Correlation between aggregated molecular cancer subtypes and selected clinical features

Thyroid Adenocarcinoma (Primary solid tumor)

23 May 2013 | analyses__2013_05_23

Maintainer Information

Citation Information

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

Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1PC30F1

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 424 patients, 44 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'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. 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 Mature cHierClus subtypes'. 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 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, 44 significant findings detected.


Clinical Features	Statistical Tests	Copy Number Ratio CNMF subtypes	METHLYATION CNMF	RPPA CNMF subtypes	RPPA cHierClus subtypes	RNAseq CNMF subtypes	RNAseq cHierClus subtypes	MIRSEQ CNMF	MIRSEQ CHIERARCHICAL	MIRseq Mature CNMF subtypes	MIRseq Mature cHierClus subtypes
Time to Death	logrank test	0.0564 (1.00)	0.536 (1.00)	0.739 (1.00)	0.0266 (1.00)	0.19 (1.00)	0.296 (1.00)	0.0223 (1.00)	0.0215 (1.00)	0.0093 (0.828)	0.0338 (1.00)
AGE	ANOVA	5.8e-05 (0.00667)	0.082 (1.00)	0.000749 (0.0764)	0.000266 (0.0287)	0.0594 (1.00)	0.0733 (1.00)	0.0823 (1.00)	0.0059 (0.555)	0.0282 (1.00)	0.00569 (0.541)
GENDER	Fisher's exact test	0.336 (1.00)	0.858 (1.00)	0.836 (1.00)	0.578 (1.00)	0.991 (1.00)	0.616 (1.00)	0.763 (1.00)	0.796 (1.00)	0.693 (1.00)	0.273 (1.00)
HISTOLOGICAL TYPE	Chi-square test	0.182 (1.00)	2.09e-20 (2.81e-18)	0.000694 (0.0715)	4.29e-07 (5.32e-05)	1.22e-25 (1.67e-23)	2.5e-27 (3.5e-25)	5.72e-24 (7.78e-22)	1e-26 (1.38e-24)	1.16e-23 (1.57e-21)	3.02e-27 (4.2e-25)
RADIATIONS RADIATION REGIMENINDICATION	Fisher's exact test	0.301 (1.00)	0.152 (1.00)	0.0132 (1.00)	0.341 (1.00)	0.0138 (1.00)	0.0594 (1.00)	0.308 (1.00)	0.0233 (1.00)	0.0481 (1.00)	0.0188 (1.00)
RADIATIONEXPOSURE	Fisher's exact test	0.201 (1.00)	0.679 (1.00)	0.63 (1.00)	0.0605 (1.00)	0.908 (1.00)	0.349 (1.00)	1 (1.00)	0.949 (1.00)	1 (1.00)	0.948 (1.00)
DISTANT METASTASIS	Chi-square test	0.0722 (1.00)	0.138 (1.00)	0.393 (1.00)	0.481 (1.00)	0.0374 (1.00)	0.00681 (0.623)	0.0153 (1.00)	0.0168 (1.00)	0.00677 (0.623)	0.0344 (1.00)
EXTRATHYROIDAL EXTENSION	Chi-square test	0.00184 (0.182)	3.72e-06 (0.000443)	0.00211 (0.207)	0.0267 (1.00)	9.75e-07 (0.000118)	8.4e-08 (1.06e-05)	1.15e-06 (0.000139)	5.49e-07 (6.69e-05)	3.68e-07 (4.6e-05)	5e-07 (6.15e-05)
LYMPH NODE METASTASIS	Chi-square test	0.187 (1.00)	3.3e-09 (4.19e-07)	0.0186 (1.00)	0.000379 (0.0405)	1.9e-09 (2.46e-07)	1.19e-10 (1.58e-08)	6.29e-10 (8.31e-08)	7.14e-10 (9.29e-08)	6.95e-10 (9.11e-08)	2.5e-09 (3.19e-07)
COMPLETENESS OF RESECTION	Chi-square test	0.422 (1.00)	0.555 (1.00)	0.39 (1.00)	0.637 (1.00)	0.472 (1.00)	0.813 (1.00)	0.503 (1.00)	0.425 (1.00)	0.174 (1.00)	0.587 (1.00)
NUMBER OF LYMPH NODES	ANOVA	0.229 (1.00)	0.000127 (0.0145)	0.463 (1.00)	0.266 (1.00)	0.00021 (0.0234)	0.0014 (0.14)	0.000258 (0.0281)	0.000396 (0.0416)	0.000172 (0.0194)	0.000243 (0.0267)
TUMOR STAGECODE	ANOVA
NEOPLASM DISEASESTAGE	Chi-square test	1.02e-05 (0.00121)	0.000191 (0.0214)	0.00211 (0.207)	0.0151 (1.00)	0.00346 (0.332)	0.000959 (0.0968)	0.000603 (0.0627)	2.75e-05 (0.00322)	0.000387 (0.041)	2.81e-05 (0.00326)
MULTIFOCALITY	Fisher's exact test	0.33 (1.00)	0.731 (1.00)	0.00622 (0.578)	0.642 (1.00)	0.0188 (1.00)	0.0518 (1.00)	0.929 (1.00)	0.553 (1.00)	1 (1.00)	0.714 (1.00)
TUMOR SIZE	ANOVA	0.00712 (0.641)	0.52 (1.00)	0.297 (1.00)	0.505 (1.00)	0.0949 (1.00)	0.0152 (1.00)	0.0211 (1.00)	0.0928 (1.00)	0.0339 (1.00)	0.0153 (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 = 0.0067

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 = 0.18

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 = 0.0012

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 = 0.64

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	61	241

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.536 (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	58	2	0.1 - 147.4 (7.1)
subtype3	240	5	0.1 - 145.4 (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 = 0.082 (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	61	43.1 (15.3)
subtype3	241	46.6 (15.7)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.858 (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	46	15
subtype3	177	64

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

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 2.09e-20 (Chi-square test), Q value = 2.8e-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	49	5	6
subtype3	3	191	19	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 = 0.152 (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	59
subtype3	11	230

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

'METHLYATION CNMF' versus 'RADIATIONEXPOSURE'

P value = 0.679 (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	53	2
subtype3	211	8

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

'METHLYATION CNMF' versus 'DISTANT.METASTASIS'

P value = 0.138 (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	35	0	26
subtype3	145	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 = 3.72e-06 (Chi-square test), Q value = 0.00044

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	44	1
subtype3	82	13	137	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 = 3.3e-09 (Chi-square test), Q value = 4.2e-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	27	6	14	11	3
subtype3	85	36	59	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 = 0.555 (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	44	7	0	4
subtype3	190	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 = 0.000127 (ANOVA), Q value = 0.015

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	48	3.8 (7.1)
subtype3	198	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 = 0.000191 (Chi-square test), Q value = 0.021

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	38	3	14	1	5	0
subtype3	133	17	58	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 = 0.731 (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	26	33
subtype3	107	129

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

'METHLYATION CNMF' versus 'TUMOR.SIZE'

P value = 0.52 (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	47	2.8 (1.5)
subtype3	192	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 = 0.076

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 = 0.071

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 = 1

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 = 0.21

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 = 0.21

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 = 0.58

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 = 0.029

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 = 5.3e-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 = 0.041

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	120	51	104	138

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.19 (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	408	10	0.0 - 147.4 (9.3)
subtype1	120	3	0.0 - 130.7 (7.3)
subtype2	49	1	0.1 - 145.4 (6.8)
subtype3	102	0	0.1 - 138.1 (11.9)
subtype4	137	6	0.1 - 147.4 (12.0)

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 = 0.0594 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	413	46.6 (15.6)
subtype1	120	49.0 (15.5)
subtype2	51	43.1 (15.8)
subtype3	104	44.5 (14.8)
subtype4	138	47.4 (16.1)

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

'RNAseq CNMF subtypes' versus 'GENDER'

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

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

nPatients	FEMALE	MALE
ALL	307	106
subtype1	90	30
subtype2	38	13
subtype3	78	26
subtype4	101	37

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 = 1.22e-25 (Chi-square test), Q value = 1.7e-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	7	286	86	34
subtype1	3	52	64	1
subtype2	1	41	4	5
subtype3	1	86	15	2
subtype4	2	107	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 = 0.0138 (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	400
subtype1	1	119
subtype2	0	51
subtype3	2	102
subtype4	10	128

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	354	16
subtype1	102	6
subtype2	44	1
subtype3	89	4
subtype4	119	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 = 0.0374 (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	231	8	173
subtype1	52	3	64
subtype2	36	0	15
subtype3	63	2	39
subtype4	80	3	55

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 = 9.75e-07 (Chi-square test), Q value = 0.00012

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	110	13	270	1
subtype1	16	1	97	0
subtype2	12	2	35	0
subtype3	27	0	74	0
subtype4	55	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 = 1.9e-09 (Chi-square test), Q value = 2.5e-07

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

nPatients	N0	N1	N1A	N1B	NX
ALL	191	48	77	59	38
subtype1	81	6	8	7	18
subtype2	21	5	14	11	0
subtype3	43	10	29	15	7
subtype4	46	27	26	26	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 = 0.472 (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	325	35	2	26
subtype1	99	6	0	7
subtype2	34	7	0	5
subtype3	85	7	1	6
subtype4	107	15	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 = 0.00021 (ANOVA), Q value = 0.023

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

	nPatients	Mean (Std.Dev)
ALL	318	3.6 (6.3)
subtype1	85	1.2 (2.8)
subtype2	42	5.7 (7.9)
subtype3	81	4.1 (6.5)
subtype4	110	4.3 (6.9)

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 = 0.00346 (Chi-square test), Q value = 0.33

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	232	46	90	2	35	5
subtype1	65	25	22	1	3	2
subtype2	36	2	9	0	4	0
subtype3	59	12	22	0	9	1
subtype4	72	7	37	1	19	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 = 0.0188 (Fisher's exact test), Q value = 1

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

nPatients	MULTIFOCAL	UNIFOCAL
ALL	177	227
subtype1	51	67
subtype2	24	25
subtype3	55	46
subtype4	47	89

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 = 0.0949 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	325	2.9 (1.6)
subtype1	93	3.1 (1.7)
subtype2	36	2.5 (1.7)
subtype3	81	2.7 (1.4)
subtype4	115	3.0 (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	81	169	104	59

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.296 (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	408	10	0.0 - 147.4 (9.3)
subtype1	80	0	0.1 - 138.1 (11.9)
subtype2	166	6	0.1 - 147.4 (11.1)
subtype3	104	3	0.2 - 112.6 (7.6)
subtype4	58	1	0.0 - 130.7 (6.9)

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 = 0.0733 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	413	46.6 (15.6)
subtype1	81	44.7 (15.0)
subtype2	169	46.6 (16.1)
subtype3	104	49.7 (15.8)
subtype4	59	43.9 (14.3)

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.616 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	307	106
subtype1	63	18
subtype2	120	49
subtype3	78	26
subtype4	46	13

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 = 2.5e-27 (Chi-square test), Q value = 3.5e-25

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	7	286	86	34
subtype1	0	68	12	1
subtype2	3	133	4	29
subtype3	3	41	60	0
subtype4	1	44	10	4

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 = 0.0594 (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	400
subtype1	2	79
subtype2	10	159
subtype3	1	103
subtype4	0	59

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 = 0.349 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	354	16
subtype1	72	1
subtype2	146	7
subtype3	90	4
subtype4	46	4

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 = 0.00681 (Chi-square test), Q value = 0.62

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

nPatients	M0	M1	MX
ALL	231	8	173
subtype1	47	2	32
subtype2	99	3	67
subtype3	42	3	58
subtype4	43	0	16

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 = 8.4e-08 (Chi-square test), Q value = 1.1e-05

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	110	13	270	1
subtype1	20	0	59	0
subtype2	66	12	81	1
subtype3	11	1	86	0
subtype4	13	0	44	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 = 1.19e-10 (Chi-square test), Q value = 1.6e-08

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

nPatients	N0	N1	N1A	N1B	NX
ALL	191	48	77	59	38
subtype1	36	9	17	13	6
subtype2	53	31	41	32	12
subtype3	73	4	3	6	18
subtype4	29	4	16	8	2

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 = 0.813 (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	325	35	2	26
subtype1	67	6	1	4
subtype2	127	19	1	11
subtype3	84	6	0	7
subtype4	47	4	0	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 = 0.0014 (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	318	3.6 (6.3)
subtype1	62	4.1 (6.6)
subtype2	141	4.5 (6.9)
subtype3	70	1.0 (2.8)
subtype4	45	3.7 (6.8)

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 = 0.000959 (Chi-square test), Q value = 0.097

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	232	46	90	2	35	5
subtype1	45	10	16	0	8	1
subtype2	93	9	44	1	20	2
subtype3	55	25	16	1	3	2
subtype4	39	2	14	0	4	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 = 0.0518 (Fisher's exact test), Q value = 1

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

nPatients	MULTIFOCAL	UNIFOCAL
ALL	177	227
subtype1	43	36
subtype2	62	104
subtype3	43	59
subtype4	29	28

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 = 0.0152 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	325	2.9 (1.6)
subtype1	62	2.7 (1.4)
subtype2	138	3.0 (1.6)
subtype3	84	3.3 (1.7)
subtype4	41	2.3 (1.6)

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 = 7.8e-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 = 0.00014

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 = 8.3e-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 = 0.028

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 = 0.063

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 = 0.55

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 = 1.4e-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 = 6.7e-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 = 9.3e-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 = 0.042

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 = 0.0032

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'

Clustering Approach #9: 'MIRseq Mature CNMF subtypes'

Table S121. Get Full Table Description of clustering approach #9: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	103	143	127

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

P value = 0.0093 (logrank test), Q value = 0.83

Table S122. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	368	10	0.0 - 147.4 (9.0)
subtype1	103	3	0.3 - 112.6 (7.6)
subtype2	140	0	0.1 - 138.1 (10.4)
subtype3	125	7	0.0 - 147.4 (8.4)

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

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.0282 (ANOVA), Q value = 1

Table S123. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	373	46.4 (15.8)
subtype1	103	48.6 (16.2)
subtype2	143	43.7 (15.0)
subtype3	127	47.7 (16.0)

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

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.693 (Fisher's exact test), Q value = 1

Table S124. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	279	94
subtype1	77	26
subtype2	104	39
subtype3	98	29

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

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

P value = 1.16e-23 (Chi-square test), Q value = 1.6e-21

Table S125. Clustering Approach #9: 'MIRseq Mature 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	254	82	31
subtype1	3	40	60	0
subtype2	1	116	14	12
subtype3	2	98	8	19

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

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

P value = 0.0481 (Fisher's exact test), Q value = 1

Table S126. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	14	359
subtype1	2	101
subtype2	10	133
subtype3	2	125

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

'MIRseq Mature CNMF subtypes' versus 'RADIATIONEXPOSURE'

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

Table S127. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

nPatients	NO	YES
ALL	318	16
subtype1	90	4
subtype2	120	6
subtype3	108	6

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

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

P value = 0.00677 (Chi-square test), Q value = 0.62

Table S128. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'DISTANT.METASTASIS'

nPatients	M0	M1	MX
ALL	195	6	171
subtype1	41	2	59
subtype2	72	3	68
subtype3	82	1	44

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

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

P value = 3.68e-07 (Chi-square test), Q value = 4.6e-05

Table S129. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

nPatients	MINIMAL (T3)	MODERATE/ADVANCED (T4A)	NONE	VERY ADVANCED (T4B)
ALL	96	12	248	1
subtype1	9	1	88	0
subtype2	42	1	92	0
subtype3	45	10	68	1

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

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

P value = 6.95e-10 (Chi-square test), Q value = 9.1e-08

Table S130. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	NX
ALL	176	38	75	49	35
subtype1	70	4	5	6	18
subtype2	59	18	40	15	11
subtype3	47	16	30	28	6

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

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

P value = 0.174 (Chi-square test), Q value = 1

Table S131. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	293	30	2	24
subtype1	82	7	0	8
subtype2	114	7	2	9
subtype3	97	16	0	7

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

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

P value = 0.000172 (ANOVA), Q value = 0.019

Table S132. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	288	3.2 (5.6)
subtype1	70	1.1 (2.9)
subtype2	113	3.1 (5.3)
subtype3	105	4.6 (6.6)

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

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

P value = 0.000387 (Chi-square test), Q value = 0.041

Table S133. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' 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	57	22	16	0	4	2
subtype2	88	16	28	0	9	1
subtype3	65	5	39	1	16	1

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

'MIRseq Mature CNMF subtypes' versus 'MULTIFOCALITY'

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

Table S134. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients	MULTIFOCAL	UNIFOCAL
ALL	165	199
subtype1	45	55
subtype2	63	75
subtype3	57	69

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

'MIRseq Mature CNMF subtypes' versus 'TUMOR.SIZE'

P value = 0.0339 (ANOVA), Q value = 1

Table S135. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

	nPatients	Mean (Std.Dev)
ALL	290	2.9 (1.6)
subtype1	84	3.2 (1.6)
subtype2	114	2.8 (1.5)
subtype3	92	2.6 (1.6)

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

Clustering Approach #10: 'MIRseq Mature cHierClus subtypes'

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

Cluster Labels	1	2	3
Number of samples	148	95	130

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

P value = 0.0338 (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	368	10	0.0 - 147.4 (9.0)
subtype1	145	7	0.0 - 147.4 (8.5)
subtype2	95	3	0.3 - 112.6 (7.4)
subtype3	128	0	0.1 - 138.1 (11.1)

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 = 0.00569 (ANOVA), Q value = 0.54

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

	nPatients	Mean (Std.Dev)
ALL	373	46.4 (15.8)
subtype1	148	47.9 (16.0)
subtype2	95	49.1 (15.6)
subtype3	130	42.9 (15.1)

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 = 0.273 (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	279	94
subtype1	116	32
subtype2	72	23
subtype3	91	39

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 = 3.02e-27 (Chi-square test), Q value = 4.2e-25

Table S140. Clustering Approach #10: 'MIRseq Mature 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	254	82	31
subtype1	2	113	9	24
subtype2	3	33	59	0
subtype3	1	108	14	7

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 = 0.0188 (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	359
subtype1	3	145
subtype2	1	94
subtype3	10	120

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 = 0.948 (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	318	16
subtype1	129	6
subtype2	82	4
subtype3	107	6

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 = 0.0344 (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	195	6	171
subtype1	91	1	56
subtype2	39	2	53
subtype3	65	3	62

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 = 5e-07 (Chi-square test), Q value = 6.2e-05

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

nPatients	MINIMAL (T3)	MODERATE/ADVANCED (T4A)	NONE	VERY ADVANCED (T4B)
ALL	96	12	248	1
subtype1	55	10	78	1
subtype2	8	0	82	0
subtype3	33	2	88	0

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 = 2.5e-09 (Chi-square test), Q value = 3.2e-07

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

nPatients	N0	N1	N1A	N1B	NX
ALL	176	38	75	49	35
subtype1	57	19	35	27	10
subtype2	68	3	3	5	16
subtype3	51	16	37	17	9

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 = 0.587 (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	293	30	2	24
subtype1	112	17	1	10
subtype2	78	5	0	6
subtype3	103	8	1	8

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 = 0.000243 (ANOVA), Q value = 0.027

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

	nPatients	Mean (Std.Dev)
ALL	288	3.2 (5.6)
subtype1	120	4.3 (6.4)
subtype2	65	0.9 (2.5)
subtype3	103	3.3 (5.5)

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 = 2.81e-05 (Chi-square test), Q value = 0.0033

Table S148. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' 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	76	6	47	1	16	1
subtype2	52	22	15	0	2	2
subtype3	82	15	21	0	11	1

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 = 0.714 (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	165	199
subtype1	64	82
subtype2	41	53
subtype3	60	64

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 = 0.0153 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	290	2.9 (1.6)
subtype1	111	2.5 (1.5)
subtype2	79	3.1 (1.6)
subtype3	100	3.0 (1.5)

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 = 424
Number of clustering approaches = 10
Number of selected clinical features = 15
Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches

CNMF clustering

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

Consensus hierarchical clustering

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

Survival analysis

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

ANOVA analysis

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

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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