Correlate_Clinical_vs_Molecular_Signatures

Thyroid Adenocarcinoma (Primary solid tumor)

22 February 2013 | analyses__2013_02_22

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): Correlate_Clinical_vs_Molecular_Signatures. Broad Institute of MIT and Harvard. doi:10.7908/C12R3PXS

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 288 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 'NEOPLASM.DISEASESTAGE'.
3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'HISTOLOGICAL.TYPE', 'EXTRATHYROIDAL.EXTENSION', 'LYMPH.NODE.METASTASIS', and 'NUMBER.OF.LYMPH.NODES'.
3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'AGE' and 'HISTOLOGICAL.TYPE'.
Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that correlate to 'AGE' and 'HISTOLOGICAL.TYPE'.
4 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes 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 3 subtypes that correlate to 'HISTOLOGICAL.TYPE', 'EXTRATHYROIDAL.EXTENSION', 'LYMPH.NODE.METASTASIS', and 'NUMBER.OF.LYMPH.NODES'.
3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'HISTOLOGICAL.TYPE', 'EXTRATHYROIDAL.EXTENSION', and 'LYMPH.NODE.METASTASIS'.
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'.
3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'HISTOLOGICAL.TYPE', 'EXTRATHYROIDAL.EXTENSION', and 'LYMPH.NODE.METASTASIS'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'HISTOLOGICAL.TYPE', 'EXTRATHYROIDAL.EXTENSION', 'LYMPH.NODE.METASTASIS', 'NUMBER.OF.LYMPH.NODES', 'NEOPLASM.DISEASESTAGE', and 'TUMOR.SIZE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 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	Copy Number Ratio CNMF subtypes	Copy Number Ratio CNMF subtypes	RPPA cHierClus subtypes	Copy Number Ratio CNMF subtypes	RNAseq cHierClus subtypes	Copy Number Ratio CNMF subtypes	MIRSEQ CHIERARCHICAL	Copy Number Ratio CNMF subtypes	MIRseq Mature cHierClus subtypes
Time to Death	logrank test	100 (1.00)	100 (1.00)	100 (1.00)	100 (1.00)	100 (1.00)	100 (1.00)	100 (1.00)	100 (1.00)	100 (1.00)	100 (1.00)
AGE	ANOVA	0.00693 (0.686)	0.0658 (1.00)	0.000355 (0.0405)	0.00128 (0.14)	0.0541 (1.00)	0.126 (1.00)	0.321 (1.00)	0.0747 (1.00)	0.123 (1.00)	0.00653 (0.653)
GENDER	Fisher's exact test	0.651 (1.00)	0.816 (1.00)	0.892 (1.00)	0.495 (1.00)	0.991 (1.00)	0.502 (1.00)	0.575 (1.00)	0.563 (1.00)	0.608 (1.00)	0.304 (1.00)
HISTOLOGICAL TYPE	Chi-square test	0.00252 (0.264)	1.31e-21 (1.75e-19)	1.63e-10 (2.15e-08)	2.87e-17 (3.82e-15)	1.42e-25 (1.93e-23)	2.52e-22 (3.41e-20)	6.45e-27 (8.84e-25)	2.04e-29 (2.83e-27)	2.96e-27 (4.09e-25)	8.11e-32 (1.14e-29)
RADIATIONS RADIATION REGIMENINDICATION	Fisher's exact test	0.402 (1.00)	0.0465 (1.00)	0.0118 (1.00)	0.271 (1.00)	0.00404 (0.408)	0.0264 (1.00)	0.333 (1.00)	0.0529 (1.00)	0.0525 (1.00)	0.019 (1.00)
RADIATIONEXPOSURE	Fisher's exact test	0.187 (1.00)	0.825 (1.00)	0.707 (1.00)	0.102 (1.00)	0.934 (1.00)	0.761 (1.00)	0.401 (1.00)	0.265 (1.00)	0.74 (1.00)	0.497 (1.00)
DISTANT METASTASIS	Chi-square test	0.254 (1.00)	0.593 (1.00)	0.254 (1.00)	0.367 (1.00)	0.206 (1.00)	0.803 (1.00)	0.0273 (1.00)	0.0343 (1.00)	0.0158 (1.00)	0.0311 (1.00)
EXTRATHYROIDAL EXTENSION	Chi-square test	0.451 (1.00)	0.000422 (0.0477)	0.0321 (1.00)	0.111 (1.00)	0.000125 (0.015)	0.00021 (0.0246)	2.82e-05 (0.00346)	0.000134 (0.0159)	1.95e-05 (0.00241)	5.62e-05 (0.0068)
LYMPH NODE METASTASIS	Chi-square test	0.0557 (1.00)	1.73e-06 (0.000217)	0.195 (1.00)	0.0138 (1.00)	1.39e-06 (0.000176)	1.5e-05 (0.00188)	1.4e-07 (1.79e-05)	6.93e-08 (8.94e-06)	1.17e-08 (1.53e-06)	3.11e-08 (4.05e-06)
COMPLETENESS OF RESECTION	Chi-square test	0.156 (1.00)	0.519 (1.00)	0.656 (1.00)	0.527 (1.00)	0.549 (1.00)	0.45 (1.00)	0.704 (1.00)	0.8 (1.00)	0.774 (1.00)	0.899 (1.00)
NUMBER OF LYMPH NODES	ANOVA	0.454 (1.00)	0.000653 (0.0731)	0.526 (1.00)	0.316 (1.00)	0.000352 (0.0405)	0.00129 (0.14)	0.00958 (0.92)	0.000845 (0.0938)	0.0072 (0.706)	0.00201 (0.215)
TUMOR STAGECODE	ANOVA
NEOPLASM DISEASESTAGE	Chi-square test	0.000157 (0.0186)	0.00813 (0.788)	0.0233 (1.00)	0.0397 (1.00)	0.0371 (1.00)	0.123 (1.00)	0.00237 (0.251)	0.000235 (0.0272)	0.00373 (0.384)	4.22e-05 (0.00515)
MULTIFOCALITY	Fisher's exact test	0.104 (1.00)	0.364 (1.00)	0.0126 (1.00)	0.846 (1.00)	0.0217 (1.00)	0.0237 (1.00)	0.908 (1.00)	0.907 (1.00)	0.964 (1.00)	0.842 (1.00)
TUMOR SIZE	ANOVA	0.0938 (1.00)	0.259 (1.00)	0.672 (1.00)	0.565 (1.00)	0.0811 (1.00)	0.139 (1.00)	0.0029 (0.302)	0.0157 (1.00)	0.00397 (0.405)	0.00109 (0.12)

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	22	220	42

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

P value = 100 (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	284	1	0.0 - 66.2 (8.0)
subtype1	22	1	0.4 - 65.9 (11.4)
subtype2	220	0	0.1 - 66.2 (8.0)
subtype3	42	0	0.0 - 65.9 (6.7)

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

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 0.00693 (ANOVA), Q value = 0.69

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

	nPatients	Mean (Std.Dev)
ALL	284	46.4 (15.5)
subtype1	22	56.3 (12.7)
subtype2	220	45.4 (15.9)
subtype3	42	46.4 (12.9)

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 = 0.651 (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	211	73
subtype1	15	7
subtype2	163	57
subtype3	33	9

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 = 0.00252 (Chi-square test), Q value = 0.26

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

nPatients	OTHER	THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL	THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED)	THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
ALL	18	167	69	30
subtype1	5	10	6	1
subtype2	10	138	46	26
subtype3	3	19	17	3

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 = 0.402 (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	270
subtype1	2	20
subtype2	11	209
subtype3	1	41

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 = 0.187 (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	237	11
subtype1	19	2
subtype2	179	9
subtype3	39	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 = 0.254 (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	124	4	155
subtype1	5	0	17
subtype2	101	3	115
subtype3	18	1	23

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 = 0.451 (Chi-square test), Q value = 1

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

nPatients	MINIMAL (T3)	MODERATE/ADVANCED (T4A)	NONE
ALL	64	5	201
subtype1	3	0	19
subtype2	53	5	150
subtype3	8	0	32

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 = 0.0557 (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	137	14	63	40	30
subtype1	15	0	0	2	5
subtype2	99	13	54	31	23
subtype3	23	1	9	7	2

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 = 0.156 (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	222	20	1	20
subtype1	17	1	0	4
subtype2	175	16	1	10
subtype3	30	3	0	6

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 = 0.454 (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	225	2.8 (5.2)
subtype1	15	1.2 (4.1)
subtype2	176	3.0 (5.4)
subtype3	34	2.8 (4.5)

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 = 0.000157 (Chi-square test), Q value = 0.019

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IVA	STAGE IVC
ALL	161	32	62	25	3
subtype1	5	10	5	2	0
subtype2	132	19	47	19	2
subtype3	24	3	10	4	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 = 0.104 (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	138	137
subtype1	9	13
subtype2	103	110
subtype3	26	14

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 = 0.0938 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	210	2.8 (1.5)
subtype1	18	3.4 (1.4)
subtype2	162	2.8 (1.4)
subtype3	30	2.4 (1.8)

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

Clustering Approach #2: 'Copy Number Ratio CNMF subtypes'

Table S16. Get Full Table Description of clustering approach #2: 'Copy Number Ratio CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	92	39	157

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

P value = 100 (logrank test), Q value = 1

Table S17. Clustering Approach #2: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	288	1	0.0 - 66.2 (7.9)
subtype1	92	1	0.0 - 66.1 (6.9)
subtype2	39	0	0.1 - 66.2 (6.4)
subtype3	157	0	0.2 - 66.1 (9.3)

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

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 0.0658 (ANOVA), Q value = 1

Table S18. Clustering Approach #2: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	288	46.4 (15.5)
subtype1	92	49.3 (15.3)
subtype2	39	43.2 (14.5)
subtype3	157	45.5 (15.8)

Figure S16. Get High-res Image Clustering Approach #2: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.816 (Fisher's exact test), Q value = 1

Table S19. Clustering Approach #2: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	213	75
subtype1	70	22
subtype2	28	11
subtype3	115	42

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

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

P value = 1.31e-21 (Chi-square test), Q value = 1.8e-19

Table S20. Clustering Approach #2: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	OTHER	THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL	THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED)	THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
ALL	18	168	72	30
subtype1	14	22	53	3
subtype2	2	26	6	5
subtype3	2	120	13	22

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

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

P value = 0.0465 (Fisher's exact test), Q value = 1

Table S21. Clustering Approach #2: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	14	274
subtype1	1	91
subtype2	1	38
subtype3	12	145

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

'Copy Number Ratio CNMF subtypes' versus 'RADIATIONEXPOSURE'

P value = 0.825 (Fisher's exact test), Q value = 1

Table S22. Clustering Approach #2: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

nPatients	NO	YES
ALL	240	11
subtype1	75	3
subtype2	32	2
subtype3	133	6

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

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

P value = 0.593 (Chi-square test), Q value = 1

Table S23. Clustering Approach #2: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'DISTANT.METASTASIS'

nPatients	M0	M1	MX
ALL	126	4	157
subtype1	35	2	54
subtype2	20	0	19
subtype3	71	2	84

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

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

P value = 0.000422 (Chi-square test), Q value = 0.048

Table S24. Clustering Approach #2: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

nPatients	MINIMAL (T3)	MODERATE/ADVANCED (T4A)	NONE
ALL	64	5	205
subtype1	9	0	78
subtype2	7	0	30
subtype3	48	5	97

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

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

P value = 1.73e-06 (Chi-square test), Q value = 0.00022

Table S25. Clustering Approach #2: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	NX
ALL	140	15	63	40	30
subtype1	65	1	7	5	14
subtype2	16	2	12	7	2
subtype3	59	12	44	28	14

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

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

P value = 0.519 (Chi-square test), Q value = 1

Table S26. Clustering Approach #2: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	226	20	1	20
subtype1	75	3	0	7
subtype2	27	4	0	4
subtype3	124	13	1	9

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

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

P value = 0.000653 (ANOVA), Q value = 0.073

Table S27. Clustering Approach #2: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	227	2.8 (5.2)
subtype1	64	0.8 (2.2)
subtype2	34	3.0 (4.5)
subtype3	129	3.8 (6.1)

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

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

P value = 0.00813 (Chi-square test), Q value = 0.79

Table S28. Clustering Approach #2: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IVA	STAGE IVC
ALL	163	34	62	25	3
subtype1	49	20	18	2	2
subtype2	24	2	9	4	0
subtype3	90	12	35	19	1

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

'Copy Number Ratio CNMF subtypes' versus 'MULTIFOCALITY'

P value = 0.364 (Fisher's exact test), Q value = 1

Table S29. Clustering Approach #2: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients	MULTIFOCAL	UNIFOCAL
ALL	140	139
subtype1	42	48
subtype2	16	21
subtype3	82	70

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

'Copy Number Ratio CNMF subtypes' versus 'TUMOR.SIZE'

P value = 0.259 (ANOVA), Q value = 1

Table S30. Clustering Approach #2: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

	nPatients	Mean (Std.Dev)
ALL	213	2.8 (1.5)
subtype1	69	3.0 (1.7)
subtype2	27	2.6 (1.3)
subtype3	117	2.7 (1.4)

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

Clustering Approach #3: 'Copy Number Ratio CNMF subtypes'

Table S31. Get Full Table Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	46	59	58

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

P value = 100 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	163	1	0.1 - 66.1 (8.6)
subtype1	46	0	0.3 - 50.5 (8.3)
subtype2	59	0	0.2 - 65.9 (9.3)
subtype3	58	1	0.1 - 66.1 (7.7)

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

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 0.000355 (ANOVA), Q value = 0.041

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

	nPatients	Mean (Std.Dev)
ALL	163	46.6 (16.1)
subtype1	46	50.0 (13.9)
subtype2	59	50.5 (16.0)
subtype3	58	40.0 (15.8)

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

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.892 (Fisher's exact test), Q value = 1

Table S34. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	110	53
subtype1	31	15
subtype2	41	18
subtype3	38	20

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

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

P value = 1.63e-10 (Chi-square test), Q value = 2.2e-08

Table S35. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	OTHER	THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL	THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED)	THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
ALL	12	84	52	15
subtype1	1	12	31	2
subtype2	10	30	8	11
subtype3	1	42	13	2

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

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

P value = 0.0118 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	13	150
subtype1	0	46
subtype2	9	50
subtype3	4	54

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

'Copy Number Ratio CNMF subtypes' versus 'RADIATIONEXPOSURE'

P value = 0.707 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	137	7
subtype1	41	1
subtype2	49	3
subtype3	47	3

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

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

P value = 0.254 (Chi-square test), Q value = 1

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

nPatients	M0	M1	MX
ALL	71	2	89
subtype1	25	1	19
subtype2	25	0	34
subtype3	21	1	36

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

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

P value = 0.0321 (Chi-square test), Q value = 1

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

nPatients	MINIMAL (T3)	MODERATE/ADVANCED (T4A)	NONE
ALL	33	3	119
subtype1	8	0	38
subtype2	17	3	36
subtype3	8	0	45

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

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

P value = 0.195 (Chi-square test), Q value = 1

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

nPatients	N0	N1	N1A	N1B	NX
ALL	76	11	33	21	22
subtype1	29	0	7	4	6
subtype2	24	6	11	10	8
subtype3	23	5	15	7	8

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

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

P value = 0.656 (Chi-square test), Q value = 1

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

nPatients	R0	R1	RX
ALL	128	11	10
subtype1	38	3	4
subtype2	45	6	3
subtype3	45	2	3

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

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

P value = 0.526 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	124	3.0 (5.6)
subtype1	32	2.4 (6.1)
subtype2	48	2.7 (4.6)
subtype3	44	3.8 (6.4)

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

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

P value = 0.0233 (Chi-square test), Q value = 1

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IVA	STAGE IVC
ALL	86	27	34	13	2
subtype1	24	9	10	1	1
subtype2	22	12	16	9	0
subtype3	40	6	8	3	1

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

'Copy Number Ratio CNMF subtypes' versus 'MULTIFOCALITY'

P value = 0.0126 (Fisher's exact test), Q value = 1

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

nPatients	MULTIFOCAL	UNIFOCAL
ALL	76	79
subtype1	28	15
subtype2	20	37
subtype3	28	27

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

'Copy Number Ratio CNMF subtypes' versus 'TUMOR.SIZE'

P value = 0.672 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	140	3.1 (1.6)
subtype1	41	3.0 (1.5)
subtype2	51	3.2 (1.7)
subtype3	48	3.0 (1.5)

Figure S42. Get High-res Image Clustering Approach #3: 'Copy Number Ratio 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	69	26	68

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 100 (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	163	1	0.1 - 66.1 (8.6)
subtype1	69	1	0.3 - 65.9 (8.1)
subtype2	26	0	1.1 - 65.9 (9.0)
subtype3	68	0	0.1 - 66.1 (9.2)

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 = 0.00128 (ANOVA), Q value = 0.14

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

	nPatients	Mean (Std.Dev)
ALL	163	46.6 (16.1)
subtype1	69	48.8 (15.6)
subtype2	26	53.8 (15.9)
subtype3	68	41.7 (15.3)

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

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.495 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	110	53
subtype1	43	26
subtype2	19	7
subtype3	48	20

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 = 2.87e-17 (Chi-square test), Q value = 3.8e-15

Table S50. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	OTHER	THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL	THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED)	THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
ALL	12	84	52	15
subtype1	0	24	41	4
subtype2	10	8	7	1
subtype3	2	52	4	10

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 = 0.271 (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	150
subtype1	3	66
subtype2	2	24
subtype3	8	60

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 = 0.102 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	137	7
subtype1	62	1
subtype2	22	3
subtype3	53	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 = 0.367 (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	71	2	89
subtype1	29	2	37
subtype2	9	0	17
subtype3	33	0	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 = 0.111 (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	33	3	119
subtype1	11	0	56
subtype2	5	0	21
subtype3	17	3	42

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 = 0.0138 (Chi-square test), Q value = 1

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

nPatients	N0	N1	N1A	N1B	NX
ALL	76	11	33	21	22
subtype1	42	2	10	6	9
subtype2	12	1	3	4	6
subtype3	22	8	20	11	7

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 = 0.527 (Chi-square test), Q value = 1

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

nPatients	R0	R1	RX
ALL	128	11	10
subtype1	56	4	5
subtype2	22	1	3
subtype3	50	6	2

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 = 0.316 (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	124	3.0 (5.6)
subtype1	51	2.1 (5.2)
subtype2	21	3.1 (5.8)
subtype3	52	3.8 (6.0)

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 = 0.0397 (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	86	27	34	13	2
subtype1	38	11	15	2	2
subtype2	7	8	7	4	0
subtype3	41	8	12	7	0

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

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

nPatients	MULTIFOCAL	UNIFOCAL
ALL	76	79
subtype1	33	33
subtype2	11	14
subtype3	32	32

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 = 0.565 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	140	3.1 (1.6)
subtype1	61	3.0 (1.6)
subtype2	21	3.4 (2.0)
subtype3	58	3.0 (1.4)

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

Clustering Approach #5: 'Copy Number Ratio CNMF subtypes'

Table S61. Get Full Table Description of clustering approach #5: 'Copy Number Ratio CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	88	30	65	88

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

P value = 100 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	271	1	0.0 - 66.2 (8.0)
subtype1	88	1	0.0 - 65.9 (6.9)
subtype2	30	0	0.1 - 66.2 (5.8)
subtype3	65	0	0.2 - 66.1 (9.9)
subtype4	88	0	0.2 - 66.2 (9.2)

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

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 0.0541 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	271	46.7 (15.6)
subtype1	88	50.0 (15.4)
subtype2	30	43.7 (14.1)
subtype3	65	43.5 (14.2)
subtype4	88	46.8 (16.9)

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

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.991 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	200	71
subtype1	65	23
subtype2	23	7
subtype3	48	17
subtype4	64	24

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

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

P value = 1.42e-25 (Chi-square test), Q value = 1.9e-23

Table S65. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	OTHER	THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL	THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED)	THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
ALL	17	159	67	28
subtype1	14	19	53	2
subtype2	1	21	3	5
subtype3	1	54	8	2
subtype4	1	65	3	19

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

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

P value = 0.00404 (Fisher's exact test), Q value = 0.41

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

nPatients	NO	YES
ALL	12	259
subtype1	1	87
subtype2	0	30
subtype3	1	64
subtype4	10	78

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

'Copy Number Ratio CNMF subtypes' versus 'RADIATIONEXPOSURE'

P value = 0.934 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	225	10
subtype1	75	3
subtype2	23	1
subtype3	53	3
subtype4	74	3

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

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

P value = 0.206 (Chi-square test), Q value = 1

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

nPatients	M0	M1	MX
ALL	123	4	143
subtype1	31	2	54
subtype2	19	0	11
subtype3	33	1	31
subtype4	40	1	47

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

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

P value = 0.000125 (Chi-square test), Q value = 0.015

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

nPatients	MINIMAL (T3)	MODERATE/ADVANCED (T4A)	NONE
ALL	62	4	191
subtype1	9	0	75
subtype2	8	0	20
subtype3	14	0	49
subtype4	31	4	47

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

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

P value = 1.39e-06 (Chi-square test), Q value = 0.00018

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

nPatients	N0	N1	N1A	N1B	NX
ALL	134	14	56	40	27
subtype1	63	0	7	5	13
subtype2	12	1	10	7	0
subtype3	27	3	19	12	4
subtype4	32	10	20	16	10

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

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

P value = 0.549 (Chi-square test), Q value = 1

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

nPatients	R0	R1	R2	RX
ALL	212	17	1	20
subtype1	71	4	0	7
subtype2	20	2	0	4
subtype3	53	3	1	3
subtype4	68	8	0	6

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

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

P value = 0.000352 (ANOVA), Q value = 0.041

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

	nPatients	Mean (Std.Dev)
ALL	213	2.9 (5.3)
subtype1	64	0.8 (2.2)
subtype2	27	4.4 (6.1)
subtype3	52	4.6 (7.3)
subtype4	70	2.9 (4.6)

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

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

P value = 0.0371 (Chi-square test), Q value = 1

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IVA	STAGE IVC
ALL	152	33	58	24	3
subtype1	46	18	19	2	2
subtype2	20	2	5	3	0
subtype3	40	8	10	7	0
subtype4	46	5	24	12	1

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

'Copy Number Ratio CNMF subtypes' versus 'MULTIFOCALITY'

P value = 0.0217 (Fisher's exact test), Q value = 1

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

nPatients	MULTIFOCAL	UNIFOCAL
ALL	133	129
subtype1	44	42
subtype2	13	15
subtype3	41	21
subtype4	35	51

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

'Copy Number Ratio CNMF subtypes' versus 'TUMOR.SIZE'

P value = 0.0811 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	200	2.7 (1.5)
subtype1	66	3.0 (1.7)
subtype2	17	2.1 (1.1)
subtype3	48	2.6 (1.4)
subtype4	69	2.7 (1.3)

Figure S70. Get High-res Image Clustering Approach #5: 'Copy Number Ratio 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
Number of samples	97	123	51

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 100 (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	271	1	0.0 - 66.2 (8.0)
subtype1	97	1	0.0 - 66.2 (6.8)
subtype2	123	0	0.2 - 66.2 (8.8)
subtype3	51	0	0.2 - 66.1 (9.6)

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 = 0.126 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	271	46.7 (15.6)
subtype1	97	49.1 (15.2)
subtype2	123	45.8 (16.0)
subtype3	51	44.1 (15.2)

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.502 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	200	71
subtype1	71	26
subtype2	88	35
subtype3	41	10

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 = 2.52e-22 (Chi-square test), Q value = 3.4e-20

Table S80. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	OTHER	THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL	THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED)	THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
ALL	17	159	67	28
subtype1	14	26	53	4
subtype2	3	90	7	23
subtype3	0	43	7	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 = 0.0264 (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	12	259
subtype1	1	96
subtype2	10	113
subtype3	1	50

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 = 0.761 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	225	10
subtype1	80	3
subtype2	101	6
subtype3	44	1

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 = 0.803 (Chi-square test), Q value = 1

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

nPatients	M0	M1	MX
ALL	123	4	143
subtype1	40	2	54
subtype2	60	1	62
subtype3	23	1	27

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 = 0.00021 (Chi-square test), Q value = 0.025

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

nPatients	MINIMAL (T3)	MODERATE/ADVANCED (T4A)	NONE
ALL	62	4	191
subtype1	11	0	80
subtype2	41	4	71
subtype3	10	0	40

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 = 1.5e-05 (Chi-square test), Q value = 0.0019

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

nPatients	N0	N1	N1A	N1B	NX
ALL	134	14	56	40	27
subtype1	66	1	9	8	13
subtype2	43	11	37	22	10
subtype3	25	2	10	10	4

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 = 0.45 (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	212	17	1	20
subtype1	78	4	0	7
subtype2	94	10	0	9
subtype3	40	3	1	4

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 = 0.00129 (ANOVA), Q value = 0.14

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

	nPatients	Mean (Std.Dev)
ALL	213	2.9 (5.3)
subtype1	70	1.1 (3.4)
subtype2	103	3.3 (4.9)
subtype3	40	4.7 (7.8)

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 = 0.123 (Chi-square test), Q value = 1

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IVA	STAGE IVC
ALL	152	33	58	24	3
subtype1	52	18	20	4	2
subtype2	71	8	29	14	1
subtype3	29	7	9	6	0

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 = 0.0237 (Fisher's exact test), Q value = 1

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

nPatients	MULTIFOCAL	UNIFOCAL
ALL	133	129
subtype1	47	46
subtype2	53	67
subtype3	33	16

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 = 0.139 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	200	2.7 (1.5)
subtype1	71	3.0 (1.7)
subtype2	92	2.6 (1.3)
subtype3	37	2.6 (1.4)

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

Clustering Approach #7: 'Copy Number Ratio CNMF subtypes'

Table S91. Get Full Table Description of clustering approach #7: 'Copy Number Ratio CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	85	104	93

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

P value = 100 (logrank test), Q value = 1

Table S92. Clustering Approach #7: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	282	1	0.0 - 66.2 (8.0)
subtype1	85	1	0.3 - 65.9 (7.2)
subtype2	104	0	0.2 - 66.2 (9.5)
subtype3	93	0	0.0 - 66.2 (7.1)

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

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 0.321 (ANOVA), Q value = 1

Table S93. Clustering Approach #7: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	282	46.6 (15.5)
subtype1	85	48.4 (16.5)
subtype2	104	45.0 (15.5)
subtype3	93	46.6 (14.4)

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

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.575 (Fisher's exact test), Q value = 1

Table S94. Clustering Approach #7: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	207	75
subtype1	59	26
subtype2	77	27
subtype3	71	22

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

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

P value = 6.45e-27 (Chi-square test), Q value = 8.8e-25

Table S95. Clustering Approach #7: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	OTHER	THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL	THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED)	THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
ALL	18	162	72	30
subtype1	14	16	54	1
subtype2	2	81	11	10
subtype3	2	65	7	19

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

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

P value = 0.333 (Fisher's exact test), Q value = 1

Table S96. Clustering Approach #7: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	14	268
subtype1	3	82
subtype2	8	96
subtype3	3	90

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

'Copy Number Ratio CNMF subtypes' versus 'RADIATIONEXPOSURE'

P value = 0.401 (Fisher's exact test), Q value = 1

Table S97. Clustering Approach #7: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

nPatients	NO	YES
ALL	235	11
subtype1	73	3
subtype2	82	6
subtype3	80	2

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

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

P value = 0.0273 (Chi-square test), Q value = 1

Table S98. Clustering Approach #7: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'DISTANT.METASTASIS'

nPatients	M0	M1	MX
ALL	120	4	157
subtype1	27	2	55
subtype2	42	2	60
subtype3	51	0	42

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

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

P value = 2.82e-05 (Chi-square test), Q value = 0.0035

Table S99. Clustering Approach #7: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

nPatients	MINIMAL (T3)	MODERATE/ADVANCED (T4A)	NONE
ALL	63	5	200
subtype1	6	0	76
subtype2	25	1	70
subtype3	32	4	54

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

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

P value = 1.4e-07 (Chi-square test), Q value = 1.8e-05

Table S100. Clustering Approach #7: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	NX
ALL	137	15	62	39	29
subtype1	57	1	5	6	16
subtype2	46	8	30	11	9
subtype3	34	6	27	22	4

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

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

P value = 0.704 (Chi-square test), Q value = 1

Table S101. Clustering Approach #7: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	221	20	1	19
subtype1	68	5	0	8
subtype2	78	8	1	7
subtype3	75	7	0	4

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

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

P value = 0.00958 (ANOVA), Q value = 0.92

Table S102. Clustering Approach #7: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	223	2.8 (5.2)
subtype1	60	1.3 (3.2)
subtype2	87	2.8 (5.7)
subtype3	76	4.0 (5.7)

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

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

P value = 0.00237 (Chi-square test), Q value = 0.25

Table S103. Clustering Approach #7: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IVA	STAGE IVC
ALL	158	34	62	24	3
subtype1	46	18	15	3	2
subtype2	65	11	20	7	1
subtype3	47	5	27	14	0

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

'Copy Number Ratio CNMF subtypes' versus 'MULTIFOCALITY'

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

Table S104. Clustering Approach #7: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients	MULTIFOCAL	UNIFOCAL
ALL	138	135
subtype1	40	42
subtype2	50	49
subtype3	48	44

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

'Copy Number Ratio CNMF subtypes' versus 'TUMOR.SIZE'

P value = 0.0029 (ANOVA), Q value = 0.3

Table S105. Clustering Approach #7: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

	nPatients	Mean (Std.Dev)
ALL	212	2.8 (1.5)
subtype1	67	3.2 (1.6)
subtype2	81	2.7 (1.4)
subtype3	64	2.3 (1.3)

Figure S98. Get High-res Image Clustering Approach #7: 'Copy Number Ratio CNMF subtypes' 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	78	107	97

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 100 (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	282	1	0.0 - 66.2 (8.0)
subtype1	78	1	0.3 - 65.9 (7.0)
subtype2	107	0	0.0 - 66.2 (7.1)
subtype3	97	0	0.2 - 66.1 (9.8)

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

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.0747 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	282	46.6 (15.5)
subtype1	78	49.5 (15.7)
subtype2	107	46.7 (15.1)
subtype3	97	44.2 (15.5)

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

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.563 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	207	75
subtype1	57	21
subtype2	82	25
subtype3	68	29

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

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 2.04e-29 (Chi-square test), Q value = 2.8e-27

Table S110. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	OTHER	THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL	THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED)	THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
ALL	18	162	72	30
subtype1	14	12	51	1
subtype2	2	74	7	24
subtype3	2	76	14	5

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 = 0.0529 (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	268
subtype1	1	77
subtype2	4	103
subtype3	9	88

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

'MIRSEQ CHIERARCHICAL' versus 'RADIATIONEXPOSURE'

P value = 0.265 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	235	11
subtype1	67	3
subtype2	92	2
subtype3	76	6

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

'MIRSEQ CHIERARCHICAL' versus 'DISTANT.METASTASIS'

P value = 0.0343 (Chi-square test), Q value = 1

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

nPatients	M0	M1	MX
ALL	120	4	157
subtype1	25	2	50
subtype2	57	0	50
subtype3	38	2	57

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

'MIRSEQ CHIERARCHICAL' versus 'EXTRATHYROIDAL.EXTENSION'

P value = 0.000134 (Chi-square test), Q value = 0.016

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

nPatients	MINIMAL (T3)	MODERATE/ADVANCED (T4A)	NONE
ALL	63	5	200
subtype1	6	0	69
subtype2	36	4	63
subtype3	21	1	68

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 = 6.93e-08 (Chi-square test), Q value = 8.9e-06

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

nPatients	N0	N1	N1A	N1B	NX
ALL	137	15	62	39	29
subtype1	56	0	3	5	14
subtype2	38	9	32	22	6
subtype3	43	6	27	12	9

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 = 0.8 (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	221	20	1	19
subtype1	64	4	0	6
subtype2	83	9	0	6
subtype3	74	7	1	7

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 = 0.000845 (ANOVA), Q value = 0.094

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

	nPatients	Mean (Std.Dev)
ALL	223	2.8 (5.2)
subtype1	56	1.0 (2.8)
subtype2	86	4.3 (6.5)
subtype3	81	2.5 (4.6)

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 = 0.000235 (Chi-square test), Q value = 0.027

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IVA	STAGE IVC
ALL	158	34	62	24	3
subtype1	40	18	15	2	2
subtype2	54	6	33	14	0
subtype3	64	10	14	8	1

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

'MIRSEQ CHIERARCHICAL' versus 'MULTIFOCALITY'

P value = 0.907 (Fisher's exact test), Q value = 1

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

nPatients	MULTIFOCAL	UNIFOCAL
ALL	138	135
subtype1	38	39
subtype2	52	53
subtype3	48	43

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

'MIRSEQ CHIERARCHICAL' versus 'TUMOR.SIZE'

P value = 0.0157 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	212	2.8 (1.5)
subtype1	63	3.2 (1.7)
subtype2	78	2.4 (1.3)
subtype3	71	2.8 (1.5)

Figure S112. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #15: 'TUMOR.SIZE'

Clustering Approach #9: 'Copy Number Ratio CNMF subtypes'

Table S121. Get Full Table Description of clustering approach #9: 'Copy Number Ratio CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	87	111	84

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

P value = 100 (logrank test), Q value = 1

Table S122. Clustering Approach #9: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	282	1	0.0 - 66.2 (8.0)
subtype1	87	1	0.3 - 65.9 (7.1)
subtype2	111	0	0.2 - 66.2 (9.6)
subtype3	84	0	0.0 - 66.2 (7.1)

Figure S113. Get High-res Image Clustering Approach #9: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 0.123 (ANOVA), Q value = 1

Table S123. Clustering Approach #9: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	282	46.6 (15.5)
subtype1	87	48.7 (16.2)
subtype2	111	44.3 (15.7)
subtype3	84	47.3 (14.3)

Figure S114. Get High-res Image Clustering Approach #9: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.608 (Fisher's exact test), Q value = 1

Table S124. Clustering Approach #9: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	207	75
subtype1	63	24
subtype2	79	32
subtype3	65	19

Figure S115. Get High-res Image Clustering Approach #9: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 2.96e-27 (Chi-square test), Q value = 4.1e-25

Table S125. Clustering Approach #9: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	OTHER	THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL	THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED)	THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
ALL	18	162	72	30
subtype1	14	17	55	1
subtype2	2	88	10	11
subtype3	2	57	7	18

Figure S116. Get High-res Image Clustering Approach #9: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

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

P value = 0.0525 (Fisher's exact test), Q value = 1

Table S126. Clustering Approach #9: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	14	268
subtype1	2	85
subtype2	10	101
subtype3	2	82

Figure S117. Get High-res Image Clustering Approach #9: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'Copy Number Ratio CNMF subtypes' versus 'RADIATIONEXPOSURE'

P value = 0.74 (Fisher's exact test), Q value = 1

Table S127. Clustering Approach #9: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

nPatients	NO	YES
ALL	235	11
subtype1	75	4
subtype2	89	5
subtype3	71	2

Figure S118. Get High-res Image Clustering Approach #9: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

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

P value = 0.0158 (Chi-square test), Q value = 1

Table S128. Clustering Approach #9: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'DISTANT.METASTASIS'

nPatients	M0	M1	MX
ALL	120	4	157
subtype1	28	2	56
subtype2	44	2	65
subtype3	48	0	36

Figure S119. Get High-res Image Clustering Approach #9: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'DISTANT.METASTASIS'

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

P value = 1.95e-05 (Chi-square test), Q value = 0.0024

Table S129. Clustering Approach #9: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

nPatients	MINIMAL (T3)	MODERATE/ADVANCED (T4A)	NONE
ALL	63	5	200
subtype1	7	0	77
subtype2	26	1	76
subtype3	30	4	47

Figure S120. Get High-res Image Clustering Approach #9: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

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

P value = 1.17e-08 (Chi-square test), Q value = 1.5e-06

Table S130. Clustering Approach #9: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	NX
ALL	137	15	62	39	29
subtype1	60	0	5	6	16
subtype2	45	9	33	13	11
subtype3	32	6	24	20	2

Figure S121. Get High-res Image Clustering Approach #9: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

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

P value = 0.774 (Chi-square test), Q value = 1

Table S131. Clustering Approach #9: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	221	20	1	19
subtype1	70	5	0	8
subtype2	84	9	1	7
subtype3	67	6	0	4

Figure S122. Get High-res Image Clustering Approach #9: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.0072 (ANOVA), Q value = 0.71

Table S132. Clustering Approach #9: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	223	2.8 (5.2)
subtype1	62	1.2 (3.1)
subtype2	91	3.0 (5.6)
subtype3	70	4.0 (5.9)

Figure S123. Get High-res Image Clustering Approach #9: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

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

P value = 0.00373 (Chi-square test), Q value = 0.38

Table S133. Clustering Approach #9: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IVA	STAGE IVC
ALL	158	34	62	24	3
subtype1	47	18	16	3	2
subtype2	70	11	20	9	1
subtype3	41	5	26	12	0

Figure S124. Get High-res Image Clustering Approach #9: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

'Copy Number Ratio CNMF subtypes' versus 'MULTIFOCALITY'

P value = 0.964 (Fisher's exact test), Q value = 1

Table S134. Clustering Approach #9: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients	MULTIFOCAL	UNIFOCAL
ALL	138	135
subtype1	41	42
subtype2	54	53
subtype3	43	40

Figure S125. Get High-res Image Clustering Approach #9: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

'Copy Number Ratio CNMF subtypes' versus 'TUMOR.SIZE'

P value = 0.00397 (ANOVA), Q value = 0.41

Table S135. Clustering Approach #9: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

	nPatients	Mean (Std.Dev)
ALL	212	2.8 (1.5)
subtype1	68	3.2 (1.6)
subtype2	85	2.8 (1.4)
subtype3	59	2.3 (1.3)

Figure S126. Get High-res Image Clustering Approach #9: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

Clustering Approach #10: 'MIRseq Mature cHierClus subtypes'

Table S136. Get Full Table Description of clustering approach #10: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	91	116	75

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

P value = 100 (logrank test), Q value = 1

Table S137. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	282	1	0.0 - 66.2 (8.0)
subtype1	91	0	0.0 - 66.2 (7.1)
subtype2	116	0	0.2 - 66.1 (9.7)
subtype3	75	1	0.3 - 65.9 (7.0)

Figure S127. 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 = 0.00653 (ANOVA), Q value = 0.65

Table S138. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	282	46.6 (15.5)
subtype1	91	48.3 (14.6)
subtype2	116	43.2 (15.3)
subtype3	75	49.7 (15.9)

Figure S128. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.304 (Fisher's exact test), Q value = 1

Table S139. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	207	75
subtype1	72	19
subtype2	81	35
subtype3	54	21

Figure S129. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 8.11e-32 (Chi-square test), Q value = 1.1e-29

Table S140. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	OTHER	THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL	THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED)	THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
ALL	18	162	72	30
subtype1	2	60	7	22
subtype2	2	93	14	7
subtype3	14	9	51	1

Figure S130. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

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

P value = 0.019 (Fisher's exact test), Q value = 1

Table S141. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	14	268
subtype1	2	89
subtype2	11	105
subtype3	1	74

Figure S131. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq Mature cHierClus subtypes' versus 'RADIATIONEXPOSURE'

P value = 0.497 (Fisher's exact test), Q value = 1

Table S142. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

nPatients	NO	YES
ALL	235	11
subtype1	79	2
subtype2	92	6
subtype3	64	3

Figure S132. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

'MIRseq Mature cHierClus subtypes' versus 'DISTANT.METASTASIS'

P value = 0.0311 (Chi-square test), Q value = 1

Table S143. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'DISTANT.METASTASIS'

nPatients	M0	M1	MX
ALL	120	4	157
subtype1	50	0	41
subtype2	46	2	68
subtype3	24	2	48

Figure S133. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'DISTANT.METASTASIS'

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

P value = 5.62e-05 (Chi-square test), Q value = 0.0068

Table S144. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

nPatients	MINIMAL (T3)	MODERATE/ADVANCED (T4A)	NONE
ALL	63	5	200
subtype1	32	4	51
subtype2	25	1	83
subtype3	6	0	66

Figure S134. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

'MIRseq Mature cHierClus subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 3.11e-08 (Chi-square test), Q value = 4e-06

Table S145. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	NX
ALL	137	15	62	39	29
subtype1	36	6	25	19	5
subtype2	46	9	35	16	10
subtype3	55	0	2	4	14

Figure S135. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

'MIRseq Mature cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.899 (Chi-square test), Q value = 1

Table S146. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	221	20	1	19
subtype1	71	7	0	6
subtype2	89	9	1	7
subtype3	61	4	0	6

Figure S136. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.00201 (ANOVA), Q value = 0.22

Table S147. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	223	2.8 (5.2)
subtype1	73	3.9 (5.8)
subtype2	97	3.2 (5.6)
subtype3	53	0.7 (2.3)

Figure S137. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

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

P value = 4.22e-05 (Chi-square test), Q value = 0.0052

Table S148. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IVA	STAGE IVC
ALL	158	34	62	24	3
subtype1	44	4	30	13	0
subtype2	76	12	18	9	1
subtype3	38	18	14	2	2

Figure S138. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

'MIRseq Mature cHierClus subtypes' versus 'MULTIFOCALITY'

P value = 0.842 (Fisher's exact test), Q value = 1

Table S149. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients	MULTIFOCAL	UNIFOCAL
ALL	138	135
subtype1	44	45
subtype2	58	52
subtype3	36	38

Figure S139. 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 = 0.00109 (ANOVA), Q value = 0.12

Table S150. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

	nPatients	Mean (Std.Dev)
ALL	212	2.8 (1.5)
subtype1	65	2.2 (1.2)
subtype2	85	2.9 (1.5)
subtype3	62	3.2 (1.7)

Figure S140. 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 = 288
Number of clustering approaches = 10
Number of selected clinical features = 15
Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches

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] 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)

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

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

[4] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)

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

[6] 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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