNMF' versus 'RADIATIONEXPOSURE'

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

Table S97. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

nPatients	NO	YES
ALL	318	16
subtype1	90	4
subtype2	112	6
subtype3	116	6

Figure S90. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

'MIRSEQ CNMF' versus 'DISTANT.METASTASIS'

P value = 0.0153 (Chi-square test), Q value = 1

Table S98. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'DISTANT.METASTASIS'

nPatients	M0	M1	MX
ALL	195	6	171
subtype1	42	2	60
subtype2	69	3	62
subtype3	84	1	49

Figure S91. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'DISTANT.METASTASIS'

'MIRSEQ CNMF' versus 'EXTRATHYROIDAL.EXTENSION'

P value = 1.15e-06 (Chi-square test), Q value = 0.00011

Table S99. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

nPatients	MINIMAL (T3)	MODERATE/ADVANCED (T4A)	NONE	VERY ADVANCED (T4B)
ALL	96	12	248	1
subtype1	10	0	89	0
subtype2	39	2	86	0
subtype3	47	10	73	1

Figure S92. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

'MIRSEQ CNMF' versus 'LYMPH.NODE.METASTASIS'

P value = 6.29e-10 (Chi-square test), Q value = 6.7e-08

Table S100. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	NX
ALL	176	38	75	49	35
subtype1	72	5	4	6	18
subtype2	53	17	39	15	10
subtype3	51	16	32	28	7

Figure S93. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

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

P value = 0.503 (Chi-square test), Q value = 1

Table S101. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	293	30	2	24
subtype1	83	7	0	8
subtype2	108	7	1	8
subtype3	102	16	1	8

Figure S94. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

'MIRSEQ CNMF' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.000258 (ANOVA), Q value = 0.023

Table S102. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	288	3.2 (5.6)
subtype1	71	1.1 (2.9)
subtype2	108	3.2 (5.4)
subtype3	109	4.5 (6.6)

Figure S95. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.000603 (Chi-square test), Q value = 0.051

Table S103. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV	STAGE IVA	STAGE IVC
ALL	210	43	83	1	29	4
subtype1	59	23	16	0	3	2
subtype2	81	14	28	0	10	1
subtype3	70	6	39	1	16	1

Figure S96. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

'MIRSEQ CNMF' versus 'MULTIFOCALITY'

P value = 0.929 (Fisher's exact test), Q value = 1

Table S104. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients	MULTIFOCAL	UNIFOCAL
ALL	165	199
subtype1	45	58
subtype2	59	69
subtype3	61	72

Figure S97. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #14: 'MULTIFOCALITY'

'MIRSEQ CNMF' versus 'TUMOR.SIZE'

P value = 0.0211 (ANOVA), Q value = 1

Table S105. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #15: 'TUMOR.SIZE'

	nPatients	Mean (Std.Dev)
ALL	290	2.9 (1.6)
subtype1	86	3.2 (1.6)
subtype2	107	2.9 (1.5)
subtype3	97	2.5 (1.5)

Figure S98. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #15: 'TUMOR.SIZE'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S106. Get Full Table Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3
Number of samples	92	149	132

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.0215 (logrank test), Q value = 1

Table S107. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	368	10	0.0 - 147.4 (9.0)
subtype1	92	3	0.3 - 112.6 (7.2)
subtype2	146	7	0.0 - 147.4 (8.5)
subtype3	130	0	0.1 - 138.1 (11.9)

Figure S99. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.0059 (ANOVA), Q value = 0.45

Table S108. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	373	46.4 (15.8)
subtype1	92	49.2 (15.5)
subtype2	149	47.8 (15.8)
subtype3	132	43.0 (15.5)

Figure S100. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

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

Table S109. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	279	94
subtype1	70	22
subtype2	113	36
subtype3	96	36

Figure S101. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 1e-26 (Chi-square test), Q value = 1.1e-24

Table S110. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: '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	6	254	82	31
subtype1	3	32	57	0
subtype2	1	114	9	25
subtype3	2	108	16	6

Figure S102. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

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

P value = 0.0233 (Fisher's exact test), Q value = 1

Table S111. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	14	359
subtype1	1	91
subtype2	3	146
subtype3	10	122

Figure S103. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CHIERARCHICAL' versus 'RADIATIONEXPOSURE'

P value = 0.949 (Fisher's exact test), Q value = 1

Table S112. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

nPatients	NO	YES
ALL	318	16
subtype1	79	4
subtype2	128	7
subtype3	111	5

Figure S104. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

'MIRSEQ CHIERARCHICAL' versus 'DISTANT.METASTASIS'

P value = 0.0168 (Chi-square test), Q value = 1

Table S113. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'DISTANT.METASTASIS'

nPatients	M0	M1	MX
ALL	195	6	171
subtype1	36	2	53
subtype2	92	1	56
subtype3	67	3	62

Figure S105. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'DISTANT.METASTASIS'

'MIRSEQ CHIERARCHICAL' versus 'EXTRATHYROIDAL.EXTENSION'

P value = 5.49e-07 (Chi-square test), Q value = 5.4e-05

Table S114. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

nPatients	MINIMAL (T3)	MODERATE/ADVANCED (T4A)	NONE	VERY ADVANCED (T4B)
ALL	96	12	248	1
subtype1	8	0	79	0
subtype2	56	10	78	1
subtype3	32	2	91	0

Figure S106. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

'MIRSEQ CHIERARCHICAL' versus 'LYMPH.NODE.METASTASIS'

P value = 7.14e-10 (Chi-square test), Q value = 7.5e-08

Table S115. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	NX
ALL	176	38	75	49	35
subtype1	67	3	2	4	16
subtype2	54	18	40	26	11
subtype3	55	17	33	19	8

Figure S107. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

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

P value = 0.425 (Chi-square test), Q value = 1

Table S116. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	293	30	2	24
subtype1	77	3	0	6
subtype2	112	17	1	10
subtype3	104	10	1	8

Figure S108. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.000396 (ANOVA), Q value = 0.034

Table S117. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	288	3.2 (5.6)
subtype1	62	0.7 (2.2)
subtype2	120	3.9 (5.3)
subtype3	106	3.8 (6.7)

Figure S109. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 2.75e-05 (Chi-square test), Q value = 0.0026

Table S118. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV	STAGE IVA	STAGE IVC
ALL	210	43	83	1	29	4
subtype1	49	21	16	0	2	2
subtype2	77	6	48	1	15	1
subtype3	84	16	19	0	12	1

Figure S110. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

'MIRSEQ CHIERARCHICAL' versus 'MULTIFOCALITY'

P value = 0.553 (Fisher's exact test), Q value = 1

Table S119. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients	MULTIFOCAL	UNIFOCAL
ALL	165	199
subtype1	40	51
subtype2	63	84
subtype3	62	64

Figure S111. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'MULTIFOCALITY'

'MIRSEQ CHIERARCHICAL' versus 'TUMOR.SIZE'

P value = 0.0928 (ANOVA), Q value = 1

Table S120. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #15: 'TUMOR.SIZE'

	nPatients	Mean (Std.Dev)
ALL	290	2.9 (1.6)
subtype1	76	3.1 (1.7)
subtype2	111	2.6 (1.5)
subtype3	103	3.0 (1.5)

Figure S112. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' 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 = 424
Number of clustering approaches = 8
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

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

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

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

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

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] 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)

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

[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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